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EVC: Towards Real-Time Neural Image Compression with Mask Decay | https://openreview.net/forum?id=XUxad2Gj40n | https://openreview.net/forum?id=XUxad2Gj40n | Wang Guo-Hua,Jiahao Li,Bin Li,Yan Lu | ICLR 2023,Poster | Neural image compression has surpassed state-of-the-art traditional codecs (H.266/VVC) for rate-distortion (RD) performance, but suffers from large complexity and separate models for different rate-distortion trade-offs. In this paper, we propose an Efficient single-model Variable-bit-rate Codec (EVC), which is able to run at 30 FPS with 768x512 input images and still outperforms VVC for the RD performance. By further reducing both encoder and decoder complexities, our small model even achieves 30 FPS with 1920x1080 input images. To bridge the performance gap between our different capacities models, we meticulously design the mask decay, which transforms the large model's parameters into the small model automatically. And a novel sparsity regularization loss is proposed to mitigate shortcomings of $L_p$ regularization. Our algorithm significantly narrows the performance gap by 50% and 30% for our medium and small models, respectively. At last, we advocate the scalable encoder for neural image compression. The encoding complexity is dynamic to meet different latency requirements. We propose decaying the large encoder multiple times to reduce the residual representation progressively. Both mask decay and residual representation learning greatly improve the RD performance of our scalable encoder. Our code is at https://github.com/microsoft/DCVC. | https://openreview.net/pdf/10eb4d0b2c9232991827cc7d0ada38e1a727ff3b.pdf |
Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information | https://openreview.net/forum?id=ICYasJBlZNs | https://openreview.net/forum?id=ICYasJBlZNs | Yulun Wu,Rob Barton,Zichen Wang,Vassilis N. Ioannidis,Carlo De Donno,Layne C Price,Luis F. Voloch,George Karypis | ICLR 2023,Poster | Predicting the responses of a cell under perturbations may bring important benefits to drug discovery and personalized therapeutics. In this work, we propose a novel graph variational Bayesian causal inference framework to predict a cell's gene expressions under counterfactual perturbations (perturbations that this cell did not factually receive), leveraging information representing biological knowledge in the form of gene regulatory networks (GRNs) to aid individualized cellular response predictions. Aiming at a data-adaptive GRN, we also developed an adjacency matrix updating technique for graph convolutional networks and used it to refine GRNs during pre-training, which generated more insights on gene relations and enhanced model performance. Additionally, we propose a robust estimator within our framework for the asymptotically efficient estimation of marginal perturbation effect, which is yet to be carried out in previous works. With extensive experiments, we exhibited the advantage of our approach over state-of-the-art deep learning models for individual response prediction. | https://openreview.net/pdf/5f9f897ae409bffc5621445608f3a70103eb6b6e.pdf |
ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actor | https://openreview.net/forum?id=HmPOzJQhbwg | https://openreview.net/forum?id=HmPOzJQhbwg | Wanqi Xue,Qingpeng Cai,Ruohan Zhan,Dong Zheng,Peng Jiang,Kun Gai,Bo An | ICLR 2023,Poster | Long-term engagement is preferred over immediate engagement in sequential recommendation as it directly affects product operational metrics such as daily active users (DAUs) and dwell time. Meanwhile, reinforcement learning (RL) is widely regarded as a promising framework for optimizing long-term engagement in sequential recommendation. However, due to expensive online interactions, it is very difficult for RL algorithms to perform state-action value estimation, exploration and feature extraction when optimizing long-term engagement. In this paper, we propose ResAct which seeks a policy that is close to, but better than, the online-serving policy. In this way, we can collect sufficient data near the learned policy so that state-action values can be properly estimated, and there is no need to perform online exploration. ResAct optimizes the policy by first reconstructing the online behaviors and then improving it via a Residual Actor. To extract long-term information, ResAct utilizes two information-theoretical regularizers to confirm the expressiveness and conciseness of features. We conduct experiments on a benchmark dataset and a large-scale industrial dataset which consists of tens of millions of recommendation requests. Experimental results show that our method significantly outperforms the state-of-the-art baselines in various long-term engagement optimization tasks. | https://openreview.net/pdf/b1263f8890ffdc4ff6b7655e8a9c65ab1bac4429.pdf |
Dataset Pruning: Reducing Training Data by Examining Generalization Influence | https://openreview.net/forum?id=4wZiAXD29TQ | https://openreview.net/forum?id=4wZiAXD29TQ | Shuo Yang,Zeke Xie,Hanyu Peng,Min Xu,Mingming Sun,Ping Li | ICLR 2023,Poster | The great success of deep learning heavily relies on increasingly larger training data, which comes at a price of huge computational and infrastructural costs. This poses crucial questions that, do all training data contribute to model's performance? How much does each individual training sample or a sub-training-set affect the model's generalization, and how to construct the smallest subset from the entire training data as a proxy training set without significantly sacrificing the model's performance? To answer these, we propose dataset pruning, an optimization-based sample selection method that can (1) examine the influence of removing a particular set of training samples on model's generalization ability with theoretical guarantee, and (2) construct the smallest subset of training data that yields strictly constrained generalization gap. The empirically observed generalization gap of dataset pruning is substantially consistent with our theoretical expectations. Furthermore, the proposed method prunes 40% training examples on the CIFAR-10 dataset, halves the convergence time with only 1.3% test accuracy decrease, which is superior to previous score-based sample selection methods. | https://openreview.net/pdf/a5df22f04dc4cf8275a555cc4ceef32677efcbb8.pdf |
StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training | https://openreview.net/forum?id=HE_75XY5Ljh | https://openreview.net/forum?id=HE_75XY5Ljh | Yuechen Yu,Yulin Li,Chengquan Zhang,Xiaoqiang Zhang,Zengyuan Guo,Xiameng Qin,Kun Yao,Junyu Han,Errui Ding,Jingdong Wang | ICLR 2023,Poster | In this paper, we present StrucTexTv2, an effective document image pre-training framework, by performing masked visual-textual prediction. It consists of two self-supervised pre-training tasks: masked image modeling and masked language modeling, based on text region-level image masking. The proposed method randomly masks some image regions according to the bounding box coordinates of text words. The objectives of our pre-training tasks are reconstructing the pixels of masked image regions and the corresponding masked tokens simultaneously. Hence the pre-trained encoder can capture more textual semantics in comparison to the masked image modeling that usually predicts the masked image patches. Compared to the masked multi-modal modeling methods for document image understanding that rely on both the image and text modalities, StrucTexTv2 models image-only input and potentially deals with more application scenarios free from OCR pre-processing. Extensive experiments on mainstream benchmarks of document image understanding demonstrate the effectiveness of StrucTexTv2. It achieves competitive or even new state-of-the-art performance in various downstream tasks such as image classification, layout analysis, table structure recognition, document OCR, and information extraction under the end-to-end scenario. | https://openreview.net/pdf/ee5c222d76dee2155c9e5e5058f2ded043e2e3f5.pdf |
Plateau in Monotonic Linear Interpolation --- A "Biased" View of Loss Landscape for Deep Networks | https://openreview.net/forum?id=z289SIQOQna | https://openreview.net/forum?id=z289SIQOQna | Xiang Wang,Annie N. Wang,Mo Zhou,Rong Ge | ICLR 2023,Poster | Monotonic linear interpolation (MLI) --- on the line connecting a random initialization with the minimizer it converges to, the loss and accuracy are monotonic --- is a phenomenon that is commonly observed in the training of neural networks. Such a phenomenon may seem to suggest that optimization of neural networks is easy. In this paper, we show that the MLI property is not necessarily related to the hardness of optimization problems, and empirical observations on MLI for deep neural networks depend heavily on the biases. In particular, we show that interpolating both weights and biases linearly leads to very different influences on the final output, and when different classes have different last-layer biases on a deep network, there will be a long plateau in both the loss and accuracy interpolation (which existing theory of MLI cannot explain). We also show how the last-layer biases for different classes can be different even on a perfectly balanced dataset using a simple model. Empirically we demonstrate that similar intuitions hold on practical networks and realistic datasets. | https://openreview.net/pdf/5f963b1090be68987c9f6af517d4e8234dbecbcb.pdf |
The KFIoU Loss for Rotated Object Detection | https://openreview.net/forum?id=qUKsCztWlKq | https://openreview.net/forum?id=qUKsCztWlKq | Xue Yang,Yue Zhou,Gefan Zhang,Jirui Yang,Wentao Wang,Junchi Yan,XIAOPENG ZHANG,Qi Tian | ICLR 2023,Poster | Differing from the well-developed horizontal object detection area whereby the computing-friendly IoU based loss is readily adopted and well fits with the detection metrics, rotation detectors often involve a more complicated loss based on SkewIoU which is unfriendly to gradient-based training. In this paper, we propose an effective approximate SkewIoU loss based on Gaussian modeling and Gaussian product, which mainly consists of two items. The first term is a scale-insensitive center point loss, which is used to quickly narrow the distance between the center points of the two bounding boxes. In the distance-independent second term, the product of the Gaussian distributions is adopted to inherently mimic the mechanism of SkewIoU by its definition, and show its alignment with the SkewIoU loss at trend-level within a certain distance (i.e. within 9 pixels). This is in contrast to recent Gaussian modeling based rotation detectors e.g. GWD loss and KLD loss that involve a human-specified distribution distance metric which require additional hyperparameter tuning that vary across datasets and detectors. The resulting new loss called KFIoU loss is easier to implement and works better compared with exact SkewIoU loss, thanks to its full differentiability and ability to handle the non-overlapping cases. We further extend our technique to the 3-D case which also suffers from the same issues as 2-D. Extensive results on various datasets with different base detectors show the effectiveness of our approach. | https://openreview.net/pdf/eb8531535f3c119950bdf295af5b855bb6efde39.pdf |
BrainBERT: Self-supervised representation learning for intracranial recordings | https://openreview.net/forum?id=xmcYx_reUn6 | https://openreview.net/forum?id=xmcYx_reUn6 | Christopher Wang,Vighnesh Subramaniam,Adam Uri Yaari,Gabriel Kreiman,Boris Katz,Ignacio Cases,Andrei Barbu | ICLR 2023,Poster | We create a reusable Transformer, BrainBERT, for intracranial recordings bringing modern representation learning approaches to neuroscience. Much like in NLP and speech recognition, this Transformer enables classifying complex concepts, i.e., decoding neural data, with higher accuracy and with much less data by being pretrained in an unsupervised manner on a large corpus of unannotated neural recordings. Our approach generalizes to new subjects with electrodes in new positions and to unrelated tasks showing that the representations robustly disentangle the neural signal. Just like in NLP where one can study language by investigating what a language model learns, this approach opens the door to investigating the brain by what a model of the brain learns. As a first step along this path, we demonstrate a new analysis of the intrinsic dimensionality of the computations in different areas of the brain. To construct these representations, we combine a technique for producing super-resolution spectrograms of neural data with an approach designed for generating contextual representations of audio by masking. In the future, far more concepts will be decodable from neural recordings by using representation learning, potentially unlocking the brain like language models unlocked language. | https://openreview.net/pdf/0ea1fa510d3c8d4dc63c0ae0775f2c2ea4c765ff.pdf |
General Neural Gauge Fields | https://openreview.net/forum?id=XWkWK2UagFR | https://openreview.net/forum?id=XWkWK2UagFR | Fangneng Zhan,Lingjie Liu,Adam Kortylewski,Christian Theobalt | ICLR 2023,Poster | The recent advance of neural fields, such as neural radiance fields, has significantly pushed the boundary of scene representation learning. Aiming to boost the computation efficiency and rendering quality of 3D scenes, a popular line of research maps the 3D coordinate system to another measuring system, e.g., 2D manifolds and hash tables, for modeling neural fields. The conversion of coordinate systems can be typically dubbed as \emph{gauge transformation}, which is usually a pre-defined mapping function, e.g., orthogonal projection or spatial hash function. This begs a question: can we directly learn a desired gauge transformation along with the neural field in an end-to-end manner? In this work, we extend this problem to a general paradigm with a taxonomy of discrete and continuous cases, and develop an end-to-end learning framework to jointly optimize the gauge transformation and neural fields. To counter the problem that the learning of gauge transformations can collapse easily, we derive a general regularization mechanism from the principle of information conservation during the gauge transformation. To circumvent the high computation cost in gauge learning with regularization, we directly derive an information-invariant gauge transformation which allows to preserve scene information inherently and yield superior performance. | https://openreview.net/pdf/e453a73c4d655b57d61a2848f7e8b13cb8c0b242.pdf |
Generate rather than Retrieve: Large Language Models are Strong Context Generators | https://openreview.net/forum?id=fB0hRu9GZUS | https://openreview.net/forum?id=fB0hRu9GZUS | Wenhao Yu,Dan Iter,Shuohang Wang,Yichong Xu,Mingxuan Ju,Soumya Sanyal,Chenguang Zhu,Michael Zeng,Meng Jiang | ICLR 2023,Poster | Knowledge-intensive tasks, such as open-domain question answering (QA), require access to a large amount of world or domain knowledge. A common approach for knowledge-intensive tasks is to employ a retrieve-then-read pipeline that first retrieves a handful of relevant contextual documents from an external corpus such as Wikipedia and then predicts an answer conditioned on the retrieved documents. In this paper, we present a novel perspective for solving knowledge-intensive tasks by replacing document retrievers with large language model generators. We call our method generate-then-read (GenRead), which first prompts a large language model to generate contextual documents based on a given question, and then reads the generated documents to produce the final answer. Furthermore, we propose a novel clustering-based prompting method that selects distinct prompts, in order to generate diverse documents that cover different perspectives, leading to better recall over acceptable answers. We conduct extensive experiments on three different knowledge-intensive tasks, including open-domain QA, fact checking, and dialogue system. Notably, GenRead achieves 71.6 and 54.4 exact match scores on TriviaQA and WebQ, significantly outperforming the state-of-the-art retrieve-then-read pipeline DPR-FiD by +4.0 and +3.9, without retrieving any documents from any external knowledge source. Lastly, we demonstrate the model performance can be further improved by combining retrieval and generation. Our code and generated documents can be found at https://github.com/wyu97/GenRead. | https://openreview.net/pdf/a4086708f91faaf89fdf42039c5895dfd8a6a372.pdf |
Discovering Informative and Robust Positives for Video Domain Adaptation | https://openreview.net/forum?id=vk-j5pQY3Gv | https://openreview.net/forum?id=vk-j5pQY3Gv | Chang Liu,Kunpeng Li,Michael Stopa,Jun Amano,Yun Fu | ICLR 2023,Poster | Unsupervised domain adaptation for video recognition is challenging where the domain shift includes both spatial variations and temporal dynamics. Previous works have focused on exploring contrastive learning for cross-domain alignment. However, limited variations in intra-domain positives, false cross-domain positives, and false negatives hinder contrastive learning from fulfilling intra-domain discrimination and cross-domain closeness. This paper presents a non-contrastive learning framework without relying on negative samples for unsupervised video domain adaptation. To address the limited variations in intra-domain positives, we set unlabeled target videos as anchors and explored to mine "informative intra-domain positives" in the form of spatial/temporal augmentations and target nearest neighbors (NNs).
