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ICLR.cc/2022/Conference | Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection | 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. | Accept (Poster) |
ICLR.cc/2020/Conference | Comparing Rewinding and Fine-tuning in Neural Network Pruning | Many neural network pruning algorithms proceed in three steps: train the network to completion, remove unwanted structure to compress the network, and retrain the remaining structure to recover lost accuracy. The standard retraining technique, fine-tuning, trains the unpruned weights from their final trained values using a small fixed learning rate. In this paper, we compare fine-tuning to alternative retraining techniques. Weight rewinding (as proposed by Frankle et al., (2019)), rewinds unpruned weights to their values from earlier in training and retrains them from there using the original training schedule. Learning rate rewinding (which we propose) trains the unpruned weights from their final values using the same learning rate schedule as weight rewinding. Both rewinding techniques outperform fine-tuning, forming the basis of a network-agnostic pruning algorithm that matches the accuracy and compression ratios of several more network-specific state-of-the-art techniques.
| Accept (Talk) |
ICLR.cc/2023/Conference | Correcting the Sub-optimal Bit Allocation | In this paper, we investigate the problem of bit allocation in Neural Video Compression (NVC). First, we reveal that a recent bit allocation approach claimed to be optimal is, in fact, sub-optimal due to its implementation. Specifically, we find that its sub-optimality lies in the improper application of semi-amortized variational inference (SAVI) on latent with non-factorized variational posterior. Then, we show that the corrected version of SAVI on non-factorized latent requires recursively applying back-propagating through gradient ascent, based on which we derive the corrected optimal bit allocation algorithm. Due to the computational in-feasibility of the corrected bit allocation, we design an efficient approximation to make it practical. Empirical results show that our proposed correction significantly improves the incorrect bit allocation in terms of R-D performance and bitrate error, and outperforms all other bit allocation methods by a large margin. The source code is provided in the supplementary material. | Withdrawn |
ICLR.cc/2020/Conference | Unsupervised Learning of Node Embeddings by Detecting Communities | We present Deep MinCut (DMC), an unsupervised approach to learn node embeddings for graph-structured data. It derives node representations based on their membership in communities. As such, the embeddings directly provide interesting insights into the graph structure, so that the separate node clustering step of existing methods is no longer needed. DMC learns both, node embeddings and communities, simultaneously by minimizing the mincut loss, which captures the number of connections between communities. Striving for high scalability, we also propose a training process for DMC based on minibatches. We provide empirical evidence that the communities learned by DMC are meaningful and that the node embeddings are competitive in different node classification benchmarks. | Reject |
ICLR.cc/2023/Conference | Diffusing Graph Attention | The dominant paradigm for machine learning on graphs uses Message Passing Graph Neural Networks~(MP-GNNs), in which node representations are updated by aggregating information in their local neighborhood. Recently, there have been increasingly more attempts to adapt the Transformer architecture to graphs in an effort to solve some known limitations of MP-GNN. A challenging aspect of designing Graph Transformers is integrating the arbitrary graph structure into the architecture. We propose \emph{Graph Diffuser}~(GD) to address this challenge. GD learns to extract structural and positional relationships between distant nodes in the graph, which it then uses to direct the Transformer's attention and node representation. We demonstrate that existing GNNs and Graph Transformers struggle to capture long-range interactions and how Graph Diffuser does so while admitting intuitive visualizations. Experiments on eight benchmarks show Graph Diffuser to be a highly competitive model, outperforming the state-of-the-art in a diverse set of domains. | Reject |
ICLR.cc/2023/Conference | Offline Reinforcement Learning with Closed-Form Policy Improvement Operators | Behavior constrained policy optimization has been demonstrated to be a successful paradigm for tackling Offline Reinforcement Learning. By exploiting historical transitions, a policy is trained to maximize a learned value function while constrained by the behavior policy to avoid a significant distributional shift. In this paper, we propose our closed-form policy improvement operators. We make a novel observation that the behavior constraint naturally motivates the use of first-order Taylor approximation, leading to a linear approximation of the policy objective. Additionally, as practical datasets are usually collected by heterogeneous policies, we model the behavior policies as a Gaussian Mixture and overcome the induced optimization difficulties by leveraging the LogSumExp's lower bound and Jensen's Inequality, giving rise to a closed-form policy improvement operator. We instantiate an offline RL algorithm with our novel policy improvement operator and empirically demonstrate its effectiveness over state-of-the-art algorithms on the standard D4RL benchmark. | Reject |
ICLR.cc/2023/Conference | Semi-Offline Reinforcement Learning for Portfolio Optimization | We introduce semi-offline reinforcement learning (RL), a new formalization of the sequential decision-making problem for portfolio optimization. Unlike the standard and the fully-offline RL settings, the unique challenge of semi-offline RL is the limited access to an actively evolving environment. Therefore, existing online/offline RL approaches are incapable of handling the distributional shift between the fixed observations in the training set and those in an out-of-distribution test domain. In this paper, we propose a novel off-policy RL algorithm named \textit{stationarity-constrained MDP} (SC-MDP), which decouples the previously-collected training observations into two streams of \textit{stationary} and \textit{non-stationary} latent variables through a probabilistic inference framework. We demonstrate that in this way, the learned policies can be persistently profitable despite rapidly-changing environment dynamics. Our approach remarkably outperforms the existing online RL algorithms, advanced offline RL methods, and state-of-the-art stock prediction models on three real-world financial datasets. | Withdrawn |
ICLR.cc/2020/Conference | Global Relational Models of Source Code | Models of code can learn distributed representations of a program's syntax and semantics to predict many non-trivial properties of a program. Recent state-of-the-art models leverage highly structured representations of programs, such as trees, graphs and paths therein (e.g. data-flow relations), which are precise and abundantly available for code. This provides a strong inductive bias towards semantically meaningful relations, yielding more generalizable representations than classical sequence-based models. Unfortunately, these models primarily rely on graph-based message passing to represent relations in code, which makes them de facto local due to the high cost of message-passing steps, quite in contrast to modern, global sequence-based models, such as the Transformer. In this work, we bridge this divide between global and structured models by introducing two new hybrid model families that are both global and incorporate structural bias: Graph Sandwiches, which wrap traditional (gated) graph message-passing layers in sequential message-passing layers; and Graph Relational Embedding Attention Transformers (GREAT for short), which bias traditional Transformers with relational information from graph edge types. By studying a popular, non-trivial program repair task, variable-misuse identification, we explore the relative merits of traditional and hybrid model families for code representation. Starting with a graph-based model that already improves upon the prior state-of-the-art for this task by 20%, we show that our proposed hybrid models improve an additional 10-15%, while training both faster and using fewer parameters. | Accept (Poster) |
