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Multiset-Equivariant Set Prediction with Approximate Implicit Differentiation | https://openreview.net/forum?id=5K7RRqZEjoS | https://openreview.net/forum?id=5K7RRqZEjoS | Yan Zhang,David W Zhang,Simon Lacoste-Julien,Gertjan J. Burghouts,Cees G. M. Snoek | ICLR 2022,Poster | Most set prediction models in deep learning use set-equivariant operations, but they actually operate on multisets. We show that set-equivariant functions cannot represent certain functions on multisets, so we introduce the more appropriate notion of multiset-equivariance. We identify that the existing Deep Set Prediction Network (DSPN) can be multiset-equivariant without being hindered by set-equivariance and improve it with approximate implicit differentiation, allowing for better optimization while being faster and saving memory. In a range of toy experiments, we show that the perspective of multiset-equivariance is beneficial and that our changes to DSPN achieve better results in most cases. On CLEVR object property prediction, we substantially improve over the state-of-the-art Slot Attention from 8% to 77% in one of the strictest evaluation metrics because of the benefits made possible by implicit differentiation. | https://openreview.net/pdf/3f0a2b8f6922fd811b23ed91b53451a3e4c8efc1.pdf |
Learned Simulators for Turbulence | https://openreview.net/forum?id=msRBojTz-Nh | https://openreview.net/forum?id=msRBojTz-Nh | Kim Stachenfeld,Drummond Buschman Fielding,Dmitrii Kochkov,Miles Cranmer,Tobias Pfaff,Jonathan Godwin,Can Cui,Shirley Ho,Peter Battaglia,Alvaro Sanchez-Gonzalez | ICLR 2022,Poster | Turbulence simulation with classical numerical solvers requires high-resolution grids to accurately resolve dynamics. Here we train learned simulators at low spatial and temporal resolutions to capture turbulent dynamics generated at high resolution. We show that our proposed model can simulate turbulent dynamics more accurately than classical numerical solvers at the comparably low resolutions across various scientifically relevant metrics. Our model is trained end-to-end from data and is capable of learning a range of challenging chaotic and turbulent dynamics at low resolution, including trajectories generated by the state-of-the-art Athena++ engine. We show that our simpler, general-purpose architecture outperforms various more specialized, turbulence-specific architectures from the learned turbulence simulation literature. In general, we see that learned simulators yield unstable trajectories; however, we show that tuning training noise and temporal downsampling solves this problem. We also find that while generalization beyond the training distribution is a challenge for learned models, training noise, added loss constraints, and dataset augmentation can help. Broadly, we conclude that our learned simulator outperforms traditional solvers run on coarser grids, and emphasize that simple design choices can offer stability and robust generalization. | https://openreview.net/pdf/a5285382bfece0e469683d88f7053a50f45acb4e.pdf |
Modular Lifelong Reinforcement Learning via Neural Composition | https://openreview.net/forum?id=5XmLzdslFNN | https://openreview.net/forum?id=5XmLzdslFNN | Jorge A Mendez,Harm van Seijen,ERIC EATON | ICLR 2022,Poster | Humans commonly solve complex problems by decomposing them into easier subproblems and then combining the subproblem solutions. This type of compositional reasoning permits reuse of the subproblem solutions when tackling future tasks that share part of the underlying compositional structure. In a continual or lifelong reinforcement learning (RL) setting, this ability to decompose knowledge into reusable components would enable agents to quickly learn new RL tasks by leveraging accumulated compositional structures. We explore a particular form of composition based on neural modules and present a set of RL problems that intuitively admit compositional solutions. Empirically, we demonstrate that neural composition indeed captures the underlying structure of this space of problems. We further propose a compositional lifelong RL method that leverages accumulated neural components to accelerate the learning of future tasks while retaining performance on previous tasks via off-line RL over replayed experiences. | https://openreview.net/pdf/a7e9350f0b9a40fc77e2f196fa086d7759fbbbd7.pdf |
Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks | https://openreview.net/forum?id=7B3IJMM1k_M | https://openreview.net/forum?id=7B3IJMM1k_M | Tong Bu,Wei Fang,Jianhao Ding,PENGLIN DAI,Zhaofei Yu,Tiejun Huang | ICLR 2022,Poster | Spiking Neural Networks (SNNs) have gained great attraction due to their distinctive properties of low power consumption and fast inference on neuromorphic hardware. As the most effective method to get deep SNNs, ANN-SNN conversion has achieved comparable performance as ANNs on large-scale datasets. Despite this, it requires long time-steps to match the firing rates of SNNs to the activation of ANNs. As a result, the converted SNN suffers severe performance degradation problems with short time-steps, which hamper the practical application of SNNs. In this paper, we theoretically analyze ANN-SNN conversion error and derive the estimated activation function of SNNs. Then we propose the quantization clip-floor-shift activation function to replace the ReLU activation function in source ANNs, which can better approximate the activation function of SNNs. We prove that the expected conversion error between SNNs and ANNs is zero, enabling us to achieve high-accuracy and ultra-low-latency SNNs. We evaluate our method on CIFAR-10/100 and ImageNet datasets, and show that it outperforms the state-of-the-art ANN-SNN and directly trained SNNs in both accuracy and time-steps. To the best of our knowledge, this is the first time to explore high-performance ANN-SNN conversion with ultra-low latency (4 time-steps). Code is available at https://github.com/putshua/SNN_conversion_QCFS | https://openreview.net/pdf/b0826b541fa995e04f8d1b450b17143170fe1d8f.pdf |
AS-MLP: An Axial Shifted MLP Architecture for Vision | https://openreview.net/forum?id=fvLLcIYmXb | https://openreview.net/forum?id=fvLLcIYmXb | Dongze Lian,Zehao Yu,Xing Sun,Shenghua Gao | ICLR 2022,Poster | An Axial Shifted MLP architecture (AS-MLP) is proposed in this paper. Different from MLP-Mixer, where the global spatial feature is encoded for information flow through matrix transposition and one token-mixing MLP, we pay more attention to the local features interaction. By axially shifting channels of the feature map, AS-MLP is able to obtain the information flow from different axial directions, which captures the local dependencies. Such an operation enables us to utilize a pure MLP architecture to achieve the same local receptive field as CNN-like architecture. We can also design the receptive field size and dilation of blocks of AS-MLP, \emph{etc}, in the same spirit of convolutional neural networks. With the proposed AS-MLP architecture, our model obtains 83.3\% Top-1 accuracy with 88M parameters and 15.2 GFLOPs on the ImageNet-1K dataset. Such a simple yet effective architecture outperforms all MLP-based architectures and achieves competitive performance compared to the transformer-based architectures (\emph{e.g.}, Swin Transformer) even with slightly lower FLOPs. In addition, AS-MLP is also the first MLP-based architecture to be applied to the downstream tasks (\emph{e.g.}, object detection and semantic segmentation). The experimental results are also impressive. Our proposed AS-MLP obtains 51.5 mAP on the COCO validation set and 49.5 MS mIoU on the ADE20K dataset, which is competitive compared to the transformer-based architectures. Our AS-MLP establishes a strong baseline of MLP-based architecture. Code is available at \url{https://github.com/svip-lab/AS-MLP}. | https://openreview.net/pdf/4ba030acd877662e2f1238c570aa52552d7f3997.pdf |
Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference | https://openreview.net/forum?id=nrGGfMbY_qK | https://openreview.net/forum?id=nrGGfMbY_qK | Hyunseo Koh,Dahyun Kim,Jung-Woo Ha,Jonghyun Choi | ICLR 2022,Poster | Despite rapid advances in continual learning, a large body of research is devoted to improving performance in the existing setups.
While a handful of work do propose new continual learning setups, they still lack practicality in certain aspects.
For better practicality, we first propose a novel continual learning setup that is online, task-free, class-incremental, of blurry task boundaries and subject to inference queries at any moment.
We additionally propose a new metric to better measure the performance of the continual learning methods subject to inference queries at any moment.
To address the challenging setup and evaluation protocol, we propose an effective method that employs a new memory management scheme and novel learning techniques.
Our empirical validation demonstrates that the proposed method outperforms prior arts by large margins. Code and data splits are available at https://github.com/naver-ai/i-Blurry. | https://openreview.net/pdf/ee1c732abb45197cf11bd542a9df4b33a403823f.pdf |
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations | https://openreview.net/forum?id=TBWA6PLJZQm | https://openreview.net/forum?id=TBWA6PLJZQm | Jiaheng Wei,Zhaowei Zhu,Hao Cheng,Tongliang Liu,Gang Niu,Yang Liu | ICLR 2022,Poster | Existing research on learning with noisy labels mainly focuses on synthetic label noise. The synthetic noise, though has clean structures which greatly enabled statistical analyses, often fails to model the real-world noise patterns. The recent literature has observed several efforts to offer real-world noisy datasets, e.g., Food-101N, WebVision, and Clothing1M. Yet the existing efforts suffer from two caveats: firstly, the lack of ground-truth verification makes it hard to theoretically study the property and treatment of real-world label noise. Secondly, these efforts are often of large scales, which may result in unfair comparisons of robust methods within reasonable and accessible computation power. To better understand real-world label noise, it is important to establish controllable, easy-to-use, and moderate-sized real-world noisy datasets with both ground-truth and noisy labels. This work presents two new benchmark datasets, which we name as CIFAR-10N, CIFAR-100N (jointly we call them CIFAR-N), equipping the training datasets of CIFAR-10 and CIFAR-100 with human-annotated real-world noisy labels we collected from Amazon Mechanical Turk. We quantitatively and qualitatively show that real-world noisy labels follow an instance-dependent pattern rather than the classically assumed and adopted ones (e.g., class-dependent label noise). We then initiate an effort to benchmarking a subset of the existing solutions using CIFAR-10N and CIFAR-100N. We further proceed to study the memorization of correct and wrong predictions, which further illustrates the difference between human noise and class-dependent synthetic noise. We show indeed the real-world noise patterns impose new and outstanding challenges as compared to synthetic label noise. These observations require us to rethink the treatment of noisy labels, and we hope the availability of these two datasets would facilitate the development and evaluation of future learning with noisy label solutions. The corresponding datasets and the leaderboard are available at http://noisylabels.com. | https://openreview.net/pdf/fcda4f135277eb47cf549ba438285dc78fe27977.pdf |
Optimization inspired Multi-Branch Equilibrium Models | https://openreview.net/forum?id=nbC8iTTXIrk | https://openreview.net/forum?id=nbC8iTTXIrk | Mingjie Li,Yisen Wang,Xingyu Xie,Zhouchen Lin | ICLR 2022,Poster | Works have shown the strong connections between some implicit models and optimization problems. However, explorations on such relationships are limited. Most works pay attention to some common mathematical properties, such as sparsity. In this work, we propose a new type of implicit model inspired by the designing of the systems' hidden objective functions, called the Multi-branch Optimization induced Equilibrium networks~(MOptEqs). The model architecture is designed based on modelling the hidden objective function for the multi-resolution recognition task. Furthermore, we also propose a new strategy inspired by our understandings of the hidden objective function. In this manner, the proposed model can better utilize the hierarchical patterns for recognition tasks and retain the abilities for interpreting the whole structure as trying to obtain the minima of the problem's goal. Comparing with the state-of-the-art models, our MOptEqs not only enjoys better explainability but are also superior to MDEQ with less parameter consumption and better performance on practical tasks. Furthermore, we also implement various experiments to demonstrate the effectiveness of our new methods and explore the applicability of the model's hidden objective function. | https://openreview.net/pdf/64ff80fa7f6ea2e747dc3daa4a44b500a5e7888a.pdf |
Learning to Annotate Part Segmentation with Gradient Matching | https://openreview.net/forum?id=zNR43c03lRy | https://openreview.net/forum?id=zNR43c03lRy | Yu Yang,Xiaotian Cheng,Hakan Bilen,Xiangyang Ji | ICLR 2022,Poster | The success of state-of-the-art deep neural networks heavily relies on the presence of large-scale labelled datasets, which are extremely expensive and time-consuming to annotate. This paper focuses on tackling semi-supervised part segmentation tasks by generating high-quality images with a pre-trained GAN and labelling the generated images with an automatic annotator. In particular, we formulate the annotator learning as a learning-to-learn problem. Given a pre-trained GAN, the annotator learns to label object parts in a set of randomly generated images such that a part segmentation model trained on these synthetic images with their predicted labels obtains low segmentation error on a small validation set of manually labelled images. We further reduce this nested-loop optimization problem to a simple gradient matching problem and efficiently solve it with an iterative algorithm. We show that our method can learn annotators from a broad range of labelled images including real images, generated images, and even analytically rendered images. Our method is evaluated with semi-supervised part segmentation tasks and significantly outperforms other semi-supervised competitors when the amount of labelled examples is extremely limited. | https://openreview.net/pdf/ad3311cb0430eae8c6960121482e58765c803178.pdf |
Vector-quantized Image Modeling with Improved VQGAN | https://openreview.net/forum?id=pfNyExj7z2 | https://openreview.net/forum?id=pfNyExj7z2 | Jiahui Yu,Xin Li,Jing Yu Koh,Han Zhang,Ruoming Pang,James Qin,Alexander Ku,Yuanzhong Xu,Jason Baldridge,Yonghui Wu | ICLR 2022,Poster | Pretraining language models with next-token prediction on massive text corpora has delivered phenomenal zero-shot, few-shot, transfer learning and multi-tasking capabilities on both generative and discriminative language tasks. Motivated by this success, we explore a Vector-quantized Image Modeling (VIM) approach that involves pretraining a Transformer to predict rasterized image tokens autoregressively. The discrete image tokens are encoded from a learned Vision-Transformer-based VQGAN (ViT-VQGAN). We first propose multiple improvements over vanilla VQGAN from architecture to codebook learning, yielding better efficiency and reconstruction fidelity. The improved ViT-VQGAN further improves vector-quantized image modeling tasks, including unconditional, class-conditioned image generation and unsupervised representation learning. When trained on ImageNet at 256x256 resolution, we achieve Inception Score (IS) of 175.1 and Fr'echet Inception Distance (FID) of 4.17, a dramatic improvement over the vanilla VQGAN, which obtains 70.6 and 17.04 for IS and FID, respectively. Based on ViT-VQGAN and unsupervised pretraining, we further evaluate the pretrained Transformer by averaging intermediate features, similar to Image GPT (iGPT). This ImageNet-pretrained VIM-L significantly beats iGPT-L on linear-probe accuracy from 60.3% to 73.2% for a similar model size. ViM-L also outperforms iGPT-XL which is trained with extra web image data and larger model size. | https://openreview.net/pdf/63ce2022df1b6352ef17e394bb00cd416cf9497c.pdf |
Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation | https://openreview.net/forum?id=R8sQPpGCv0 | https://openreview.net/forum?id=R8sQPpGCv0 | Ofir Press,Noah Smith,Mike Lewis | ICLR 2022,Poster | Since the introduction of the transformer model by Vaswani et al. (2017), a fundamental question has yet to be answered: how does a model achieve extrapolation at inference time for sequences that are longer than it saw during training? We first show that extrapolation can be enabled by simply changing the position representation method, though we find that current methods do not allow for efficient extrapolation. We therefore introduce a simpler and more efficient position method, Attention with Linear Biases (ALiBi). ALiBi does not add positional embeddings to word embeddings; instead, it biases query-key attention scores with a penalty that is proportional to their distance. We show that this method trains a 1.3 billion parameter model on input sequences of length 1024 that extrapolates to input sequences of length 2048, achieving the same perplexity as a sinusoidal position embedding model trained on inputs of length 2048 but training 11% faster and using 11% less memory. ALiBi's inductive bias towards recency also leads it to outperform multiple strong position methods on the WikiText-103 benchmark. | https://openreview.net/pdf/76259151174b85b4bf1f526f9f72da486f40418b.pdf |