To tackle the false cross-domain positives led by noisy pseudo-labels, we reversely set source videos as anchors and sample the synthesized target videos as "robust cross-domain positives" from an estimated target distribution, which are naturally more robust to the pseudo-label noise. Our approach is demonstrated to be superior to state-of-the-art methods through extensive experiments on several cross-domain action recognition benchmarks.
| https://openreview.net/pdf/a84f553396a5cb7380355eabf385194e1ab0612a.pdf |
Understanding Why Generalized Reweighting Does Not Improve Over ERM | https://openreview.net/forum?id=ashPce_W8F- | https://openreview.net/forum?id=ashPce_W8F- | Runtian Zhai,Chen Dan,J Zico Kolter,Pradeep Kumar Ravikumar | ICLR 2023,Poster | Empirical risk minimization (ERM) is known to be non-robust in practice to distributional shift where the training and the test distributions are different. A suite of approaches, such as importance weighting, and variants of distributionally robust optimization (DRO), have been proposed to solve this problem. But a line of recent work has empirically shown that these approaches do not significantly improve over ERM in real applications with distribution shift. The goal of this work is to obtain a comprehensive theoretical understanding of this intriguing phenomenon. We first posit the class of Generalized Reweighting (GRW) algorithms, as a broad category of approaches that iteratively update model parameters based on iterative reweighting of the training samples. We show that when overparameterized models are trained under GRW, the resulting models are close to that obtained by ERM. We also show that adding small regularization which does not greatly affect the empirical training accuracy does not help. Together, our results show that a broad category of what we term GRW approaches are not able to achieve distributionally robust generalization. Our work thus has the following sobering takeaway: to make progress towards distributionally robust generalization, we either have to develop non-GRW approaches, or perhaps devise novel classification/regression loss functions that are adapted to GRW approaches. | https://openreview.net/pdf/6dfa64283e85d840377a06006209d9a9d973c45c.pdf |
Linear Connectivity Reveals Generalization Strategies | https://openreview.net/forum?id=hY6M0JHl3uL | https://openreview.net/forum?id=hY6M0JHl3uL | Jeevesh Juneja,Rachit Bansal,Kyunghyun Cho,João Sedoc,Naomi Saphra | ICLR 2023,Poster | In the mode connectivity literature, it is widely accepted that there are common circumstances in which two neural networks, trained similarly on the same data, will maintain loss when interpolated in the weight space. In particular, transfer learning is presumed to ensure the necessary conditions for linear mode connectivity across training runs. In contrast to existing results from image classification, we find that among text classifiers (trained on MNLI, QQP, and CoLA), some pairs of finetuned models have large barriers of increasing loss on the linear paths between them. On each task, we find distinct clusters of models which are linearly connected on the test loss surface, but are disconnected from models outside the cluster---models that occupy separate basins on the surface. By measuring performance on specially-crafted diagnostic datasets, we find that these clusters correspond to different generalization strategies. For example, on MNLI, one cluster behaves like a bag of words model under domain shift, while another cluster uses syntactic heuristics. Our work demonstrates how the geometry of the loss surface can guide models towards different heuristic functions in standard finetuning settings. | https://openreview.net/pdf/628b1742a0bfcfa4e76ad755ea5019c650cb2a0d.pdf |
Gradient-Guided Importance Sampling for Learning Binary Energy-Based Models | https://openreview.net/forum?id=9DZKk85Z4zA | https://openreview.net/forum?id=9DZKk85Z4zA | Meng Liu,Haoran Liu,Shuiwang Ji | ICLR 2023,Poster | Learning energy-based models (EBMs) is known to be difficult especially on discrete data where gradient-based learning strategies cannot be applied directly. Although ratio matching is a sound method to learn discrete EBMs, it suffers from expensive computation and excessive memory requirements, thereby resulting in difficulties in learning EBMs on high-dimensional data. Motivated by these limitations, in this study, we propose ratio matching with gradient-guided importance sampling (RMwGGIS). Particularly, we use the gradient of the energy function w.r.t. the discrete data space to approximately construct the provably optimal proposal distribution, which is subsequently used by importance sampling to efficiently estimate the original ratio matching objective. We perform experiments on density modeling over synthetic discrete data, graph generation, and training Ising models to evaluate our proposed method. The experimental results demonstrate that our method can significantly alleviate the limitations of ratio matching, perform more effectively in practice, and scale to high-dimensional problems. Our implementation is available at https://github.com/divelab/RMwGGIS. | https://openreview.net/pdf/5b2f9ad18b0930a16ef891e03d537cc91c0c7e09.pdf |
Composing Ensembles of Pre-trained Models via Iterative Consensus | https://openreview.net/forum?id=gmwDKo-4cY | https://openreview.net/forum?id=gmwDKo-4cY | Shuang Li,Yilun Du,Joshua B. Tenenbaum,Antonio Torralba,Igor Mordatch | ICLR 2023,Poster | Large pre-trained models exhibit distinct and complementary capabilities dependent on the data they are trained on. Language models such as GPT-3 are capable of textual reasoning but cannot understand visual information, while vision models such as DALL-E can generate photorealistic photos but fail to understand complex language descriptions. In this work, we propose a unified framework for composing ensembles of different pre-trained models -- combining the strengths of each individual model to solve various multimodal problems in a zero-shot manner. We use pre-trained models as "generators" or "scorers" and compose them via closed-loop iterative consensus optimization. The generator constructs proposals and the scorers iteratively provide feedback to refine the generated result. Such closed-loop communication enables models to correct errors caused by other models, significantly boosting performance on downstream tasks, e.g. improving accuracy on grade school math problems by 7.5%, without requiring any model finetuning. We demonstrate that consensus achieved by an ensemble of scorers outperforms the feedback of a single scorer, by leveraging the strengths of each expert model. Results show that the proposed method can be used as a general purpose framework for a wide range of zero-shot multimodal tasks, such as image generation, video question answering, mathematical reasoning, and robotic manipulation.
| https://openreview.net/pdf/3a0f22bd7748e3a5e79b5973a182c5777b2aab02.pdf |
Automated Data Augmentations for Graph Classification | https://openreview.net/forum?id=vTb1JI0Gps_ | https://openreview.net/forum?id=vTb1JI0Gps_ | Youzhi Luo,Michael Curtis McThrow,Wing Yee Au,Tao Komikado,Kanji Uchino,Koji Maruhashi,Shuiwang Ji | ICLR 2023,Poster | Data augmentations are effective in improving the invariance of learning machines. We argue that the core challenge of data augmentations lies in designing data transformations that preserve labels. This is relatively straightforward for images, but much more challenging for graphs. In this work, we propose GraphAug, a novel automated data augmentation method aiming at computing label-invariant augmentations for graph classification. Instead of using uniform transformations as in existing studies, GraphAug uses an automated augmentation model to avoid compromising critical label-related information of the graph, thereby producing label-invariant augmentations at most times. To ensure label-invariance, we develop a training method based on reinforcement learning to maximize an estimated label-invariance probability. Experiments show that GraphAug outperforms previous graph augmentation methods on various graph classification tasks. | https://openreview.net/pdf/37dd16041ff68ffd646b0c5eae31689019c5b3c6.pdf |
Riemannian Metric Learning via Optimal Transport | https://openreview.net/forum?id=v3y68gz-WEz | https://openreview.net/forum?id=v3y68gz-WEz | Christopher Scarvelis,Justin Solomon | ICLR 2023,Poster | We introduce an optimal transport-based model for learning a metric tensor from cross-sectional samples of evolving probability measures on a common Riemannian manifold. We neurally parametrize the metric as a spatially-varying matrix field and efficiently optimize our model's objective using a simple alternating scheme. Using this learned metric, we can non-linearly interpolate between probability measures and compute geodesics on the manifold. We show that metrics learned using our method improve the quality of trajectory inference on scRNA and bird migration data at the cost of little additional cross-sectional data. | https://openreview.net/pdf/ef540eb6d8d75498298baa5bf7417a3542a7b4e8.pdf |
Reliability of CKA as a Similarity Measure in Deep Learning | https://openreview.net/forum?id=8HRvyxc606 | https://openreview.net/forum?id=8HRvyxc606 | MohammadReza Davari,Stefan Horoi,Amine Natik,Guillaume Lajoie,Guy Wolf,Eugene Belilovsky | ICLR 2023,Poster | Comparing learned neural representations in neural networks is a challenging but important problem, which has been approached in different ways. The Centered Kernel Alignment (CKA) similarity metric, particularly its linear variant, has recently become a popular approach and has been widely used to compare representations of a network's different layers, of architecturally similar networks trained differently, or of models with different architectures trained on the same data. A wide variety of claims about similarity and dissimilarity of these various representations have been made using CKA results. In this work we present analysis that formally characterizes CKA sensitivity to a large class of simple transformations, which can naturally occur in the context of modern machine learning. This provides a concrete explanation to CKA sensitivity to outliers, which has been observed in past works, and to transformations that preserve the linear separability of the data, an important generalization attribute. We empirically investigate several weaknesses of the CKA similarity metric, demonstrating situations in which it gives unexpected or counterintuitive results. Finally we study approaches for modifying representations to maintain functional behaviour while changing the CKA value. Our results illustrate that, in many cases, the CKA value can be easily manipulated without substantial changes to the functional behaviour of the models, and call for caution when leveraging activation alignment metrics. | https://openreview.net/pdf/402c531d6593a053e759e45ba18de2f2cc84dd23.pdf |
Fair Attribute Completion on Graph with Missing Attributes | https://openreview.net/forum?id=9vcXCMp9VEp | https://openreview.net/forum?id=9vcXCMp9VEp | Dongliang Guo,Zhixuan Chu,Sheng Li | ICLR 2023,Poster | Tackling unfairness in graph learning models is a challenging task, as the unfairness issues on graphs involve both attributes and topological structures. Existing work on fair graph learning simply assumes that attributes of all nodes are available for model training and then makes fair predictions. In practice, however, the attributes of some nodes might not be accessible due to missing data or privacy concerns, which makes fair graph learning even more challenging. In this paper, we propose FairAC, a fair attribute completion method, to complement missing information and learn fair node embeddings for graphs with missing attributes. FairAC adopts an attention mechanism to deal with the attribute missing problem and meanwhile, it mitigates two types of unfairness, i.e., feature unfairness from attributes and topological unfairness due to attribute completion. FairAC can work on various types of homogeneous graphs and generate fair embeddings for them and thus can be applied to most downstream tasks to improve their fairness performance. To our best knowledge, FairAC is the first method that jointly addresses the graph attribution completion and graph unfairness problems. Experimental results on benchmark datasets show that our method achieves better fairness performance with less sacrifice in accuracy, compared with the state-of-the-art methods of fair graph learning. Code is available at: https://github.com/donglgcn/FairAC. | https://openreview.net/pdf/4b58d2b063bb99126b2cfd1be7282c3425918024.pdf |
Deep Ranking Ensembles for Hyperparameter Optimization | https://openreview.net/forum?id=_ruvo2KCL2x | https://openreview.net/forum?id=_ruvo2KCL2x | Abdus Salam Khazi,Sebastian Pineda Arango,Josif Grabocka | ICLR 2023,Poster | Automatically optimizing the hyperparameters of Machine Learning algorithms is one of the primary open questions in AI. Existing work in Hyperparameter Optimization (HPO) trains surrogate models for approximating the response surface of hyperparameters as a regression task. In contrast, we hypothesize that the optimal strategy for training surrogates is to preserve the ranks of the performances of hyperparameter configurations as a Learning to Rank problem. As a result, we present a novel method that meta-learns neural network surrogates optimized for ranking the configurations' performances while modeling their uncertainty via ensembling. In a large-scale experimental protocol comprising 12 baselines, 16 HPO search spaces and 86 datasets/tasks, we demonstrate that our method achieves new state-of-the-art results in HPO. | https://openreview.net/pdf/33706864dbb8e9f87a9e325cfb7774ebd45da395.pdf |
Robustness to corruption in pre-trained Bayesian neural networks | https://openreview.net/forum?id=kUI41mY8bHl | https://openreview.net/forum?id=kUI41mY8bHl | Xi Wang,Laurence Aitchison | ICLR 2023,Poster | We develop ShiftMatch, a new training-data-dependent likelihood for robustness to corruption in Bayesian neural networks (BNNs). ShiftMatch is inspired by the training-data-dependent “EmpCov” priors from Izmailov et al. (2021a), and efficiently matches test-time spatial correlations to those at training time. Critically, ShiftMatch is designed to leave the neural network’s training time likelihood unchanged, allowing it to use publicly available samples from pre-trained BNNs. Using pre-trained HMC samples, ShiftMatch gives strong performance improvements on CIFAR-10-C, outperforms EmpCov priors (though ShiftMatch uses extra information from a minibatch of corrupted test points), and is perhaps the first Bayesian method capable of convincingly outperforming plain deep ensembles. | https://openreview.net/pdf/5173cc630678a9428fb2ccf35130eb4b6e7107fd.pdf |
Weakly-supervised HOI Detection via Prior-guided Bi-level Representation Learning | https://openreview.net/forum?id=resApVNcqSB | https://openreview.net/forum?id=resApVNcqSB | Bo Wan,Yongfei Liu,Desen Zhou,Tinne Tuytelaars,Xuming He | ICLR 2023,Poster | Human object interaction (HOI) detection plays a crucial role in human-centric scene understanding and serves as a fundamental building block for many vision tasks. One generalizable and scalable strategy for HOI detection is to use weak supervision, learning from image-level annotations only. This is inherently challenging due to ambiguous human-object associations, large search space of detecting HOIs and highly noisy training signal. A promising strategy to address those challenges is to exploit knowledge from large-scale pretrained models (e.g., CLIP), but a direct knowledge distillation strategy does not perform well on the weakly-supervised setting. In contrast, we develop a CLIP-guided HOI representation capable of incorporating the prior knowledge at both image level and HOI instance level, and adopt a self-taught mechanism to prune incorrect human-object associations. Experimental results on HICO-DET and V-COCO
show that our method outperforms the previous works by a sizable margin, showing the efficacy of our HOI representation. | https://openreview.net/pdf/8ff52c6cf6af8897d2ec87baeb7f01134169c98c.pdf |
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction | https://openreview.net/forum?id=KXRSh0sdVTP | https://openreview.net/forum?id=KXRSh0sdVTP | Wenlin Chen,Austin Tripp,José Miguel Hernández-Lobato | ICLR 2023,Poster | We propose Adaptive Deep Kernel Fitting with Implicit Function Theorem (ADKF-IFT), a novel framework for learning deep kernel Gaussian processes (GPs) by interpolating between meta-learning and conventional deep kernel learning. Our approach employs a bilevel optimization objective where we meta-learn generally useful feature representations across tasks, in the sense that task-specific GP models estimated on top of such features achieve the lowest possible predictive loss on average. We solve the resulting nested optimization problem using the implicit function theorem (IFT). We show that our ADKF-IFT framework contains previously proposed Deep Kernel Learning (DKL) and Deep Kernel Transfer (DKT) as special cases. Although ADKF-IFT is a completely general method, we argue that it is especially well-suited for drug discovery problems and demonstrate that it significantly outperforms previous state-of-the-art methods on a variety of real-world few-shot molecular property prediction tasks and out-of-domain molecular property prediction and optimization tasks. | https://openreview.net/pdf/60d3227f50f202e62dbd4dd6030d8ac2b29b5872.pdf |
ERL-Re$^2$: Efficient Evolutionary Reinforcement Learning with Shared State Representation and Individual Policy Representation | https://openreview.net/forum?id=FYZCHEtt6H0 | https://openreview.net/forum?id=FYZCHEtt6H0 | Jianye HAO,Pengyi Li,Hongyao Tang,YAN ZHENG,Xian Fu,Zhaopeng Meng | ICLR 2023,Poster | Deep Reinforcement Learning (Deep RL) and Evolutionary Algorithm (EA) are two major paradigms of policy optimization with distinct learning principles, i.e., gradient-based v.s. gradient-free. An appealing research direction is integrating Deep RL and EA to devise new methods by fusing their complementary advantages. However, existing works on combining Deep RL and EA have two common drawbacks:1) the RL agent and EA agents learn their policies individually, neglecting efficient sharing of useful common knowledge; 2) parameter-level policy optimization guarantees no semantic level of behavior evolution for the EA side. In this paper, we propose Evolutionary Reinforcement Learning with Two-scale State Representation and Policy Representation (ERL-Re$^2$), a novel solution to the aforementioned two drawbacks. The key idea of ERL-Re$^2$ is two-scale representation: all EA and RL policies share the same nonlinear state representation while maintaining individual linear policy representations. The state representation conveys expressive common features of the environment learned by all the agents collectively; the linear policy representation provides a favorable space for efficient policy optimization, where novel behavior-level crossover and mutation operations can be performed. Moreover, the linear policy representation allows convenient generalization of policy fitness with the help of Policy-extended Value Function Approximator (PeVFA), further improving the sample efficiency of fitness estimation. The experiments on a range of continuous control tasks show that ERL-Re$^2$ consistently outperforms advanced baselines and achieves the State Of The Art (SOTA). Our code is available on https://github.com/yeshenpy/ERL-Re2. | https://openreview.net/pdf/9b3a060321395c18a721125540f51db282b2e6a6.pdf |
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models | https://openreview.net/forum?id=WZH7099tgfM | https://openreview.net/forum?id=WZH7099tgfM | Denny Zhou,Nathanael Schärli,Le Hou,Jason Wei,Nathan Scales,Xuezhi Wang,Dale Schuurmans,Claire Cui,Olivier Bousquet,Quoc V Le,Ed H. Chi | ICLR 2023,Poster | Chain-of-thought prompting has demonstrated remarkable performance on various natural language reasoning tasks. However, it tends to perform poorly on tasks which requires solving problems harder than the exemplars shown in the prompts. To overcome this challenge of easy-to-hard generalization, we propose a novel prompting strategy, least-to-most prompting. The key idea in this strategy is to break down a complex problem into a series of simpler subproblems and then solve them in sequence. Solving each subproblem is facilitated by the answers to previously solved subproblems. Our experimental results on tasks related to symbolic manipulation, compositional generalization, and math reasoning reveal that least-to-most prompting is capable of generalizing to more difficult problems than those seen in the prompts. A notable finding is that when the GPT-3 code-davinci-002 model is used with least-to-most prompting, it can solve the compositional generalization benchmark SCAN in any split (including length split) with an accuracy of at least 99\% using just 14 exemplars, compared to only 16\% accuracy with chain-of-thought prompting. This is particularly noteworthy because neural-symbolic models in the literature that specialize in solving SCAN are trained on the entire training set containing over 15,000 examples. We have included prompts for all the tasks in the Appendix. | https://openreview.net/pdf/328fd7b9b742a2398905672f07b91af643001cb5.pdf |