ICLR.cc/2023/Conference | Co-Evolution As More Than a Scalable Alternative for Multi-Agent Reinforcement Learning | In recent years, gradient based multi-agent reinforcement learning is growing in success. One contributing factor is the use of shared parameters for learning policy networks. While this approach scales well with the number of agents during execution it lacks this ambiguity for training as the number of produced samples grows linearly with the number of agents. For a very large number of agents, this could lead to an inefficient use of the circumstantial amount of produced samples. Moreover in single-agent reinforcement learning policy search with evolutionary algorithms showed viable success when sampling can be parallelized on a larger scale. The here proposed method does not only consider sampling in concurrent environments but further investigates sampling diverse parameters from the population in co-evolution in joint environments during training. This co-evolutionary policy search has shown to be capable of training a large number of agents. Beyond that, it has been shown to produce competitive results in smaller environments in comparison to gradient descent based methods. This surprising result make evolutionary algorithms a promising candidate for further research in the context of multi-agent reinforcement learning. | Reject |
ICLR.cc/2022/Conference | Generate Triggers in Neural Relation Extraction | In the relation extraction task, the relationship between two entities is determined by some specific words in their source text. These words are called relation triggers, which are the evidence to explain the relationship; other words are called ir-relevant words. The current relationship extraction neural network model aims at identifying the relation type between two entities mentioned in source text by encoding the text and entities. However, these models cannot output the relation triggers, but only gives the result of relation classification. Although models can generate weights for every single word through the improvement of attention mechanism, the weights will be affected by irrelevant words essentially, which are not required by the relation extraction task. In order to output re-lation triggers accurately, we propose a novel training frame-work for Relation Extraction (RE) that reduces the negative effect of irrelevant words on them in the encoding stage. In specific, we leverage Evolutive Mask based Point Network (EMPN) as a decoder to generate relation triggers and encode these words again. For an ordered output in relation triggers, we utilize order loss to constrain the output order in them. Ex-tensive experiment results demonstrate that the effectiveness of our proposed model achieves state-of-the-art performance on three RE benchmark datasets. | Desk_Rejected |
ICLR.cc/2022/Conference | On Evaluation Metrics for Graph Generative Models | In image generation, generative models can be evaluated naturally by visually inspecting model outputs. However, this is not always the case for graph generative models (GGMs), making their evaluation challenging. Currently, the standard process for evaluating GGMs suffers from three critical limitations: i) it does not produce a single score which makes model selection challenging, ii) in many cases it fails to consider underlying edge and node features, and iii) it is prohibitively slow to perform. In this work, we mitigate these issues by searching for \emph{scalar, domain-agnostic, and scalable metrics} for evaluating and ranking GGMs. To this end, we study existing GGM metrics and neural-network-based metrics emerging from generative models of images that use embeddings extracted from a task-specific network. Motivated by the power of Graph Neural Networks (GNNs) to extract meaningful graph representations \emph{without any training}, we introduce several metrics based on the features extracted by an untrained random GNN. We design experiments to thoroughly test and objectively score metrics on their ability to measure the diversity and fidelity of generated graphs, as well as their sample and computational efficiency. Depending on the quantity of samples, we recommend one of two metrics from our collection of random-GNN-based metrics. We show these two metrics to be more expressive than pre-existing and alternative random-GNN-based metrics using our objective scoring. While we focus on applying these metrics to GGM evaluation, in practice this enables the ability to easily compute the dissimilarity between any two sets of graphs \emph{regardless of domain}. Our code is released at: https://github.com/uoguelph-mlrg/GGM-metrics. | Accept (Poster) |
ICLR.cc/2023/Conference | Brain2GAN; Reconstructing perceived faces from the primate brain via StyleGAN3 | Neural coding characterizes the relationship between stimuli and their corresponding neural responses. The usage of synthesized yet photorealistic reality by generative adversarial networks (GANs) allows for superior control over these data: the underlying feature representations that account for the semantics in synthesized data are known a priori and their relationship is perfect rather than approximated post-hoc by feature extraction models. We exploit this property in neural decoding of multi-unit activity responses that we recorded from the primate brain upon presentation with synthesized face images in a passive fixation experiment. The face reconstructions we acquired from brain activity were astonishingly similar to the originally perceived face stimuli. This provides strong evidence that the neural face manifold and the disentangled w-latent space conditioned on StyleGAN3 (rather than the z-latent space of arbitrary GANs or other feature representations we encountered so far) share how they represent the high-level semantics of the high-dimensional space of faces. | Reject |
ICLR.cc/2020/Conference | Policy Message Passing: A New Algorithm for Probabilistic Graph Inference | A general graph-structured neural network architecture operates on graphs through two core components: (1) complex enough message functions; (2) a fixed information aggregation process. In this paper, we present the Policy Message Passing algorithm, which takes a probabilistic perspective and reformulates the whole information aggregation as stochastic sequential processes. The algorithm works on a much larger search space, utilizes reasoning history to perform inference, and is robust to noisy edges. We apply our algorithm to multiple complex graph reasoning and prediction tasks and show that our algorithm consistently outperforms state-of-the-art graph-structured models by a significant margin. | Reject |
ICLR.cc/2020/Conference | Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? | Modern deep learning methods provide effective means to learn good representations. However, is a good representation itself sufficient for sample efficient reinforcement learning? This question has largely been studied only with respect to (worst-case) approximation error, in the more classical approximate dynamic programming literature. With regards to the statistical viewpoint, this question is largely unexplored, and the extant body of literature mainly focuses on conditions which \emph{permit} sample efficient reinforcement learning with little understanding of what are \emph{necessary} conditions for efficient reinforcement learning.