Learning Representation from Neural Fisher Kernel with Low-rank Approximation | https://openreview.net/forum?id=J1rhANsCY9 | https://openreview.net/forum?id=J1rhANsCY9 | Ruixiang ZHANG,Shuangfei Zhai,Etai Littwin,Joshua M. Susskind | ICLR 2022,Poster | In this paper, we study the representation of neural networks from the view of kernels. We first define the Neural Fisher Kernel (NFK), which is the Fisher Kernel applied to neural networks. We show that NFK can be computed for both supervised and unsupervised learning models, which can serve as a unified tool for representation extraction. Furthermore, we show that practical NFKs exhibit low-rank structures. We then propose an efficient algorithm that computes a low-rank approximation of NFK, which scales to large datasets and networks. We show that the low-rank approximation of NFKs derived from unsupervised generative models and supervised learning models gives rise to high-quality compact representations of data, achieving competitive results on a variety of machine learning tasks. | https://openreview.net/pdf/decd70c5ae7ce929f66d927827dec0e696609320.pdf |
Learning Temporally Causal Latent Processes from General Temporal Data | https://openreview.net/forum?id=RDlLMjLJXdq | https://openreview.net/forum?id=RDlLMjLJXdq | Weiran Yao,Yuewen Sun,Alex Ho,Changyin Sun,Kun Zhang | ICLR 2022,Poster | Our goal is to recover time-delayed latent causal variables and identify their relations from measured temporal data. Estimating causally-related latent variables from observations is particularly challenging as the latent variables are not uniquely recoverable in the most general case. In this work, we consider both a nonparametric, nonstationary setting and a parametric setting for the latent processes and propose two provable conditions under which temporally causal latent processes can be identified from their nonlinear mixtures. We propose LEAP, a theoretically-grounded framework that extends Variational AutoEncoders (VAEs) by enforcing our conditions through proper constraints in causal process prior. Experimental results on various datasets demonstrate that temporally causal latent processes are reliably identified from observed variables under different dependency structures and that our approach considerably outperforms baselines that do not properly leverage history or nonstationarity information. This demonstrates that using temporal information to learn latent processes from their invertible nonlinear mixtures in an unsupervised manner, for which we believe our work is one of the first, seems promising even without sparsity or minimality assumptions. | https://openreview.net/pdf/917054f5da79e94676388fea7fc2c7b9714dd8d9.pdf |
The Rich Get Richer: Disparate Impact of Semi-Supervised Learning | https://openreview.net/forum?id=DXPftn5kjQK | https://openreview.net/forum?id=DXPftn5kjQK | Zhaowei Zhu,Tianyi Luo,Yang Liu | ICLR 2022,Poster | Semi-supervised learning (SSL) has demonstrated its potential to improve the model accuracy for a variety of learning tasks when the high-quality supervised data is severely limited. Although it is often established that the average accuracy for the entire population of data is improved, it is unclear how SSL fares with different sub-populations. Understanding the above question has substantial fairness implications when different sub-populations are defined by the demographic groups that we aim to treat fairly. In this paper, we reveal the disparate impacts of deploying SSL: the sub-population who has a higher baseline accuracy without using SSL (the "rich" one) tends to benefit more from SSL; while the sub-population who suffers from a low baseline accuracy (the "poor" one) might even observe a performance drop after adding the SSL module. We theoretically and empirically establish the above observation for a broad family of SSL algorithms, which either explicitly or implicitly use an auxiliary "pseudo-label". Experiments on a set of image and text classification tasks confirm our claims. We introduce a new metric, Benefit Ratio, and promote the evaluation of the fairness of SSL (Equalized Benefit Ratio). We further discuss how the disparate impact can be mitigated. We hope our paper will alarm the potential pitfall of using SSL and encourage a multifaceted evaluation of future SSL algorithms. | https://openreview.net/pdf/512dd2bf00378d8e1ddb8ff29166f99a1339d921.pdf |
Neural Relational Inference with Node-Specific Information | https://openreview.net/forum?id=HBsJNesj2S | https://openreview.net/forum?id=HBsJNesj2S | Ershad Banijamali | ICLR 2022,Poster | Inferring interactions among entities is an important problem in studying dynamical systems, which greatly impacts the performance of downstream tasks, such as prediction. In this paper, we tackle the relational inference problem in a setting where each entity can potentially have a set of individualized information that other entities cannot have access to. Specifically, we represent the system using a graph in which the individualized information become node-specific information (NSI). We build our model in the framework of Neural Relation Inference (NRI), where the interaction among entities are uncovered using variational inference. We adopt NRI model to incorporate the individualized information by introducing private nodes in the graph that represent NSI. Such representation enables us to uncover more accurate relations among the agents and therefore leads to better performance on the downstream tasks. Our experiment results over real-world datasets validate the merit of our proposed algorithm. | https://openreview.net/pdf/96c13a371051c12e097a483a7c11e21162aa63da.pdf |
Bregman Gradient Policy Optimization | https://openreview.net/forum?id=ZU-zFnTum1N | https://openreview.net/forum?id=ZU-zFnTum1N | Feihu Huang,Shangqian Gao,Heng Huang | ICLR 2022,Poster | In the paper, we design a novel Bregman gradient policy optimization framework for reinforcement learning based on Bregman divergences and momentum techniques. Specifically, we propose a Bregman gradient policy optimization (BGPO) algorithm based on the basic momentum technique and mirror descent iteration. Meanwhile, we further propose an accelerated Bregman gradient policy optimization (VR-BGPO) algorithm based on the variance reduced technique. Moreover, we provide a convergence analysis framework for our Bregman gradient policy optimization under the nonconvex setting. We prove that our BGPO achieves a sample complexity of $O(\epsilon^{-4})$ for finding $\epsilon$-stationary policy only requiring one trajectory at each iteration, and our VR-BGPO reaches the best known sample complexity of $O(\epsilon^{-3})$, which also only requires one trajectory at each iteration. In particular, by using different Bregman divergences, our BGPO framework unifies many existing policy optimization algorithms such as the existing (variance reduced) policy gradient algorithms such as natural policy gradient algorithm. Extensive experimental results on multiple reinforcement learning tasks demonstrate the efficiency of our new algorithms. | https://openreview.net/pdf/e120a3566088f53c36d56bd58eba97a8e2c4ffc6.pdf |
A generalization of the randomized singular value decomposition | https://openreview.net/forum?id=hgKtwSb4S2 | https://openreview.net/forum?id=hgKtwSb4S2 | Nicolas Boulle,Alex Townsend | ICLR 2022,Poster | The randomized singular value decomposition (SVD) is a popular and effective algorithm for computing a near-best rank $k$ approximation of a matrix $A$ using matrix-vector products with standard Gaussian vectors. Here, we generalize the theory of randomized SVD to multivariate Gaussian vectors, allowing one to incorporate prior knowledge of $A$ into the algorithm. This enables us to explore the continuous analogue of the randomized SVD for Hilbert--Schmidt (HS) operators using operator-function products with functions drawn from a Gaussian process (GP). We then construct a new covariance kernel for GPs, based on weighted Jacobi polynomials, which allows us to rapidly sample the GP and control the smoothness of the randomly generated functions. Numerical examples on matrices and HS operators demonstrate the applicability of the algorithm. | https://openreview.net/pdf/fa63296901b49d968d4f9bcdde4847cb73c0b1a3.pdf |
Dropout Q-Functions for Doubly Efficient Reinforcement Learning | https://openreview.net/forum?id=xCVJMsPv3RT | https://openreview.net/forum?id=xCVJMsPv3RT | Takuya Hiraoka,Takahisa Imagawa,Taisei Hashimoto,Takashi Onishi,Yoshimasa Tsuruoka | ICLR 2022,Poster | Randomized ensembled double Q-learning (REDQ) (Chen et al., 2021b) has recently achieved state-of-the-art sample efficiency on continuous-action reinforcement learning benchmarks. This superior sample efficiency is made possible by using a large Q-function ensemble. However, REDQ is much less computationally efficient than non-ensemble counterparts such as Soft Actor-Critic (SAC) (Haarnoja et al., 2018a). To make REDQ more computationally efficient, we propose a method of improving computational efficiency called DroQ, which is a variant of REDQ that uses a small ensemble of dropout Q-functions. Our dropout Q-functions are simple Q-functions equipped with dropout connection and layer normalization. Despite its simplicity of implementation, our experimental results indicate that DroQ is doubly (sample and computationally) efficient. It achieved comparable sample efficiency with REDQ, much better computational efficiency than REDQ, and comparable computational efficiency with that of SAC. | https://openreview.net/pdf/b31fb60261f2746e9fc9ccca341e609e0b152e43.pdf |
QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization | https://openreview.net/forum?id=ySQH0oDyp7 | https://openreview.net/forum?id=ySQH0oDyp7 | Xiuying Wei,Ruihao Gong,Yuhang Li,Xianglong Liu,Fengwei Yu | ICLR 2022,Poster | Recently, post-training quantization (PTQ) has driven much attention to produce efficient neural networks without long-time retraining. Despite the low cost, current PTQ works always fail under the extremely low-bit setting. In this study, we pioneeringly confirm that properly incorporating activation quantization into the PTQ reconstruction benefits the final accuracy. To deeply understand the inherent reason, a theoretical framework is established, which inspires us that the flatness of the optimized low-bit model on calibration and test data is crucial. Based on the conclusion, a simple yet effective approach dubbed as \textsc{QDrop} is proposed, which randomly drops the quantization of activations during reconstruction. Extensive experiments on various tasks including computer vision (image classification, object detection) and natural language processing (text classification and question answering) prove its superiority. With \textsc{QDrop}, the limit of PTQ is pushed to the 2-bit activation for the first time and the accuracy boost can be up to 51.49\%. Without bells and whistles, \textsc{QDrop} establishes a new state of the art for PTQ. | https://openreview.net/pdf/d4b98e0b2a7f155f01c30723878a99c839a19f05.pdf |
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction | https://openreview.net/forum?id=POxF-LEqnF | https://openreview.net/forum?id=POxF-LEqnF | Osama Makansi,Julius Von Kügelgen,Francesco Locatello,Peter Vincent Gehler,Dominik Janzing,Thomas Brox,Bernhard Schölkopf | ICLR 2022,Poster | Predicting the future trajectory of a moving agent can be easy when the past trajectory continues smoothly but is challenging when complex interactions with other agents are involved. Recent deep learning approaches for trajectory prediction show promising performance and partially attribute this to successful reasoning about agent-agent interactions. However, it remains unclear which features such black-box models actually learn to use for making predictions. This paper proposes a procedure that quantifies the contributions of different cues to model performance based on a variant of Shapley values. Applying this procedure to state-of-the-art trajectory prediction methods on standard benchmark datasets shows that they are, in fact, unable to reason about interactions. Instead, the past trajectory of the target is the only feature used for predicting its future. For a task with richer social interaction patterns, on the other hand, the tested models do pick up such interactions to a certain extent, as quantified by our feature attribution method. We discuss the limits of the proposed method and its links to causality. | https://openreview.net/pdf/c2255d7b6f3e4ca52c2e54c74469ac86211d02ca.pdf |
Rethinking Class-Prior Estimation for Positive-Unlabeled Learning | https://openreview.net/forum?id=aYAA-XHKyk | https://openreview.net/forum?id=aYAA-XHKyk | Yu Yao,Tongliang Liu,Bo Han,Mingming Gong,Gang Niu,Masashi Sugiyama,Dacheng Tao | ICLR 2022,Poster | Given only positive (P) and unlabeled (U) data, PU learning can train a binary classifier without any negative data. It has two building blocks: PU class-prior estimation (CPE) and PU classification; the latter has been well studied while the former has received less attention. Hitherto, the distributional-assumption-free CPE methods rely on a critical assumption that the support of the positive data distribution cannot be contained in the support of the negative data distribution. If this is violated, those CPE methods will systematically overestimate the class prior; it is even worse that we cannot verify the assumption based on the data. In this paper, we rethink CPE for PU learning—can we remove the assumption to make CPE always valid? We show an affirmative answer by proposing Regrouping CPE (ReCPE) that builds an auxiliary probability distribution such that the support of the positive data distribution is never contained in the support of the negative data distribution. ReCPE can work with any CPE method by treating it as the base method. Theoretically, ReCPE does not affect its base if the assumption already holds for the original probability distribution; otherwise, it reduces the positive bias of its base. Empirically, ReCPE improves all state-of-the-art CPE methods on various datasets, implying that the assumption has indeed been violated here. | https://openreview.net/pdf/edf5d059e7a69954e63ffc928b52490879c12cdb.pdf |
Learning Efficient Online 3D Bin Packing on Packing Configuration Trees | https://openreview.net/forum?id=bfuGjlCwAq | https://openreview.net/forum?id=bfuGjlCwAq | Hang Zhao,Yang Yu,Kai Xu | ICLR 2022,Poster | Online 3D Bin Packing Problem (3D-BPP) has widespread applications in industrial automation and has aroused enthusiastic research interest recently. Existing methods usually solve the problem with limited resolution of spatial discretization, and/or cannot deal with complex practical constraints well. We propose to enhance the practical applicability of online 3D-BPP via learning on a novel hierarchical representation – packing configuration tree (PCT). PCT is a full-fledged description of the state and action space of bin packing which can support packing policy learning based on deep reinforcement learning (DRL). The size of the packing action space is proportional to the number of leaf nodes, making the DRL model easy to train and well-performing even with continuous solution space. During training, PCT expands based on heuristic rules, however, the DRL model learns a much more effective and robust packing policy than heuristic methods. Through extensive evaluation, we demonstrate that our method outperforms all existing online BPP methods and is versatile in terms of incorporating various practical constraints. | https://openreview.net/pdf/e5e3ec7c1a72d597a2bb22fa50e8e20915ce740c.pdf |
Uncertainty Modeling for Out-of-Distribution Generalization | https://openreview.net/forum?id=6HN7LHyzGgC | https://openreview.net/forum?id=6HN7LHyzGgC | Xiaotong Li,Yongxing Dai,Yixiao Ge,Jun Liu,Ying Shan,LINGYU DUAN | ICLR 2022,Poster | Though remarkable progress has been achieved in various vision tasks, deep neural networks still suffer obvious performance degradation when tested in out-of-distribution scenarios. We argue that the feature statistics (mean and standard deviation), which carry the domain characteristics of the training data, can be properly manipulated to improve the generalization ability of deep learning models. Common methods often consider the feature statistics as deterministic values measured from the learned features and do not explicitly consider the uncertain statistics discrepancy caused by potential domain shifts during testing. In this paper, we improve the network generalization ability by modeling the uncertainty of domain shifts with synthesized feature statistics during training. Specifically, we hypothesize that the feature statistic, after considering the potential uncertainties, follows a multivariate Gaussian distribution. Hence, each feature statistic is no longer a deterministic value, but a probabilistic point with diverse distribution possibilities. With the uncertain feature statistics, the models can be trained to alleviate the domain perturbations and achieve better robustness against potential domain shifts. Our method can be readily integrated into networks without additional parameters. Extensive experiments demonstrate that our proposed method consistently improves the network generalization ability on multiple vision tasks, including image classification, semantic segmentation, and instance retrieval. | https://openreview.net/pdf/8b458e772747ce913aafc8c12549f3d6efa75dd9.pdf |