Deep Ensembles for Graphs with Higher-order Dependencies | https://openreview.net/forum?id=hZftxQGJ4Re | https://openreview.net/forum?id=hZftxQGJ4Re | Steven Krieg,William Burgis,Patrick Soga,Nitesh Chawla | ICLR 2023,Poster | Graph neural networks (GNNs) continue to achieve state-of-the-art performance on many graph learning tasks, but rely on the assumption that a given graph is a sufficient approximation of the true neighborhood structure. In the presence of higher-order sequential dependencies, we show that the tendency of traditional graph representations to underfit each node's neighborhood causes existing GNNs to generalize poorly. To address this, we propose a novel Deep Graph Ensemble (DGE), which captures neighborhood variance by training an ensemble of GNNs on different neighborhood subspaces of the same node within a higher-order network structure. We show that DGE consistently outperforms existing GNNs on semisupervised and supervised tasks on six real-world data sets with known higher-order dependencies, even under a similar parameter budget. We demonstrate that learning diverse and accurate base classifiers is central to DGE's success, and discuss the implications of these findings for future work on GNNs. | https://openreview.net/pdf/167908de362ffeac6eed2ac92250a7a9f4822cd3.pdf |
Towards Understanding Why Mask Reconstruction Pretraining Helps in Downstream Tasks | https://openreview.net/forum?id=PaEUQiY40Dk | https://openreview.net/forum?id=PaEUQiY40Dk | Jiachun Pan,Pan Zhou,Shuicheng YAN | ICLR 2023,Poster | For unsupervised pretraining, mask-reconstruction pretraining (MRP) approaches, e.g. MAE and data2vec, randomly mask input patches and then reconstruct the pixels or semantic features of these masked patches via an auto-encoder. Then for a downstream task, supervised fine-tuning the pretrained encoder remarkably surpasses the conventional "supervised learning" (SL) trained from scratch. However, it is still unclear 1) how MRP performs semantic (feature) learning in the pretraining phase and 2) why it helps in downstream tasks. To solve these problems, we first theoretically show that on an auto-encoder of a two/one-layered convolution encoder/decoder, MRP can capture all discriminative semantics of each potential semantic class in the pretraining dataset. Then considering the fact that the pretraining dataset is of huge size and high diversity and thus covers most semantics in downstream dataset, in fine-tuning phase, the pretrained encoder can capture as much semantics as it can in downstream datasets, and would not lost these semantics with theoretical guarantees. In contrast, SL only randomly captures some semantics due to lottery ticket hypothesis. So MRP provably achieves better performance than SL on the classification tasks. Experimental results testify to our data assumptions and also our theoretical implications. | https://openreview.net/pdf/32a6fee6d96a49f6781a05a36d15b391c55b0bc6.pdf |
Self-Supervised Category-Level Articulated Object Pose Estimation with Part-Level SE(3) Equivariance | https://openreview.net/forum?id=20GtJ6hIaPA | https://openreview.net/forum?id=20GtJ6hIaPA | Xueyi Liu,Ji Zhang,Ruizhen Hu,Haibin Huang,He Wang,Li Yi | ICLR 2023,Poster | Category-level articulated object pose estimation aims to estimate a hierarchy of articulation-aware object poses of an unseen articulated object from a known category. To reduce the heavy annotations needed for supervised learning methods, we present a novel self-supervised strategy that solves this problem without any human labels. Our key idea is to factorize canonical shapes and articulated object poses from input articulated shapes through part-level equivariant shape analysis. Specifically, we first introduce the concept of part-level SE(3) equivariance and devise a network to learn features of such property. Then, through a carefully designed fine-grained pose-shape disentanglement strategy, we expect that canonical spaces to support pose estimation could be induced automatically. Thus, we could further predict articulated object poses as per-part rigid transformations describing how parts transform from their canonical part spaces to the camera space. Extensive experiments demonstrate the effectiveness of our method on both complete and partial point clouds from synthetic and real articulated object datasets. | https://openreview.net/pdf/58588985182b7adae53071aa8b2da82ff6db8141.pdf |
Thalamus: a brain-inspired algorithm for biologically-plausible continual learning and disentangled representations | https://openreview.net/forum?id=6orC5MvgPBK | https://openreview.net/forum?id=6orC5MvgPBK | Ali Hummos | ICLR 2023,Poster | Animals thrive in a constantly changing environment and leverage the temporal structure to learn well-factorized causal representations. In contrast, traditional neural networks suffer from forgetting in changing environments and many methods have been proposed to limit forgetting with different trade-offs. Inspired by the brain thalamocortical circuit, we introduce a simple algorithm that uses optimization at inference time to generate internal representations of the current task dynamically. The algorithm alternates between updating the model weights and a latent task embedding, allowing the agent to parse the stream of temporal experience into discrete events and organize learning about them. On a continual learning benchmark, it achieves competitive end average accuracy by mitigating forgetting, but importantly, the interaction between the weights dynamics and the latent dynamics organizes knowledge into flexible structures with a cognitive interface to control them. Tasks later in the sequence can be solved through knowledge transfer as they become reachable within the well-factorized latent space. The algorithm meets many of the desiderata of an ideal continually learning agent in open-ended environments, and its simplicity suggests fundamental computations in circuits with abundant feedback control loops such as the thalamocortical circuits in the brain | https://openreview.net/pdf/ee4dce013cdb125a610759d64c058b9dd35caa6e.pdf |
Deep Variational Implicit Processes | https://openreview.net/forum?id=8aeSJNbmbQq | https://openreview.net/forum?id=8aeSJNbmbQq | Luis A. Ortega,Simon Rodriguez Santana,Daniel Hernández-Lobato | ICLR 2023,Poster | Implicit processes (IPs) are a generalization of Gaussian processes (GPs). IPs may lack a closed-form expression but are easy to sample from. Examples include, among others, Bayesian neural networks or neural samplers. IPs can be used as priors over functions, resulting in flexible models with well-calibrated prediction uncertainty estimates. Methods based on IPs usually carry out function-space approximate inference, which overcomes some of the difficulties of parameter-space approximate inference. Nevertheless, the approximations employed often limit the expressiveness of the final model, resulting, e.g., in a Gaussian predictive distribution, which can be restrictive. We propose here a multi-layer generalization of IPs called the Deep Variational Implicit process (DVIP). This generalization is similar to that of deep GPs over GPs, but it is more flexible due to the use of IPs as the prior distribution over the latent functions. We describe a scalable variational inference algorithm for training DVIP and show that it outperforms previous IP-based methods and also deep GPs. We support these claims via extensive regression and classification experiments. We also evaluate DVIP on large datasets with up to several million data instances to illustrate its good scalability and performance. | https://openreview.net/pdf/8d72ac24107a3c5441bed16c6444aface829b02f.pdf |
Denoising Masked Autoencoders Help Robust Classification | https://openreview.net/forum?id=zDjtZZBZtqK | https://openreview.net/forum?id=zDjtZZBZtqK | QuanLin Wu,Hang Ye,Yuntian Gu,Huishuai Zhang,Liwei Wang,Di He | ICLR 2023,Poster | In this paper, we propose a new self-supervised method, which is called denoising masked autoencoders (DMAE), for learning certified robust classifiers of images. In DMAE, we corrupt each image by adding Gaussian noises to each pixel value and randomly masking several patches. A Transformer-based encoder-decoder model is then trained to reconstruct the original image from the corrupted one. In this learning paradigm, the encoder will learn to capture relevant semantics for the downstream tasks, which is also robust to Gaussian additive noises. We show that the pre-trained encoder can naturally be used as the base classifier in Gaussian smoothed models, where we can analytically compute the certified radius for any data point. Although the proposed method is simple, it yields significant performance improvement in downstream classification tasks. We show that the DMAE ViT-Base model, which just uses 1/10 parameters of the model developed in recent work (Carlini et al., 2022), achieves competitive or better certified accuracy in various settings. The DMAE ViT-Large model significantly surpasses all previous results, establishing a new state-of-the-art on ImageNet dataset. We further demonstrate that the pre-trained model has good transferability to the CIFAR-10 dataset, suggesting its wide adaptability. Models and code are available at
https://github.com/quanlin-wu/dmae. | https://openreview.net/pdf/eb383598b65499174e21e2475c8f9f0442264ccb.pdf |
Estimating individual treatment effects under unobserved confounding using binary instruments | https://openreview.net/forum?id=ULsuEVQbV-9 | https://openreview.net/forum?id=ULsuEVQbV-9 | Dennis Frauen,Stefan Feuerriegel | ICLR 2023,Poster | Estimating conditional average treatment effects (CATEs) from observational data is relevant in many fields such as personalized medicine. However, in practice, the treatment assignment is usually confounded by unobserved variables and thus introduces bias. A remedy to remove the bias is the use of instrumental variables (IVs). Such settings are widespread in medicine (e.g., trials where the treatment assignment is used as binary IV). In this paper, we propose a novel, multiply robust machine learning framework, called MRIV, for estimating CATEs using binary IVs and thus yield an unbiased CATE estimator. Different from previous work for binary IVs, our framework estimates the CATE directly via a pseudo-outcome regression. (1)~We provide a theoretical analysis where we show that our framework yields multiple robust convergence rates: our CATE estimator achieves fast convergence even if several nuisance estimators converge slowly. (2)~We further show that our framework asymptotically outperforms state-of-the-art plug-in IV methods for CATE estimation, in the sense that it achieves a faster rate of convergence if the CATE is smoother than the individual outcome surfaces. (3)~We build upon our theoretical results and propose a tailored deep neural network architecture called MRIV-Net for CATE estimation using binary IVs. Across various computational experiments, we demonstrate empirically that our MRIV-Net achieves state-of-the-art performance. To the best of our knowledge, our MRIV is the first multiply robust machine learning framework tailored to estimating CATEs in the binary IV setting. | https://openreview.net/pdf/2213116e2bc69d245d15ec4f4ff82d2de202dbe1.pdf |
Approximate Bayesian Inference with Stein Functional Variational Gradient Descent | https://openreview.net/forum?id=a2-aoqmeYM4 | https://openreview.net/forum?id=a2-aoqmeYM4 | Tobias Pielok,Bernd Bischl,David Rügamer | ICLR 2023,Poster | We propose a general-purpose variational algorithm that forms a natural analogue of Stein variational gradient descent (SVGD) in function space. While SVGD successively updates a set of particles to match a target density, the method introduced here of Stein functional variational gradient descent (SFVGD) updates a set of particle functions to match a target stochastic process (SP). The update step is found by minimizing the functional derivative of the Kullback-Leibler divergence between SPs. SFVGD can either be used to train Bayesian neural networks (BNNs) or for ensemble gradient boosting. We show the efficacy of training BNNs with SFVGD on various real-world datasets. | https://openreview.net/pdf/e46a8d9dc8a67950ba6f68c14b150e0c2d8c9290.pdf |
SCoMoE: Efficient Mixtures of Experts with Structured Communication | https://openreview.net/forum?id=s-c96mSU0u5 | https://openreview.net/forum?id=s-c96mSU0u5 | zhiyuan zeng,Deyi Xiong | ICLR 2023,Poster | Mixture-of-Experts (MoE) models are promising architectures for massively multilingual neural machine translation and large language models due to the advantage of sublinear scaling. However, the training of large MoE models is usually bottlenecked by the all-to-all communication (Lepikhin et al., 2020). To reduce the communication cost, we propose SCoMoE, an MoE architecture with structured all-to-all communication, inspired by the hierarchical architecture of the communication topology. SCoMoE encourages data to be communicated across devices through fast intra-accelerator/node communication channels, reducing communication throughput in the slow inter-node communication channel. We slice the data on the sequence dimension (SCoMoE-Seq) into three communication groups and project the data on the feature dimension (SCoMoE-Feat) into low-dimensional representations. To compensate the potential performance drop caused by the routing locality in SCoMoE, we further propose a token clustering approach to aggregating related tokens from different devices before the MoE layers. The sigmoid gating in the balanced router used in the token clustering is substituted with the softmax gating with differential sorting. Experiments on bilingual and massively multilingual machine translation demonstrate that SCoMoE achieves a speedup of 1.44x over GShard with comparable performance, and substantially outperforms Gshard (2.8 BLEU) on OPUS-100 with a speedup of 1.25x. | https://openreview.net/pdf/ac600913a3de976ce9df830677f9ddacb13a4838.pdf |
An Additive Instance-Wise Approach to Multi-class Model Interpretation | https://openreview.net/forum?id=5OygDd-4Eeh | https://openreview.net/forum?id=5OygDd-4Eeh | Vy Vo,Van Nguyen,Trung Le,Quan Hung Tran,Reza Haf,Seyit Camtepe,Dinh Phung | ICLR 2023,Poster | Interpretable machine learning offers insights into what factors drive a certain prediction of a black-box system. A large number of interpreting methods focus on identifying explanatory input features, which generally fall into two main categories: attribution and selection. A popular attribution-based approach is to exploit local neighborhoods for learning instance-specific explainers in an additive manner. The process is thus inefficient and susceptible to poorly-conditioned samples. Meanwhile, many selection-based methods directly optimize local feature distributions in an instance-wise training framework, thereby being capable of leveraging global information from other inputs. However, they can only interpret single-class predictions and many suffer from inconsistency across different settings, due to a strict reliance on a pre-defined number of features selected. This work exploits the strengths of both methods and proposes a framework for learning local explanations simultaneously for multiple target classes. Our model explainer significantly outperforms additive and instance-wise counterparts on faithfulness with more compact and comprehensible explanations. We also demonstrate the capacity to select stable and important features through extensive experiments on various data sets and black-box model architectures. | https://openreview.net/pdf/eec9f35dfb2a2cd0ffec4b6c7bf3eb7b59674819.pdf |
LDMIC: Learning-based Distributed Multi-view Image Coding | https://openreview.net/forum?id=ILQVw4cA5F9 | https://openreview.net/forum?id=ILQVw4cA5F9 | Xinjie Zhang,Jiawei Shao,Jun Zhang | ICLR 2023,Poster | Multi-view image compression plays a critical role in 3D-related applications. Existing methods adopt a predictive coding architecture, which requires joint encoding to compress the corresponding disparity as well as residual information. This demands collaboration among cameras and enforces the epipolar geometric constraint between different views, which makes it challenging to deploy these methods in distributed camera systems with randomly overlapping fields of view. Meanwhile, distributed source coding theory indicates that efficient data compression of correlated sources can be achieved by independent encoding and joint decoding, which motivates us to design a learning-based distributed multi-view image coding (LDMIC) framework. With independent encoders, LDMIC introduces a simple yet effective joint context transfer module based on the cross-attention mechanism at the decoder to effectively capture the global inter-view correlations, which is insensitive to the geometric relationships between images. Experimental results show that LDMIC significantly outperforms both traditional and learning-based MIC methods while enjoying fast encoding speed. Code is released at https://github.com/Xinjie-Q/LDMIC. | https://openreview.net/pdf/4a0637d97e03d3ced4f839174d866a19d66d54e3.pdf |
Sound Randomized Smoothing in Floating-Point Arithmetic | https://openreview.net/forum?id=HaHCoGcpV9 | https://openreview.net/forum?id=HaHCoGcpV9 | Vaclav Voracek,Matthias Hein | ICLR 2023,Poster | Randomized smoothing is sound when using infinite precision. However, we show that randomized smoothing is no longer sound for limited floating-point precision. We present a simple example where randomized smoothing certifies a radius of $1.26$ around a point, even though there is an adversarial example in the distance $0.8$ and show how this can be abused to give false certificates for CIFAR10. We discuss the implicit assumptions of randomized smoothing and show that they do not apply to generic image classification models whose smoothed versions are commonly certified. In order to overcome this problem, we propose a sound approach to randomized smoothing when using floating-point precision with essentially equal speed for quantized input. It yields sound certificates or image classifiers which for the ones tested so far are very similar to the unsound practice of randomized smoothing. Our only assumption is that we have access to a fair coin. | https://openreview.net/pdf/cc4b6006471a5fcac57cbfcd5bb73ca9ef9b392f.pdf |
Collaborative Pure Exploration in Kernel Bandit | https://openreview.net/forum?id=hLbeJ6jObDD | https://openreview.net/forum?id=hLbeJ6jObDD | Yihan Du,Wei Chen,Yuko Kuroki,Longbo Huang | ICLR 2023,Poster | In this paper, we propose a novel Collaborative Pure Exploration in Kernel Bandit model (CoPE-KB), where multiple agents collaborate to complete different but related tasks with limited communication. Our model generalizes prior CoPE formulation with the single-task and classic MAB setting to allow multiple tasks and general reward structures. We propose a novel communication scheme with an efficient kernelized estimator, and design optimal algorithms CoKernelFC and CoKernelFB for CoPE-KB with fixed-confidence and fixed-budget objectives, respectively. Nearly matching upper and lower bounds in both sampling and communication complexity are established to demonstrate the optimality of our algorithms. Our theoretical results explicitly quantify how task similarities influence learning speedup, and only depend on the effective dimension of feature space. Our novel techniques including an efficient kernelized estimator and linear structured instance transformation, which overcome the communication difficulty in high-dimensional feature space and derive communication round lower bounds, can be of independent interests. | https://openreview.net/pdf/de61b7fc244459159df63514d2e06e2a3eaa49f5.pdf |
Provably Efficient Risk-Sensitive Reinforcement Learning: Iterated CVaR and Worst Path | https://openreview.net/forum?id=Yn0xg-kHNW- | https://openreview.net/forum?id=Yn0xg-kHNW- | Yihan Du,Siwei Wang,Longbo Huang | ICLR 2023,Poster | In this paper, we study a novel episodic risk-sensitive Reinforcement Learning (RL) problem, named Iterated CVaR RL, which aims to maximize the tail of the reward-to-go at each step, and focuses on tightly controlling the risk of getting into catastrophic situations at each stage. This formulation is applicable to real-world tasks that demand strong risk avoidance throughout the decision process, such as autonomous driving, clinical treatment planning and robotics. We investigate two performance metrics under Iterated CVaR RL, i.e., Regret Minimization and Best Policy Identification. For both metrics, we design efficient algorithms ICVaR-RM and ICVaR-BPI, respectively, and provide nearly matching upper and lower bounds with respect to the number of episodes $K$. We also investigate an interesting limiting case of Iterated CVaR RL, called Worst Path RL, where the objective becomes to maximize the minimum possible cumulative reward. For Worst Path RL, we propose an efficient algorithm with constant upper and lower bounds. Finally, the techniques we develop for bounding the change of CVaR due to the value function shift and decomposing the regret via a distorted visitation distribution are novel, and can find applications in other risk-sensitive online learning problems. | https://openreview.net/pdf/522515c753abedbb1309e0ee55893ed3b4e91f6c.pdf |
Test-Time Robust Personalization for Federated Learning | https://openreview.net/forum?id=3aBuJEza5sq | https://openreview.net/forum?id=3aBuJEza5sq | Liangze Jiang,Tao Lin | ICLR 2023,Poster | Federated Learning (FL) is a machine learning paradigm where many clients collaboratively learn a shared global model with decentralized training data. Personalization on FL models additionally adapts the global model to different clients, achieving promising results on consistent local training & test distributions. However, for real-world personalized FL applications, it is crucial to go one step further: robustifying FL models under the evolving local test set during deployment, where various types of distribution shifts can arise. In this work, we identify the pitfalls of existing works under test-time distribution shifts and propose Federated Test-time Head Ensemble plus tuning (FedTHE+), which personalizes FL models with robustness to various test-time distribution shifts. We illustrate the advancement of FedTHE+ (and its degraded computationally efficient variant FedTHE) over strong competitors, for training various neural architectures (CNN, ResNet, and Transformer) on CIFAR10 and ImageNet and evaluating on diverse test distributions. Along with this, we build a benchmark for assessing the performance and robustness of personalized FL methods during deployment. Code: \url{https://github.com/LINs-lab/FedTHE}.