This work shows that, from the statistical viewpoint, the situation is far subtler than suggested by the more traditional approximation viewpoint, where the requirements on the representation that suffice for sample efficient RL are even more stringent. Our main results provide sharp thresholds for reinforcement learning methods, showing that there are hard limitations on what constitutes good function approximation (in terms of the dimensionality of the representation), where we focus on natural representational conditions relevant to value-based, model-based, and policy-based learning. These lower bounds highlight that having a good (value-based, model-based, or policy-based) representation in and of itself is insufficient for efficient reinforcement learning, unless the quality of this approximation passes certain hard thresholds. Furthermore, our lower bounds also imply exponential separations on the sample complexity between 1) value-based learning with perfect representation and value-based learning with a good-but-not-perfect representation, 2) value-based learning and policy-based learning, 3) policy-based learning and supervised learning and 4) reinforcement learning and imitation learning. | Accept (Spotlight) |
ICLR.cc/2021/Conference | Balancing Robustness and Sensitivity using Feature Contrastive Learning | It is generally believed that robust training of extremely large networks is critical to their success in real-world applications. However, when taken to the extreme, methods that promote robustness can hurt the model’s sensitivity to rare or underrepresented patterns. In this paper, we discuss this trade-off between robustness and sensitivity by introducing two notions: contextual feature utility and contextual feature sensitivity. We propose Feature Contrastive Learning (FCL) that encourages the model to be more sensitive to the features that have higher contextual utility. Empirical results demonstrate that models trained with FCL achieve a better balance of robustness and sensitivity, leading to improved generalization in the presence of noise. | Reject |
ICLR.cc/2023/Conference | FedPSE: Personalized Sparsification with Element-wise Aggregation for Federated Learning | Federated learning (FL) is a popular distributed machine learning framework in which clients aggregate models' parameters instead of sharing their individual data. In FL, clients communicate with the server under limited network bandwidth frequently, which arises the communication challenge. To resolve this challenge, multiple compression methods have been proposed to reduce the transmitted parameters. However, these techniques show that the federated performance degrades significantly with Non-IID (non-identically independently distributed) datasets. To address this issue, we propose an effective method, called FedPSE, which solves the efficiency challenge of FL with heterogeneous data. FedPSE compresses the local updates on clients using Top-K sparsification and aggregates these updates on the server by element-wise average. Then clients download the personalized sparse updates from the server to update their individual local models. We then theoretically analyze the convergence of FedPSE under the non-convex setting. Moreover, extensive experiments on four benchmark tasks demonstrate that our FedPSE outperforms the state-of-the-art methods on Non-IID datasets in terms of both efficiency and accuracy. | Reject |
ICLR.cc/2021/Conference | Early Stopping in Deep Networks: Double Descent and How to Eliminate it | Over-parameterized models, such as large deep networks, often exhibit a double descent phenomenon, whereas a function of model size, error first decreases, increases, and decreases at last. This intriguing double descent behavior also occurs as a function of training epochs and has been conjectured to arise because training epochs control the model complexity. In this paper, we show that such epoch-wise double descent occurs for a different reason: It is caused by a superposition of two or more bias-variance tradeoffs that arise because different parts of the network are learned at different epochs, and mitigating this by proper scaling of stepsizes can significantly improve the early stopping performance. We show this analytically for i) linear regression, where differently scaled features give rise to a superposition of bias-variance tradeoffs, and for ii) a wide two-layer neural network, where the first and second layers govern bias-variance tradeoffs. Inspired by this theory, we study two standard convolutional networks empirically and show that eliminating epoch-wise double descent through adjusting stepsizes of different layers improves the early stopping performance. | Accept (Poster) |
ICLR.cc/2021/Conference | A Probabilistic Model for Discriminative and Neuro-Symbolic Semi-Supervised Learning | Strong progress has been achieved in semi-supervised learning (SSL) by combining several methods, some of which relate to properties of the data distribution p(x), others to the model outputs p(y|x), e.g. minimising the entropy of unlabelled predictions. Focusing on the latter, we fill a gap in the standard text by introducing a probabilistic model for discriminative semi-supervised learning, mirroring the classical generative model. Several SSL methods are theoretically explained by our model as inducing (approximate) strong priors over parameters of p(y|x). Applying this same probabilistic model to tasks in which labels represent binary attributes, we theoretically justify a family of neuro-symbolic SSL approaches, taking a step towards bridging the divide between statistical learning and logical reasoning. | Reject |
ICLR.cc/2022/Conference | DICE: A Simple Sparsification Method for Out-of-distribution Detection | Detecting out-of-distribution (OOD) inputs is a central challenge for safely deploying machine learning models in the real world. Previous methods commonly rely on an OOD score derived from the overparameterized weight space, while largely overlooking the role of sparsification. In this paper, we reveal important insights that reliance on unimportant weights and units can directly attribute to the brittleness of OOD detection. To mitigate the issue, we propose a sparsification-based OOD detection framework termed DICE. Our key idea is to rank weights based on a measure of contribution, and selectively use the most salient weights to derive the output for OOD detection. We provide both empirical and theoretical insights, characterizing and explaining the mechanism by which DICE improves OOD detection. By pruning away noisy signals, DICE provably reduces the output variance for OOD data, resulting in a sharper output distribution and stronger separability from ID data. DICE establishes state-of-the-art performance, reducing the FPR95 by up to 24.69% compared to the previous best method. | Withdrawn |
ICLR.cc/2021/Conference | Knowledge distillation via softmax regression representation learning | This paper addresses the problem of model compression via knowledge distillation. We advocate for a method that optimizes the output feature of the penultimate layer of the student network and hence is directly related to representation learning. Previous distillation methods which typically impose direct feature matching between the student and the teacher do not take into account the classification problem at hand. On the contrary, our distillation method decouples representation learning and classification and utilizes the teacher's pre-trained classifier to train the student's penultimate layer feature. In particular, for the same input image, we wish the teacher's and student's feature to produce the same output when passed through the teacher's classifier which is achieved with a simple $L_2$ loss. Our method is extremely simple to implement and straightforward to train and is shown to consistently outperform previous state-of-the-art methods over a large set of experimental settings including different (a) network architectures, (b) teacher-student capacities, (c) datasets, and (d) domains. The code will be available at \url{https://github.com/jingyang2017/KD_SRRL}. | Accept (Poster) |