Online Adversarial Attacks | https://openreview.net/forum?id=bYGSzbCM_i | https://openreview.net/forum?id=bYGSzbCM_i | Andjela Mladenovic,Joey Bose,Hugo Berard,William L. Hamilton,Simon Lacoste-Julien,Pascal Vincent,Gauthier Gidel | ICLR 2022,Poster | Adversarial attacks expose important vulnerabilities of deep learning models, yet little attention has been paid to settings where data arrives as a stream. In this paper, we formalize the online adversarial attack problem, emphasizing two key elements found in real-world use-cases: attackers must operate under partial knowledge of the target model, and the decisions made by the attacker are irrevocable since they operate on a transient data stream. We first rigorously analyze a deterministic variant of the online threat model by drawing parallels to the well-studied $k$-secretary problem in theoretical computer science and propose Virtual+, a simple yet practical online algorithm. Our main theoretical result shows Virtual+ yields provably the best competitive ratio over all single-threshold algorithms for $k<5$---extending the previous analysis of the $k$-secretary problem. We also introduce the \textit{stochastic $k$-secretary}---effectively reducing online blackbox transfer attacks to a $k$-secretary problem under noise---and prove theoretical bounds on the performance of Virtual+ adapted to this setting. Finally, we complement our theoretical results by conducting experiments on MNIST, CIFAR-10, and Imagenet classifiers, revealing the necessity of online algorithms in achieving near-optimal performance and also the rich interplay between attack strategies and online attack selection, enabling simple strategies like FGSM to outperform stronger adversaries. | https://openreview.net/pdf/24f13e61662f3513c6032f30cf8bf30a47bdd2d1.pdf |
Anytime Dense Prediction with Confidence Adaptivity | https://openreview.net/forum?id=kNKFOXleuC | https://openreview.net/forum?id=kNKFOXleuC | Zhuang Liu,Zhiqiu Xu,Hung-Ju Wang,Trevor Darrell,Evan Shelhamer | ICLR 2022,Poster | Anytime inference requires a model to make a progression of predictions which might be halted at any time. Prior research on anytime visual recognition has mostly focused on image classification.We propose the first unified and end-to-end approach for anytime dense prediction. A cascade of "exits" is attached to the model to make multiple predictions. We redesign the exits to account for the depth and spatial resolution of the features for each exit. To reduce total computation, and make full use of prior predictions, we develop a novel spatially adaptive approach to avoid further computation on regions where early predictions are already sufficiently confident. Our full method, named anytime dense prediction with confidence (ADP-C), achieves the same level of final accuracy, and meanwhile significantly reduces total computation. We evaluate our method on Cityscapes semantic segmentation and MPII human pose estimation: ADP-C enables anytime inference without sacrificing accuracy while also reducing the total FLOPs of its base models by 44.4% and 59.1%. We compare with anytime inference by deep equilibrium networks and feature-based stochastic sampling, showing that ADP-C dominates both across the accuracy-computation curve. Our code is available at https://github.com/liuzhuang13/anytime. | https://openreview.net/pdf/32a205bf9187cb9f1f299230fb996fb4ca7c920a.pdf |
Declarative nets that are equilibrium models | https://openreview.net/forum?id=q4HaTeMO--y | https://openreview.net/forum?id=q4HaTeMO--y | Russell Tsuchida,Suk Yee Yong,Mohammad Ali Armin,Lars Petersson,Cheng Soon Ong | ICLR 2022,Poster | Implicit layers are computational modules that output the solution to some problem depending on the input and the layer parameters. Deep equilibrium models (DEQs) output a solution to a fixed point equation. Deep declarative networks (DDNs) solve an optimisation problem in their forward pass, an arguably more intuitive, interpretable problem than finding a fixed point. We show that solving a kernelised regularised maximum likelihood estimate as an inner problem in a DDN yields a large class of DEQ architectures. Our proof uses the exponential family in canonical form, and provides a closed-form expression for the DEQ parameters in terms of the kernel. The activation functions have interpretations in terms of the derivative of the log partition function. Building on existing literature, we interpret DEQs as fine-tuned, unrolled classical algorithms, giving an intuitive justification for why DEQ models are sensible. We use our theoretical result to devise an initialisation scheme for DEQs that allows them to solve kGLMs in their forward pass at initialisation. We empirically show that this initialisation scheme improves training stability and performance over random initialisation. | https://openreview.net/pdf/691b2e9a113f764639503e6882404ede8e06e75c.pdf |
A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning | https://openreview.net/forum?id=AcrlgZ9BKed | https://openreview.net/forum?id=AcrlgZ9BKed | Yunchang Yang,Tianhao Wu,Han Zhong,Evrard Garcelon,Matteo Pirotta,Alessandro Lazaric,Liwei Wang,Simon Shaolei Du | ICLR 2022,Poster | We study bandits and reinforcement learning (RL) subject to a conservative constraint where the agent is asked to perform at least as well as a given baseline policy. This setting is particular relevant in real-world domains including digital marketing, healthcare, production, finance, etc. In this paper, we present a reduction-based framework for conservative bandits and RL, in which our core technique is to calculate the necessary and sufficient budget obtained from running the baseline policy. For lower bounds, we improve the existing lower bound for conservative multi-armed bandits and obtain new lower bounds for conservative linear bandits, tabular RL and low-rank MDP, through a black-box reduction that turns a certain lower bound in the nonconservative setting into a new lower bound in the conservative setting. For upper bounds, in multi-armed bandits, linear bandits and tabular RL, our new upper bounds tighten or match existing ones with significantly simpler analyses. We also obtain a new upper bound for conservative low-rank MDP. | https://openreview.net/pdf/559947894a1c34a3506ac8af2c0fe052117babd0.pdf |
Wisdom of Committees: An Overlooked Approach To Faster and More Accurate Models | https://openreview.net/forum?id=MvO2t0vbs4- | https://openreview.net/forum?id=MvO2t0vbs4- | Xiaofang Wang,Dan Kondratyuk,Eric Christiansen,Kris M. Kitani,Yair Movshovitz-Attias,Elad Eban | ICLR 2022,Poster | Committee-based models (ensembles or cascades) construct models by combining existing pre-trained ones. While ensembles and cascades are well-known techniques that were proposed before deep learning, they are not considered a core building block of deep model architectures and are rarely compared to in recent literature on developing efficient models. In this work, we go back to basics and conduct a comprehensive analysis of the efficiency of committee-based models. We find that even the most simplistic method for building committees from existing, independently pre-trained models can match or exceed the accuracy of state-of-the-art models while being drastically more efficient. These simple committee-based models also outperform sophisticated neural architecture search methods (e.g., BigNAS). These findings hold true for several tasks, including image classification, video classification, and semantic segmentation, and various architecture families, such as ViT, EfficientNet, ResNet, MobileNetV2, and X3D. Our results show that an EfficientNet cascade can achieve a 5.4x speedup over B7 and a ViT cascade can achieve a 2.3x speedup over ViT-L-384 while being equally accurate. | https://openreview.net/pdf/30614ee406abf0fbe954e884f90eb52e58cb5336.pdf |
Unsupervised Discovery of Object Radiance Fields | https://openreview.net/forum?id=rwE8SshAlxw | https://openreview.net/forum?id=rwE8SshAlxw | Hong-Xing Yu,Leonidas Guibas,Jiajun Wu | ICLR 2022,Poster | We study the problem of inferring an object-centric scene representation from a single image, aiming to derive a representation that explains the image formation process, captures the scene's 3D nature, and is learned without supervision. Most existing methods on scene decomposition lack one or more of these characteristics, due to the fundamental challenge in integrating the complex 3D-to-2D image formation process into powerful inference schemes like deep networks. In this paper, we propose unsupervised discovery of Object Radiance Fields (uORF), integrating recent progresses in neural 3D scene representations and rendering with deep inference networks for unsupervised 3D scene decomposition. Trained on multi-view RGB images without annotations, uORF learns to decompose complex scenes with diverse, textured background from a single image. We show that uORF enables novel tasks, such as scene segmentation and editing in 3D, and it performs well on these tasks and on novel view synthesis on three datasets. | https://openreview.net/pdf/cfb2b52583e94904f05e5386618edd96d4521272.pdf |
Gradient Step Denoiser for convergent Plug-and-Play | https://openreview.net/forum?id=fPhKeld3Okz | https://openreview.net/forum?id=fPhKeld3Okz | Samuel Hurault,Arthur Leclaire,Nicolas Papadakis | ICLR 2022,Poster | Plug-and-Play methods constitute a class of iterative algorithms for imaging problems where regularization is performed by an off-the-shelf denoiser. Although Plug-and-Play methods can lead to tremendous visual performance for various image problems, the few existing convergence guarantees are based on unrealistic (or suboptimal) hypotheses on the denoiser, or limited to strongly convex data terms. In this work, we propose a new type of Plug-and-Play methods, based on half-quadratic splitting, for which the denoiser is realized as a gradient descent step on a functional parameterized by a deep neural network. Exploiting convergence results for proximal gradient descent algorithms in the non-convex setting, we show that the proposed Plug-and-Play algorithm is a convergent iterative scheme that targets stationary points of an explicit global functional. Besides, experiments show that it is possible to learn such a deep denoiser while not compromising the performance in comparison to other state-of-the-art deep denoisers used in Plug-and-Play schemes. We apply our proximal gradient algorithm to various ill-posed inverse problems, e.g. deblurring, super-resolution and inpainting. For all these applications, numerical results empirically confirm the convergence results. Experiments also show that this new algorithm reaches state-of-the-art performance, both quantitatively and qualitatively. | https://openreview.net/pdf/ebee4c9eada2179f7cee9f61783b96bb27df1819.pdf |
Surrogate Gap Minimization Improves Sharpness-Aware Training | https://openreview.net/forum?id=edONMAnhLu- | https://openreview.net/forum?id=edONMAnhLu- | Juntang Zhuang,Boqing Gong,Liangzhe Yuan,Yin Cui,Hartwig Adam,Nicha C Dvornek,sekhar tatikonda,James s Duncan,Ting Liu | ICLR 2022,Poster | The recently proposed Sharpness-Aware Minimization (SAM) improves generalization by minimizing a perturbed loss defined as the maximum loss within a neighborhood in the parameter space. However, we show that both sharp and flat minima can have a low perturbed loss, implying that SAM does not always prefer flat minima. Instead, we define a surrogate gap, a measure equivalent to the dominant eigenvalue of Hessian at a local minimum when the radius of neighborhood (to derive the perturbed loss) is small. The surrogate gap is easy to compute and feasible for direct minimization during training. Based on the above observations, we propose Surrogate Gap Guided Sharpness-Aware Minimization (GSAM), a novel improvement over SAM with negligible computation overhead. Conceptually, GSAM consists of two steps: 1) a gradient descent like SAM to minimize the perturbed loss, and 2) an ascent step in the orthogonal direction (after gradient decomposition) to minimize the surrogate gap and yet not affect the perturbed loss. GSAM seeks a region with both small loss (by step 1) and low sharpness (by step 2), giving rise to a model with high generalization capabilities. Theoretically, we show the convergence of GSAM and provably better generalization than SAM.Empirically, GSAM consistently improves generalization (e.g., +3.2% over SAM and +5.4% over AdamW on ImageNet top-1 accuracy for ViT-B/32). Code is released at https://sites.google.com/view/gsam-iclr22/home | https://openreview.net/pdf/a8445fdd027dcffe865628c3024581cae637dca1.pdf |
R4D: Utilizing Reference Objects for Long-Range Distance Estimation | https://openreview.net/forum?id=MQ2sAGunyBP | https://openreview.net/forum?id=MQ2sAGunyBP | Yingwei Li,Tiffany Chen,Maya Kabkab,Ruichi Yu,Longlong Jing,Yurong You,Hang Zhao | ICLR 2022,Poster | Estimating the distance of objects is a safety-critical task for autonomous driving. Focusing on short-range objects, existing methods and datasets neglect the equally important long-range objects. In this paper, we introduce a challenging and under-explored task, which we refer to as Long-Range Distance Estimation, as well as two datasets to validate new methods developed for this task. We then proposeR4D, the first framework to accurately estimate the distance of long-range objects by using references with known distances in the scene. Drawing inspiration from human perception, R4D builds a graph by connecting a target object to all references. An edge in the graph encodes the relative distance information between a pair of target and reference objects. An attention module is then used to weigh the importance of reference objects and combine them into one target object distance prediction. Experiments on the two proposed datasets demonstrate the effectiveness and robustness of R4D by showing significant improvements compared to existing baselines. We’re looking to make the proposed dataset, Waymo OpenDataset - Long-Range Labels, available publicly at waymo.com/open/download. | https://openreview.net/pdf/1f644c94f6909e0b3a0ceb6f97174f34a0130d65.pdf |
Understanding Dimensional Collapse in Contrastive Self-supervised Learning | https://openreview.net/forum?id=YevsQ05DEN7 | https://openreview.net/forum?id=YevsQ05DEN7 | Li Jing,Pascal Vincent,Yann LeCun,Yuandong Tian | ICLR 2022,Poster | Self-supervised visual representation learning aims to learn useful representations without relying on human annotations. Joint embedding approach bases on maximizing the agreement between embedding vectors from different views of the same image. Various methods have been proposed to solve the collapsing problem where all embedding vectors collapse to a trivial constant solution. Among these methods, contrastive learning prevents collapse via negative sample pairs. It has been shown that non-contrastive methods suffer from a lesser collapse problem of a different nature: dimensional collapse, whereby the embedding vectors end up spanning a lower-dimensional subspace instead of the entire available embedding space. Here, we show that dimensional collapse also happens in contrastive learning. In this paper, we shed light on the dynamics at play in contrastive learning that leads to dimensional collapse. Inspired by our theory, we propose a novel contrastive learning method, called DirectCLR, which directly optimizes the representation space without relying on a trainable projector. Experiments show that DirectCLR outperforms SimCLR with a trainable linear projector on ImageNet. | https://openreview.net/pdf/29c47b5da8f299ffc2b636c45a4854bc2a62e102.pdf |
FedPara: Low-rank Hadamard Product for Communication-Efficient Federated Learning | https://openreview.net/forum?id=d71n4ftoCBy | https://openreview.net/forum?id=d71n4ftoCBy | Nam Hyeon-Woo,Moon Ye-Bin,Tae-Hyun Oh | ICLR 2022,Poster | In this work, we propose a communication-efficient parameterization, $\texttt{FedPara}$, for federated learning (FL) to overcome the burdens on frequent model uploads and downloads. Our method re-parameterizes weight parameters of layers using low-rank weights followed by the Hadamard product. Compared to the conventional low-rank parameterization, our $\texttt{FedPara}$ method is not restricted to low-rank constraints, and thereby it has a far larger capacity. This property enables to achieve comparable performance while requiring 3 to 10 times lower communication costs than the model with the original layers, which is not achievable by the traditional low-rank methods. The efficiency of our method can be further improved by combining with other efficient FL optimizers. In addition, we extend our method to a personalized FL application, $\texttt{pFedPara}$, which separates parameters into global and local ones. We show that $\texttt{pFedPara}$ outperforms competing personalized FL methods with more than three times fewer parameters. | https://openreview.net/pdf/4dc185d690323acd6419fa90d304ed213b53a7e6.pdf |
RegionViT: Regional-to-Local Attention for Vision Transformers | https://openreview.net/forum?id=T__V3uLix7V | https://openreview.net/forum?id=T__V3uLix7V | Chun-Fu Chen,Rameswar Panda,Quanfu Fan | ICLR 2022,Poster | Vision transformer (ViT) has recently shown its strong capability in achieving comparable results to convolutional neural networks (CNNs) on image classification. However, vanilla ViT simply inherits the same architecture from the natural language processing directly, which is often not optimized for vision applications. Motivated by this, in this paper, we propose a new architecture that adopts the pyramid structure and employ novel regional-to-local attention rather than global self-attention in vision transformers. More specifically, our model first generates regional tokens and local tokens from an image with different patch sizes, where each regional token is associated with a set of local tokens based on the spatial location. The regional-to-local attention includes two steps: first, the regional self-attention extracts global information among all regional tokens and then the local self-attention exchanges the information among one regional token and the associated local tokens via self-attention. Therefore, even though local self-attention confines the scope in a local region but it can still receive global information.