| https://openreview.net/pdf/760207455793019468b1695bd64f24537a7765bc.pdf |
Learning to Linearize Deep Neural Networks for Secure and Efficient Private Inference | https://openreview.net/forum?id=BGF9IeDfmlH | https://openreview.net/forum?id=BGF9IeDfmlH | Souvik Kundu,Shunlin Lu,Yuke Zhang,Jacqueline Tiffany Liu,Peter Anthony Beerel | ICLR 2023,Poster | The large number of ReLU non-linearity operations in existing deep neural networks makes them ill-suited for latency-efficient private inference (PI). Existing techniques to reduce ReLU operations often involve manual effort and sacrifice significant accuracy. In this paper, we first present a novel measure of non-linearity layers’ ReLU sensitivity, enabling mitigation of the time-consuming manual efforts in identifying the same. Based on this sensitivity, we then present SENet, a three-stage training method that for a given ReLU budget, automatically assigns per-layer ReLU counts, decides the ReLU locations for each layer’s activation map, and trains a model with significantly fewer ReLUs to potentially yield latency and communication efficient PI. Experimental evaluations with multiple models on various datasets show SENet’s superior performance both in terms of reduced ReLUs and improved classification accuracy compared to existing alternatives. In particular, SENet can yield models that require up to ∼2× fewer ReLUs while yielding similar accuracy. For a similar ReLU budget SENet can yield models with ∼2.32% improved classification accuracy, evaluated on CIFAR-100. | https://openreview.net/pdf/35f3e94fd4c6765836d89bf0ee8307a99020a02c.pdf |
Meta Knowledge Condensation for Federated Learning | https://openreview.net/forum?id=TDf-XFAwc79 | https://openreview.net/forum?id=TDf-XFAwc79 | Ping Liu,Xin Yu,Joey Tianyi Zhou | ICLR 2023,Poster | Existing federated learning paradigms usually extensively exchange distributed models, rather than original data, at a central solver to achieve a more powerful model. However, this would incur severe communication burden between a server and multiple clients especially when data distributions are heterogeneous. As a result, current federated learning methods often require plenty of communication rounds in training. Unlike existing paradigms, we introduce an alternative perspective to significantly decrease the federate learning communication cost without leaking original data. In this work, we first present a meta knowledge representation method that extracts meta knowledge from distributed clients. The extracted meta knowledge encodes essential information that can be used to improve the current model. As the training progresses, the contributions of the same training samples to a federated model should also vary. Thus, we introduce a dynamic weight assignment mechanism that enables informative samples to contribute adaptively to the current model update. Then, informative meta knowledge from all active clients is sent to the server for model update. Training model on the combined meta knowledge that is regarded as a condense form of original data can significantly mitigate the heterogeneity issues. Moreover, to further ameliorate data heterogeneity, we also exchange meta knowledge among clients as conditional initialisation for meta knowledge extraction. Extensive experiments demonstrate the effectiveness and efficiency of our proposed method. Remarkably, our method outperforms the state-of-the-art by a large margin (from $74.07\%$ to $92.95\%$) on MNIST with a restricted communication budget (\textit{i.e.}, 10 rounds). | https://openreview.net/pdf/5cab9457b013d537fb818c57e3ac00c817524faa.pdf |
Masked Frequency Modeling for Self-Supervised Visual Pre-Training | https://openreview.net/forum?id=9-umxtNPx5E | https://openreview.net/forum?id=9-umxtNPx5E | Jiahao Xie,Wei Li,Xiaohang Zhan,Ziwei Liu,Yew-Soon Ong,Chen Change Loy | ICLR 2023,Poster | We present Masked Frequency Modeling (MFM), a unified frequency-domain-based approach for self-supervised pre-training of visual models. Instead of randomly inserting mask tokens to the input embeddings in the spatial domain, in this paper, we shift the perspective to the frequency domain. Specifically, MFM first masks out a portion of frequency components of the input image and then predicts the missing frequencies on the frequency spectrum. Our key insight is that predicting masked components in the frequency domain is more ideal to reveal underlying image patterns rather than predicting masked patches in the spatial domain, due to the heavy spatial redundancy. Our findings suggest that with the right configuration of mask-and-predict strategy, both the structural information within high-frequency components and the low-level statistics among low-frequency counterparts are useful in learning good representations. For the first time, MFM demonstrates that, for both ViT and CNN, a simple non-Siamese framework can learn meaningful representations even using none of the following: (i) extra data, (ii) extra model, (iii) mask token. Experimental results on image classification and semantic segmentation, as well as several robustness benchmarks show the competitive performance and advanced robustness of MFM compared with recent masked image modeling approaches. Furthermore, we also comprehensively investigate the effectiveness of classical image restoration tasks for representation learning from a unified frequency perspective and reveal their intriguing relations with our MFM approach. Project page: https://www.mmlab-ntu.com/project/mfm/index.html. | https://openreview.net/pdf/3612ffc6cb79e236fa2961f3a99e5138d9a04f36.pdf |
Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning | https://openreview.net/forum?id=DHyHRBwJUTN | https://openreview.net/forum?id=DHyHRBwJUTN | Pan Lu,Liang Qiu,Kai-Wei Chang,Ying Nian Wu,Song-Chun Zhu,Tanmay Rajpurohit,Peter Clark,Ashwin Kalyan | ICLR 2023,Poster | Mathematical reasoning, a core ability of human intelligence, presents unique challenges for machines in abstract thinking and logical reasoning. Recent large pre-trained language models such as GPT-3 have achieved remarkable progress on mathematical reasoning tasks written in text form, such as math word problems (MWP). However, it is unknown if the models can handle more complex problems that involve math reasoning over heterogeneous information, such as tabular data. To fill the gap, we present Tabular Math Word Problems (TabMWP), a new dataset containing 38,431 open-domain grade-level problems that require mathematical reasoning on both textual and tabular data. Each question in TabMWP is aligned with a tabular context, which is presented as an image, semi-structured text, and a structured table. There are two types of questions: free-text and multi-choice, and each problem is annotated with gold solutions to reveal the multi-step reasoning process. We evaluate different pre-trained models on TabMWP, including the GPT-3 model in a few-shot setting. As earlier studies suggest, since few-shot GPT-3 relies on the selection of in-context examples, its performance is unstable and can degrade to near chance. The unstable issue is more severe when handling complex problems like TabMWP. To mitigate this, we further propose a novel approach, PromptPG, which utilizes policy gradient to learn to select in-context examples from a small amount of training data and then constructs the corresponding prompt for the test example. Experimental results show that our method outperforms the best baseline by 5.31% on the accuracy metric and reduces the prediction variance significantly compared to random selection, which verifies its effectiveness in selecting in-context examples. The data and code are available at https://promptpg.github.io. | https://openreview.net/pdf/1f2f51f57875ec48e1bb27c936aa39ee2e65d06e.pdf |
Learning Object-Language Alignments for Open-Vocabulary Object Detection | https://openreview.net/forum?id=mjHlitXvReu | https://openreview.net/forum?id=mjHlitXvReu | Chuang Lin,Peize Sun,Yi Jiang,Ping Luo,Lizhen Qu,Gholamreza Haffari,Zehuan Yuan,Jianfei Cai | ICLR 2023,Poster | Existing object detection methods are bounded in a fixed-set vocabulary by costly labeled data. When dealing with novel categories, the model has to be retrained with more bounding box annotations. Natural language supervision is an attractive alternative for its annotation-free attributes and broader object concepts. However, learning open-vocabulary object detection from language is challenging since image-text pairs do not contain fine-grained object-language alignments. Previous solutions rely on either expensive grounding annotations or distilling classification-oriented vision models. In this paper, we propose a novel open-vocabulary object detection framework directly learning from image-text pair data. We formulate object-language alignment as a set matching problem between a set of image region features and a set of word embeddings. It enables us to train an open-vocabulary object detector on image-text pairs in a much simple and effective way. Extensive experiments on two benchmark datasets, COCO and LVIS, demonstrate our superior performance over the competing approaches on novel categories, e.g. achieving 32.0% mAP on COCO and 21.7% mask mAP on LVIS. Code will be released. | https://openreview.net/pdf/cb8cac570e10d08a902ce48c0fa2bed07668f5ff.pdf |
Phase transition for detecting a small community in a large network | https://openreview.net/forum?id=iN3Lh-Vy2TH | https://openreview.net/forum?id=iN3Lh-Vy2TH | Jiashun Jin,Tracy Ke,Paxton Turner,Anru Zhang | ICLR 2023,Poster | How to detect a small community in a large network is an interesting problem, including clique detection as a special case, where a naive degree-based $\chi^2$-test was shown to be powerful in the presence of an Erdös-Renyi (ER) background. Using Sinkhorn's theorem, we show that the signal captured by the $\chi^2$-test may be a modeling artifact, and it may disappear once we replace the Erdös-Renyi model by a broader network model. We show that the recent SgnQ test is more appropriate for such a setting. The test is optimal in detecting communities with sizes comparable to the whole network, but has never been studied for our setting, which is substantially different and more challenging. Using a degree-corrected block model (DCBM), we establish phase transitions of this testing problem concerning the size of the small community and the edge densities in small and large communities. When the size of the small community is larger than $\sqrt{n}$, the SgnQ test is optimal for it attains the computational lower bound (CLB), the information lower bound for methods allowing polynomial computation time. When the size of the small community is smaller than $\sqrt{n}$, we establish the parameter regime where the SgnQ test has full power and make some conjectures of the CLB. We also study the classical information lower bound (LB) and show that there is always a gap between the CLB and LB in our range of interest. | https://openreview.net/pdf/c00c92abf1ef98a46e3daf06a972016b311dc7df.pdf |
On the Word Boundaries of Emergent Languages Based on Harris's Articulation Scheme | https://openreview.net/forum?id=b4t9_XASt6G | https://openreview.net/forum?id=b4t9_XASt6G | Ryo Ueda,Taiga Ishii,Yusuke Miyao | ICLR 2023,Poster | This paper shows that emergent languages in signaling games lack meaningful word boundaries in terms of Harris's Articulation Scheme (HAS), a universal property of natural language. Emergent Languages are artificial communication protocols arising among agents. However, it is not obvious whether such a simulated language would have the same properties as natural language. In this paper, we test if they satisfy HAS. HAS states that word boundaries can be obtained solely from phonemes in natural language. We adopt HAS-based word segmentation and verify whether emergent languages have meaningful word segments. The experiment suggested they do not have, although they meet some preconditions for HAS. We discovered a gap between emergent and natural languages to be bridged, indicating that the standard signaling game satisfies prerequisites but is still missing some necessary ingredients. | https://openreview.net/pdf/41416040a375f7713f32fe3cce94e4465ba66a86.pdf |
TempCLR: Temporal Alignment Representation with Contrastive Learning | https://openreview.net/forum?id=CIFOsnhZvON | https://openreview.net/forum?id=CIFOsnhZvON | Yuncong Yang,Jiawei Ma,Shiyuan Huang,Long Chen,Xudong Lin,Guangxing Han,Shih-Fu Chang | ICLR 2023,Poster | Video representation learning has been successful in video-text pre-training for zero-shot transfer, where each sentence is trained to be close to the paired video clips in a common feature space. For long videos, given a paragraph of description where the sentences describe different segments of the video, by matching all sentence-clip pairs, the paragraph and the full video are aligned implicitly. However, such unit-level similarity measure may ignore the global temporal context over a long time span, which inevitably limits the generalization ability. In this paper, we propose a contrastive learning framework TempCLR to compare the full video and the paragraph explicitly. As the video/paragraph is formulated as a sequence of clips/sentences, under the constraint of their temporal order, we use dynamic time warping to compute the minimum cumulative cost over sentence-clip pairs as the sequence-level distance. To explore the temporal dynamics, we break the consistency of temporal order by shuffling the video clips or sentences according to the temporal granularity. In this way, we obtain the representations for clips/sentences, which perceive the temporal information and thus facilitate the sequence alignment. In addition to pre-training on the video and paragraph, our approach can also generalize on the matching between different video instances. We evaluate our approach on video retrieval, action step localization, and few-shot action recognition, and achieve consistent performance gain over all three tasks. Detailed ablation studies are provided to justify the approach design. | https://openreview.net/pdf/f4df84ba55e6a74dd51327e33ccbe729ed2f166c.pdf |
Bort: Towards Explainable Neural Networks with Bounded Orthogonal Constraint | https://openreview.net/forum?id=My57qBufZWs | https://openreview.net/forum?id=My57qBufZWs | Borui Zhang,Wenzhao Zheng,Jie Zhou,Jiwen Lu | ICLR 2023,Poster | Deep learning has revolutionized human society, yet the black-box nature of deep neural networks hinders further application to reliability-demanded industries. In the attempt to unpack them, many works observe or impact internal variables to improve the comprehensibility and invertibility of the black-box models. However, existing methods rely on intuitive assumptions and lack mathematical guarantees. To bridge this gap, we introduce Bort, an optimizer for improving model explainability with boundedness and orthogonality constraints on model parameters, derived from the sufficient conditions of model comprehensibility and invertibility. We perform reconstruction and backtracking on the model representations optimized by Bort and observe a clear improvement in model explainability. Based on Bort, we are able to synthesize explainable adversarial samples without additional parameters and training. Surprisingly, we find Bort constantly improves the classification accuracy of various architectures including ResNet and DeiT on MNIST, CIFAR-10, and ImageNet. Code: https://github.com/zbr17/Bort. | https://openreview.net/pdf/568c492c97564ec3f94f6ce7dd4e56d9f2fd5efe.pdf |
The Power of Regularization in Solving Extensive-Form Games | https://openreview.net/forum?id=bPiHuNUNv_R | https://openreview.net/forum?id=bPiHuNUNv_R | Mingyang Liu,Asuman E. Ozdaglar,Tiancheng Yu,Kaiqing Zhang | ICLR 2023,Poster | In this paper, we investigate the power of {\it regularization}, a common technique in reinforcement learning and optimization, in solving extensive-form games (EFGs). We propose a series of new algorithms based on regularizing the payoff functions of the game, and establish a set of convergence results that strictly improve over the existing ones, with either weaker assumptions or stronger convergence guarantees. In particular, we first show that dilated optimistic mirror descent (DOMD), an efficient variant of OMD for solving EFGs, with adaptive regularization can achieve a fast $\tilde O(1/T)$ last-iterate convergence in terms of duality gap and distance to the set of Nash equilibrium (NE) without uniqueness assumption of the NE. Second, we show that regularized counterfactual regret minimization (\texttt{Reg-CFR}), with a variant of optimistic mirror descent algorithm as regret-minimizer, can achieve $O(1/T^{1/4})$ best-iterate, and $O(1/T^{3/4})$ average-iterate convergence rate for finding NE in EFGs. Finally, we show that \texttt{Reg-CFR} can achieve asymptotic last-iterate convergence, and optimal $O(1/T)$ average-iterate convergence rate, for finding the NE of perturbed EFGs, which is useful for finding approximate extensive-form perfect equilibria (EFPE). To the best of our knowledge, they constitute the first last-iterate convergence results for CFR-type algorithms, while matching the state-of-the-art average-iterate convergence rate in finding NE for non-perturbed EFGs. We also provide numerical results to corroborate the advantages of our algorithms. | https://openreview.net/pdf/e182118c8044afa1567ce02597914314835a0da7.pdf |
MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization | https://openreview.net/forum?id=P8YIphWNEGO | https://openreview.net/forum?id=P8YIphWNEGO | Xiaotian Han,Tong Zhao,Yozen Liu,Xia Hu,Neil Shah | ICLR 2023,Poster | Training graph neural networks (GNNs) on large graphs is complex and extremely time consuming. This is attributed to overheads caused by sparse matrix multiplication, which are sidestepped when training multi-layer perceptrons (MLPs) with only node features. MLPs, by ignoring graph context, are simple and faster for graph data, however they usually sacrifice prediction accuracy, limiting their applications for graph data. We observe that for most message passing-based GNNs, we can trivially derive an analog MLP (we call this a PeerMLP) with an equivalent weight space, by setting the trainable parameters with the same shapes, making us curious about how do GNNs using weights from a fully trained PeerMLP perform? Surprisingly, we find that GNNs initialized with such weights significantly outperform their PeerMLPs, motivating us to use PeerMLP training as a precursor, initialization step to GNN training. To this end, we propose an embarrassingly simple, yet hugely effective initialization method for GNN training acceleration, called \mlpinit. Our extensive experiments on multiple large-scale graph datasets with diverse GNN architectures validate that MLPInit can accelerate the training of GNNs (up to 33× speedup on OGB-Products) and often improve prediction performance (e.g., up to $7.97\%$ improvement for GraphSAGE across $7$ datasets for node classification, and up to $17.81\%$ improvement across $4$ datasets for link prediction on metric Hits@10). The code is available at https://github.com/snap-research/MLPInit-for-GNNs. | https://openreview.net/pdf/5504596be9b6c6444f19a89e626afd15c735b736.pdf |