ICLR.cc/2023/Conference | 3D-IntPhys: Learning 3D Visual Intuitive Physics for Fluids, Rigid Bodies, and Granular Materials | Given a visual scene, humans have strong intuitions about how a scene can evolve over time under given actions. The intuition, often termed visual intuitive physics, is a critical ability that allows us to make effective plans to manipulate the scene to achieve desired outcomes without relying on extensive trial and error. In this paper, we present a framework capable of learning 3D-grounded visual intuitive physics models purely from unlabeled images. Our method is composed of a conditional Neural Radiance Field (NeRF)-style visual frontend and a 3D point-based dynamics prediction backend, in which we impose strong relational and structural inductive bias to capture the structure of the underlying environment. Unlike existing intuitive point-based dynamics works that rely on the supervision of dense point trajectory from simulators, we relax the requirements and only assume access to multi-view RGB images and (imperfect) instance masks. This enables the proposed model to handle scenarios where accurate point estimation and tracking are hard or impossible. We evaluate the models on three challenging scenarios involving fluid, granular materials, and rigid objects, where standard detection and tracking methods are not applicable. We show our model can make long-horizon future predictions by learning from raw images and significantly outperforms models that do not employ an explicit 3D representation space. We also show that, once trained, our model can achieve strong generalization in complex scenarios under extrapolate settings. | Reject |
ICLR.cc/2021/Conference | Manifold-aware Training: Increase Adversarial Robustness with Feature Clustering | The problem of defending against adversarial attacks has attracted increasing attention in recent years. While various types of defense methods ($\textit{e.g.}$, adversarial training, detection and rejection, and recovery) were proven empirically to bring robustness to the network, their weakness was shown by later works. Inspired by the observation from the distribution properties of the features extracted by the CNNs in the feature space and their link to robustness, this work designs a novel training process called Manifold-Aware Training (MAT), which forces CNNs to learn compact features to increase robustness. The effectiveness of the proposed method is evaluated via comparisons with existing defense mechanisms, $\textit{i.e.}$, the TRADES algorithm, which has been recognized as a representative state-of-the-art technology, and the MMC method, which also aims to learn compact features. Further verification is also conducted using the attack adaptive to our method. Experimental results show that MAT-trained CNNs exhibit significantly higher performance than state-of-the-art robustness. | Reject |
ICLR.cc/2020/Conference | Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems | First-order methods such as stochastic gradient descent (SGD) are currently the standard algorithm for training deep neural networks. Second-order methods, despite their better convergence rate, are rarely used in practice due to the pro- hibitive computational cost in calculating the second-order information. In this paper, we propose a novel Gram-Gauss-Newton (GGN) algorithm to train deep neural networks for regression problems with square loss. Our method draws inspiration from the connection between neural network optimization and kernel regression of neural tangent kernel (NTK). Different from typical second-order methods that have heavy computational cost in each iteration, GGN only has minor overhead compared to first-order methods such as SGD. We also give theoretical results to show that for sufficiently wide neural networks, the convergence rate of GGN is quadratic. Furthermore, we provide convergence guarantee for mini-batch GGN algorithm, which is, to our knowledge, the first convergence result for the mini-batch version of a second-order method on overparameterized neural net- works. Preliminary experiments on regression tasks demonstrate that for training standard networks, our GGN algorithm converges much faster and achieves better performance than SGD. | Reject |
ICLR.cc/2020/Conference | Physics-Aware Flow Data Completion Using Neural Inpainting | In this paper we propose a physics-aware neural network for inpainting fluid flow data. We consider that flow field data inherently follows the solution of the Navier-Stokes equations and hence our network is designed to capture physical laws. We use a DenseBlock U-Net architecture combined with a stream function formulation to inpaint missing velocity data. Our loss functions represent the relevant physical quantities velocity, velocity Jacobian, vorticity and divergence. Obstacles are treated as known priors, and each layer of the network receives the relevant information through concatenation with the previous layer's output. Our results demonstrate the network's capability for physics-aware completion tasks, and the presented ablation studies show the effectiveness of each proposed component. | Reject |
ICLR.cc/2018/Conference | Fast and Accurate Text Classification: Skimming, Rereading and Early Stopping | Recent advances in recurrent neural nets (RNNs) have shown much promise in many applications in natural language processing. For most of these tasks, such as sentiment analysis of customer reviews, a recurrent neural net model parses the entire review before forming a decision. We argue that reading the entire input is not always necessary in practice, since a lot of reviews are often easy to classify, i.e., a decision can be formed after reading some crucial sentences or words in the provided text. In this paper, we present an approach of fast reading for text classification. Inspired by several well-known human reading techniques, our approach implements an intelligent recurrent agent which evaluates the importance of the current snippet in order to decide whether to make a prediction, or to skip some texts, or to re-read part of the sentence. Our agent uses an RNN module to encode information from the past and the current tokens, and applies a policy module to form decisions. With an end-to-end training algorithm based on policy gradient, we train and test our agent on several text classification datasets and achieve both higher efficiency and better accuracy compared to previous approaches.
| Invite to Workshop Track |
ICLR.cc/2021/Conference | Structural Knowledge Distillation | Knowledge distillation is a critical technique to transfer knowledge between models, typically from a large model (the teacher) to a smaller one (the student). The objective function of knowledge distillation is typically the cross-entropy between the teacher and the student's output distributions. However, for structured prediction problems, the output space is exponential in size; therefore, the cross-entropy objective becomes intractable to compute and optimize directly. In this paper, we derive a factorized form of the knowledge distillation objective for structured prediction, which is tractable for many typical choices of the teacher and student models. In particular, we show the tractability and empirical effectiveness of structural knowledge distillation between sequence labeling and dependency parsing models under four different scenarios: 1) the teacher and student share the same factorization form of the output structure scoring function; 2) the student factorization produces smaller substructures than the teacher factorization; 3) the teacher factorization produces smaller substructures than the student factorization; 4) the factorization forms from the teacher and the student are incompatible. | Withdrawn |
ICLR.cc/2021/Conference | DEMI: Discriminative Estimator of Mutual Information | Estimating mutual information between continuous random variables is often intractable and extremely challenging for high-dimensional data. Recent progress has leveraged neural networks to optimize variational lower bounds on mutual information. Although showing promise for this difficult problem, the variational methods have been theoretically and empirically proven to have serious statistical limitations: 1) many methods struggle to produce accurate estimates when the underlying mutual information is either low or high; 2) the resulting estimators may suffer from high variance. Our approach is based on training a classifier that provides the probability that a data sample pair is drawn from the joint distribution rather than from the product of its marginal distributions. Moreover, we establish a direct connection between mutual information and the average log odds estimate produced by the classifier on a test set, leading to a simple and accurate estimator of mutual information. We show theoretically that our method and other variational approaches are equivalent when they achieve their optimum, while our method sidesteps the variational bound. Empirical results demonstrate high accuracy of our approach and the advantages of our estimator in the context of representation learning.