Extensive experiments on four vision tasks, including image classification, object and keypoint detection, semantics segmentation and action recognition, show that our approach outperforms or is on par with state-of-the-art ViT variants including many concurrent works. Our source codes and models are available at \url{https://github.com/IBM/RegionViT}. | https://openreview.net/pdf/a70d9722581424639326f684a4184d8a5af3dcb3.pdf |
Quadtree Attention for Vision Transformers | https://openreview.net/forum?id=fR-EnKWL_Zb | https://openreview.net/forum?id=fR-EnKWL_Zb | Shitao Tang,Jiahui Zhang,Siyu Zhu,Ping Tan | ICLR 2022,Poster | Transformers have been successful in many vision tasks, thanks to their capability of capturing long-range dependency. However, their quadratic computational complexity poses a major obstacle for applying them to vision tasks requiring dense predictions, such as object detection, feature matching, stereo, etc. We introduce QuadTree Attention, which reduces the computational complexity from quadratic to linear. Our quadtree transformer builds token pyramids and computes attention in a coarse-to-fine manner. At each level, the top K patches with the highest attention scores are selected, such that at the next level, attention is only evaluated within the relevant regions corresponding to these top K patches. We demonstrate that quadtree attention achieves state-of-the-art performance in various vision tasks, e.g. with 4.0% improvement in feature matching on ScanNet, about 50% flops reduction in stereo matching, 0.4-1.5% improvement in top-1 accuracy on ImageNet classification, 1.2-1.8% improvement on COCO object detection, and 0.7-2.4% improvement on semantic segmentation over previous state-of-the-art transformers. The codes are available at https://github.com/Tangshitao/QuadtreeAttention. | https://openreview.net/pdf/b7f143e3e44e4a03ec6c8a7bb34ff7bf86d510f6.pdf |
Visual Correspondence Hallucination | https://openreview.net/forum?id=jaLDP8Hp_gc | https://openreview.net/forum?id=jaLDP8Hp_gc | Hugo Germain,Vincent Lepetit,Guillaume Bourmaud | ICLR 2022,Poster | Given a pair of partially overlapping source and target images and a keypoint in the source image, the keypoint's correspondent in the target image can be either visible, occluded or outside the field of view. Local feature matching methods are only able to identify the correspondent's location when it is visible, while humans can also hallucinate its location when it is occluded or outside the field of view through geometric reasoning. In this paper, we bridge this gap by training a network to output a peaked probability distribution over the correspondent's location, regardless of this correspondent being visible, occluded, or outside the field of view. We experimentally demonstrate that this network is indeed able to hallucinate correspondences on pairs of images captured in scenes that were not seen at training-time. We also apply this network to an absolute camera pose estimation problem and find it is significantly more robust than state-of-the-art local feature matching-based competitors. | https://openreview.net/pdf/6374bf841920e8781ce8a4dbbf3877649df2ae13.pdf |
What’s Wrong with Deep Learning in Tree Search for Combinatorial Optimization | https://openreview.net/forum?id=mk0HzdqY7i1 | https://openreview.net/forum?id=mk0HzdqY7i1 | Maximilian Böther,Otto Kißig,Martin Taraz,Sarel Cohen,Karen Seidel,Tobias Friedrich | ICLR 2022,Poster | Combinatorial optimization lies at the core of many real-world problems. Especially since the rise of graph neural networks (GNNs), the deep learning community has been developing solvers that derive solutions to NP-hard problems by learning the problem-specific solution structure. However, reproducing the results of these publications proves to be difficult. We make three contributions. First, we present an open-source benchmark suite for the NP-hard Maximum Independent Set problem, in both its weighted and unweighted variants. The suite offers a unified interface to various state-of-the-art traditional and machine learning-based solvers. Second, using our benchmark suite, we conduct an in-depth analysis of the popular guided tree search algorithm by Li et al. [NeurIPS 2018], testing various configurations on small and large synthetic and real-world graphs. By re-implementing their algorithm with a focus on code quality and extensibility, we show that the graph convolution network used in the tree search does not learn a meaningful representation of the solution structure, and can in fact be replaced by random values. Instead, the tree search relies on algorithmic techniques like graph kernelization to find good solutions. Thus, the results from the original publication are not reproducible. Third, we extend the analysis to compare the tree search implementations to other solvers, showing that the classical algorithmic solvers often are faster, while providing solutions of similar quality. Additionally, we analyze a recent solver based on reinforcement learning and observe that for this solver, the GNN is responsible for the competitive solution quality. | https://openreview.net/pdf/e5f8071b2a7b4ea5b72a544d5351737d28998d92.pdf |
Deep Attentive Variational Inference | https://openreview.net/forum?id=T4-65DNlDij | https://openreview.net/forum?id=T4-65DNlDij | Ifigeneia Apostolopoulou,Ian Char,Elan Rosenfeld,Artur Dubrawski | ICLR 2022,Poster | Stochastic Variational Inference is a powerful framework for learning large-scale probabilistic latent variable models. However, typical assumptions on the factorization or independence of the latent variables can substantially restrict its capacity for inference and generative modeling. A major line of active research aims at building more expressive variational models by designing deep hierarchies of interdependent latent variables. Although these models exhibit superior performance and enable richer latent representations, we show that they incur diminishing returns: adding more stochastic layers to an already very deep model yields small predictive improvement while substantially increasing the inference and training time. Moreover, the architecture for this class of models favors local interactions among the latent variables between neighboring layers when designing the conditioning factors of the involved distributions. This is the first work that proposes attention mechanisms to build more expressive variational distributions in deep probabilistic models by explicitly modeling both local and global interactions in the latent space. Specifically, we propose deep attentive variational autoencoder and test it on a variety of established datasets. We show it achieves state-of-the-art log-likelihoods while using fewer latent layers and requiring less training time than existing models. The proposed non-local inference reduces computational footprint by alleviating the need for deep hierarchies. Project code:
https://github.com/ifiaposto/Deep_Attentive_VI | https://openreview.net/pdf/e146a17aaa803dbcfae93f2869963af182eb5007.pdf |
ARTEMIS: Attention-based Retrieval with Text-Explicit Matching and Implicit Similarity | https://openreview.net/forum?id=CVfLvQq9gLo | https://openreview.net/forum?id=CVfLvQq9gLo | Ginger Delmas,Rafael S. Rezende,Gabriela Csurka,Diane Larlus | ICLR 2022,Poster | An intuitive way to search for images is to use queries composed of an example image and a complementary text. While the first provides rich and implicit context for the search, the latter explicitly calls for new traits, or specifies how some elements of the example image should be changed to retrieve the desired target image. Current approaches typically combine the features of each of the two elements of the query into a single representation, which can then be compared to the ones of the potential target images. Our work aims at shedding new light on the task by looking at it through the prism of two familiar and related frameworks: text-to-image and image-to-image retrieval. Taking inspiration from them, we exploit the specific relation of each query element with the targeted image and derive light-weight attention mechanisms which enable to mediate between the two complementary modalities. We validate our approach on several retrieval benchmarks, querying with images and their associated free-form text modifiers. Our method obtains state-of-the-art results without resorting to side information, multi-level features, heavy pre-training nor large architectures as in previous works. | https://openreview.net/pdf/7417abca3de0104a752284ee1cbdc467dd175c20.pdf |
Trivial or Impossible --- dichotomous data difficulty masks model differences (on ImageNet and beyond) | https://openreview.net/forum?id=C_vsGwEIjAr | https://openreview.net/forum?id=C_vsGwEIjAr | Kristof Meding,Luca M. Schulze Buschoff,Robert Geirhos,Felix A. Wichmann | ICLR 2022,Poster | "The power of a generalization system follows directly from its biases" (Mitchell 1980). Today, CNNs are incredibly powerful generalisation systems---but to what degree have we understood how their inductive bias influences model decisions? We here attempt to disentangle the various aspects that determine how a model decides. In particular, we ask: what makes one model decide differently from another? In a meticulously controlled setting, we find that (1.) irrespective of the network architecture or objective (e.g. self-supervised, semi-supervised, vision transformers, recurrent models) all models end up with a similar decision boundary. (2.) To understand these findings, we analysed model decisions on the ImageNet validation set from epoch to epoch and image by image. We find that the ImageNet validation set, among others, suffers from dichotomous data difficulty (DDD): For the range of investigated models and their accuracies, it is dominated by 46.0% "trivial" and 11.5% "impossible" images (beyond label errors). Only 42.5% of the images could possibly be responsible for the differences between two models' decision boundaries. (3.) Only removing the "impossible" and "trivial" images allows us to see pronounced differences between models. (4.) Humans are highly accurate at predicting which images are "trivial" and "impossible" for CNNs (81.4%). This implies that in future comparisons of brains, machines and behaviour, much may be gained from investigating the decisive role of images and the distribution of their difficulties. | https://openreview.net/pdf/d76062ba60abbde95bd2a493d0d7749efdfd41c0.pdf |
Group equivariant neural posterior estimation | https://openreview.net/forum?id=u6s8dSporO8 | https://openreview.net/forum?id=u6s8dSporO8 | Maximilian Dax,Stephen R Green,Jonathan Gair,Michael Deistler,Bernhard Schölkopf,Jakob H. Macke | ICLR 2022,Poster | Simulation-based inference with conditional neural density estimators is a powerful approach to solving inverse problems in science. However, these methods typically treat the underlying forward model as a black box, with no way to exploit geometric properties such as equivariances. Equivariances are common in scientific models, however integrating them directly into expressive inference networks (such as normalizing flows) is not straightforward. We here describe an alternative method to incorporate equivariances under joint transformations of parameters and data. Our method---called group equivariant neural posterior estimation (GNPE)---is based on self-consistently standardizing the "pose" of the data while estimating the posterior over parameters. It is architecture-independent, and applies both to exact and approximate equivariances. As a real-world application, we use GNPE for amortized inference of astrophysical binary black hole systems from gravitational-wave observations. We show that GNPE achieves state-of-the-art accuracy while reducing inference times by three orders of magnitude. | https://openreview.net/pdf/57dd5a689e8ee692e40ff4ca7c15b889e41e29c2.pdf |
Fast Differentiable Matrix Square Root | https://openreview.net/forum?id=-AOEi-5VTU8 | https://openreview.net/forum?id=-AOEi-5VTU8 | Yue Song,Nicu Sebe,Wei Wang | ICLR 2022,Poster | Computing the matrix square root or its inverse in a differentiable manner is important in a variety of computer vision tasks. Previous methods either adopt the Singular Value Decomposition (SVD) to explicitly factorize the matrix or use the Newton-Schulz iteration (NS iteration) to derive the approximate solution. However, both methods are not computationally efficient enough in either the forward pass or in the backward pass. In this paper, we propose two more efficient variants to compute the differentiable matrix square root. For the forward propagation, one method is to use Matrix Taylor Polynomial (MTP), and the other method is to use Matrix Pad\'e Approximants (MPA). The backward gradient is computed by iteratively solving the continuous-time Lyapunov equation using the matrix sign function. Both methods yield considerable speed-up compared with the SVD or the Newton-Schulz iteration. Experimental results on the de-correlated batch normalization and second-order vision transformer demonstrate that our methods can also achieve competitive and even slightly better performances. The code is available at \href{https://github.com/KingJamesSong/FastDifferentiableMatSqrt}{https://github.com/KingJamesSong/FastDifferentiableMatSqrt}. | https://openreview.net/pdf/641879a2e12839331fe8fe974fdc1ef6494cbb9c.pdf |
SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation | https://openreview.net/forum?id=JXhROKNZzOc | https://openreview.net/forum?id=JXhROKNZzOc | Cong Guo,Yuxian Qiu,Jingwen Leng,Xiaotian Gao,Chen Zhang,Yunxin Liu,Fan Yang,Yuhao Zhu,Minyi Guo | ICLR 2022,Poster | Quantization of deep neural networks (DNN) has been proven effective for compressing and accelerating DNN models. Data-free quantization (DFQ) is a promising approach without the original datasets under privacy-sensitive and confidential scenarios. However, current DFQ solutions degrade accuracy, need synthetic data to calibrate networks, and are time-consuming and costly. This paper proposes an on-the-fly DFQ framework with sub-second quantization time, called SQuant, which can quantize networks on inference-only devices with low computation and memory requirements. With the theoretical analysis of the second-order information of DNN task loss, we decompose and approximate the Hessian-based optimization objective into three diagonal sub-items, which have different areas corresponding to three dimensions of weight tensor: element-wise, kernel-wise, and output channel-wise. Then, we progressively compose sub-items and propose a novel data-free optimization objective in the discrete domain, minimizing Constrained Absolute Sum of Error (or CASE in short), which surprisingly does not need any dataset and is even not aware of network architecture. We also design an efficient algorithm without back-propagation to further reduce the computation complexity of the objective solver. Finally, without fine-tuning and synthetic datasets, SQuant accelerates the data-free quantization process to a sub-second level with >30% accuracy improvement over the existing data-free post-training quantization works, with the evaluated models under 4-bit quantization. We have open-sourced the SQuant framework at https://github.com/clevercool/SQuant. | https://openreview.net/pdf/2bb26dab9a7db75000cffe21e5229acfdca132af.pdf |
Neural Variational Dropout Processes | https://openreview.net/forum?id=lyLVzukXi08 | https://openreview.net/forum?id=lyLVzukXi08 | Insu Jeon,Youngjin Park,Gunhee Kim | ICLR 2022,Poster | Learning to infer the conditional posterior model is a key step for robust meta-learning. This paper presents a new Bayesian meta-learning approach called Neural Variational Dropout Processes (NVDPs). NVDPs model the conditional posterior distribution based on a task-specific dropout; a low-rank product of Bernoulli experts meta-model is utilized for a memory-efficient mapping of dropout rates from a few observed contexts. It allows for a quick reconfiguration of a globally learned and shared neural network for new tasks in multi-task few-shot learning. In addition, NVDPs utilize a novel prior conditioned on the whole task data to optimize the conditional dropout posterior in the amortized variational inference. Surprisingly, this enables the robust approximation of task-specific dropout rates that can deal with a wide range of functional ambiguities and uncertainties. We compared the proposed method with other meta-learning approaches in the few-shot learning tasks such as 1D stochastic regression, image inpainting, and classification. The results show the excellent performance of NVDPs. | https://openreview.net/pdf/8553b02c69dba637b2b54e4d035a7c5c7277d5ed.pdf |
Towards Better Understanding and Better Generalization of Low-shot Classification in Histology Images with Contrastive Learning | https://openreview.net/forum?id=kQ2SOflIOVC | https://openreview.net/forum?id=kQ2SOflIOVC | Jiawei Yang,Hanbo Chen,Jiangpeng Yan,Xiaoyu Chen,Jianhua Yao | ICLR 2022,Poster | Few-shot learning is an established topic in natural images for years, but few work is attended to histology images, which is of high clinical value since well-labeled datasets and rare abnormal samples are expensive to collect. Here, we facilitate the study of few-shot learning in histology images by setting up three cross-domain tasks that simulate real clinics problems. To enable label-efficient learning and better generalizability, we propose to incorporate contrastive learning (CL) with latent augmentation (LA) to build a few-shot system. CL learns useful representations without manual labels, while LA transfers semantic variations of the base dataset in an unsupervised way. These two components fully exploit unlabeled training data and can scale gracefully to other label-hungry problems. In experiments, we find i) models learned by CL generalize better than supervised learning for histology images in unseen classes, and ii) LA brings consistent gains over baselines. Prior studies of self-supervised learning mainly focus on ImageNet-like images, which only present a dominant object in their centers. Recent attention has been paid to images with multi-objects and multi-textures. Histology images are a natural choice for such a study. We show the superiority of CL over supervised learning in terms of generalization for such data and provide our empirical understanding for this observation. The findings in this work could contribute to understanding how the model generalizes in the context of both representation learning and histological image analysis. Code is available. | https://openreview.net/pdf/e31401d33fda8d1009bf242636e6aa638632482a.pdf |
Distilling GANs with Style-Mixed Triplets for X2I Translation with Limited Data | https://openreview.net/forum?id=QjOQkpzKbNk | https://openreview.net/forum?id=QjOQkpzKbNk | Yaxing Wang,Joost van de weijer,Lu Yu,SHANGLING JUI | ICLR 2022,Poster | Conditional image synthesis is an integral part of many X2I translation systems, including image-to-image, text-to-image and audio-to-image translation systems. Training these large systems generally requires huge amounts of training data.
Therefore, we investigate knowledge distillation to transfer knowledge from a high-quality unconditioned generative model (e.g., StyleGAN) to a conditioned synthetic image generation modules in a variety of systems. To initialize the conditional and reference branch (from a unconditional GAN) we exploit the style mixing characteristics of high-quality GANs to generate an infinite supply of style-mixed triplets to perform the knowledge distillation. Extensive experimental results in a number of image generation tasks (i.e., image-to-image, semantic segmentation-to-image, text-to-image and audio-to-image) demonstrate qualitatively and quantitatively that our method successfully transfers knowledge to the synthetic image generation modules, resulting in more realistic images than previous methods as confirmed by a significant drop in the FID. | https://openreview.net/pdf/5a2c69f76a578d6db9205be58713ad872914604e.pdf |
Handling Distribution Shifts on Graphs: An Invariance Perspective | https://openreview.net/forum?id=FQOC5u-1egI | https://openreview.net/forum?id=FQOC5u-1egI | Qitian Wu,Hengrui Zhang,Junchi Yan,David Wipf | ICLR 2022,Poster | There is increasing evidence suggesting neural networks' sensitivity to distribution shifts, so that research on out-of-distribution (OOD) generalization comes into the spotlight. Nonetheless, current endeavors mostly focus on Euclidean data, and its formulation for graph-structured data is not clear and remains under-explored, given two-fold fundamental challenges: 1) the inter-connection among nodes in one graph, which induces non-IID generation of data points even under the same environment, and 2) the structural information in the input graph, which is also informative for prediction. In this paper, we formulate the OOD problem on graphs and develop a new invariant learning approach, Explore-to-Extrapolate Risk Minimization (EERM), that facilitates graph neural networks to leverage invariance principles for prediction. EERM resorts to multiple context explorers (specified as graph structure editers in our case) that are adversarially trained to maximize the variance of risks from multiple virtual environments. Such a design enables the model to extrapolate from a single observed environment which is the common case for node-level prediction. We prove the validity of our method by theoretically showing its guarantee of a valid OOD solution and further demonstrate its power on various real-world datasets for handling distribution shifts from artificial spurious features, cross-domain transfers and dynamic graph evolution. | https://openreview.net/pdf/f55776307b1b806dc62cd1e642289d70df555fd2.pdf |
Automatic Loss Function Search for Predict-Then-Optimize Problems with Strong Ranking Property | https://openreview.net/forum?id=hSktDu-h94 | https://openreview.net/forum?id=hSktDu-h94 | Boshi Wang,Jialin Yi,Hang Dong,Bo Qiao,Chuan Luo,Qingwei Lin | ICLR 2022,Poster | Combinatorial optimization problems with parameters to be predicted from side information are commonly seen in a variety of problems during the paradigm shift from reactive decision making to proactive decision making. Due to the misalignment between the continuous prediction results and the discrete decisions in optimization problems, it is hard to achieve a satisfactory prediction result with the ordinary $l_2$ loss in the prediction phase. To properly connect the prediction loss with the optimization goal, in this paper we propose a total group preorder (TGP) loss and its differential version called approximated total group preorder (ATGP) loss for predict-then-optimize (PTO) problems with strong ranking property. These new losses are provably more robust than the usual $l_2$ loss in a linear regression setting and have great potential to extend to other settings. We also propose an automatic searching algorithm that adapts the ATGP loss to PTO problems with different combinatorial structures. Extensive experiments on the ranking problem, the knapsack problem, and the shortest path problem have demonstrated that our proposed method can achieve a significant performance compared to the other methods designed for PTO problems. | https://openreview.net/pdf/98fea8b922621c46bbee3b4b58290769005b0897.pdf |
Generalized Demographic Parity for Group Fairness | https://openreview.net/forum?id=YigKlMJwjye | https://openreview.net/forum?id=YigKlMJwjye | Zhimeng Jiang,Xiaotian Han,Chao Fan,Fan Yang,Ali Mostafavi,Xia Hu | ICLR 2022,Poster | This work aims to generalize demographic parity to continuous sensitive attributes while preserving tractable computation. Current fairness metrics for continuous sensitive attributes largely rely on intractable statistical independence between variables, such as Hirschfeld-Gebelein-Renyi (HGR) and mutual information. Statistical fairness metrics estimation relying on either tractable bounds or neural network approximation, however, are not sufficiently trustful to rank algorithms prediction bias due to lack of estimation accuracy guarantee.