Progressively Compressed Auto-Encoder for Self-supervised Representation Learning | https://openreview.net/forum?id=8T4qmZbTkW7 | https://openreview.net/forum?id=8T4qmZbTkW7 | Jin Li,Yaoming Wang,XIAOPENG ZHANG,Yabo Chen,Dongsheng Jiang,Wenrui Dai,Chenglin Li,Hongkai Xiong,Qi Tian | ICLR 2023,Poster | As a typical self-supervised learning strategy, Masked Image Modeling (MIM) is driven by recovering all masked patches from visible ones. However, patches from the same image are highly correlated and it is redundant to reconstruct all the masked patches. We find that this redundancy is neglected by existing MIM based methods and causes non-negligible overheads in computation that do not necessarily benefit self-supervised representation. In this paper, we present a novel approach named PCAE, short for Progressively Compressed AutoEncoder, to address the redundant reconstruction issue by progressively compacting tokens and only retaining necessary information for forward propagation and reconstruction. In particular, we identify those redundant tokens in an image via a simple yet effective similarity metric between each token with the mean of the token sequence. Those redundant tokens that other ones can probably represent are progressively dropped accordingly during the forward propagation, and importantly, we only focus on reconstructing these retained tokens. As a result, we are able to achieve a better trade-off between performance and efficiency for pre-training. Besides, benefitting from the flexible strategy, PCAE can be also directly employed for downstream fine-tuning tasks and enable scalable deployment. Experiments show that PCAE achieves comparable performance to MAE with only 1/8 GPU days. The code is available at https://github.com/caddyless/PCAE/. | https://openreview.net/pdf/216b76a14f9bcf830842688219297d41c90a0935.pdf |
S-NeRF: Neural Radiance Fields for Street Views | https://openreview.net/forum?id=gx2yJS-ENqI | https://openreview.net/forum?id=gx2yJS-ENqI | Ziyang Xie,Junge Zhang,Wenye Li,Feihu Zhang,Li Zhang | ICLR 2023,Poster | Neural Radiance Fields (NeRFs) aim to synthesize novel views of objects and scenes, given the object-centric camera views with large overlaps. However, we conjugate that this paradigm does not fit the nature of the street views that are collected by many self-driving cars from the large-scale unbounded scenes. Also, the onboard cameras perceive scenes without much overlapping. Thus, existing NeRFs often produce blurs, "floaters" and other artifacts on street-view synthesis. In this paper, we propose a new street-view NeRF (S-NeRF) that considers novel view synthesis of both the large-scale background scenes and the foreground moving vehicles jointly. Specifically, we improve the scene parameterization function and the camera poses for learning better neural representations from street views. We also use the the noisy and sparse LiDAR points to boost the training and learn a robust geometry and reprojection based confidence to address the depth outliers. Moreover, we extend our S-NeRF for reconstructing moving vehicles that is impracticable for conventional NeRFs. Thorough experiments on the large-scale driving datasets (e.g., nuScenes and Waymo) demonstrate that our method beats the state-of-the-art rivals by reducing 7～40% of the mean-squared error in the street-view synthesis and a 45% PSNR gain for the moving vehicles rendering. | https://openreview.net/pdf/f620fccf8a60810c2d760b1cf94ad8c82f499dac.pdf |
Cycle-consistent Masked AutoEncoder for Unsupervised Domain Generalization | https://openreview.net/forum?id=wC98X1qpDBA | https://openreview.net/forum?id=wC98X1qpDBA | Haiyang Yang,Xiaotong Li,SHIXIANG TANG,Feng Zhu,Yizhou Wang,Meilin Chen,LEI BAI,Rui Zhao,Wanli Ouyang | ICLR 2023,Poster | Self-supervised learning methods undergo undesirable performance drops when there exists a significant domain gap between training and testing scenarios. Therefore, unsupervised domain generalization (UDG) is proposed to tackle the problem, which requires the model to be trained on several different domains without supervision and generalize well on unseen test domains. Existing methods either rely on a cross-domain and semantically consistent image pair in contrastive methods or the reconstruction pair in generative methods, while the precious image pairs are not available without semantic labels. In this paper, we propose a cycle cross-domain reconstruction task for unsupervised domain generalization in the absence of paired images. The cycle cross-domain reconstruction task converts a masked image from one domain to another domain and then reconstructs the original image from the converted images. To preserve the divergent domain knowledge of decoders in the cycle reconstruction task, we propose a novel domain-contrastive loss to regularize the domain information in reconstructed images encoded with the desirable domain style. Qualitative results on extensive datasets illustrate our method improves the state-of-the-art unsupervised domain generalization methods by average $\textbf{+5.59\%}, \textbf{+4.52\%}, \textbf{+4.22\%}, \textbf{+7.02\%}$ on $1\%, 5\%, 10\%, 100\%$ PACS, and $\textbf{+5.08\%}, \textbf{+6.49\%}, \textbf{+1.79\%}, \textbf{+0.53\%}$ on $1\%, 5\%, 10\%, 100\%$ DomainNet, respectively. | https://openreview.net/pdf/80923f306be26ea8c160f03fb85027b691b34dbb.pdf |
CFlowNets: Continuous Control with Generative Flow Networks | https://openreview.net/forum?id=yAYHho4fATa | https://openreview.net/forum?id=yAYHho4fATa | Yinchuan Li,Shuang Luo,Haozhi Wang,Jianye HAO | ICLR 2023,Poster | Generative flow networks (GFlowNets), as an emerging technique, can be used as an alternative to reinforcement learning for exploratory control tasks. GFlowNets aims to sample actions with a probability proportional to the reward, similar to sampling different candidates in an active learning fashion. However, existing GFlowNets cannot adapt to continuous control tasks because GFlowNets need to form a DAG and compute the flow matching loss by traversing the inflows and outflows of each node in the trajectory. In this paper, we propose generative continuous flow networks (CFlowNets) that can be applied to continuous control tasks. First, we present the theoretical formulation of CFlowNets. Then, a training framework for CFlowNets is proposed, including the action selection process, the flow approximation algorithm, and the continuous flow matching loss function. Afterward, we theoretically prove the error bound of the flow approximation. The error decreases rapidly as the number of flow samples increases. Finally, experimental results on continuous control tasks demonstrate the performance advantages of CFlowNets compared to many reinforcement learning methods, especially regarding exploration ability. | https://openreview.net/pdf/d04aea952ea57b0759baa3d16e05576bc4d9fd2b.pdf |
Differentiable Gaussianization Layers for Inverse Problems Regularized by Deep Generative Models | https://openreview.net/forum?id=OXP9Ns0gnIq | https://openreview.net/forum?id=OXP9Ns0gnIq | Dongzhuo Li | ICLR 2023,Poster | Deep generative models such as GANs, normalizing flows, and diffusion models are powerful regularizers for inverse problems. They exhibit great potential for helping reduce ill-posedness and attain high-quality results. However, the latent tensors of such deep generative models can fall out of the desired high-dimensional standard Gaussian distribution during inversion, particularly in the presence of data noise and inaccurate forward models, leading to low-fidelity solutions. To address this issue, we propose to reparameterize and Gaussianize the latent tensors using novel differentiable data-dependent layers wherein custom operators are defined by solving optimization problems. These proposed layers constrain inverse problems to obtain high-fidelity in-distribution solutions. We validate our technique on three inversion tasks: compressive-sensing MRI, image deblurring, and eikonal tomography (a nonlinear PDE-constrained inverse problem) using two representative deep generative models: StyleGAN2 and Glow. Our approach achieves state-of-the-art performance in terms of accuracy and consistency. | https://openreview.net/pdf/0d918b07dda7aa6571c2c0ac1eff7f920d47bf7b.pdf |
DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detection | https://openreview.net/forum?id=ZccFLU-Yk65 | https://openreview.net/forum?id=ZccFLU-Yk65 | Jinrong Yang,Lin Song,Songtao Liu,Weixin Mao,Zeming Li,Xiaoping Li,Hongbin Sun,Jian Sun,Nanning Zheng | ICLR 2023,Poster | Many point-based 3D detectors adopt point-feature sampling strategies to drop some points for efficient inference. These strategies are typically based on fixed and handcrafted rules, making it difficult to handle complicated scenes. Different from them, we propose a Dynamic Ball Query (DBQ) network to adaptively select a subset of input points according to the input features, and assign the feature transform with a suitable receptive field for each selected point. It can be embedded into some state-of-the-art 3D detectors and trained in an end-to-end manner, which significantly reduces the computational cost. Extensive experiments demonstrate that our method can reduce latency by 30%-100% on KITTI, Waymo, and ONCE datasets. Specifically, the inference speed of our detector can reach 162 FPS on KITTI scene, and 30 FPS on Waymo and ONCE scenes without performance degradation. Due to skipping the redundant points, some evaluation metrics show significant improvements. | https://openreview.net/pdf/7e4f348e394155ee1fbc1dcf63d19d0aa47abccb.pdf |
Exploring Low-Rank Property in Multiple Instance Learning for Whole Slide Image Classification | https://openreview.net/forum?id=01KmhBsEPFO | https://openreview.net/forum?id=01KmhBsEPFO | Jinxi Xiang,Jun Zhang | ICLR 2023,Poster | The classification of gigapixel-sized whole slide images (WSIs) with slide-level labels can be formulated as a multiple-instance-learning (MIL) problem. State-of-the-art models often consist of two decoupled parts: local feature embedding with a pre-trained model followed by a global feature aggregation network for classification. We leverage the properties of the apparent similarity in high-resolution WSIs, which essentially exhibit \textit{low-rank} structures in the data manifold, to develop a novel MIL with a boost in both feature embedding and feature aggregation. We extend the contrastive learning with a pathology-specific Low-Rank Constraint (LRC) for feature embedding to pull together samples (i.e., patches) belonging to the same pathological tissue in the low-rank subspace and simultaneously push apart those from different latent subspaces. At the feature aggregation stage, we introduce an iterative low-rank attention MIL (ILRA-MIL) model to aggregate features with low-rank learnable latent vectors to model global interactions among all instances. We highlight the importance of instance correlation modeling but refrain from directly using the transformer encoder considering the $O(n^2)$ complexity. ILRA-MIL with LRC pre-trained features achieves strong empirical results across various benchmarks, including (i) 96.49\% AUC on the CAMELYON16 for binary metastasis classification, (ii) 97.63\% AUC on the TCGA-NSCLC for lung cancer subtyping, and (iii) 0.6562 kappa on the large-scale PANDA dataset for prostate cancer classification. The code is available at https://github.com/jinxixiang/low_rank_wsi. | https://openreview.net/pdf/d0eda3d45dbc986243ff39276e4dd535fc40aa9a.pdf |
Causal Balancing for Domain Generalization | https://openreview.net/forum?id=F91SROvVJ_6 | https://openreview.net/forum?id=F91SROvVJ_6 | Xinyi Wang,Michael Saxon,Jiachen Li,Hongyang Zhang,Kun Zhang,William Yang Wang | ICLR 2023,Poster | While machine learning models rapidly advance the state-of-the-art on various real-world tasks, out-of-domain (OOD) generalization remains a challenging problem given the vulnerability of these models to spurious correlations. We propose a balanced mini-batch sampling strategy to transform a biased data distribution into a spurious-free balanced distribution, based on the invariance of the underlying causal mechanisms for the data generation process. We argue that the Bayes optimal classifiers trained on such balanced distribution are minimax optimal across a diverse enough environment space. We also provide an identifiability guarantee of the latent variable model of the proposed data generation process, when utilizing enough train environments. Experiments are conducted on DomainBed, demonstrating empirically that our method obtains the best performance across 20 baselines reported on the benchmark. | https://openreview.net/pdf/6b88930a2f4b17b072ce937d41b10c43e6d346fd.pdf |
Towards Addressing Label Skews in One-Shot Federated Learning | https://openreview.net/forum?id=rzrqh85f4Sc | https://openreview.net/forum?id=rzrqh85f4Sc | Yiqun Diao,Qinbin Li,Bingsheng He | ICLR 2023,Poster | Federated learning (FL) has been a popular research area, where multiple clients collaboratively train a model without sharing their local raw data. Among existing FL solutions, one-shot FL is a promising and challenging direction, where the clients conduct FL training with a single communication round. However, while label skew is a common real-world scenario where some clients may have few or no data of some classes, existing one-shot FL approaches that conduct voting on the local models are not able to produce effective global models. Due to the limited number of classes in each party, the local models misclassify the data from unseen classes into seen classes, which leads to very ineffective global models from voting. To address the label skew issue in one-shot FL, we propose a novel approach named FedOV which generates diverse outliers and introduces them as an additional unknown class in local training to improve the voting performance. Specifically, based on open-set recognition, we propose novel outlier generation approaches by corrupting the original features and further develop adversarial learning to enhance the outliers. Our extensive experiments show that FedOV can significantly improve the test accuracy compared to state-of-the-art approaches in various label skew settings. | https://openreview.net/pdf/8eeb662e01315fcdeccb79b7f1631b46d561813e.pdf |
Breaking Correlation Shift via Conditional Invariant Regularizer | https://openreview.net/forum?id=-jTaz3CMk72 | https://openreview.net/forum?id=-jTaz3CMk72 | Mingyang Yi,Ruoyu Wang,Jiacheng Sun,Zhenguo Li,Zhi-Ming Ma | ICLR 2023,Poster | Recently, generalization on out-of-distribution (OOD) data with correlation shift has attracted great attentions. The correlation shift is caused by the spurious attributes that correlate to the class label, as the correlation between them may vary in training and test data. For such a problem, we show that given the class label, the models that are conditionally independent of spurious attributes are OOD generalizable. Based on this, a metric Conditional Spurious Variation (CSV) which controls the OOD generalization error, is proposed to measure such conditional independence. To improve the OOD generalization, we regularize the training process with the proposed CSV. Under mild assumptions, our training objective can be formulated as a nonconvex-concave mini-max problem. An algorithm with a provable convergence rate is proposed to solve the problem. Extensive empirical results verify our algorithm's efficacy in improving OOD generalization. | https://openreview.net/pdf/f48bf469639b94c10700368ae8e9c35026d31565.pdf |
Towards One-shot Neural Combinatorial Solvers: Theoretical and Empirical Notes on the Cardinality-Constrained Case | https://openreview.net/forum?id=h21yJhdzbwz | https://openreview.net/forum?id=h21yJhdzbwz | Runzhong Wang,Li Shen,Yiting Chen,Xiaokang Yang,Dacheng Tao,Junchi Yan | ICLR 2023,Poster | One-shot non-autoregressive neural networks, different from RL-based ones, have been actively adopted for solving combinatorial optimization (CO) problems, which can be trained by the objective score in a self-supervised manner. Such methods have shown their superiority in efficiency (e.g. by parallelization) and potential for tackling predictive CO problems for decision-making under uncertainty. While the discrete constraints often become a bottleneck for gradient-based neural solvers, as currently handled in three typical ways: 1) adding a soft penalty in the objective, where a bounded violation of the constraints cannot be guaranteed, being critical to many constraint-sensitive scenarios; 2) perturbing the input to generate an approximate gradient in a black-box manner, though the constraints are exactly obeyed while the approximate gradients can hurt the performance on the objective score; 3) a compromise by developing soft algorithms whereby the output of neural networks obeys a relaxed constraint, and there can still occur an arbitrary degree of constraint-violation. Towards the ultimate goal of establishing a general framework for neural CO solver with the ability to control an arbitrary-small degree of constraint violation, in this paper, we focus on a more achievable and common setting: the cardinality constraints, which in fact can be readily encoded by a differentiable optimal transport (OT) layer. Based on this observation, we propose OT-based cardinality constraint encoding for end-to-end CO problem learning with two variants: Sinkhorn and Gumbel-Sinkhorn, whereby their violation of the constraints can be exactly characterized and bounded by our theoretical results. On synthetic and real-world CO problem instances, our methods surpass the state-of-the-art CO network and are comparable to (if not superior to) the commercial solver Gurobi. In particular, we further showcase a case study of applying our approach to the predictive portfolio optimization task on real-world asset price data, improving the Sharpe ratio from 1.1 to 2.0 of a strong LSTM+Gurobi baseline under the classic predict-then-optimize paradigm. | https://openreview.net/pdf/47ff06773ffd6757c65cd362fde9c7bfd3176168.pdf |
Block and Subword-Scaling Floating-Point (BSFP) : An Efficient Non-Uniform Quantization For Low Precision Inference | https://openreview.net/forum?id=VWm4o4l3V9e | https://openreview.net/forum?id=VWm4o4l3V9e | Yun-Chen Lo,Tse-Kuang Lee,Ren-Shuo Liu | ICLR 2023,Poster | In this paper, we propose Block and Subword-Scaling Floating-Point (BSFP), a non-uniform quantization scheme for the skewed and non-uniform distribution of weight vectors in neural networks. By quantizing each weight vector as the superposition of multiple subword vectors (in two's complement) with scaling factors (in Low-bit Floating-Point, LBFP), BSFP can effectively fit the distribution of weight vectors while maintaining high computation efficiency. Furthermore, we present a grid search-based MSE-optimal quantization flow and a scaled serial processing engine to complete the quantization pipeline and the infrastructure.