| Reject |
ICLR.cc/2021/Conference | Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets | Despite the success of recent Neural Architecture Search (NAS) methods on various tasks which have shown to output networks that largely outperform human-designed networks, conventional NAS methods have mostly tackled the optimization of searching for the network architecture for a single task (dataset), which does not generalize well across multiple tasks (datasets). Moreover, since such task-specific methods search for a neural architecture from scratch for every given task, they incur a large computational cost, which is problematic when the time and monetary budget are limited. In this paper, we propose an efficient NAS framework that is trained once on a database consisting of datasets and pretrained networks and can rapidly search for a neural architecture for a novel dataset. The proposed MetaD2A (Meta Dataset-to-Architecture) model can stochastically generate graphs (architectures) from a given set (dataset) via a cross-modal latent space learned with amortized meta-learning. Moreover, we also propose a meta-performance predictor to estimate and select the best architecture without direct training on target datasets. The experimental results demonstrate that our model meta-learned on subsets of ImageNet-1K and architectures from NAS-Bench 201 search space successfully generalizes to multiple unseen datasets including CIFAR-10 and CIFAR-100, with an average search time of 33 GPU seconds. Even under MobileNetV3 search space, MetaD2A is 5.5K times faster than NSGANetV2, a transferable NAS method, with comparable performance. We believe that the MetaD2A proposes a new research direction for rapid NAS as well as ways to utilize the knowledge from rich databases of datasets and architectures accumulated over the past years. Code is available at https://github.com/HayeonLee/MetaD2A. | Accept (Poster) |
ICLR.cc/2023/Conference | Multiscale Multimodal Transformer for Multimodal Action Recognition | While action recognition has been an active research area for several years, most existing approaches merely leverage the video modality as opposed to humans that efficiently process video and audio cues simultaneously. This limits the usage of recent models to applications where the actions are visually well-defined. On the other hand, audio and video can be perceived in a hierarchical structure, e.g., from audio signal per sampling time point to audio activities and the whole category in the audio classification. In this work, we develop a multiscale multimodal Transformer (MMT) that employs hierarchical representation learning. Particularly, MMT is composed of a novel multiscale audio Transformer (MAT) and a multiscale video Transformer. Furthermore, we propose a set of multimodal supervised contrastive objectives called audio-video contrastive loss (AVC) and intra-modal contrastive loss (IMC) that specifically align the two modalities for robust multimodal representation fusion. MMT surpasses previous state-of-the-art approaches by 7.3%, 1.6% and 2.1% on Kinetics-Sounds, Epic-Kitchens-100 and VGGSound in terms of the top-1 accuracy without external training data. Moreover, our MAT significantly outperforms AST by 22.2%, 4.4% and 4.7% on the three public benchmark datasets and is 3x more efficient based on the number of FLOPs. Through extensive ablation studies and visualizations, we demonstrate that the proposed MMT can effectively capture semantically more separable feature representations from a combination of video and audio signals. | Withdrawn |
ICLR.cc/2021/Conference | SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing | Conversational Semantic Parsing (CSP) is the task of converting a sequence of natural language queries to formal language (e.g., SQL, SPARQL) that can be executed against a structured ontology (e.g. databases, knowledge bases). To accomplish this task, a CSP system needs to model the relation between the unstructured language utterance and the structured ontology while representing the multi-turn dynamics of the dialog. Pre-trained language models (LMs) are the state-of-the-art for various natural language processing tasks. However, existing pre-trained LMs that use language modeling training objectives over free-form text have limited ability to represent natural language references to contextual structural data. In this work, we present SCORE, a new pre-training approach for CSP tasks designed to induce representations that capture the alignment between the dialogue flow and the structural context. We demonstrate the broad applicability of SCORE to CSP tasks by combining SCORE with strong base systems on four different tasks (SPARC, COSQL, MWOZ, and SQA). We show that SCORE can improve the performance over all these base systems by a significant margin and achieves state-of-the-art results on three of them. | Accept (Poster) |
ICLR.cc/2023/Conference | Excess Risk of Two-Layer ReLU Neural Networks in Teacher-Student Settings and its Superiority to Kernel Methods | While deep learning has outperformed other methods for various tasks, theoretical frameworks that explain its reason have not been fully established. We investigate the excess risk of two-layer ReLU neural networks in a teacher-student regression model, in which a student network learns an unknown teacher network through its outputs. Especially, we consider the student network that has the same width as the teacher network and is trained in two phases: first by noisy gradient descent and then by the vanilla gradient descent. Our result shows that the student network provably reaches a near-global optimal solution and outperforms any kernel methods estimator (more generally, linear estimators), including neural tangent kernel approach, random feature model, and other kernel methods, in a sense of the minimax optimal rate. The key concept inducing this superiority is the non-convexity of the neural network models. Even though the loss landscape is highly non-convex, the student network adaptively learns the teacher neurons. | Accept: poster |
ICLR.cc/2021/Conference | Spherical Motion Dynamics: Learning Dynamics of Neural Network with Normalization, Weight Decay, and SGD | In this work, we comprehensively reveal the learning dynamics of neural network with normalization, weight decay (WD), and SGD (with momentum), named as Spherical Motion Dynamics (SMD). Most related works study SMD by focusing on "effective learning rate" in "equilibrium" condition, where weight norm remains unchanged. However, their discussions on why equilibrium condition can be reached in SMD is either absent or less convincing. Our work investigates SMD by directly exploring the cause of equilibrium condition. Specifically, 1) we introduce the assumptions that can lead to equilibrium condition in SMD, and prove that weight norm can converge at linear rate with given assumptions; 2) we propose "angular update" as a substitute for effective learning rate to measure the evolving of neural network in SMD, and prove angular update can also converge to its theoretical value at linear rate; 3) we verify our assumptions and theoretical results on various computer vision tasks including ImageNet and MSCOCO with standard settings. Experiment results show our theoretical findings agree well with empirical observations. | Reject |
ICLR.cc/2018/Conference | Adversarial Learning for Semi-Supervised Semantic Segmentation | We propose a method for semi-supervised semantic segmentation using the adversarial network. While most existing discriminators are trained to classify input images as real or fake on the image level, we design a discriminator in a fully convolutional manner to differentiate the predicted probability maps from the ground truth segmentation distribution with the consideration of the spatial resolution. We show that the proposed discriminator can be used to improve the performance on semantic segmentation by coupling the adversarial loss with the standard cross entropy loss on the segmentation network. In addition, the fully convolutional discriminator enables the semi-supervised learning through discovering the trustworthy regions in prediction results of unlabeled images, providing additional supervisory signals. In contrast to existing methods that utilize weakly-labeled images, our method leverages unlabeled images without any annotation to enhance the segmentation model. Experimental results on both the PASCAL VOC 2012 dataset and the Cityscapes dataset demonstrate the effectiveness of our algorithm. | Reject |