To make fairness metrics trustable, we propose \textit{\underline{G}eneralized \underline{D}emographic \underline{P}arity} (GDP), a group fairness metric for continuous and discrete attributes. We show the understanding of GDP from the probability perspective and theoretically reveal the connection between GDP regularizer and adversarial debiasing. To estimate GDP, we adopt hard and soft group strategies via the one-hot or the soft group indicator, representing the membership of each sample in different groups of the sensitive attribute. We provably and numerically show that the soft group strategy achieves a faster estimation error convergence rate. Experiments show the better bias mitigation performance of GDP regularizer, compared with adversarial debiasing, for regression and classification tasks in tabular and graph benchmarks. | https://openreview.net/pdf/3f9ffe7eafbd44f0205f3629edbcfb60ec738e7c.pdf |
Closed-form Sample Probing for Learning Generative Models in Zero-shot Learning | https://openreview.net/forum?id=ljxWpdBl4V | https://openreview.net/forum?id=ljxWpdBl4V | Samet Cetin,Orhun Buğra Baran,Ramazan Gokberk Cinbis | ICLR 2022,Poster | Generative model based approaches have led to significant advances in zero-shot learning (ZSL) over the past few years. These approaches typically aim to learn a conditional generator that synthesizes training samples of classes conditioned on class definitions. The final zero-shot learning model is then obtained by training a supervised classification model over the real and/or synthesized training samples of seen and unseen classes, combined. Therefore, naturally, the generative model needs to produce not only relevant samples, but also those that are sufficiently rich for classifier training purposes, which is handled by various heuristics in existing works. In this paper, we introduce a principled approach for training generative models {\em directly} for training data generation purposes. Our main observation is that the use of closed-form models opens doors to end-to-end training thanks to the differentiability of the solvers. In our approach, at each generative model update step, we fit a task-specific closed-form ZSL model from generated samples, and measure its loss on novel samples all within the compute graph, a procedure that we refer to as {\em sample probing}. In this manner, the generator receives feedback directly based on the value of its samples for model training purposes. Our experimental results show that the proposed sample probing approach improves the ZSL results even when integrated into state-of-the-art generative models.
| https://openreview.net/pdf/991863550ac668773c180067c71b28b180a005d0.pdf |
DKM: Differentiable k-Means Clustering Layer for Neural Network Compression | https://openreview.net/forum?id=J_F_qqCE3Z5 | https://openreview.net/forum?id=J_F_qqCE3Z5 | Minsik Cho,Keivan Alizadeh-Vahid,Saurabh Adya,Mohammad Rastegari | ICLR 2022,Poster | Deep neural network (DNN) model compression for efficient on-device inference is becoming increasingly important to reduce memory requirements and keep user data on-device. To this end, we propose a novel differentiable k-means clustering layer (DKM) and its application to train-time weight clustering-based DNN model compression. DKM casts k-means clustering as an attention problem and enables joint optimization of the DNN parameters and clustering centroids. Unlike prior works that rely on additional regularizers and parameters, DKM-based compression keeps the original loss function and model architecture fixed. We evaluated DKM-based compression on various DNN models for computer vision and natural language processing (NLP) tasks. Our results demonstrate that DKM delivers superior compression and accuracy trade-off on ImageNet1k and GLUE benchmarks. For example, DKM-based compression can offer 74.5% top-1 ImageNet1k accuracy on ResNet50 DNN model with 3.3MB model size (29.4x model compression factor). For MobileNet-v1, which is a challenging DNN to compress, DKM delivers 63.9% top-1 ImageNet1k accuracy with 0.72 MB model size (22.4x model compression factor). This result is 6.8% higher top-1accuracy and 33% relatively smaller model size than the current state-of-the-art DNN compression algorithms. Additionally, DKM enables compression of DistilBERT model by 11.8x with minimal (1.1%) accuracy loss on GLUE NLP benchmarks. | https://openreview.net/pdf/32e93d22db59833cad75b3a81ec8d15aa537a574.pdf |
Fixed Neural Network Steganography: Train the images, not the network | https://openreview.net/forum?id=hcMvApxGSzZ | https://openreview.net/forum?id=hcMvApxGSzZ | Varsha Kishore,Xiangyu Chen,Yan Wang,Boyi Li,Kilian Q Weinberger | ICLR 2022,Poster | Recent attempts at image steganography make use of advances in deep learning to train an encoder-decoder network pair to hide and retrieve secret messages in images. These methods are able to hide large amounts of data, but they also incur high decoding error rates (around 20%). In this paper, we propose a novel algorithm for steganography that takes advantage of the fact that neural networks are sensitive to tiny perturbations. Our method, Fixed Neural Network Steganography (FNNS), yields significantly lower error rates when compared to prior state-of-the-art methods and achieves 0% error reliably for hiding up to 3 bits per pixel (bpp) of secret information in images. FNNS also successfully evades existing statistical steganalysis systems and can be modified to evade neural steganalysis systems as well. Recovering every bit correctly, up to 3 bpp, enables novel applications that requires encryption. We introduce one specific use case for facilitating anonymized and safe image sharing. Our code is available at https://github.com/varshakishore/FNNS. | https://openreview.net/pdf/c905cc7ba9e62713ac3a302451e12d3a75523a2c.pdf |
Steerable Partial Differential Operators for Equivariant Neural Networks | https://openreview.net/forum?id=N9W24a4zU | https://openreview.net/forum?id=N9W24a4zU | Erik Jenner,Maurice Weiler | ICLR 2022,Poster | Recent work in equivariant deep learning bears strong similarities to physics. Fields over a base space are fundamental entities in both subjects, as are equivariant maps between these fields. In deep learning, however, these maps are usually defined by convolutions with a kernel, whereas they are partial differential operators (PDOs) in physics. Developing the theory of equivariant PDOs in the context of deep learning could bring these subjects even closer together and lead to a stronger flow of ideas. In this work, we derive a $G$-steerability constraint that completely characterizes when a PDO between feature vector fields is equivariant, for arbitrary symmetry groups $G$. We then fully solve this constraint for several important groups. We use our solutions as equivariant drop-in replacements for convolutional layers and benchmark them in that role. Finally, we develop a framework for equivariant maps based on Schwartz distributions that unifies classical convolutions and differential operators and gives insight about the relation between the two.
| https://openreview.net/pdf/b60b61cfed84b22e5393025d981343e0ed46ddcf.pdf |
Divergence-aware Federated Self-Supervised Learning | https://openreview.net/forum?id=oVE1z8NlNe | https://openreview.net/forum?id=oVE1z8NlNe | Weiming Zhuang,Yonggang Wen,Shuai Zhang | ICLR 2022,Poster | Self-supervised learning (SSL) is capable of learning remarkable representations from centrally available data. Recent works further implement federated learning with SSL to learn from rapidly growing decentralized unlabeled images (e.g., from cameras and phones), often resulted from privacy constraints. Extensive attention has been paid to SSL approaches based on Siamese networks. However, such an effort has not yet revealed deep insights into various fundamental building blocks for the federated self-supervised learning (FedSSL) architecture. We aim to fill in this gap via in-depth empirical study and propose a new method to tackle the non-independently and identically distributed (non-IID) data problem of decentralized data. Firstly, we introduce a generalized FedSSL framework that embraces existing SSL methods based on Siamese networks and presents flexibility catering to future methods. In this framework, a server coordinates multiple clients to conduct SSL training and periodically updates local models of clients with the aggregated global model. Using the framework, our study uncovers unique insights of FedSSL: 1) stop-gradient operation, previously reported to be essential, is not always necessary in FedSSL; 2) retaining local knowledge of clients in FedSSL is particularly beneficial for non-IID data. Inspired by the insights, we then propose a new approach for model update, Federated Divergence-aware Exponential Moving Average update (FedEMA). FedEMA updates local models of clients adaptively using EMA of the global model, where the decay rate is dynamically measured by model divergence. Extensive experiments demonstrate that FedEMA outperforms existing methods by 3-4% on linear evaluation. We hope that this work will provide useful insights for future research. | https://openreview.net/pdf/0f42d73623138cacd0d8e8d263d5d4fcaa2c5a65.pdf |
Neural Spectral Marked Point Processes | https://openreview.net/forum?id=0rcbOaoBXbg | https://openreview.net/forum?id=0rcbOaoBXbg | Shixiang Zhu,Haoyun Wang,Zheng Dong,Xiuyuan Cheng,Yao Xie | ICLR 2022,Poster | Self- and mutually-exciting point processes are popular models in machine learning and statistics for dependent discrete event data. To date, most existing models assume stationary kernels (including the classical Hawkes processes) and simple parametric models. Modern applications with complex event data require more general point process models that can incorporate contextual information of the events, called marks, besides the temporal and location information. Moreover, such applications often require non-stationary models to capture more complex spatio-temporal dependence. To tackle these challenges, a key question is to devise a versatile influence kernel in the point process model. In this paper, we introduce a novel and general neural network-based non-stationary influence kernel with high expressiveness for handling complex discrete events data while providing theoretical performance guarantees. We demonstrate the superior performance of our proposed method compared with the state-of-the-art on synthetic and real data. | https://openreview.net/pdf/999254e545c44db20626baabcfcd415ccd206c6a.pdf |
How to Inject Backdoors with Better Consistency: Logit Anchoring on Clean Data | https://openreview.net/forum?id=Bn09TnDngN | https://openreview.net/forum?id=Bn09TnDngN | Zhiyuan Zhang,Lingjuan Lyu,Weiqiang Wang,Lichao Sun,Xu Sun | ICLR 2022,Poster | Since training a large-scale backdoored model from scratch requires a large training dataset, several recent attacks have considered to inject backdoors into a trained clean model without altering model behaviors on the clean data. Previous work finds that backdoors can be injected into a trained clean model with Adversarial Weight Perturbation (AWP), which means the variation of parameters are small in backdoor learning. In this work, we observe an interesting phenomenon that the variations of parameters are always AWPs when tuning the trained clean model to inject backdoors. We further provide theoretical analysis to explain this phenomenon. We are the first to formulate the behavior of maintaining accuracy on clean data as the consistency of backdoored models, which includes both global consistency and instance-wise consistency. We extensively analyze the effects of AWPs on the consistency of backdoored models. In order to achieve better consistency, we propose a novel anchoring loss to anchor or freeze the model behaviors on the clean data, with a theoretical guarantee. | https://openreview.net/pdf/84f1213ba4770c743a5a5d48d699687b230f8c18.pdf |
A Biologically Interpretable Graph Convolutional Network to Link Genetic Risk Pathways and Imaging Phenotypes of Disease | https://openreview.net/forum?id=Lwr8We4MIxn | https://openreview.net/forum?id=Lwr8We4MIxn | Sayan Ghosal,Qiang Chen,Giulio Pergola,Aaron L Goldman,William Ulrich,Daniel R Weinberger,Archana Venkataraman | ICLR 2022,Poster | We propose a novel end-to-end framework for whole-brain and whole-genome imaging-genetics. Our genetics network uses hierarchical graph convolution and pooling operations to embed subject-level data onto a low-dimensional latent space. The hierarchical network implicitly tracks the convergence of genetic risk across well-established biological pathways, while an attention mechanism automatically identifies the salient edges of this network at the subject level. In parallel, our imaging network projects multimodal data onto a set of latent embeddings. For interpretability, we implement a Bayesian feature selection strategy to extract the discriminative imaging biomarkers; these feature weights are optimized alongside the other model parameters. We couple the imaging and genetic embeddings with a predictor network, to ensure that the learned representations are linked to phenotype. We evaluate our framework on a schizophrenia dataset that includes two functional MRI paradigms and gene scores derived from Single Nucleotide Polymorphism data. Using repeated 10-fold cross-validation, we show that our imaging-genetics fusion achieves the better classification performance than state-of-the-art baselines. In an exploratory analysis, we further show that the biomarkers identified by our model are reproducible and closely associated with deficits in schizophrenia. | https://openreview.net/pdf/e88ef5a6ee79b4188e9a4d9cb222409009729992.pdf |
Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners | https://openreview.net/forum?id=ek9a0qIafW | https://openreview.net/forum?id=ek9a0qIafW | Ningyu Zhang,Luoqiu Li,Xiang Chen,Shumin Deng,Zhen Bi,Chuanqi Tan,Fei Huang,Huajun Chen | ICLR 2022,Poster | Large-scale pre-trained language models have contributed significantly to natural language processing by demonstrating remarkable abilities as few-shot learners. However, their effectiveness depends mainly on scaling the model parameters and prompt design, hindering their implementation in most real-world applications. This study proposes a novel pluggable, extensible, and efficient approach named DifferentiAble pRompT (DART), which can convert small language models into better few-shot learners. The main principle behind this approach involves reformulating potential natural language processing tasks into the task of a pre-trained language model and differentially optimizing the prompt template as well as the target label with backpropagation. Furthermore, the proposed approach can be: (i) Plugged to any pre-trained language models; (ii) Extended to widespread classification tasks. A comprehensive evaluation of standard NLP tasks demonstrates that the proposed approach achieves a better few-shot performance. | https://openreview.net/pdf/d0850eb512cbbd2b27f6ff0ab0edf71861cd4829.pdf |
OntoProtein: Protein Pretraining With Gene Ontology Embedding | https://openreview.net/forum?id=yfe1VMYAXa4 | https://openreview.net/forum?id=yfe1VMYAXa4 | Ningyu Zhang,Zhen Bi,Xiaozhuan Liang,Siyuan Cheng,Haosen Hong,Shumin Deng,Qiang Zhang,Jiazhang Lian,Huajun Chen | ICLR 2022,Poster | Self-supervised protein language models have proved their effectiveness in learning the proteins representations. With the increasing computational power, current protein language models pre-trained with millions of diverse sequences can advance the parameter scale from million-level to billion-level and achieve remarkable improvement. However, those prevailing approaches rarely consider incorporating knowledge graphs (KGs), which can provide rich structured knowledge facts for better protein representations. We argue that informative biology knowledge in KGs can enhance protein representation with external knowledge. In this work, we propose OntoProtein, the first general framework that makes use of structure in GO (Gene Ontology) into protein pre-training models. We construct a novel large-scale knowledge graph that consists of GO and its related proteins, and gene annotation texts or protein sequences describe all nodes in the graph. We propose novel contrastive learning with knowledge-aware negative sampling to jointly optimize the knowledge graph and protein embedding during pre-training. Experimental results show that OntoProtein can surpass state-of-the-art methods with pre-trained protein language models in TAPE benchmark and yield better performance compared with baselines in protein-protein interaction and protein function prediction. | https://openreview.net/pdf/45d0f729743dae28d50e8c7e62050d0d95afbf43.pdf |
Permutation Compressors for Provably Faster Distributed Nonconvex Optimization | https://openreview.net/forum?id=GugZ5DzzAu | https://openreview.net/forum?id=GugZ5DzzAu | Rafał Szlendak,Alexander Tyurin,Peter Richtárik | ICLR 2022,Poster | In this work we study the MARINA method of Gorbunov et al (ICML, 2021) -- the current state-of-the-art distributed non-convex optimization method in terms of theoretical communication complexity. Theoretical superiority of this method can be largely attributed to two sources: a carefully engineered biased stochastic gradient estimator, which leads to a reduction in the number of communication rounds, and the reliance on
{\em independent} stochastic communication compression, which leads to a reduction in the number of transmitted bits within each communication round. In this paper we i) extend the theory of MARINA to support a much wider class of potentially {\em correlated} compressors, extending the reach of the method beyond the classical independent compressors setting, ii) show that a new quantity, for which we coin the name {\em Hessian variance}, allows us to significantly refine the original analysis of MARINA without any additional assumptions, and iii) identify a special class of correlated compressors based on the idea of {\em random permutations}, for which we coin the term Perm$K$, the use of which leads to up to $O(\sqrt{n})$ (resp. $O(1 + d/\sqrt{n})$) improvement in the theoretical communication complexity of MARINA in the low Hessian variance regime when $d\geq n$ (resp. $d \leq n$), where $n$ is the number of workers and $d$ is the number of parameters describing the model we are learning. We corroborate our theoretical results with carefully engineered synthetic experiments with minimizing the average of nonconvex quadratics, and on autoencoder training with the MNIST dataset. | https://openreview.net/pdf/0e0632efe3d09e539cb046531a252270ab830deb.pdf |
Few-shot Learning via Dirichlet Tessellation Ensemble | https://openreview.net/forum?id=6kCiVaoQdx9 | https://openreview.net/forum?id=6kCiVaoQdx9 | Chunwei Ma,Ziyun Huang,Mingchen Gao,Jinhui Xu | ICLR 2022,Poster | Few-shot learning (FSL) is the process of rapid generalization from abundant base samples to inadequate novel samples. Despite extensive research in recent years, FSL is still not yet able to generate satisfactory solutions for a wide range of real-world applications. To confront this challenge, we study the FSL problem from a geometric point of view in this paper. One observation is that the widely embraced ProtoNet model is essentially a Voronoi Diagram (VD) in the feature space. We retrofit it by making use of a recent advance in computational geometry called Cluster-induced Voronoi Diagram (CIVD). Starting from the simplest nearest neighbor model, CIVD gradually incorporates cluster-to-point and then cluster-to-cluster relationships for space subdivision, which is used to improve the accuracy and robustness at multiple stages of FSL. Specifically, we use CIVD (1) to integrate parametric and nonparametric few-shot classifiers; (2) to combine feature representation and surrogate representation; (3) and to leverage feature-level, transformation-level, and geometry-level heterogeneities for a better ensemble. Our CIVD-based workflow enables us to achieve new state-of-the-art results on mini-ImageNet, CUB, and tiered-ImagenNet datasets, with ${\sim}2\%{-}5\%$ improvements upon the next best. To summarize, CIVD provides a mathematically elegant and geometrically interpretable framework that compensates for extreme data insufficiency, prevents overfitting, and allows for fast geometric ensemble for thousands of individual VD. These together make FSL stronger. | https://openreview.net/pdf/f02d54e514e3af112f1ef58d64a80b6533b4d63f.pdf |
Deep Point Cloud Reconstruction | https://openreview.net/forum?id=mKDtUtxIGJ | https://openreview.net/forum?id=mKDtUtxIGJ | Jaesung Choe,ByeongIn Joung,Francois Rameau,Jaesik Park,In So Kweon | ICLR 2022,Poster | Point cloud obtained from 3D scanning is often sparse, noisy, and irregular. To cope with these issues, recent studies have been separately conducted to densify, denoise, and complete inaccurate point cloud. In this paper, we advocate that jointly solving these tasks leads to significant improvement for point cloud reconstruction. To this end, we propose a deep point cloud reconstruction network consisting of two stages: 1) a 3D sparse stacked-hourglass network as for the initial densification and denoising, 2) a refinement via transformers converting the discrete voxels into continuous 3D points. In particular, we further improve the performance of the transformers by a newly proposed module called amplified positional encoding. This module has been designed to differently amplify the magnitude of positional encoding vectors based on the points' distances for adaptive refinements. Extensive experiments demonstrate that our network achieves state-of-the-art performance among the recent studies in the ScanNet, ICL-NUIM, and ShapeNet datasets. Moreover, we underline the ability of our network to generalize toward real-world and unmet scenes.