The experimental results on the ImageNet classification task show that our proposed method outperforms state-of-the-art Microsoft Floating Point (MSFP) by up to 20.56% top-1 accuracy at the same weight precision and reduces up to 10.3% model size. Furthermore, BSFP outperforms MSFP by up to 2.0$\times$ computing throughput and up to 5.3$\times$ energy efficiency under the same silicon area budget. | https://openreview.net/pdf/f1e497711d6deb4a3d3385c8784c05ae56a0a3b2.pdf |
Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning | https://openreview.net/forum?id=0qmwFNJyxCL | https://openreview.net/forum?id=0qmwFNJyxCL | Rundong Luo,Yifei Wang,Yisen Wang | ICLR 2023,Poster | Recent works have shown that self-supervised learning can achieve remarkable robustness when integrated with adversarial training (AT). However, the robustness gap between supervised AT (sup-AT) and self-supervised AT (self-AT) remains significant. Motivated by this observation, we revisit existing self-AT methods and discover an inherent dilemma that affects self-AT robustness: either strong or weak data augmentations are harmful to self-AT, and a medium strength is insufficient to bridge the gap. To resolve this dilemma, we propose a simple remedy named DYNACL (Dynamic Adversarial Contrastive Learning). In particular, we propose an augmentation schedule that gradually anneals from a strong augmentation to a weak one to benefit from both extreme cases. Besides, we adopt a fast post-processing stage for adapting it to downstream tasks. Through extensive experiments, we show that DYNACL can improve state-of-the-art self-AT robustness by 8.84% under Auto-Attack on the CIFAR-10 dataset, and can even outperform vanilla supervised adversarial training for the first time. Our code is available at \url{https://github.com/PKU-ML/DYNACL}. | https://openreview.net/pdf/0674733fd56c180a9e32157d194ad9d1fb37d85f.pdf |
Semi-supervised Community Detection via Structural Similarity Metrics | https://openreview.net/forum?id=cxvEGLCHpgl | https://openreview.net/forum?id=cxvEGLCHpgl | Yicong Jiang,Tracy Ke | ICLR 2023,Poster | Motivated by the interests of social network analysis and network-based recommendation systems, we consider a semi-supervised community detection problem, where the goal is to estimate the community label of a new node by leveraging on the network structure and partially observed community labels of existing nodes.
We model the network with a degree-corrected stochastic block model, which allows for severe degree heterogeneity and potentially non-assortative communities.
We propose a fast algorithm that computes a `structural similarity metric' between the new node and each of the $K$ communities, aggregating information in labeled and unlabeled data. The estimated label of the new node is equal to the value of $k$ that maximizes this similarity metric. Our method is computationally fast and compares favorably with existing semi-supervised algorithms on numerical performance. In theory, we derive explicit bounds for the misclassification error and show the efficiency of our method by comparing it with an ideal classifier. To our best knowledge, our results provide the first semi-supervised community detection algorithm with theoretical guarantees. | https://openreview.net/pdf/13e99b83f8e745c4c8b94b195d29ff73903bb11a.pdf |
DDM$^2$: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models | https://openreview.net/forum?id=0vqjc50HfcC | https://openreview.net/forum?id=0vqjc50HfcC | Tiange Xiang,Mahmut Yurt,Ali B Syed,Kawin Setsompop,Akshay Chaudhari | ICLR 2023,Poster | Magnetic resonance imaging (MRI) is a common and life-saving medical imaging technique. However, acquiring high signal-to-noise ratio MRI scans requires long scan times, resulting in increased costs and patient discomfort, and decreased throughput. Thus, there is great interest in denoising MRI scans, especially for the subtype of diffusion MRI scans that are severely SNR-limited. While most prior MRI denoising methods are supervised in nature, acquiring supervised training datasets for the multitude of anatomies, MRI scanners, and scan parameters proves impractical. Here, we propose Denoising Diffusion Models for Denoising Diffusion MRI (DDM^2), a self-supervised denoising method for MRI denoising using diffusion denoising generative models. Our three-stage framework integrates statistic-based denoising theory into diffusion models and performs denoising through conditional generation. During inference, we represent input noisy measurements as a sample from an intermediate posterior distribution within the diffusion Markov chain. We conduct experiments on 4 real-world in-vivo diffusion MRI datasets and show that our DDM^2 demonstrates superior denoising performances ascertained with clinically-relevant visual qualitative and quantitative metrics. | https://openreview.net/pdf/ba3ad1d1f754d30b7ce210a7b9c4d169705d5234.pdf |
Multivariate Time-series Imputation with Disentangled Temporal Representations | https://openreview.net/forum?id=rdjeCNUS6TG | https://openreview.net/forum?id=rdjeCNUS6TG | SHUAI LIU,Xiucheng Li,Gao Cong,Yile Chen,YUE JIANG | ICLR 2023,Poster | Multivariate time series often faces the problem of missing value. Many time series imputation methods have been developed in the literature. However, these methods all rely on an entangled representation to model dynamics of time series, which may fail to fully exploit the multiple factors (e.g., periodic patterns) contained in the time series. Moreover, the entangled representation usually has no semantic meaning, and thus they often lack interpretability. In addition, many recent models are proposed to deal with the whole time series to capture cross-channel correlations and identify temporal dynamics, but they are not scalable to large-scale datasets. Different from existing approaches, we propose TIDER, a novel matrix factorization-based method with disentangled temporal representations that account for multiple factors, namely trend, seasonality, and local bias, to model complex dynamics. The learned disentanglement makes the imputation process more reliable and offers explainability for imputation results. Moreover, TIDER is scalable to large datasets. Empirical results show that our method not only outperforms existing approaches by notable margins on three real-world datasets, but also scales well to large datasets on which existing deep learning based methods struggle. Disentanglement validation experiments further demonstrate the robustness of our model in obtaining accurate and explainable disentangled components. | https://openreview.net/pdf/7c6584c4824cce684dda0337c35779e83ed93b78.pdf |
Unified Detoxifying and Debiasing in Language Generation via Inference-time Adaptive Optimization | https://openreview.net/forum?id=FvevdI0aA_h | https://openreview.net/forum?id=FvevdI0aA_h | Zonghan Yang,Xiaoyuan Yi,Peng Li,Yang Liu,Xing Xie | ICLR 2023,Poster | Recently pre-trained language models (PLMs) have prospered in various natural language generation (NLG) tasks due to their ability to generate fairly fluent text. Nevertheless, these models are observed to capture and reproduce harmful contents in training corpora, typically toxic language and social biases, raising severe moral issues. Prior works on ethical NLG tackle detoxifying and debiasing separately, which is problematic since we find debiased models still exhibit toxicity while detoxified ones even exacerbate biases. To address such a challenge, we propose the first unified framework of detoxifying and debiasing called UDDIA, which jointly formalizes these two problems as rectifying the output space. We theoretically interpret our framework as learning a text distribution mixing weighted attributes. Besides, UDDIA conducts adaptive optimization of only a few parameters during decoding based on a parameter-efficient tuning schema without any training data. This leads to minimal generation quality loss and improved rectification performance with acceptable computational cost. Experimental results demonstrate that compared to several strong baselines, UDDIA achieves debiasing and detoxifying simultaneously and better balances efficiency and effectiveness, taking a further step towards practical ethical NLG. | https://openreview.net/pdf/d5308cde03505a14781fa29264be9ecd9cba9bc4.pdf |
Automating Nearest Neighbor Search Configuration with Constrained Optimization | https://openreview.net/forum?id=KfptQCEKVW4 | https://openreview.net/forum?id=KfptQCEKVW4 | Philip Sun,Ruiqi Guo,Sanjiv Kumar | ICLR 2023,Poster | The approximate nearest neighbor (ANN) search problem is fundamental to efficiently serving many real-world machine learning applications. A number of techniques have been developed for ANN search that are efficient, accurate, and scalable. However, such techniques typically have a number of parameters that affect the speed-recall tradeoff, and exhibit poor performance when such parameters aren't properly set. Tuning these parameters has traditionally been a manual process, demanding in-depth knowledge of the underlying search algorithm. This is becoming an increasingly unrealistic demand as ANN search grows in popularity. To tackle this obstacle to ANN adoption, this work proposes a constrained optimization-based approach to tuning quantization-based ANN algorithms. Our technique takes just a desired search cost or recall as input, and then generates tunings that, empirically, are very close to the speed-recall Pareto frontier and give leading performance on standard benchmarks. | https://openreview.net/pdf/7baf4a44b22ee9059ca564133650828b1290566e.pdf |
Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoders | https://openreview.net/forum?id=HDxgaKk956l | https://openreview.net/forum?id=HDxgaKk956l | Huangjie Zheng,Pengcheng He,Weizhu Chen,Mingyuan Zhou | ICLR 2023,Poster | Employing a forward diffusion chain to gradually map the data to a noise distribution, diffusion-based generative models learn how to generate the data by inferring a reverse diffusion chain. However, this approach is slow and costly because it needs many forward and reverse steps. We propose a faster and cheaper approach that adds noise not until the data become pure random noise, but until they reach a hidden noisy data distribution that we can confidently learn. Then, we use fewer reverse steps to generate data by starting from this hidden distribution that is made similar to the noisy data. We reveal that the proposed model can be cast as an adversarial auto-encoder empowered by both the diffusion process and a learnable implicit prior. Experimental results show even with a significantly smaller number of reverse diffusion steps, the proposed truncated diffusion probabilistic models can provide consistent improvements over the non-truncated ones in terms of performance in both unconditional and text-guided image generations. | https://openreview.net/pdf/32c55b679758b7a52dda4923d5a985cace1bd4f5.pdf |
NTK-SAP: Improving neural network pruning by aligning training dynamics | https://openreview.net/forum?id=-5EWhW_4qWP | https://openreview.net/forum?id=-5EWhW_4qWP | Yite Wang,Dawei Li,Ruoyu Sun | ICLR 2023,Poster | Pruning neural networks before training has received increasing interest due to its potential to reduce training time and memory. One popular method is to prune the connections based on a certain metric, but it is not entirely clear what metric is the best choice. Recent advances in neural tangent kernel (NTK) theory suggest that the training dynamics of large enough neural networks is closely related to the spectrum of the NTK. Motivated by this finding, we propose to prune the connections that have the least influence on the spectrum of the NTK. This method can help maintain the NTK spectrum, which may help align the training dynamics to that of its dense counterpart. However, one possible issue is that the fixed-weight-NTK corresponding to a given initial point can be very different from the NTK corresponding to later iterates during the training phase. We further propose to sample multiple realizations of random weights to estimate the NTK spectrum. Note that our approach is weight-agnostic, which is different from most existing methods that are weight-dependent. In addition, we use random inputs to compute the fixed-weight-NTK, making our method data-agnostic as well. We name our foresight pruning algorithm Neural Tangent Kernel Spectrum-Aware Pruning (NTK-SAP). Empirically, our method achieves better performance than all baselines on multiple datasets. | https://openreview.net/pdf/0c900b18137ea74d6ca0a934667ba24676032540.pdf |
Effective Self-supervised Pre-training on Low-compute Networks without Distillation | https://openreview.net/forum?id=cbpRzMy-UZH | https://openreview.net/forum?id=cbpRzMy-UZH | Fuwen Tan,Fatemeh Sadat Saleh,Brais Martinez | ICLR 2023,Poster | Despite the impressive progress of self-supervised learning (SSL), its applicability to low-compute networks has received limited attention. Reported performance has trailed behind standard supervised pre-training by a large margin, barring self-supervised learning from making an impact on models that are deployed on device. Most prior works attribute this poor performance to the capacity bottleneck of the low-compute networks and opt to bypass the problem through the use of knowledge distillation (KD). In this work, we revisit SSL for efficient neural networks, taking a closer at what are the detrimental factors causing the practical limitations, and whether they are intrinsic to the self-supervised low-compute setting. We find that, contrary to accepted knowledge, there is no intrinsic architectural bottleneck, we diagnose that the performance bottleneck is related to the model complexity vs regularization strength trade-off. In particular, we start by empirically observing that the use of local views can have a dramatic impact on the effectiveness of the SSL methods. This hints at view sampling being one of the performance bottlenecks for SSL on low-capacity networks. We hypothesize that the view sampling strategy for large neural networks, which requires matching views in very diverse spatial scales and contexts, is too demanding for low-capacity architectures. We systematize the design of the view sampling mechanism, leading to a new training methodology that consistently improves the performance across different SSL methods (e.g. MoCo-v2, SwAV or DINO), different low-size networks (convolution-based networks, e.g. MobileNetV2, ResNet18, ResNet34 and vision transformer, e.g. ViT-Ti), and different tasks (linear probe, object detection, instance segmentation and semi-supervised learning). Our best models establish new state-of-the-art for SSL methods on low-compute networks despite not using a KD loss term. Code is publicly available at github.com/saic-fi/SSLight. | https://openreview.net/pdf/ac14c4b70b2efd3b6c4e244137ae56017267bf5d.pdf |
CoRTX: Contrastive Framework for Real-time Explanation | https://openreview.net/forum?id=L2MUOUp0beo | https://openreview.net/forum?id=L2MUOUp0beo | Yu-Neng Chuang,Guanchu Wang,Fan Yang,Quan Zhou,Pushkar Tripathi,Xuanting Cai,Xia Hu | ICLR 2023,Poster | Recent advancements in explainable machine learning provide effective and faithful solutions for interpreting model behaviors. However, many explanation methods encounter efficiency issues, which largely limit their deployments in practical scenarios. Real-time explainer (RTX) frameworks have thus been proposed to accelerate the model explanation process by learning an one-feed-forward explainer. Existing RTX frameworks typically build the explainer under the supervised learning paradigm, which requires large amounts of explanation labels as the ground truth. Considering that accurate explanation labels are usually hard to obtain, due to constrained computational resources and limited human efforts, effective explainer training is still challenging in practice. In this work, we propose a COntrastive Real-Time eXplanation (CoRTX) framework to learn the explanation-oriented representation and relieve the intensive dependence of explainer training on explanation labels. Specifically, we design a synthetic strategy to select positive and negative instances for explanation representation learning. Theoretical analysis show that our selection strategy can benefit the contrastive learning process on explanation tasks. Experimental results on three real-world datasets further demonstrate the efficiency and efficacy of our proposed CoRTX framework. | https://openreview.net/pdf/8e9825a8e9446ab79ae7406ff203d26aef5828ed.pdf |
OTOv2: Automatic, Generic, User-Friendly | https://openreview.net/forum?id=7ynoX1ojPMt | https://openreview.net/forum?id=7ynoX1ojPMt | Tianyi Chen,Luming Liang,Tianyu DING,Zhihui Zhu,Ilya Zharkov | ICLR 2023,Poster | The existing model compression methods via structured pruning typically require complicated multi-stage procedures. Each individual stage necessitates numerous engineering efforts and domain-knowledge from the end-users which prevent their wider applications onto broader scenarios. We propose the second generation of Only-Train-Once (OTOv2), which first automatically trains and compresses a general DNN only once from scratch to produce a more compact model with competitive performance without fine-tuning. OTOv2 is automatic and pluggable into various deep learning applications, and requires almost minimal engineering efforts from the users. Methodologically, OTOv2 proposes two major improvements: (i) Autonomy: automatically exploits the dependency of general DNNs, partitions the trainable variables into Zero-Invariant Groups (ZIGs), and constructs the compressed model; and (ii) Dual Half-Space Projected Gradient (DHSPG): a novel optimizer to more reliably solve structured-sparsity problems. Numerically, we demonstrate the generality and autonomy of OTOv2 on a variety of model architectures such as VGG, ResNet, CARN, ConvNeXt, DenseNet and StackedUnets, the majority of which cannot be handled by other methods without extensive handcrafting efforts. Together with benchmark datasets including CIFAR10/100, DIV2K, Fashion-MNIST, SVNH and ImageNet, its effectiveness is validated by performing competitively or even better than the state-of-the-arts. The source code is available at https://github.com/tianyic/only_train_once. | https://openreview.net/pdf/cb3780e5995c75c4e42b85e2802143445ea5bf63.pdf |
Filter-Recovery Network for Multi-Speaker Audio-Visual Speech Separation | https://openreview.net/forum?id=fiB2RjmgwQ6 | https://openreview.net/forum?id=fiB2RjmgwQ6 | Haoyue Cheng,Zhaoyang Liu,Wayne Wu,Limin Wang | ICLR 2023,Poster | In this paper, we systematically study the audio-visual speech separation task in a multi-speaker scenario. Given the facial information of each speaker, the goal of this task is to separate the corresponding speech from the mixed speech. The existing works are designed for speech separation in a controlled setting with a fixed number of speakers (mostly 2 or 3 speakers), which seems to be impractical for real applications. As a result, we try to utilize a single model to separate the voices with a variable number of speakers. Based on the observation, there are two prominent issues for multi-speaker separation: 1) There are some noisy voice pieces belonging to other speakers in the separation results; 2) Part of the target speech is missing after separation. Accordingly, we propose \textbf{BFRNet}, including a {\bf B}asic audio-visual speech separator and a Filter-Recovery Network (\textbf{FRNet}). FRNet can refine the coarse audio separated by basic audio-visual speech separator. To have fair comparisons, we build a comprehensive benchmark for multi-speaker audio-visual speech separation to verify the performance of various methods. Experimental results show that our method is able to achieve the state-of-the-art performance. Furthermore, we also find that FRNet can boost the performance of other off-the-shelf speech separators, which exhibits its ability of generalization. | https://openreview.net/pdf/3e900c96203c599eac58f365c0b6e536a3fa37ed.pdf |
Can discrete information extraction prompts generalize across language models? | https://openreview.net/forum?id=sbWVtxq8-zE | https://openreview.net/forum?id=sbWVtxq8-zE | Nathanaël Carraz Rakotonirina,Roberto Dessi,Fabio Petroni,Sebastian Riedel,Marco Baroni | ICLR 2023,Poster | We study whether automatically-induced prompts that effectively extract information from a language model can also be used, out-of-the-box, to probe other language models for the same information. After confirming that discrete prompts induced with the AutoPrompt algorithm outperform manual and semi-manual prompts on the slot-filling task, we demonstrate a drop in performance for AutoPrompt prompts learned on a model and tested on another. We introduce a way to induce prompts by mixing language models at training time that results in prompts that generalize well across models. We conduct an extensive analysis of the induced prompts, finding that the more general prompts include a larger proportion of existing English words and have a less order-dependent and more uniform distribution of information across their component tokens. Our work provides preliminary evidence that it's possible to generate discrete prompts that can be induced once and used with a number of different models, and gives insights on the properties characterizing such prompts. | https://openreview.net/pdf/027788e7f8d7f512b53ca6e6935d18aa5150e77f.pdf |
A view of mini-batch SGD via generating functions: conditions of convergence, phase transitions, benefit from negative momenta. | https://openreview.net/forum?id=bzaPGEllsjE | https://openreview.net/forum?id=bzaPGEllsjE | Maksim Velikanov,Denis Kuznedelev,Dmitry Yarotsky | ICLR 2023,Poster | Mini-batch SGD with momentum is a fundamental algorithm for learning large predictive models. In this paper we develop a new analytic framework to analyze noise-averaged properties of mini-batch SGD for linear models at constant learning rates, momenta and sizes of batches. Our key idea is to consider the dynamics of the second moments of model parameters for a special family of "Spectrally Expressible" approximations. This allows to obtain an explicit expression for the generating function of the sequence of loss values. By analyzing this generating function, we find, in particular, that 1) the SGD dynamics exhibits several convergent and divergent regimes depending on the spectral distributions of the problem; 2) the convergent regimes admit explicit stability conditions, and explicit loss asymptotics in the case of power-law spectral distributions; 3) the optimal convergence rate can be achieved at negative momenta. We verify our theoretical predictions by extensive experiments with MNIST and synthetic problems, and find a good quantitative agreement. | https://openreview.net/pdf/90d22c6df28d1d2983dce952bd3a17a819ab5b3b.pdf |
Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes | https://openreview.net/forum?id=PFbzoWZyZRX | https://openreview.net/forum?id=PFbzoWZyZRX | Zecheng Hao,Jianhao Ding,Tong Bu,Tiejun Huang,Zhaofei Yu | ICLR 2023,Poster | Spiking Neural Networks (SNNs) have attracted great attention due to their distinctive characteristics of low power consumption and temporal information processing. ANN-SNN conversion, as the most commonly used training method for applying SNNs, can ensure that converted SNNs achieve comparable performance to ANNs on large-scale datasets. However, the performance degrades severely under low quantities of time-steps, which hampers the practical applications of SNNs to neuromorphic chips.