ICLR.cc/2020/Conference | The Dual Information Bottleneck | The Information-Bottleneck (IB) framework suggests a general characterization of optimal representations in learning, and deep learning in particular. It is based on the optimal trade off between the representation complexity and accuracy, both of which are quantified by mutual information. The problem is solved by alternating projections between the encoder and decoder of the representation, which can be performed locally at each representation level. The framework, however, has practical drawbacks, in that mutual information is notoriously difficult to handle at high dimension, and only has closed form solutions in special cases. Further, because it aims to extract representations which are minimal sufficient statistics of the data with respect to the desired label, it does not necessarily optimize the actual prediction of unseen labels. Here we present a formal dual problem to the IB which has several interesting properties. By switching the order in the KL-divergence between the representation decoder and data, the optimal decoder becomes the geometric rather than the arithmetic mean of the input points. While providing a good approximation to the original IB, it also preserves the form of exponential families, and optimizes the mutual information on the predicted label rather than the desired one. We also analyze the critical points of the dualIB and discuss their importance for the quality of this approach. | Reject |
ICLR.cc/2020/Conference | Individualised Dose-Response Estimation using Generative Adversarial Nets | The problem of estimating treatment responses from observational data is by now a well-studied one. Less well studied, though, is the problem of treatment response estimation when the treatments are accompanied by a continuous dosage parameter. In this paper, we tackle this lesser studied problem by building on a modification of the generative adversarial networks (GANs) framework that has already demonstrated effectiveness in the former problem. Our model, DRGAN, is flexible, capable of handling multiple treatments each accompanied by a dosage parameter. The key idea is to use a significantly modified GAN model to generate entire dose-response curves for each sample in the training data which will then allow us to use standard supervised methods to learn an inference model capable of estimating these curves for a new sample. Our model consists of 3 blocks: (1) a generator, (2) a discriminator, (3) an inference block. In order to address the challenge presented by the introduction of dosages, we propose novel architectures for both our generator and discriminator. We model the generator as a multi-task deep neural network. In order to address the increased complexity of the treatment space (because of the addition of dosages), we develop a hierarchical discriminator consisting of several networks: (a) a treatment discriminator, (b) a dosage discriminator for each treatment. In the experiments section, we introduce a new semi-synthetic data simulation for use in the dose-response setting and demonstrate improvements over the existing benchmark models. | Reject |
ICLR.cc/2018/Conference | A Flexible Approach to Automated RNN Architecture Generation | The process of designing neural architectures requires expert knowledge and extensive trial and error.
While automated architecture search may simplify these requirements, the recurrent neural network (RNN) architectures generated by existing methods are limited in both flexibility and components.
We propose a domain-specific language (DSL) for use in automated architecture search which can produce novel RNNs of arbitrary depth and width.
The DSL is flexible enough to define standard architectures such as the Gated Recurrent Unit and Long Short Term Memory and allows the introduction of non-standard RNN components such as trigonometric curves and layer normalization. Using two different candidate generation techniques, random search with a ranking function and reinforcement learning,
we explore the novel architectures produced by the RNN DSL for language modeling and machine translation domains.
The resulting architectures do not follow human intuition yet perform well on their targeted tasks, suggesting the space of usable RNN architectures is far larger than previously assumed. | Invite to Workshop Track |
ICLR.cc/2018/Conference | A Tensor Analysis on Dense Connectivity via Convolutional Arithmetic Circuits | Several state of the art convolutional networks rely on inter-connecting different layers to ease the flow of information and gradient between their input and output layers. These techniques have enabled practitioners to successfully train deep convolutional networks with hundreds of layers. Particularly, a novel way of interconnecting layers was introduced as the Dense Convolutional Network (DenseNet) and has achieved state of the art performance on relevant image recognition tasks. Despite their notable empirical success, their theoretical understanding is still limited. In this work, we address this problem by analyzing the effect of layer interconnection on the overall expressive power of a convolutional network. In particular, the connections used in DenseNet are compared with other types of inter-layer connectivity. We carry out a tensor analysis on the expressive power inter-connections on convolutional arithmetic circuits (ConvACs) and relate our results to standard convolutional networks. The analysis leads to performance bounds and practical guidelines for design of ConvACs. The generalization of these results are discussed for other kinds of convolutional networks via generalized tensor decompositions. | Reject |
ICLR.cc/2023/Conference | Differentially Private Optimization on Large Model at Small Cost | Differentially private (DP) optimization is the standard paradigm to learn large neural networks that are accurate and privacy-preserving. The computational cost for DP deep learning, however, is notoriously heavy due to the per-sample gradient clipping. Existing DP implementations are $2-1000\times$ more costly in time and space complexity than the standard (non-private) training. In this work, we develop a novel Book-Keeping (BK) technique that implements existing DP optimizers (thus achieving the same accuracy), with a substantial improvement on the computational cost. Specifically, BK enables DP training on large models and high dimensional data to be roughly as efficient as the standard training, whereas previous DP algorithms can be inefficient or incapable of training due to memory error. The computational advantage of BK is supported by the complexity analysis as well as extensive experiments on vision and language tasks. Our implementation achieves state-of-the-art (SOTA) accuracy with very small extra cost: on GPT2 and at the same memory cost, BK has 1.0$\times$ the time complexity of the standard training (0.75$\times$ training speed in practice), and 0.6$\times$ the time complexity of the most efficient DP implementation (1.24$\times$ training speed in practice). We will open-source the codebase for the BK algorithm. | Reject |
ICLR.cc/2022/Conference | Optimal Transport for Long-Tailed Recognition with Learnable Cost Matrix | It is attracting attention to the long-tailed recognition problem, a burning issue that has become very popular recently. Distinctive from conventional recognition is that it posits that the allocation of the training set is supremely distorted. Predictably, it will pose challenges to the generalisation behaviour of the model. Approaches to these challenges revolve into two groups: firstly, training-aware methods, with the aim of enhancing the generalisability of the model by exploiting its potential in the training period; and secondly, post-hoc correction, liberally coupled with training-aware methods, which is intended to refine the predictions to the extent possible in the post-processing stage, offering the advantages of simplicity and effectiveness. This paper introduces an alternative direction to do the post-hoc correction, which goes beyond the statistical methods. Mathematically, we approach this issue from the perspective of optimal transport (OT), yet, choosing the exact cost matrix when applying OT is challenging and requires expert knowledge of various tasks. To overcome this limitation, we propose to employ linear mapping to learn the cost matrix without necessary configurations adaptively. Testing our methods in practice, along with high efficiency and excellent performance, our method surpasses all previous methods and has the best performance to date. | Accept (Poster) |
ICLR.cc/2023/Conference | MetaP: How to Transfer Your Knowledge on Learning Hidden Physics | Gradient-based meta-learning methods have primarily focused on classical machine learning tasks such as image classification and function regression, where they were found to perform well by recovering the underlying common representation among a set of given tasks. Recently, PDE-solving deep learning methods, such as neural operators, are starting to make an important impact on learning and predicting the response of a complex physical system directly from observational data. Since the data acquisition in this context is commonly challenging and costly, the call of utilization and transfer of existing knowledge to new and unseen physical systems is even more acute.