| https://openreview.net/pdf/f30ff432e93adac626108cbcf0a36c3601b912d2.pdf |
$\beta$-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap | https://openreview.net/forum?id=q7n2RngwOM | https://openreview.net/forum?id=q7n2RngwOM | Pengzhou Abel Wu,Kenji Fukumizu | ICLR 2022,Poster | As an important problem in causal inference, we discuss the identification and estimation of treatment effects (TEs) under limited overlap; that is, when subjects with certain features belong to a single treatment group. We use a latent variable to model a prognostic score which is widely used in biostatistics and sufficient for TEs; i.e., we build a generative prognostic model. We prove that the latent variable recovers a prognostic score, and the model identifies individualized treatment effects. The model is then learned as $\beta$-Intact-VAE––a new type of variational autoencoder (VAE). We derive the TE error bounds that enable representations balanced for treatment groups conditioned on individualized features. The proposed method is compared with recent methods using (semi-)synthetic datasets. | https://openreview.net/pdf/478449496537cfe4195dfc3df5666eac40c4aaeb.pdf |
Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection | https://openreview.net/forum?id=BZnnMbt0pW | https://openreview.net/forum?id=BZnnMbt0pW | Wei Ji,Jingjing Li,Qi Bi,chuan guo,Jie Liu,Li Cheng | ICLR 2022,Poster | Growing interests in RGB-D salient object detection (RGB-D SOD) have been witnessed in recent years, owing partly to the popularity of depth sensors and the rapid progress of deep learning techniques. Unfortunately, existing RGB-D SOD methods typically demand large quantity of training images being thoroughly annotated at pixel-level. The laborious and time-consuming manual annotation has become a real bottleneck in various practical scenarios. On the other hand, current unsupervised RGB-D SOD methods still heavily rely on handcrafted feature representations. This inspires us to propose in this paper a deep unsupervised RGB-D saliency detection approach, which requires no manual pixel-level annotation during training. It is realized by two key ingredients in our training pipeline. First, a depth-disentangled saliency update (DSU) framework is designed to automatically produce pseudo-labels with iterative follow-up refinements, which provides more trustworthy supervision signals for training the saliency network. Second, an attentive training strategy is introduced to tackle the issue of noisy pseudo-labels, by properly re-weighting to highlight the more reliable pseudo-labels. Extensive experiments demonstrate the superior efficiency and effectiveness of our approach in tackling the challenging unsupervised RGB-D SOD scenarios. Moreover, our approach can also be adapted to work in fully-supervised situation. Empirical studies show the incorporation of our approach gives rise to notably performance improvement in existing supervised RGB-D SOD models. | https://openreview.net/pdf/a7126368cdf1f32c8b60e48936a7acf1b8e7599e.pdf |
Retriever: Learning Content-Style Representation as a Token-Level Bipartite Graph | https://openreview.net/forum?id=AXWygMvuT6Q | https://openreview.net/forum?id=AXWygMvuT6Q | Dacheng Yin,Xuanchi Ren,Chong Luo,Yuwang Wang,Zhiwei Xiong,Wenjun Zeng | ICLR 2022,Poster | This paper addresses the unsupervised learning of content-style decomposed representation. We first give a definition of style and then model the content-style representation as a token-level bipartite graph. An unsupervised framework, named Retriever, is proposed to learn such representations. First, a cross-attention module is employed to retrieve permutation invariant (P.I.) information, defined as style, from the input data. Second, a vector quantization (VQ) module is used, together with man-induced constraints, to produce interpretable content tokens. Last, an innovative link attention module serves as the decoder to reconstruct data from the decomposed content and style, with the help of the linking keys. Being modal-agnostic, the proposed Retriever is evaluated in both speech and image domains. The state-of-the-art zero-shot voice conversion performance confirms the disentangling ability of our framework. Top performance is also achieved in the part discovery task for images, verifying the interpretability of our representation. In addition, the vivid part-based style transfer quality demonstrates the potential of Retriever to support various fascinating generative tasks. Project page at https://ydcustc.github.io/retriever-demo/. | https://openreview.net/pdf/40312e47e819cfbbcfde577838af08ac592b9013.pdf |
Neural Markov Controlled SDE: Stochastic Optimization for Continuous-Time Data | https://openreview.net/forum?id=7DI6op61AY | https://openreview.net/forum?id=7DI6op61AY | Sung Woo Park,Kyungjae Lee,Junseok Kwon | ICLR 2022,Poster | We propose a novel probabilistic framework for modeling stochastic dynamics with the rigorous use of stochastic optimal control theory. The proposed model called the neural Markov controlled stochastic differential equation (CSDE) overcomes the fundamental and structural limitations of conventional dynamical models by introducing the following two components: (1) Markov dynamic programming to efficiently train the proposed CSDE and (2) multi-conditional forward-backward losses to provide rich information for accurate inference and to assure theoretical optimality. We demonstrate that our dynamical model efficiently generates a complex time series in the data space without extra networks while showing comparable performance against existing model-based methods on several datasets. | https://openreview.net/pdf/d7e9dca5dfd0f6600c830cea3a5efca6ae4b8ae0.pdf |
CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention | https://openreview.net/forum?id=_PHymLIxuI | https://openreview.net/forum?id=_PHymLIxuI | Wenxiao Wang,Lu Yao,Long Chen,Binbin Lin,Deng Cai,Xiaofei He,Wei Liu | ICLR 2022,Poster | Transformers have made great progress in dealing with computer vision tasks. However, existing vision transformers have not yet possessed the ability of building the interactions among features of different scales, which is perceptually important to visual inputs. The reasons are two-fold: (1) Input embeddings of each layer are equal-scale, so no cross-scale feature can be extracted; (2) to lower the computational cost, some vision transformers merge adjacent embeddings inside the self-attention module, thus sacrificing small-scale (fine-grained) features of the embeddings and also disabling the cross-scale interactions. To this end, we propose Cross-scale Embedding Layer (CEL) and Long Short Distance Attention (LSDA). On the one hand, CEL blends each embedding with multiple patches of different scales, providing the self-attention module itself with cross-scale features. On the other hand, LSDA splits the self-attention module into a short-distance one and a long-distance counterpart, which not only reduces the computational burden but also keeps both small-scale and large-scale features in the embeddings. Through the above two designs, we achieve cross-scale attention. Besides, we put forward a dynamic position bias for vision transformers to make the popular relative position bias apply to variable-sized images. Hinging on the cross-scale attention module, we construct a versatile vision architecture, dubbed CrossFormer, which accommodates variable-sized inputs. Extensive experiments show that CrossFormer outperforms the other vision transformers on image classification, object detection, instance segmentation, and semantic segmentation tasks. | https://openreview.net/pdf/6d2cbac2997d9b594cd4e0076cfceef1cdfc3319.pdf |
Adversarially Robust Conformal Prediction | https://openreview.net/forum?id=9L1BsI4wP1H | https://openreview.net/forum?id=9L1BsI4wP1H | Asaf Gendler,Tsui-Wei Weng,Luca Daniel,Yaniv Romano | ICLR 2022,Poster | Conformal prediction is a model-agnostic tool for constructing prediction sets that are valid under the common i.i.d. assumption, which has been applied to quantify the prediction uncertainty of deep net classifiers. In this paper, we generalize this framework to the case where adversaries exist during inference time, under which the i.i.d. assumption is grossly violated. By combining conformal prediction with randomized smoothing, our proposed method forms a prediction set with finite-sample coverage guarantee that holds for any data distribution with $\ell_2$-norm bounded adversarial noise, generated by any adversarial attack algorithm. The core idea is to bound the Lipschitz constant of the non-conformity score by smoothing it with Gaussian noise and leverage this knowledge to account for the effect of the unknown adversarial perturbation. We demonstrate the necessity of our method in the adversarial setting and the validity of our theoretical guarantee on three widely used benchmark data sets: CIFAR10, CIFAR100, and ImageNet. | https://openreview.net/pdf/8c5857f5c0f9cb907a44054df718409de9263370.pdf |
Hot-Refresh Model Upgrades with Regression-Free Compatible Training in Image Retrieval | https://openreview.net/forum?id=HTp-6yLGGX | https://openreview.net/forum?id=HTp-6yLGGX | Binjie Zhang,Yixiao Ge,Yantao Shen,Yu Li,Chun Yuan,XUYUAN XU,Yexin Wang,Ying Shan | ICLR 2022,Poster | The task of hot-refresh model upgrades of image retrieval systems plays an essential role in the industry but has never been investigated in academia before. Conventional cold-refresh model upgrades can only deploy new models after the gallery is overall backfilled, taking weeks or even months for massive data. In contrast, hot-refresh model upgrades deploy the new model immediately and then gradually improve the retrieval accuracy by backfilling the gallery on-the-fly. Compatible training has made it possible, however, the problem of model regression with negative flips poses a great challenge to the stable improvement of user experience. We argue that it is mainly due to the fact that new-to-old positive query-gallery pairs may show less similarity than new-to-new negative pairs. To solve the problem, we introduce a Regression-Alleviating Compatible Training (RACT) method to properly constrain the feature compatibility while reducing negative flips. The core is to encourage the new-to-old positive pairs to be more similar than both the new-to-old negative pairs and the new-to-new negative pairs. An efficient uncertainty-based backfilling strategy is further introduced to fasten accuracy improvements. Extensive experiments on large-scale retrieval benchmarks (e.g., Google Landmark) demonstrate that our RACT effectively alleviates the model regression for one more step towards seamless model upgrades. | https://openreview.net/pdf/19b014f7d493937c96b4232e3959811fd2b47ce5.pdf |
Visual Representation Learning over Latent Domains | https://openreview.net/forum?id=kG0AtPi6JI1 | https://openreview.net/forum?id=kG0AtPi6JI1 | Lucas Deecke,Timothy Hospedales,Hakan Bilen | ICLR 2022,Poster | A fundamental shortcoming of deep neural networks is their specialization to a single task and domain. While multi-domain learning enables the learning of compact models that span multiple visual domains, these rely on the presence of domain labels, in turn requiring laborious curation of datasets. This paper proposes a less explored, but highly realistic new setting called latent domain learning: learning over data from different domains, without access to domain annotations. Experiments show that this setting is challenging for standard models and existing multi-domain approaches, calling for new customized solutions: a sparse adaptation strategy is formulated which enhances performance by accounting for latent domains in data. Our method can be paired seamlessly with existing models, and benefits conceptually related tasks, e.g. empirical fairness problems and long-tailed recognition. | https://openreview.net/pdf/c43020f29d1ac17488c3b808196c9b0243182218.pdf |
Chemical-Reaction-Aware Molecule Representation Learning | https://openreview.net/forum?id=6sh3pIzKS- | https://openreview.net/forum?id=6sh3pIzKS- | Hongwei Wang,Weijiang Li,Xiaomeng Jin,Kyunghyun Cho,Heng Ji,Jiawei Han,Martin D. Burke | ICLR 2022,Poster | Molecule representation learning (MRL) methods aim to embed molecules into a real vector space. However, existing SMILES-based (Simplified Molecular-Input Line-Entry System) or GNN-based (Graph Neural Networks) MRL methods either take SMILES strings as input that have difficulty in encoding molecule structure information, or over-emphasize the importance of GNN architectures but neglect their generalization ability. Here we propose using chemical reactions to assist learning molecule representation. The key idea of our approach is to preserve the equivalence of molecules with respect to chemical reactions in the embedding space, i.e., forcing the sum of reactant embeddings and the sum of product embeddings to be equal for each chemical equation. This constraint is proven effective to 1) keep the embedding space well-organized and 2) improve the generalization ability of molecule embeddings. Moreover, our model can use any GNN as the molecule encoder and is thus agnostic to GNN architectures. Experimental results demonstrate that our method achieves state-of-the-art performance in a variety of downstream tasks, e.g., reaction product prediction, molecule property prediction, reaction classification, and graph-edit-distance prediction. The code is available at https://github.com/hwwang55/MolR. | https://openreview.net/pdf/90448ed39b2846f07097bae6944cbb6a3e2bdaf5.pdf |
Skill-based Meta-Reinforcement Learning | https://openreview.net/forum?id=jeLW-Fh9bV | https://openreview.net/forum?id=jeLW-Fh9bV | Taewook Nam,Shao-Hua Sun,Karl Pertsch,Sung Ju Hwang,Joseph J Lim | ICLR 2022,Poster | While deep reinforcement learning methods have shown impressive results in robot learning, their sample inefficiency makes the learning of complex, long-horizon behaviors with real robot systems infeasible. To mitigate this issue, meta-reinforcement learning methods aim to enable fast learning on novel tasks by learning how to learn. Yet, the application has been limited to short-horizon tasks with dense rewards. To enable learning long-horizon behaviors, recent works have explored leveraging prior experience in the form of offline datasets without reward or task annotations. While these approaches yield improved sample efficiency, millions of interactions with environments are still required to solve complex tasks. In this work, we devise a method that enables meta-learning on long-horizon, sparse-reward tasks, allowing us to solve unseen target tasks with orders of magnitude fewer environment interactions. Our core idea is to leverage prior experience extracted from offline datasets during meta-learning. Specifically, we propose to (1) extract reusable skills and a skill prior from offline datasets, (2) meta-train a high-level policy that learns to efficiently compose learned skills into long-horizon behaviors, and (3) rapidly adapt the meta-trained policy to solve an unseen target task. Experimental results on continuous control tasks in navigation and manipulation demonstrate that the proposed method can efficiently solve long-horizon novel target tasks by combining the strengths of meta-learning and the usage of offline datasets, while prior approaches in RL, meta-RL, and multi-task RL require substantially more environment interactions to solve the tasks. | https://openreview.net/pdf/8c5b0157e0e15aedce63445fa2114ab02797c487.pdf |
InfinityGAN: Towards Infinite-Pixel Image Synthesis | https://openreview.net/forum?id=ufGMqIM0a4b | https://openreview.net/forum?id=ufGMqIM0a4b | Chieh Hubert Lin,Hsin-Ying Lee,Yen-Chi Cheng,Sergey Tulyakov,Ming-Hsuan Yang | ICLR 2022,Poster | We present InfinityGAN, a method to generate arbitrary-sized images. The problem is associated with several key challenges. First, scaling existing models to an arbitrarily large image size is resource-constrained, both in terms of computation and availability of large-field-of-view training data. InfinityGAN trains and infers patch-by-patch seamlessly with low computational resources. Second, large images should be locally and globally consistent, avoid repetitive patterns, and look realistic. To address these, InfinityGAN takes global appearance, local structure and texture into account. With this formulation, we can generate images with spatial size and level of detail not attainable before. Experimental evaluation supports that InfinityGAN generates images with superior global structure compared to baselines and features parallelizable inference. Finally, we show several applications unlocked by our approach, such as fusing styles spatially, multi-modal outpainting and image inbetweening at arbitrary input and output sizes. | https://openreview.net/pdf/0907e14b44a58aca8f7577b9e7f45870865cbfc4.pdf |
Shuffle Private Stochastic Convex Optimization | https://openreview.net/forum?id=DrZXuTGg2A- | https://openreview.net/forum?id=DrZXuTGg2A- | Albert Cheu,Matthew Joseph,Jieming Mao,Binghui Peng | ICLR 2022,Poster | In shuffle privacy, each user sends a collection of randomized messages to a trusted shuffler, the shuffler randomly permutes these messages, and the resulting shuffled collection of messages must satisfy differential privacy. Prior work in this model has largely focused on protocols that use a single round of communication to compute algorithmic primitives like means, histograms, and counts. In this work, we present interactive shuffle protocols for stochastic convex optimization. Our optimization protocols rely on a new noninteractive protocol for summing vectors of bounded $\ell_2$ norm. By combining this sum subroutine with techniques including mini-batch stochastic gradient descent, accelerated gradient descent, and Nesterov's smoothing method, we obtain loss guarantees for a variety of convex loss functions that significantly improve on those of the local model and sometimes match those of the central model. | https://openreview.net/pdf/7c8c9c62ecd536de1d742c826d9a85c22f8e430b.pdf |
Know Your Action Set: Learning Action Relations for Reinforcement Learning | https://openreview.net/forum?id=MljXVdp4A3N | https://openreview.net/forum?id=MljXVdp4A3N | Ayush Jain,Norio Kosaka,Kyung-Min Kim,Joseph J Lim | ICLR 2022,Poster | Intelligent agents can solve tasks in various ways depending on their available set of actions. However, conventional reinforcement learning (RL) assumes a fixed action set. This work asserts that tasks with varying action sets require reasoning of the relations between the available actions. For instance, taking a nail-action in a repair task is meaningful only if a hammer-action is also available. To learn and utilize such action relations, we propose a novel policy architecture consisting of a graph attention network over the available actions. We show that our model makes informed action decisions by correctly attending to other related actions in both value-based and policy-based RL. Consequently, it outperforms non-relational architectures on applications where the action space often varies, such as recommender systems and physical reasoning with tools and skills. Results and code at https://sites.google.com/view/varyingaction . | https://openreview.net/pdf/e227e96a6b5847d80aeaa7803fa8a32a8b03f28c.pdf |