In this paper, instead of evaluating different conversion errors and then eliminating these errors, we define an offset spike to measure the degree of deviation between actual and desired SNN firing rates. We perform a detailed analysis of offset spike and note that the firing of one additional (or one less) spike is the main cause of conversion errors. Based on this, we propose an optimization strategy based on shifting the initial membrane potential and we theoretically prove the corresponding optimal shifting distance for calibrating the spike. In addition, we also note that our method has a unique iterative property that enables further reduction of conversion errors. The experimental results show that our proposed method achieves state-of-the-art performance on CIFAR-10, CIFAR-100, and ImageNet datasets. For example, we reach a top-1 accuracy of 67.12% on ImageNet when using 6 time-steps. To the best of our knowledge, this is the first time an ANN-SNN conversion has been shown to simultaneously achieve high accuracy and ultralow latency on complex datasets. Code is available at https://github.com/hzc1208/ANN2SNN_COS. | https://openreview.net/pdf/42de109f414e67f76460868cbe626f2883ef913e.pdf |
ESD: Expected Squared Difference as a Tuning-Free Trainable Calibration Measure | https://openreview.net/forum?id=bHW9njOSON | https://openreview.net/forum?id=bHW9njOSON | Hee Suk Yoon,Joshua Tian Jin Tee,Eunseop Yoon,Sunjae Yoon,Gwangsu Kim,Yingzhen Li,Chang D. Yoo | ICLR 2023,Poster | Studies have shown that modern neural networks tend to be poorly calibrated due to over-confident predictions. Traditionally, post-processing methods have been used to calibrate the model after training. In recent years, various trainable calibration measures have been proposed to incorporate them directly into the training process. However, these methods all incorporate internal hyperparameters, and the performance of these calibration objectives relies on tuning these hyperparameters, incurring more computational costs as the size of neural networks and datasets become larger. As such, we present Expected Squared Difference (ESD), a tuning-free (i.e., hyperparameter-free) trainable calibration objective loss, where we view the calibration error from the perspective of the squared difference between the two expectations. With extensive experiments on several architectures (CNNs, Transformers) and datasets, we demonstrate that (1) incorporating ESD into the training improves model calibration in various batch size settings without the need for internal hyperparameter tuning, (2) ESD yields the best-calibrated results compared with previous approaches, and (3) ESD drastically improves the computational costs required for calibration during training due to the absence of internal hyperparameter. The code is publicly accessible at https://github.com/hee-suk-yoon/ESD. | https://openreview.net/pdf/0a56ada821e2179d876898972a4e0bb37b79f7d7.pdf |
Interactive Portrait Harmonization | https://openreview.net/forum?id=AP0iZoaRaS | https://openreview.net/forum?id=AP0iZoaRaS | Jeya Maria Jose Valanarasu,HE Zhang,Jianming Zhang,Yilin Wang,Zhe Lin,Jose Echevarria,Yinglan Ma,Zijun Wei,Kalyan Sunkavalli,Vishal Patel | ICLR 2023,Poster | Current image harmonization methods consider the entire background as the guidance for harmonization. However, this may limit the capability for user to choose any specific object/person in the background to guide the harmonization. To enable flexible interaction between user and harmonization, we introduce interactive harmonization, a new setting where the harmonization is performed with respect to a selected region in the reference image instead of the entire background. A new flexible framework that allows users to pick certain regions of the background image and use it to guide the harmonization is proposed. Inspired by professional portrait harmonization users, we also introduce a new luminance matching loss to optimally match the color/luminance conditions between the composite foreground and select reference region. This framework provides more control to the image harmonization pipeline achieving visually pleasing portrait edits. Furthermore, we also introduce a new dataset carefully curated for validating portrait harmonization. Extensive experiments on both synthetic and real-world datasets show that the proposed approach is efficient and robust compared to previous harmonization baselines, especially for portraits. | https://openreview.net/pdf/8fecedc380fae9d1025815dc9e8f0bdac4616474.pdf |
Self-Distillation for Further Pre-training of Transformers | https://openreview.net/forum?id=kj6oK_Hj40 | https://openreview.net/forum?id=kj6oK_Hj40 | Seanie Lee,Minki Kang,Juho Lee,Sung Ju Hwang,Kenji Kawaguchi | ICLR 2023,Poster | Pre-training a large transformer model on a massive amount of unlabeled data and fine-tuning it on labeled datasets for diverse downstream tasks has proven to be a successful strategy, for a variety of vision and natural language processing tasks. However, direct fine-tuning of the pre-trained model may be suboptimal if there exist large discrepancies across data domains for pre-training and fine-tuning. To tackle this issue, several previous studies have proposed further pre-training strategies, where we continue to pre-train the model on the target unlabeled dataset before fine-tuning. However, all of them solely focus on language models and we empirically find that a Vision Transformer is vulnerable to overfitting as we continue to pretrain the model on target unlabeled data. In order to tackle this limitation, we propose self-distillation as a regularization for a further pre-training stage. Specifically, we first further pre-train the initial pre-trained model on the target unlabeled data and then consider it as a teacher for self-distillation. Then we take the same initial pre-trained model as a student and enforce its hidden representations to be close to those of the teacher while optimizing the student with a masked auto-encoding objective. We empirically validate the efficacy of self-distillation on a variety of benchmark datasets for image and text classification tasks. Experimentally, we show that our proposed method outperforms all the relevant baselines. Theoretically, we analyze the proposed method with a simplified model to understand how self-distillation for further pre-training can potentially help improve the performance of the downstream tasks. | https://openreview.net/pdf/008646e566fce92cb8bb6248dcc7c7508818680e.pdf |
Contextual Convolutional Networks | https://openreview.net/forum?id=PldynS56bN | https://openreview.net/forum?id=PldynS56bN | Shuxian Liang,Xu Shen,Tongliang Liu,Xian-Sheng Hua | ICLR 2023,Poster | This paper presents a new Convolutional Neural Network, named Contextual Convolutional Network, that capably serves as a general-purpose backbone for visual recognition. Most existing convolutional backbones follow the representation-to-classification paradigm, where representations of the input are firstly generated by category-agnostic convolutional operations, and then fed into classifiers for specific perceptual tasks (e.g., classification and segmentation). In this paper, we deviate from this classic paradigm and propose to augment potential category memberships as contextual priors in the convolution for contextualized representation learning. Specifically, top-k likely classes from the preceding stage are encoded as a contextual prior vector. Based on this vector and the preceding features, offsets for spatial sampling locations and kernel weights are generated to modulate the convolution operations. The new convolutions can readily replace their plain counterparts in existing CNNs and can be easily trained end-to-end by standard back-propagation without additional supervision. The qualities of Contextual Convolutional Networks make it compatible with a broad range of vision tasks and boost the state-of-the-art architecture ConvNeXt-Tiny by 1.8% on top-1 accuracy of ImageNet classification. The superiority of the proposed model reveals the potential of contextualized representation learning for vision tasks. Code is available at: \url{https://github.com/liang4sx/contextual_cnn}.
| https://openreview.net/pdf/38cfc7fcfd551fcbfba2794ad8a4d6b6c719b47a.pdf |
Statistical Inference for Fisher Market Equilibrium | https://openreview.net/forum?id=KemSBwOYJC | https://openreview.net/forum?id=KemSBwOYJC | Luofeng Liao,Yuan Gao,Christian Kroer | ICLR 2023,Poster | Statistical inference under market equilibrium effects has attracted increasing attention recently. In this paper we focus on the specific case of linear Fisher markets. They have been widely use in fair resource allocation of food/blood donations and budget management in large-scale Internet ad auctions. In resource allocation, it is crucial to quantify the variability of the resource received by the agents (such as blood banks and food banks) in addition to fairness and efficiency properties of the systems. For ad auction markets, it is important to establish statistical properties of the platform's revenues in addition to their expected values. To this end, we propose a statistical framework based on the concept of infinite-dimensional Fisher markets. In our framework, we observe a market formed by a finite number of items sampled from an underlying distribution (the ``observed market'') and aim to infer several important equilibrium quantities of the underlying long-run market. These equilibrium quantities include individual utilities, social welfare, and pacing multipliers. Through the lens of sample average approximation (SSA), we derive a collection of statistical results and show that the observed market provides useful statistical information of the long-run market. In other words, the equilibrium quantities of the observed market converge to the true ones of the long-run market with strong statistical guarantees. These include consistency, finite sample bounds, asymptotics, and confidence. As an extension, we discuss revenue inference in quasilinear Fisher markets. | https://openreview.net/pdf/c29b36c06fd3175639e80ff30178fad03267ce5c.pdf |
Scenario-based Question Answering with Interacting Contextual Properties | https://openreview.net/forum?id=tPrRs6YB2P | https://openreview.net/forum?id=tPrRs6YB2P | Haitian Sun,William W. Cohen,Ruslan Salakhutdinov | ICLR 2023,Poster | In the scenario-based Question Answering (QA) task, models are asked to find answers that are appropriate to the user scenarios associated with the question and identify information that is missing from the scenarios but is necessary for the answers to hold. Scenarios commonly include multiple properties of users, such as age, employment status, and income level for the question “How much can I claim from this benefit”. The properties relevant to a potential answer are given in a document, which will state conditions necessary for the answer to hold. Documents also may specify how conditions interact with each other, e.g. with text like “one of the conditions below must apply”. Although understanding the relationship between conditions is crucial for solving this challenging QA task, limited work has been done so far in modeling this. In this paper, we propose the T-Reasoner model, which solves this problem with three jointly learned modules: an entailment module which checks whether a condition has been satisfied by the scenario, a decoding module which locates eligible answers from documents, and a reasoning module which infers the relationship between conditions and performs a reasoning step to determine the logically consistent answers and identify missing conditions. T-Reasoner outperforms strong baselines on a synthetic scenario-based QA dataset and achieves a new state-of-the-art on two scenario-based QA benchmarks, outperforming the prior best models by 3-10 points. | https://openreview.net/pdf/3fd9ea33c70845a298ecbb8cf8b7cdb1cb25c4c1.pdf |
Easy Differentially Private Linear Regression | https://openreview.net/forum?id=rSUCajhLsQ | https://openreview.net/forum?id=rSUCajhLsQ | Kareem Amin,Matthew Joseph,Mónica Ribero,Sergei Vassilvitskii | ICLR 2023,Poster | Linear regression is a fundamental tool for statistical analysis. This has motivated the development of linear regression methods that also satisfy differential privacy and thus guarantee that the learned model reveals little about any one data point used to construct it. However, existing differentially private solutions assume that the end user can easily specify good data bounds and hyperparameters. Both present significant practical obstacles. In this paper, we study an algorithm which uses the exponential mechanism to select a model with high Tukey depth from a collection of non-private regression models. Given $n$ samples of $d$-dimensional data used to train $m$ models, we construct an efficient analogue using an approximate Tukey depth that runs in time $O(d^2n + dm\log(m))$. We find that this algorithm obtains strong empirical performance in the data-rich setting with no data bounds or hyperparameter selection required. | https://openreview.net/pdf/9089bcf6b829ee1ffef90ee7aa9727703d8ae5f3.pdf |
LPT: Long-tailed Prompt Tuning for Image Classification | https://openreview.net/forum?id=8pOVAeo8ie | https://openreview.net/forum?id=8pOVAeo8ie | Bowen Dong,Pan Zhou,Shuicheng Yan,Wangmeng Zuo | ICLR 2023,Poster | For long-tailed classification tasks, most works often pretrain a big model on a large-scale (unlabeled) dataset, and then fine-tune the whole pretrained model for adapting to long-tailed data. Though promising, fine-tuning the whole pretrained model tends to suffer from high cost in computation and deployment of different models for different tasks, as well as weakened generalization capability for overfitting to certain features of long-tailed data. To alleviate these issues, we propose an effective Long-tailed Prompt Tuning (LPT) method for long-tailed classification tasks. LPT introduces several trainable prompts into a frozen pretrained model to adapt it to long-tailed data. For better effectiveness, we divide prompts into two groups: 1) a shared prompt for the whole long-tailed dataset to learn general features and to adapt a pretrained model into the target long-tailed domain; and 2) group-specific prompts to gather group-specific features for the samples which have similar features and also to empower the pretrained model with fine-grained discrimination ability. Then we design a two-phase training paradigm to learn these prompts. In the first phase, we train the shared prompt via conventional supervised prompt tuning to adapt a pretrained model to the desired long-tailed domain. In the second phase, we use the learnt shared prompt as query to select a small best matched set for a group of similar samples from the group-specific prompt set to dig the common features of these similar samples, and then optimize these prompts with a dual sampling strategy and the asymmetric Gaussian Clouded Logit loss. By only fine-tuning a few prompts while fixing the pretrained model, LPT can reduce training cost and deployment cost by storing a few prompts, and enjoys a strong generalization ability of the pretrained model. Experiments show that on various long-tailed benchmarks, with only $\sim$1.1\% extra trainable parameters, LPT achieves comparable or higher performance than previous whole model fine-tuning methods, and is more robust to domain-shift. | https://openreview.net/pdf/fcaa69d5aae6afe2bc1093525d8040992a2a38fd.pdf |
DamoFD: Digging into Backbone Design on Face Detection | https://openreview.net/forum?id=NkJOhtNKX91 | https://openreview.net/forum?id=NkJOhtNKX91 | Yang Liu,Jiankang Deng,Fei Wang,Lei Shang,Xuansong Xie,Baigui Sun | ICLR 2023,Poster | Face detection (FD) has achieved remarkable success over the past few years, yet,
these leaps often arrive when consuming enormous computation costs. Moreover,
when considering a realistic situation, i.e., building a lightweight face detector
under a computation-scarce scenario, such heavy computation cost limits the application
of the face detector. To remedy this, several pioneering works design
tiny face detectors through off-the-shelf neural architecture search (NAS) technologies,
which are usually applied to the classification task. Thus, the searched
architectures are sub-optimal for the face detection task since some design criteria
between detection and classification task are different. As a representative, the
face detection backbone design needs to guarantee the stage-level detection ability
while it is not required for the classification backbone. Furthermore, the detection
backbone consumes a vast body of inference budgets in the whole detection framework.