Herein, we propose a novel meta-learnt approach for transfer-learning knowledge between neural operators, which can be seen as transferring the knowledge of solution operators between governing (unknown) PDEs with varying parameter fields. With the key theoretical observation that the underlying parameter field can be captured in the first layer of the neural operator model, in contrast to typical final-layer transfer in existing meta-learning methods, our approach is a provably universal solution operator for multiple PDE solving tasks. As applications, we demonstrate the efficacy of our proposed approach on heterogeneous material modeling tasks, which shows that our method can handle complex and nonlinear physical response learning tasks while greatly improving the sampling efficiency in new and unseen materials. | Reject |
ICLR.cc/2023/Conference | Social Network Structure Shapes Innovation: Experience-sharing in RL with SAPIENS |
The human cultural repertoire relies on innovation: our ability to continuously explore how existing elements can be combined to create new ones. Innovation is not solitary, it relies on collective accumulation and merging of previous solutions. Machine learning approaches commonly assume that fully connected multi-agent networks are best suited for innovation. However, human laboratory and field studies have shown that hierarchical innovation is more robustly achieved by dynamic social network structures. In dynamic settings, humans oscillate between innovating individually or in small clusters, and then sharing outcomes with others. To our knowledge, the role of multi-agent topology on innovation has not been systematically studied in machine learning. It remains unclear a) which social network topologies are optimal for which innovation tasks, and b) which properties of experience sharing improve multi-level innovation. Here we use a multi-level hierarchical problem setting (WordCraft), with three different innovation tasks. We systematically design networks of DQNs sharing experiences from their replay buffers in varying topologies (fully connected, small world, dynamic, ring). Comparing the level of innovation achieved by different experience-sharing topologies across different tasks shows that, first, consistent with human findings, experience sharing within a dynamic topology achieves the highest level of innovation across tasks. Second, experience sharing is not as helpful when there is a single clear path to innovation. Third, two metrics we propose, conformity and diversity of shared experience, can explain the success of different topologies on different tasks. These contributions can advance our understanding of optimal AI-AI, human-human, and human-AI collaborative networks, inspiring future tools for fostering collective innovation in large organizations. | Reject |
ICLR.cc/2023/Conference | Prompt-Based Metric Learning for Few-Shot NER | Few-shot named entity recognition (NER) targets generalizing to unseen labels and/or domains with few labeled examples.
Existing metric learning methods compute token-level similarities between query and support sets, but are not able to fully incorporate label semantics into modeling. To address this issue, we propose a simple method to largely improve metric learning for NER: 1) multiple prompt schemas are designed to enhance label semantics; 2) we propose a novel architecture to effectively combine multiple prompt-based representations. Empirically, our method achieves new state-of-the-art (SOTA) results under 16 of the 18 considered settings, substantially outperforming the previous SOTA by an average of 8.84% and a maximum of 34.51% in relative gains of micro F1. | Withdrawn |
ICLR.cc/2022/Conference | Coresets for Kernel Clustering | We devise the first coreset for kernel $k$-Means, and use it to obtain new, more efficient, algorithms. Kernel $k$-Means has superior clustering capability compared to classical $k$-Means particularly when clusters are separable non-linearly, but it also introduces significant computational challenges. We address this computational issue by constructing a coreset, which is a reduced dataset that accurately preserves the clustering costs.
Our main result is the first coreset for kernel $k$-Means, whose size is independent of the number of input points $n$, and moreover is constructed in time near-linear in $n$. This result immediately implies new algorithms for kernel $k$-Means, such as a $(1+\epsilon)$-approximation in time near-linear in $n$, and a streaming algorithm using space and update time $\mathrm{poly}(k \epsilon^{-1} \log n)$.