On the Importance of Difficulty Calibration in Membership Inference Attacks | https://openreview.net/forum?id=3eIrli0TwQ | https://openreview.net/forum?id=3eIrli0TwQ | Lauren Watson,Chuan Guo,Graham Cormode,Alexandre Sablayrolles | ICLR 2022,Poster | The vulnerability of machine learning models to membership inference attacks has received much attention in recent years. However, existing attacks mostly remain impractical due to having high false positive rates, where non-member samples are often erroneously predicted as members. This type of error makes the predicted membership signal unreliable, especially since most samples are non-members in real world applications. In this work, we argue that membership inference attacks can benefit drastically from difficulty calibration, where an attack's predicted membership score is adjusted to the difficulty of correctly classifying the target sample. We show that difficulty calibration can significantly reduce the false positive rate of a variety of existing attacks without a loss in accuracy. | https://openreview.net/pdf/245838afd6ed33e19d36ead3f797f80899b36b37.pdf |
Entroformer: A Transformer-based Entropy Model for Learned Image Compression | https://openreview.net/forum?id=VrjOFfcnSV8 | https://openreview.net/forum?id=VrjOFfcnSV8 | Yichen Qian,Xiuyu Sun,Ming Lin,Zhiyu Tan,Rong Jin | ICLR 2022,Poster | One critical component in lossy deep image compression is the entropy model, which predicts the probability distribution of the quantized latent representation in the encoding and decoding modules. Previous works build entropy models upon convolutional neural networks which are inefficient in capturing global dependencies. In this work, we propose a novel transformer-based entropy model, termed Entroformer, to capture long-range dependencies in probability distribution estimation effectively and efficiently. Different from vision transformers in image classification, the Entroformer is highly optimized for image compression, including a top-k self-attention and a diamond relative position encoding. Meanwhile, we further expand this architecture with a parallel bidirectional context model to speed up the decoding process. The experiments show that the Entroformer achieves state-of-the-art performance on image compression while being time-efficient. | https://openreview.net/pdf/375073115f9e0608eacb1f009688d213ac8f1d11.pdf |
Dual Lottery Ticket Hypothesis | https://openreview.net/forum?id=fOsN52jn25l | https://openreview.net/forum?id=fOsN52jn25l | Yue Bai,Huan Wang,ZHIQIANG TAO,Kunpeng Li,Yun Fu | ICLR 2022,Poster | Fully exploiting the learning capacity of neural networks requires overparameterized dense networks. On the other side, directly training sparse neural networks typically results in unsatisfactory performance. Lottery Ticket Hypothesis (LTH) provides a novel view to investigate sparse network training and maintain its capacity. Concretely, it claims there exist winning tickets from a randomly initialized network found by iterative magnitude pruning and preserving promising trainability (or we say being in trainable condition). In this work, we regard the winning ticket from LTH as the subnetwork which is in trainable condition and its performance as our benchmark, then go from a complementary direction to articulate the Dual Lottery Ticket Hypothesis (DLTH): Randomly selected subnetworks from a randomly initialized dense network can be transformed into a trainable condition and achieve admirable performance compared with LTH --- random tickets in a given lottery pool can be transformed into winning tickets. Specifically, by using uniform-randomly selected subnetworks to represent the general cases, we propose a simple sparse network training strategy, Random Sparse Network Transformation (RST), to substantiate our DLTH. Concretely, we introduce a regularization term to borrow learning capacity and realize information extrusion from the weights which will be masked. After finishing the transformation for the randomly selected subnetworks, we conduct the regular finetuning to evaluate the model using fair comparisons with LTH and other strong baselines. Extensive experiments on several public datasets and comparisons with competitive approaches validate our DLTH as well as the effectiveness of the proposed model RST. Our work is expected to pave a way for inspiring new research directions of sparse network training in the future. Our code is available at https://github.com/yueb17/DLTH. | https://openreview.net/pdf/839164c6325efc6887b07ade7198a9c83757669d.pdf |
GNN is a Counter? Revisiting GNN for Question Answering | https://openreview.net/forum?id=hzmQ4wOnSb | https://openreview.net/forum?id=hzmQ4wOnSb | Kuan Wang,Yuyu Zhang,Diyi Yang,Le Song,Tao Qin | ICLR 2022,Poster | Question Answering (QA) has been a long-standing research topic in AI and NLP fields, and a wealth of studies has been conducted to attempt to equip QA systems with human-level reasoning capability. To approximate the complicated human reasoning process, state-of-the-art QA systems commonly use pre-trained language models (LMs) to access knowledge encoded in LMs together with elaborately designed modules based on Graph Neural Networks (GNNs) to perform reasoning over knowledge graphs (KGs). However, many problems remain open regarding the reasoning functionality of these GNN-based modules. Can these GNN-based modules really perform a complex reasoning process? Are they under- or over-complicated for QA? To open the black box of GNN and investigate these problems, we dissect state-of-the-art GNN modules for QA and analyze their reasoning capability. We discover that even a very simple graph neural counter can outperform all the existing GNN modules on CommonsenseQA and OpenBookQA, two popular QA benchmark datasets which heavily rely on knowledge-aware reasoning. Our work reveals that existing knowledge-aware GNN modules may only carry out some simple reasoning such as counting. It remains a challenging open problem to build comprehensive reasoning modules for knowledge-powered QA. | https://openreview.net/pdf/b4b3e479c999df701d9e74120833e9a3c1edb8f3.pdf |
IFR-Explore: Learning Inter-object Functional Relationships in 3D Indoor Scenes | https://openreview.net/forum?id=OT3mLgR8Wg8 | https://openreview.net/forum?id=OT3mLgR8Wg8 | QI LI,Kaichun Mo,Yanchao Yang,Hang Zhao,Leonidas Guibas | ICLR 2022,Poster | Building embodied intelligent agents that can interact with 3D indoor environments has received increasing research attention in recent years. While most works focus on single-object or agent-object visual functionality and affordances, our work proposes to study a novel, underexplored, kind of visual relations that is also important to perceive and model -- inter-object functional relationships (e.g., a switch on the wall turns on or off the light, a remote control operates the TV). Humans often spend no effort or only a little to infer these relationships, even when entering a new room, by using our strong prior knowledge (e.g., we know that buttons control electrical devices) or using only a few exploratory interactions in cases of uncertainty (e.g., multiple switches and lights in the same room). In this paper, we take the first step in building AI system learning inter-object functional relationships in 3D indoor environments with key technical contributions of modeling prior knowledge by training over large-scale scenes and designing interactive policies for effectively exploring the training scenes and quickly adapting to novel test scenes. We create a new dataset based on the AI2Thor and PartNet datasets and perform extensive experiments that prove the effectiveness of our proposed method. | https://openreview.net/pdf/683c0979083ae6f6094f9293b8fdc7408ff7b1c6.pdf |
VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects | https://openreview.net/forum?id=iEx3PiooLy | https://openreview.net/forum?id=iEx3PiooLy | Ruihai Wu,Yan Zhao,Kaichun Mo,Zizheng Guo,Yian Wang,Tianhao Wu,Qingnan Fan,Xuelin Chen,Leonidas Guibas,Hao Dong | ICLR 2022,Poster | Perceiving and manipulating 3D articulated objects (e.g., cabinets, doors) in human environments is an important yet challenging task for future home-assistant robots. The space of 3D articulated objects is exceptionally rich in their myriad semantic categories, diverse shape geometry, and complicated part functionality. Previous works mostly abstract kinematic structure with estimated joint parameters and part poses as the visual representations for manipulating 3D articulated objects. In this paper, we propose object-centric actionable visual priors as a novel perception-interaction handshaking point that the perception system outputs more actionable guidance than kinematic structure estimation, by predicting dense geometry-aware, interaction-aware, and task-aware visual action affordance and trajectory proposals. We design an interaction-for-perception framework VAT-Mart to learn such actionable visual representations by simultaneously training a curiosity-driven reinforcement learning policy exploring diverse interaction trajectories and a perception module summarizing and generalizing the explored knowledge for pointwise predictions among diverse shapes. Experiments prove the effectiveness of the proposed approach using the large-scale PartNet-Mobility dataset in SAPIEN environment and show promising generalization capabilities to novel test shapes, unseen object categories, and real-world data. | https://openreview.net/pdf/5a365ba429a2d4c37365ec69749ea712c8ea6d5c.pdf |
Neural graphical modelling in continuous-time: consistency guarantees and algorithms | https://openreview.net/forum?id=SsHBkfeRF9L | https://openreview.net/forum?id=SsHBkfeRF9L | Alexis Bellot,Kim Branson,Mihaela van der Schaar | ICLR 2022,Poster | The discovery of structure from time series data is a key problem in fields of study working with complex systems. Most identifiability results and learning algorithms assume the underlying dynamics to be discrete in time. Comparatively few, in contrast, explicitly define dependencies in infinitesimal intervals of time, independently of the scale of observation and of the regularity of sampling. In this paper, we consider score-based structure learning for the study of dynamical systems. We prove that for vector fields parameterized in a large class of neural networks, least squares optimization with adaptive regularization schemes consistently recovers directed graphs of local independencies in systems of stochastic differential equations. Using this insight, we propose a score-based learning algorithm based on penalized Neural Ordinary Differential Equations (modelling the mean process) that we show to be applicable to the general setting of irregularly-sampled multivariate time series and to outperform the state of the art across a range of dynamical systems. | https://openreview.net/pdf/53828fbbd7e3c6be425428ad3c70cdf5514e080a.pdf |
C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks | https://openreview.net/forum?id=K2JfSnLBD9 | https://openreview.net/forum?id=K2JfSnLBD9 | Tianjun Zhang,Benjamin Eysenbach,Ruslan Salakhutdinov,Sergey Levine,Joseph E. Gonzalez | ICLR 2022,Poster | Goal-conditioned reinforcement learning (RL) has shown great success recently at solving a wide range of tasks(e.g., navigation, robotic manipulation). However, learning to reach distant goals remains a central challenge to the field, and the task is particularly hard without any offline data, expert demonstrations, and reward shaping. In this paper, we propose to solve the distant goal-reaching task by using search at training time to generate a curriculum of intermediate states. Specifically, we introduce the algorithm Classifier-Planning (C-Planning) by framing the learning of the goal-conditioned policies as variational inference. C-Planning naturally follows expectation maximization (EM): the E-step corresponds to planning an optimal sequence of waypoints using graph search, while the M-step aims to learn a goal-conditioned policy to reach those waypoints. One essential difficulty of designing such an algorithm is accurately modeling the distribution over way-points to sample from. In C-Planning, we propose to sample the waypoints using contrastive methods to learn a value function. Unlike prior methods that combine goal-conditioned RL with graph search, ours performs search only during training and not testing, significantly decreasing the compute costs of deploying the learned policy. Empirically, we demonstrate that our method not only improves the sample efficiency of prior methods but also successfully solves temporally extended navigation and manipulation tasks, where prior goal-conditioned RL methods (including those based on graph search) fail to solve. | https://openreview.net/pdf/94d8bdac5cba49cee62dd6963b40612d903f60af.pdf |
NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy | https://openreview.net/forum?id=0DLwqQLmqV | https://openreview.net/forum?id=0DLwqQLmqV | Yash Mehta,Colin White,Arber Zela,Arjun Krishnakumar,Guri Zabergja,Shakiba Moradian,Mahmoud Safari,Kaicheng Yu,Frank Hutter | ICLR 2022,Poster | The release of tabular benchmarks, such as NAS-Bench-101 and NAS-Bench-201, has significantly lowered the computational overhead for conducting scientific research in neural architecture search (NAS). Although they have been widely adopted and used to tune real-world NAS algorithms, these benchmarks are limited to small search spaces and focus solely on image classification. Recently, several new NAS benchmarks have been introduced that cover significantly larger search spaces over a wide range of tasks, including object detection, speech recognition, and natural language processing. However, substantial differences among these NAS benchmarks have so far prevented their widespread adoption, limiting researchers to using just a few benchmarks. In this work, we present an in-depth analysis of popular NAS algorithms and performance prediction methods across 25 different combinations of search spaces and datasets, finding that many conclusions drawn from a few NAS benchmarks do \emph{not} generalize to other benchmarks. To help remedy this problem, we introduce \nasbs, a comprehensive and extensible collection of NAS benchmarks, accessible through a unified interface, created with the aim to facilitate reproducible, generalizable, and rapid NAS research. Our code is available at https://github.com/automl/naslib. | https://openreview.net/pdf/3e2df1273dd62fef9940be12e0bdd7e3fd0a5b78.pdf |
Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality | https://openreview.net/forum?id=mhYUBYNoGz | https://openreview.net/forum?id=mhYUBYNoGz | Yiping Lu,Haoxuan Chen,Jianfeng Lu,Lexing Ying,Jose Blanchet | ICLR 2022,Poster | In this paper, we study the statistical limits of deep learning techniques for solving elliptic partial differential equations (PDEs) from random samples using the Deep Ritz Method (DRM) and Physics-Informed Neural Networks (PINNs). To simplify the problem, we focus on a prototype elliptic PDE: the Schr\"odinger equation on a hypercube with zero Dirichlet boundary condition, which has wide application in the quantum-mechanical systems. We establish upper and lower bounds for both methods, which improves upon concurrently developed upper bounds for this problem via a fast rate generalization bound. We discover that the current Deep Ritz Methods is sub-optimal and propose a modified version of it. We also prove that PINN and the modified version of DRM can achieve minimax optimal bounds over Sobolev spaces. Empirically, following recent work which has shown that the deep model accuracy will improve with growing training sets according to a power law, we supply computational experiments to show a similar behavior of dimension dependent power law for deep PDE solvers. | https://openreview.net/pdf/f419ff83079cd27aae97a07c87ba18f22a355d19.pdf |
Variational oracle guiding for reinforcement learning | https://openreview.net/forum?id=pjqqxepwoMy | https://openreview.net/forum?id=pjqqxepwoMy | Dongqi Han,Tadashi Kozuno,Xufang Luo,Zhao-Yun Chen,Kenji Doya,Yuqing Yang,Dongsheng Li | ICLR 2022,Poster | How to make intelligent decisions is a central problem in machine learning and artificial intelligence. Despite recent successes of deep reinforcement learning (RL) in various decision making problems, an important but under-explored aspect is how to leverage oracle observation (the information that is invisible during online decision making, but is available during offline training) to facilitate learning. For example, human experts will look at the replay after a Poker game, in which they can check the opponents' hands to improve their estimation of the opponents' hands from the visible information during playing. In this work, we study such problems based on Bayesian theory and derive an objective to leverage oracle observation in RL using variational methods. Our key contribution is to propose a general learning framework referred to as variational latent oracle guiding (VLOG) for DRL. VLOG is featured with preferable properties such as its robust and promising performance and its versatility to incorporate with any value-based DRL algorithm. We empirically demonstrate the effectiveness of VLOG in online and offline RL domains with tasks ranging from video games to a challenging tile-based game Mahjong. Furthermore, we publish the Mahjong environment and an offline RL dataset as a benchmark to facilitate future research on oracle guiding (https://github.com/Agony5757/mahjong). | https://openreview.net/pdf/88d5d80331a1704592f9a66d052c33f976661945.pdf |
CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation | https://openreview.net/forum?id=XGzk5OKWFFc | https://openreview.net/forum?id=XGzk5OKWFFc | Tongkun Xu,Weihua Chen,Pichao WANG,Fan Wang,Hao Li,Rong Jin | ICLR 2022,Poster | Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled source domain to a different unlabeled target domain. Most existing UDA methods focus on learning domain-invariant feature representation, either from the domain level or category level, using convolution neural networks (CNNs)-based frameworks. One fundamental problem for the category level based UDA is the production of pseudo labels for samples in target domain, which are usually too noisy for accurate domain alignment, inevitably compromising the UDA performance. With the success of Transformer in various tasks, we find that the cross-attention in Transformer is robust to the noisy input pairs for better feature alignment, thus in this paper Transformer is adopted for the challenging UDA task. Specifically, to generate accurate input pairs, we design a two-way center-aware labeling algorithm to produce pseudo labels for target samples. Along with the pseudo labels, a weight-sharing triple-branch transformer framework is proposed to apply self-attention and cross-attention for source/target feature learning and source-target domain alignment, respectively.
Such design explicitly enforces the framework to learn discriminative domain-specific and domain-invariant representations simultaneously. The proposed method is dubbed CDTrans (cross-domain transformer), and it provides one of the first attempts to solve UDA tasks with a pure transformer solution. Experiments show that our proposed method achieves the best performance on public UDA datasets, e.g. VisDA-2017 and DomainNet. Code and models are available at https://github.com/CDTrans/CDTrans. | https://openreview.net/pdf/5af495124dcff5b772396db65ab98725f7b036b7.pdf |
Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains | https://openreview.net/forum?id=QkRV50TZyP | https://openreview.net/forum?id=QkRV50TZyP | Qilong Zhang,Xiaodan Li,YueFeng Chen,Jingkuan Song,Lianli Gao,Yuan He,Hui Xue' | ICLR 2022,Poster | Adversarial examples have posed a severe threat to deep neural networks due to their transferable nature. Currently, various works have paid great efforts to enhance the cross-model transferability, which mostly assume the substitute model is trained in the same domain as the target model.