Considering the intrinsic design requirement and the virtual importance role
of the face detection backbone, we thus ask a critical question: How to employ
NAS to search FD-friendly backbone architecture? To cope with this question,
we propose a distribution-dependent stage-aware ranking score (DDSAR-Score)
to explicitly characterize the stage-level expressivity and identify the individual
importance of each stage, thus satisfying the aforementioned design criterion of
the FD backbone. Based on our proposed DDSAR-Score, we conduct comprehensive
experiments on the challenging Wider Face benchmark dataset and achieve
dominant performance across a wide range of compute regimes. In particular,
compared to the tiniest face detector SCRFD-0.5GF, our method is +2.5 % better
in Average Precision (AP) score when using the same amount of FLOPs. The
code is avaliable at https://github.com/ly19965/FaceMaas/tree/master/face_project/face_detection/DamoFD. | https://openreview.net/pdf/de07bb8709651bf73dcec7308c3610ce657af47a.pdf |
Towards Smooth Video Composition | https://openreview.net/forum?id=W918Ora75q | https://openreview.net/forum?id=W918Ora75q | Qihang Zhang,Ceyuan Yang,Yujun Shen,Yinghao Xu,Bolei Zhou | ICLR 2023,Poster | Video generation, with the purpose of producing a sequence of frames, requires synthesizing consistent and persistent dynamic contents over time. This work investigates how to model the temporal relations for composing a video with arbitrary number of frames, from a few to even infinite, using generative adversarial networks (GANs). First, towards composing adjacent frames, we show that the alias-free operation for single image generation, together with adequately pre-learned knowledge, bring a smooth frame transition without harming the per-frame quality. Second, through incorporating a temporal shift module (TSM), which is originally designed for video understanding, into the discriminator, we manage to advance the generator in synthesizing more reasonable dynamics. Third, we develop a novel B-Spline based motion representation to ensure the temporal smoothness, and hence achieve infinite-length video generation, going beyond the frame number used in training. We evaluate our approach on a range of datasets and show substantial improvements over baselines on video generation. Code and models are publicly available at \url{https://genforce.github.io/StyleSV}. | https://openreview.net/pdf/329ba4d9bb03e63be9d7d8da77ceb6af6d68d209.pdf |
DiffMimic: Efficient Motion Mimicking with Differentiable Physics | https://openreview.net/forum?id=06mk-epSwZ | https://openreview.net/forum?id=06mk-epSwZ | Jiawei Ren,Cunjun Yu,Siwei Chen,Xiao Ma,Liang Pan,Ziwei Liu | ICLR 2023,Poster | Motion mimicking is a foundational task in physics-based character animation. However, most existing motion mimicking methods are built upon reinforcement learning (RL) and suffer from heavy reward engineering, high variance, and slow convergence with hard explorations. Specifically, they usually take tens of hours or even days of training to mimic a simple motion sequence, resulting in poor scalability. In this work, we leverage differentiable physics simulators (DPS) and propose an efficient motion mimicking method dubbed $\textbf{DiffMimic}$. Our key insight is that DPS casts a complex policy learning task to a much simpler state matching problem. In particular, DPS learns a stable policy by analytical gradients with ground-truth physical priors hence leading to significantly faster and stabler convergence than RL-based methods. Moreover, to escape from local optima, we utilize an \textit{Demonstration Replay} mechanism to enable stable gradient backpropagation in a long horizon. Extensive experiments on standard benchmarks show that DiffMimic has a better sample efficiency and time efficiency than existing methods (e.g., DeepMimic). Notably, DiffMimic allows a physically simulated character to learn back-flip after 10 minutes of training and be able to cycle it after 3 hours of training, while DeepMimic requires about a day of training to cycle back-flip. More importantly, we hope DiffMimic can benefit more differentiable animation systems with techniques like differentiable clothes simulation in future research. Our code is available at https://github.com/diffmimic/diffmimic. Qualitative results can be viewed at https://diffmimic-demo-main-g7h0i8.streamlitapp.com. | https://openreview.net/pdf/7914d8c5f3839927d73bea5c42cd4594584c9522.pdf |
Towards Inferential Reproducibility of Machine Learning Research | https://openreview.net/forum?id=li4GQCQWkv | https://openreview.net/forum?id=li4GQCQWkv | Michael Hagmann,Philipp Meier,Stefan Riezler | ICLR 2023,Poster | Reliability of machine learning evaluation --- the consistency of observed evaluation scores across replicated model training runs --- is affected by several sources of nondeterminism which can be regarded as measurement noise. Current tendencies to remove noise in order to enforce reproducibility of research results neglect inherent nondeterminism at the implementation level and disregard crucial interaction effects between algorithmic noise factors and data properties. This limits the scope of conclusions that can be drawn from such experiments. Instead of removing noise, we propose to incorporate several sources of variance, including their interaction with data properties, into an analysis of significance and reliability of machine learning evaluation, with the aim to draw inferences beyond particular instances of trained models. We show how to use linear mixed effects models (LMEMs) to analyze performance evaluation scores, and to conduct statistical inference with a generalized likelihood ratio test (GLRT). This allows us to incorporate arbitrary sources of noise like meta-parameter variations into statistical significance testing, and to assess performance differences conditional on data properties. Furthermore, a variance component analysis (VCA) enables the analysis of the contribution of noise sources to overall variance and the computation of a reliability coefficient by the ratio of substantial to total variance. | https://openreview.net/pdf/bb958f5122207874421ebc6aa75cf0588d96b378.pdf |
Knowledge Distillation based Degradation Estimation for Blind Super-Resolution | https://openreview.net/forum?id=Fg3mYW8owg | https://openreview.net/forum?id=Fg3mYW8owg | Bin Xia,Yulun Zhang,Yitong Wang,Yapeng Tian,Wenming Yang,Radu Timofte,Luc Van Gool | ICLR 2023,Poster | Blind image super-resolution (Blind-SR) aims to recover a high-resolution (HR) image from its corresponding low-resolution (LR) input image with unknown degradations. Most of the existing works design an explicit degradation estimator for each degradation to guide SR. However, it is infeasible to provide concrete labels of multiple degradation combinations (\eg, blur, noise, jpeg compression) to supervise the degradation estimator training. In addition, these special designs for certain degradation, such as blur, impedes the models from being generalized to handle different degradations. To this end, it is necessary to design an implicit degradation estimator that can extract discriminative degradation representation for all degradations without relying on the supervision of degradation ground-truth. In this paper, we propose a Knowledge Distillation based Blind-SR network (KDSR). It consists of a knowledge distillation based implicit degradation estimator network (KD-IDE) and an efficient SR network. To learn the KDSR model, we first train a teacher network: KD-IDE$_{T}$. It takes paired HR and LR patches as inputs and is optimized with the SR network jointly. Then, we further train a student network KD-IDE$_{S}$, which only takes LR images as input and learns to extract the same implicit degradation representation (IDR) as KD-IDE$_{T}$. In addition, to fully use extracted IDR, we design a simple, strong, and efficient IDR based dynamic convolution residual block (IDR-DCRB) to build an SR network. We conduct extensive experiments under classic and real-world degradation settings. The results show that KDSR achieves SOTA performance and can generalize to various degradation processes. The source codes and pre-trained models will be released. | https://openreview.net/pdf/ddc06a1dba96714dc462fb36685cd562658c9d62.pdf |
Graph Contrastive Learning for Skeleton-based Action Recognition | https://openreview.net/forum?id=PLUXnnxUdr4 | https://openreview.net/forum?id=PLUXnnxUdr4 | Xiaohu Huang,Hao Zhou,Jian Wang,Haocheng Feng,Junyu Han,Errui Ding,Jingdong Wang,Xinggang Wang,Wenyu Liu,Bin Feng | ICLR 2023,Poster | In the field of skeleton-based action recognition, current top-performing graph convolutional networks (GCNs) exploit intra-sequence context to construct adaptive graphs for feature aggregation. However, we argue that such context is still $\textit{local}$ since the rich cross-sequence relations have not been explicitly investigated. In this paper, we propose a graph contrastive learning framework for skeleton-based action recognition ($\textit{SkeletonGCL}$) to explore the $\textit{global}$ context across all sequences. In specific, SkeletonGCL associates graph learning across sequences by enforcing graphs to be class-discriminative, i.e., intra-class compact and inter-class dispersed, which improves the GCN capacity to distinguish various action patterns. Besides, two memory banks are designed to enrich cross-sequence context from two complementary levels, i.e., instance and semantic levels, enabling graph contrastive learning in multiple context scales. Consequently, SkeletonGCL establishes a new training paradigm, and it can be seamlessly incorporated into current GCNs. Without loss of generality, we combine SkeletonGCL with three GCNs (2S-ACGN, CTR-GCN, and InfoGCN), and achieve consistent improvements on NTU60, NTU120, and NW-UCLA benchmarks. | https://openreview.net/pdf/2d3ef5812dccf804b4de18d9cc5fb5cc98d3ba9a.pdf |
Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation | https://openreview.net/forum?id=s4WVupnJjmX | https://openreview.net/forum?id=s4WVupnJjmX | Jie Yang,Ailing Zeng,Shilong Liu,Feng Li,Ruimao Zhang,Lei Zhang | ICLR 2023,Poster | This paper presents a novel end-to-end framework with Explicit box Detection for multi-person Pose estimation, called ED-Pose, where it unifies the contextual learning between human-level (global) and keypoint-level (local) information. Different from previous one-stage methods, ED-Pose re-considers this task as two explicit box detection processes with a unified representation and regression supervision. First, we introduce a human detection decoder from encoded tokens to extract global features. It can provide a good initialization for the latter keypoint detection, making the training process converge fast. Second, to bring in contextual information near keypoints, we regard pose estimation as a keypoint box detection problem to learn both box positions and contents for each keypoint. A human-to-keypoint detection decoder adopts an interactive learning strategy between human and keypoint features to further enhance global and local feature aggregation. In general, ED-Pose is conceptually simple without post-processing and dense heatmap supervision. It demonstrates its effectiveness and efficiency compared with both two-stage and one-stage methods. Notably, explicit box detection boosts the pose estimation performance by 4.5 AP on COCO and 9.9 AP on CrowdPose. For the first time, as a fully end-to-end framework with a L1 regression loss, ED-Pose surpasses heatmap-based Top-down methods under the same backbone by 1.2 AP on COCO and achieves the state-of-the-art with 76.6 AP on CrowdPose without bells and whistles. Code is available at https://github.com/IDEA-Research/ED-Pose. | https://openreview.net/pdf/ba7f900048548e47e5aa74e11d4e2bbefff676b8.pdf |
Spikformer: When Spiking Neural Network Meets Transformer | https://openreview.net/forum?id=frE4fUwz_h | https://openreview.net/forum?id=frE4fUwz_h | Zhaokun Zhou,Yuesheng Zhu,Chao He,Yaowei Wang,Shuicheng YAN,Yonghong Tian,Li Yuan | ICLR 2023,Poster | We consider two biologically plausible structures, the Spiking Neural Network (SNN) and the self-attention mechanism. The former offers an energy-efficient and event-driven paradigm for deep learning, while the latter has the ability to capture feature dependencies, enabling Transformer to achieve good performance. It is intuitively promising to explore the marriage between them. In this paper, we consider leveraging both self-attention capability and biological properties of SNNs, and propose a novel Spiking Self Attention (SSA) as well as a powerful framework, named Spiking Transformer (Spikformer). The SSA mechanism in Spikformer models the sparse visual feature by using spike-form Query, Key, and Value without softmax. Since its computation is sparse and avoids multiplication, SSA is efficient and has low computational energy consumption. It is shown that Spikformer with SSA can outperform the state-of-the-art SNNs-like frameworks in image classification on both neuromorphic and static datasets. Spikformer (66.3M parameters) with comparable size to SEW-ResNet-152 (60.2M,69.26%) can achieve 74.81% top1 accuracy on ImageNet using 4 time steps, which is the state-of-the-art in directly trained SNNs models. Code is avaiable at https://github.com/ZK-Zhou/spikformer. | https://openreview.net/pdf/f73e61d78afbf6a46ce5de2f6af699bacae174f8.pdf |
Multimodal Analogical Reasoning over Knowledge Graphs | https://openreview.net/forum?id=NRHajbzg8y0P | https://openreview.net/forum?id=NRHajbzg8y0P | Ningyu Zhang,Lei Li,Xiang Chen,Xiaozhuan Liang,Shumin Deng,Huajun Chen | ICLR 2023,Poster | Analogical reasoning is fundamental to human cognition and holds an important place in various fields. However, previous studies mainly focus on single-modal analogical reasoning and ignore taking advantage of structure knowledge. Notably, the research in cognitive psychology has demonstrated that information from multimodal sources always brings more powerful cognitive transfer than single modality sources. To this end, we introduce the new task of multimodal analogical reasoning over knowledge graphs, which requires multimodal reasoning ability with the help of background knowledge. Specifically, we construct a Multimodal Analogical Reasoning dataSet (MARS) and a multimodal knowledge graph MarKG. We evaluate with multimodal knowledge graph embedding and pre-trained Transformer baselines, illustrating the potential challenges of the proposed task. We further propose a novel model-agnostic Multimodal analogical reasoning framework with Transformer (MarT) motivated by the structure mapping theory, which can obtain better performance. We hope our work can deliver benefits and inspire future research. Code and datasets are available in https://github.com/zjunlp/MKG_Analogy. | https://openreview.net/pdf/0932fa51d71959373e6ffd7a76954ac870fb458c.pdf |
MECTA: Memory-Economic Continual Test-Time Model Adaptation | https://openreview.net/forum?id=N92hjSf5NNh | https://openreview.net/forum?id=N92hjSf5NNh | Junyuan Hong,Lingjuan Lyu,Jiayu Zhou,Michael Spranger | ICLR 2023,Poster | Continual Test-time Adaptation (CTA) is a promising art to secure accuracy gains in continually-changing environments. The state-of-the-art adaptations improve out-of-distribution model accuracy via computation-efficient online test-time gradient descents but meanwhile cost about times of memory versus the inference, even if only a small portion of parameters are updated. Such high memory consumption of CTA substantially impedes wide applications of advanced CTA on memory-constrained devices. In this paper, we provide a novel solution, dubbed MECTA, to drastically improve the memory efficiency of gradient-based CTA. Our profiling shows that the major memory overhead comes from the intermediate cache for back-propagation, which scales by the batch size, channel, and layer number. Therefore, we propose to reduce batch sizes, adopt an adaptive normalization layer to maintain stable and accurate predictions, and stop the back-propagation caching heuristically. On the other hand, we prune the networks to reduce the computation and memory overheads in optimization and recover the parameters afterward to avoid forgetting. The proposed MECTA is efficient and can be seamlessly plugged into state-of-the-art CTA algorithms at negligible overhead on computation and memory. On three datasets, CIFAR10, CIFAR100, and ImageNet, MECTA improves the accuracy by at least 6% with constrained memory and significantly reduces the memory costs of ResNet50 on ImageNet by at least 70% with comparable accuracy. Our codes can be accessed at https://github.com/SonyAI/MECTA. | https://openreview.net/pdf/251a7a3699b3f24e5039dd2c53f82accdf4afab6.pdf |
Interpretability with full complexity by constraining feature information | https://openreview.net/forum?id=R_OL5mLhsv | https://openreview.net/forum?id=R_OL5mLhsv | Kieran A Murphy,Danielle Bassett | ICLR 2023,Poster | Interpretability is a pressing issue for machine learning. Common approaches to interpretable machine learning constrain interactions between features of the input, sacrificing model complexity in order to render more comprehensible the effects of those features on the model's output. We approach interpretability from a new angle: constrain the information about the features without restricting the complexity of the model. We use the Distributed Information Bottleneck to optimally compress each feature so as to maximally preserve information about the output. The learned information allocation, by feature and by feature value, provides rich opportunities for interpretation, particularly in problems with many features and complex feature interactions. The central object of analysis is not a single trained model, but rather a spectrum of models serving as approximations that leverage variable amounts of information about the inputs. Information is allocated to features by their relevance to the output, thereby solving the problem of feature selection by constructing a learned continuum of feature inclusion-to-exclusion. The optimal compression of each feature---at every stage of approximation---allows fine-grained inspection of the distinctions among feature values that are most impactful for prediction. We develop a framework for extracting insight from the spectrum of approximate models and demonstrate its utility on a range of tabular datasets. | https://openreview.net/pdf/4b6115cd611db557e90d8bc2827aaf0d6b2e73b1.pdf |
What shapes the loss landscape of self supervised learning? | https://openreview.net/forum?id=3zSn48RUO8M | https://openreview.net/forum?id=3zSn48RUO8M | Liu Ziyin,Ekdeep Singh Lubana,Masahito Ueda,Hidenori Tanaka | ICLR 2023,Poster | Prevention of complete and dimensional collapse of representations has recently become a design principle for self-supervised learning (SSL). However, questions remain in our theoretical understanding: When do those collapses occur? What are the mechanisms and causes? We answer these questions by deriving and thoroughly analyzing an analytically tractable theory of SSL loss landscapes. In this theory, we identify the causes of the dimensional collapse and study the effect of normalization and bias. Finally, we leverage the interpretability afforded by the analytical theory to understand how dimensional collapse can be beneficial and what affects the robustness of SSL against data imbalance. | https://openreview.net/pdf/9fe0d11856705a878971e0912c12bf584b1f2f3a.pdf |
Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies | https://openreview.net/forum?id=-z9hdsyUwVQ | https://openreview.net/forum?id=-z9hdsyUwVQ | Rui Yuan,Simon Shaolei Du,Robert M. Gower,Alessandro Lazaric,Lin Xiao | ICLR 2023,Poster | We consider infinite-horizon discounted Markov decision processes and study the convergence rates of the natural policy gradient (NPG) and the Q-NPG methods with the log-linear policy class. Using the compatible function approximation framework, both methods with log-linear policies can be written as approximate versions of the policy mirror descent (PMD) method. We show that both methods attain linear convergence rates and $\tilde{\mathcal{O}}(1/\epsilon^2)$ sample complexities using a simple, non-adaptive geometrically increasing step size, without resorting to entropy or other strongly convex regularization. Lastly, as a byproduct, we obtain sublinear convergence rates for both methods with arbitrary constant step size. | https://openreview.net/pdf/f5aa5904837a28aced14461d3df0197008d01e98.pdf |
Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game | https://openreview.net/forum?id=UP_GHHPw7rP | https://openreview.net/forum?id=UP_GHHPw7rP | Wei Xiong,Han Zhong,Chengshuai Shi,Cong Shen,Liwei Wang,Tong Zhang | ICLR 2023,Poster | Offline reinforcement learning (RL) aims at learning an optimal strategy using a pre-collected dataset without further interactions with the environment. While various algorithms have been proposed for offline RL in the previous literature, the minimax optimality has only been (nearly) established for tabular Markov decision processes (MDPs). In this paper, we focus on offline RL with linear function approximation and propose a new pessimism-based algorithm for offline linear MDP. At the core of our algorithm is the uncertainty decomposition via a reference function, which is new in the literature of offline RL under linear function approximation. Theoretical analysis demonstrates that our algorithm can match the performance lower bound up to logarithmic factors. We also extend our techniques to the two-player zero-sum Markov games (MGs), and establish a new performance lower bound for MGs, which tightens the existing result, and verifies the nearly minimax optimality of the proposed algorithm. To the best of our knowledge, these are the first computationally efficient and nearly minimax optimal algorithms for offline single-agent MDPs and MGs with linear function approximation. | https://openreview.net/pdf/4ec7b1dd165364db85e719d8b2232962869eb44d.pdf |
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