However, in reality, the relevant information of the deployed model is unlikely to leak.
Hence, it is vital to build a more practical black-box threat model to overcome this limitation and evaluate the vulnerability of deployed models.
In this paper, with only the knowledge of the ImageNet domain, we propose a Beyond ImageNet Attack (BIA) to investigate the transferability towards black-box domains (unknown classification tasks). Specifically, we leverage a generative model to learn the adversarial function for disrupting low-level features of input images.
Based on this framework, we further propose two variants to narrow the gap between the source and target domains from the data and model perspectives, respectively. Extensive experiments on coarse-grained and fine-grained domains demonstrate the effectiveness of our proposed methods. Notably,
our methods outperform state-of-the-art approaches by up to 7.71\% (towards coarse-grained domains) and 25.91\% (towards fine-grained domains) on average. Our code is available at \url{https://github.com/Alibaba-AAIG/Beyond-ImageNet-Attack}. | https://openreview.net/pdf/e3edfd55ba8b10084cbe821c5e89dd92bb887262.pdf |
Learning to Schedule Learning rate with Graph Neural Networks | https://openreview.net/forum?id=k7efTb0un9z | https://openreview.net/forum?id=k7efTb0un9z | Yuanhao Xiong,Li-Cheng Lan,Xiangning Chen,Ruochen Wang,Cho-Jui Hsieh | ICLR 2022,Poster | Recent decades have witnessed great development of stochastic optimization in training deep neural networks. Learning rate scheduling is one of the most important factors that influence the performance of stochastic optimizers like Adam. Traditional methods seek to find a relatively proper scheduling among a limited number of pre-defined rules and might not accommodate a particular target problem. Instead, we propose a novel Graph-Network-based Scheduler (GNS), aiming at learning a specific scheduling mechanism without restrictions to existing principles. By constructing a directed graph for the underlying neural network of the target problem, GNS encodes current dynamics with a graph message passing network and trains an agent to control the learning rate accordingly via reinforcement learning. The proposed scheduler can capture the intermediate layer information while being able to generalize to problems of varying scales. Besides, an efficient reward collection procedure is leveraged to speed up training. We evaluate our framework on benchmarking datasets, Fashion-MNIST and CIFAR10 for image classification, and GLUE for language understanding. GNS shows consistent improvement over popular baselines when training CNN and Transformer models. Moreover, GNS demonstrates great generalization to different datasets and network structures. | https://openreview.net/pdf/70093080d93ffa224e3b7bb6cfa481cad374f005.pdf |
SketchODE: Learning neural sketch representation in continuous time | https://openreview.net/forum?id=c-4HSDAWua5 | https://openreview.net/forum?id=c-4HSDAWua5 | Ayan Das,Yongxin Yang,Timothy Hospedales,Tao Xiang,Yi-Zhe Song | ICLR 2022,Poster | Learning meaningful representations for chirographic drawing data such as sketches, handwriting, and flowcharts is a gateway for understanding and emulating human creative expression. Despite being inherently continuous-time data, existing works have treated these as discrete-time sequences, disregarding their true nature. In this work, we model such data as continuous-time functions and learn compact representations by virtue of Neural Ordinary Differential Equations. To this end, we introduce the first continuous-time Seq2Seq model and demonstrate some remarkable properties that set it apart from traditional discrete-time analogues. We also provide solutions for some practical challenges for such models, including introducing a family of parameterized ODE dynamics & continuous-time data augmentation particularly suitable for the task. Our models are validated on several datasets including VectorMNIST, DiDi and Quick, Draw!. | https://openreview.net/pdf/9c21c733257184e97d7da6ff51910cd4e914fbb4.pdf |
Measuring the Interpretability of Unsupervised Representations via Quantized Reversed Probing | https://openreview.net/forum?id=HFPTzdwN39 | https://openreview.net/forum?id=HFPTzdwN39 | Iro Laina,Yuki M Asano,Andrea Vedaldi | ICLR 2022,Poster | Self-supervised visual representation learning has recently attracted significant research interest. While a common way to evaluate self-supervised representations is through transfer to various downstream tasks, we instead investigate the problem of measuring their interpretability, i.e. understanding the semantics encoded in raw representations. We formulate the latter as estimating the mutual information between the representation and a space of manually labelled concepts. To quantify this we introduce a decoding bottleneck: information must be captured by simple predictors, mapping concepts to clusters in representation space. This approach, which we call reverse linear probing, provides a single number sensitive to the semanticity of the representation. This measure is also able to detect when the representation contains combinations of concepts (e.g., "red apple'') instead of just individual attributes ("red'' and "apple'' independently). Finally, we propose to use supervised classifiers to automatically label large datasets in order to enrich the space of concepts used for probing. We use our method to evaluate a large number of self-supervised representations, ranking them by interpretability, highlight the differences that emerge compared to the standard evaluation with linear probes and discuss several qualitative insights. Code at: https://github.com/iro-cp/ssl-qrp. | https://openreview.net/pdf/24d5777c7ce2fb03b77a283be66a63b1b952d589.pdf |
GradMax: Growing Neural Networks using Gradient Information | https://openreview.net/forum?id=qjN4h_wwUO | https://openreview.net/forum?id=qjN4h_wwUO | Utku Evci,Bart van Merrienboer,Thomas Unterthiner,Fabian Pedregosa,Max Vladymyrov | ICLR 2022,Poster | The architecture and the parameters of neural networks are often optimized independently, which requires costly retraining of the parameters whenever the architecture is modified. In this work we instead focus on growing the architecture without requiring costly retraining. We present a method that adds new neurons during training without impacting what is already learned, while improving the training dynamics. We achieve the latter by maximizing the gradients of the new weights and efficiently find the optimal initialization by means of the singular value decomposition (SVD). We call this technique Gradient Maximizing Growth (GradMax) and demonstrate its effectiveness in variety of vision tasks and architectures. We open sourced our code at https://github.com/google-research/growneuron | https://openreview.net/pdf/217711e1fabb5f74caa7140f98b25bb2d847e75b.pdf |
Online Coreset Selection for Rehearsal-based Continual Learning | https://openreview.net/forum?id=f9D-5WNG4Nv | https://openreview.net/forum?id=f9D-5WNG4Nv | Jaehong Yoon,Divyam Madaan,Eunho Yang,Sung Ju Hwang | ICLR 2022,Poster | A dataset is a shred of crucial evidence to describe a task. However, each data point in the dataset does not have the same potential, as some of the data points can be more representative or informative than others. This unequal importance among the data points may have a large impact in rehearsal-based continual learning, where we store a subset of the training examples (coreset) to be replayed later to alleviate catastrophic forgetting. In continual learning, the quality of the samples stored in the coreset directly affects the model's effectiveness and efficiency. The coreset selection problem becomes even more important under realistic settings, such as imbalanced continual learning or noisy data scenarios. To tackle this problem, we propose Online Coreset Selection (OCS), a simple yet effective method that selects the most representative and informative coreset at each iteration and trains them in an online manner. Our proposed method maximizes the model's adaptation to a target dataset while selecting high-affinity samples to past tasks, which directly inhibits catastrophic forgetting. We validate the effectiveness of our coreset selection mechanism over various standard, imbalanced, and noisy datasets against strong continual learning baselines, demonstrating that it improves task adaptation and prevents catastrophic forgetting in a sample-efficient manner. | https://openreview.net/pdf/2e384d1d48f24a63d604505e79eb70f45ecbfe91.pdf |
Switch to Generalize: Domain-Switch Learning for Cross-Domain Few-Shot Classification | https://openreview.net/forum?id=H-iABMvzIc | https://openreview.net/forum?id=H-iABMvzIc | Zhengdong Hu,Yifan Sun,Yi Yang | ICLR 2022,Poster | This paper considers few-shot learning under the cross-domain scenario. The cross-domain setting imposes a critical challenge, i.e., using very few (support) samples to generalize the already-learned model to a novel domain. We hold a hypothesis, i.e., if a deep model is capable to fast generalize itself to different domains (using very few samples) during training, it will maintain such domain generalization capacity for testing. It motivates us to propose a novel Domain-Switch Learning (DSL) framework. DSL embeds the cross-domain scenario into the training stage in a ``fast switching'' manner. Specifically, DSL uses a single domain for a training iteration and switches into another domain for the following iteration. During the switching, DSL enforces two constraints: 1) the deep model should not over-fit the domain in the current iteration and 2) the deep model should not forget the already-learned knowledge of other domains. These two constraints jointly promote fast generalization across different domains. Experimental results confirm that the cross-domain generalization capacity can be inherited from the training stage to the testing stage, validating our key hypothesis. Consequentially, DSL significantly improves cross-domain few-shot classification and sets up new state of the art. | https://openreview.net/pdf/d34f743c2c676ac34213466790c222f7aa5a1b28.pdf |
Zero-CL: Instance and Feature decorrelation for negative-free symmetric contrastive learning | https://openreview.net/forum?id=RAW9tCdVxLj | https://openreview.net/forum?id=RAW9tCdVxLj | Shaofeng Zhang,Feng Zhu,Junchi Yan,Rui Zhao,Xiaokang Yang | ICLR 2022,Poster | For self-supervised contrastive learning, models can easily collapse and generate trivial constant solutions. The issue has been mitigated by recent improvement on objective design, which however often requires square complexity either for the size of instances ($\mathcal{O}(N^{2})$) or feature dimensions ($\mathcal{O}(d)^2$). To prevent such collapse, we develop two novel methods by decorrelating on different dimensions on the instance embedding stacking matrix, i.e., \textbf{I}nstance-wise (ICL) and \textbf{F}eature-wise (FCL) \textbf{C}ontrastive \textbf{L}earning. The proposed two methods (FCL, ICL) can be combined synthetically, called Zero-CL, where ``Zero'' means negative samples are \textbf{zero} relevant, which allows Zero-CL to completely discard negative pairs i.e., with \textbf{zero} negative samples. Compared with previous methods, Zero-CL mainly enjoys three advantages: 1) Negative free in symmetric architecture. 2) By whitening transformation, the correlation of the different features is equal to zero, alleviating information redundancy. 3) Zero-CL remains original information to a great extent after transformation, which improves the accuracy against other whitening transformation techniques. Extensive experimental results on CIFAR-10/100 and ImageNet show that Zero-CL outperforms or is on par with state-of-the-art symmetric contrastive learning methods. | https://openreview.net/pdf/545b532dc138dbfc098e9384dbc805e96fceed40.pdf |
Random matrices in service of ML footprint: ternary random features with no performance loss | https://openreview.net/forum?id=qwULHx9zld | https://openreview.net/forum?id=qwULHx9zld | Hafiz Tiomoko Ali,Zhenyu Liao,Romain Couillet | ICLR 2022,Poster | In this article, we investigate the spectral behavior of random features kernel matrices of the type ${\bf K} = \mathbb{E}_{{\bf w}} \left[\sigma\left({\bf w}^{\sf T}{\bf x}_i\right)\sigma\left({\bf w}^{\sf T}{\bf x}_j\right)\right]_{i,j=1}^n$, with nonlinear function $\sigma(\cdot)$, data ${\bf x}_1, \ldots, {\bf x}_n \in \mathbb{R}^p$, and random projection vector ${\bf w} \in \mathbb{R}^p$ having i.i.d. entries. In a high-dimensional setting where the number of data $n$ and their dimension $p$ are both large and comparable, we show, under a Gaussian mixture model for the data, that the eigenspectrum of ${\bf K}$ is independent of the distribution of the i.i.d.(zero-mean and unit-variance) entries of ${\bf w}$, and only depends on $\sigma(\cdot)$ via its (generalized) Gaussian moments $\mathbb{E}_{z\sim \mathcal N(0,1)}[\sigma'(z)]$ and $\mathbb{E}_{z\sim \mathcal N(0,1)}[\sigma''(z)]$. As a result, for any kernel matrix ${\bf K}$ of the form above, we propose a novel random features technique, called Ternary Random Features (TRFs), that (i) asymptotically yields the same limiting kernel as the original ${\bf K}$ in a spectral sense and (ii) can be computed and stored much more efficiently, by wisely tuning (in a data-dependent manner) the function $\sigma$ and the random vector ${\bf w}$, both taking values in $\{-1,0,1\}$. The computation of the proposed random features requires no multiplication, and a factor of $b$ times less bits for storage compared to classical random features such as random Fourier features, with $b$ the number of bits to store full precision values. Besides, it appears in our experiments on real data that the substantial gains in computation and storage are accompanied with somewhat improved performances compared to state-of-the-art random features methods. | https://openreview.net/pdf/a1f6abc6863f0c0b0a7a4a0bedd62b6b5b103968.pdf |
Global Convergence of Multi-Agent Policy Gradient in Markov Potential Games | https://openreview.net/forum?id=gfwON7rAm4 | https://openreview.net/forum?id=gfwON7rAm4 | Stefanos Leonardos,Will Overman,Ioannis Panageas,Georgios Piliouras | ICLR 2022,Poster | Potential games are arguably one of the most important and widely studied classes of normal form games. They define the archetypal setting of multi-agent coordination in which all agents utilities are perfectly aligned via a common potential function. Can this intuitive framework be transplanted in the setting of Markov games? What are the similarities and differences between multi-agent coordination with and without state dependence? To answer these questions, we study a natural class of Markov Potential Games (MPGs) that generalize prior attempts at capturing complex stateful multi-agent coordination. Counter-intuitively, insights from normal-form potential games do not carry over as MPGs involve settings where state-games can be zero-sum games. In the opposite direction, Markov games where every state-game is a potential game are not necessarily MPGs. Nevertheless, MPGs showcase standard desirable properties such as the existence of deterministic Nash policies. In our main technical result, we prove convergence of independent policy gradient and its stochastic counterpart to Nash policies (polynomially fast in the approximation error) by adapting recent gradient dominance property arguments developed for single-agent Markov decision processes to multi-agent learning settings.
| https://openreview.net/pdf/91e8010784f5d9c14cf26f0a2e86f8b46808d3ae.pdf |
Rethinking Adversarial Transferability from a Data Distribution Perspective | https://openreview.net/forum?id=gVRhIEajG1k | https://openreview.net/forum?id=gVRhIEajG1k | Yao Zhu,Jiacheng Sun,Zhenguo Li | ICLR 2022,Poster | Adversarial transferability enables attackers to generate adversarial examples from the source model to attack the target model, which has raised security concerns about the deployment of DNNs in practice. In this paper, we rethink adversarial transferability from a data distribution perspective and further enhance transferability by score matching based optimization. We identify that some samples with injecting small Gaussian noise can fool different target models, and their adversarial examples under different source models have much stronger transferability. We hypothesize that these samples are in the low-density region of the ground truth distribution where models are not well trained. To improve the attack success rate of adversarial examples, we match the adversarial attacks with the directions which effectively decrease the ground truth density. We propose Intrinsic Adversarial Attack (IAA), which smooths the activation function and decreases the impact of the later layers of a given normal model, to increase the alignment of adversarial attack and the gradient of joint data distribution. We conduct comprehensive transferable attacks against multiple DNNs and show that our IAA can boost the transferability of the crafted attacks in all cases and go beyond state-of-the-art methods. | https://openreview.net/pdf/d835f2b3333622a19adc0a569eaa27153c426c28.pdf |
Transformers Can Do Bayesian Inference | https://openreview.net/forum?id=KSugKcbNf9 | https://openreview.net/forum?id=KSugKcbNf9 | Samuel Müller,Noah Hollmann,Sebastian Pineda Arango,Josif Grabocka,Frank Hutter | ICLR 2022,Poster | Currently, it is hard to reap the benefits of deep learning for Bayesian methods, which allow the explicit specification of prior knowledge and accurately capture model uncertainty. We present Prior-Data Fitted Networks (PFNs). PFNs leverage large-scale machine learning techniques to approximate a large set of posteriors. The only requirement for PFNs to work is the ability to sample from a prior distribution over supervised learning tasks (or functions). Our method restates the objective of posterior approximation as a supervised classification problem with a set-valued input: it repeatedly draws a task (or function) from the prior, draws a set of data points and their labels from it, masks one of the labels and learns to make probabilistic predictions for it based on the set-valued input of the rest of the data points. Presented with a set of samples from a new supervised learning task as input, PFNs make probabilistic predictions for arbitrary other data points in a single forward propagation, having learned to approximate Bayesian inference. We demonstrate that PFNs can near-perfectly mimic Gaussian processes and also enable efficient Bayesian inference for intractable problems, with over 200-fold speedups in multiple setups compared to current methods. We obtain strong results in very diverse areas such as Gaussian process regression, Bayesian neural networks, classification for small tabular data sets, and few-shot image classification, demonstrating the generality of PFNs. Code and trained PFNs are released at https://github.com/automl/TransformersCanDoBayesianInference. | https://openreview.net/pdf/0eee14e64526e89aa03be830dcf8d0b5024fd405.pdf |
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