We validate our coreset on various datasets with different kernels. Our coreset performs consistently well, achieving small errors while using very few points. We show that our coresets can speed up kernel $k$-Means++ (the kernelized version of the widely used $k$-Means++ algorithm), and we further use this faster kernel $k$-Means++ for spectral clustering. In both applications, we achieve up to 1000x speedup while the error is comparable to baselines that do not use coresets. | Reject |
ICLR.cc/2023/Conference | Enforcing Delayed-Impact Fairness Guarantees | Recent research has shown that seemingly fair machine learning models, when used to inform decisions that have an impact on people's lives or well-being (e.g., applications involving education, employment, and lending), can inadvertently increase social inequality in the long term. Existing fairness-aware algorithms consider static fairness constraints, such as equal opportunity or demographic parity, but enforcing constraints of this type may result in models that have a negative long-term impact on disadvantaged individuals and communities. We introduce ELF (Enforcing Long-term Fairness), the first classification algorithm that provides high-confidence fairness guarantees in terms of long-term, or delayed, impact. Importantly, ELF solves the open problem of providing such guarantees based only on historical data that includes observations of delayed impact. Prior methods, by contrast, require prior knowledge (or an estimate) of analytical models describing the relationship between a classifier's predictions and their corresponding delayed impact. We prove that ELF satisfies delayed-impact fairness constraints with high confidence and that it is guaranteed to identify a fair solution, if one exists, given sufficient data. We show empirically, using real-life data, that ELF can successfully mitigate long-term unfairness with high confidence. | Reject |
ICLR.cc/2020/Conference | Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity | We provide a theoretical explanation for the effectiveness of gradient clipping in training deep neural networks. The key ingredient is a new smoothness condition derived from practical neural network training examples. We observe that gradient smoothness, a concept central to the analysis of first-order optimization algorithms that is often assumed to be a constant, demonstrates significant variability along the training trajectory of deep neural networks. Further, this smoothness positively correlates with the gradient norm, and contrary to standard assumptions in the literature, it can grow with the norm of the gradient. These empirical observations limit the applicability of existing theoretical analyses of algorithms that rely on a fixed bound on smoothness. These observations motivate us to introduce a novel relaxation of gradient smoothness that is weaker than the commonly used Lipschitz smoothness assumption. Under the new condition, we prove that two popular methods, namely, gradient clipping and normalized gradient, converge arbitrarily faster than gradient descent with fixed stepsize. We further explain why such adaptively scaled gradient methods can accelerate empirical convergence and verify our results empirically in popular neural network training settings. | Accept (Talk) |
ICLR.cc/2020/Conference | Learning Surrogate Losses | The minimization of loss functions is the heart and soul of Machine Learning. In this paper, we propose an off-the-shelf optimization approach that can seamlessly minimize virtually any non-differentiable and non-decomposable loss function (e.g. Miss-classification Rate, AUC, F1, Jaccard Index, Mathew Correlation Coefficient, etc.). Our strategy learns smooth relaxation versions of the true losses by approximating them through a surrogate neural network. The proposed loss networks are set-wise models which are invariant to the order of mini-batch instances. Ultimately, the surrogate losses are learned jointly with the prediction model via bilevel optimization. Empirical results on multiple datasets with diverse real-life loss functions compared with state-of-the-art baselines demonstrate the efficiency of learning surrogate losses. | Reject |
ICLR.cc/2021/Conference | Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers | We detect out-of-training-distribution sentences in Neural Machine Translation using the Bayesian Deep Learning equivalent of Transformer models. For this we develop a new measure of uncertainty designed specifically for long sequences of discrete random variables—i.e. words in the output sentence. Our new measure of uncertainty solves a major intractability in the naive application of existing approaches on long sentences. We use our new measure on a Transformer model trained with dropout approximate inference. On the task of German-English translation using WMT13 and Europarl, we show that with dropout uncertainty our measure is able to identify when Dutch source sentences, sentences which use the same word types as German, are given to the model instead of German. | Reject |
ICLR.cc/2018/Conference | Kronecker-factored Curvature Approximations for Recurrent Neural Networks | Kronecker-factor Approximate Curvature (Martens & Grosse, 2015) (K-FAC) is a 2nd-order optimization method which has been shown to give state-of-the-art performance on large-scale neural network optimization tasks (Ba et al., 2017). It is based on an approximation to the Fisher information matrix (FIM) that makes assumptions about the particular structure of the network and the way it is parameterized. The original K-FAC method was applicable only to fully-connected networks, although it has been recently extended by Grosse & Martens (2016) to handle convolutional networks as well. In this work we extend the method to handle RNNs by introducing a novel approximation to the FIM for RNNs. This approximation works by modelling the covariance structure between the gradient contributions at different time-steps using a chain-structured linear Gaussian graphical model, summing the various cross-covariances, and computing the inverse in closed form. We demonstrate in experiments that our method significantly outperforms general purpose state-of-the-art optimizers like SGD with momentum and Adam on several challenging RNN training tasks. | Accept (Poster) |
ICLR.cc/2022/Conference | Bandits for Black-box Attacks to Graph Neural Networks with Structure Perturbation | Graph neural networks (GNNs) have achieved state-of-the-art performance in many graph-based tasks such as node classification and graph classification. However, many recent works have demonstrated that an attacker can mislead GNN models by slightly perturbing the graph structure. Existing attacks to GNNs are either under the less practical threat model where the attacker is assumed to access the GNN model parameters, or under the practical black-box threat model but consider perturbing node features that are shown to be not enough effective. In this paper, we aim to bridge this gap and consider black-box attacks to GNNs with structure perturbation. We address this challenge motivated by bandits. Specifically, we formulate our attack as an online optimization with bandit feedback. This original problem is essentially NP-hard due to the fact that perturbing the graph structure is a binary optimization problem. We then propose a bandit convex optimization based attack which is proven to be {sublinear} to the query number $T$, i.e., $\mathcal{O}(\sqrt{N}T^{3/4})$ where $N$ is the number of nodes in the graph. Finally, we evaluate our proposed attack algorithm by conducting experiments over multiple datasets and GNN models. The experimental results on various citation graphs and image graphs show that our attack is both effective and efficient. | Withdrawn |
ICLR.cc/2022/Conference | Accelerating HEP simulations with Neural Importance Sampling | Virtually all high-energy-physics (HEP) simulations for the LHC rely on Monte Carlo using importance sampling by means of the VEGAS algorithm. However, complex high-precision calculations have become a challenge for the standard toolbox.
As a result, there has been keen interest in HEP for modern machine learning to power adaptive sampling. Despite previous work proving that normalizing-flow-powered neural importance sampling (NIS) sometimes outperforms VEGAS, existing research has still left major questions open, which we intend to solve by introducing ZüNIS, a fully automated NIS library.
We first show how to extend the original formulation of NIS to reuse samples over multiple gradient steps, yielding a significant improvement for slow functions. We then benchmark ZüNIS over a range of problems and show high performance with limited fine-tuning. This is crucial for ZüNIS to be a mature tool for the wider HEP public. We outline how the the library allows for non-experts to employ it with minimal effort, an essential condition to widely assess the value of NIS for LHC simulations. | Reject |
ICLR.cc/2021/Conference | Pea-KD: Parameter-efficient and accurate Knowledge Distillation | How can we efficiently compress a model while maintaining its performance? Knowledge Distillation (KD) is one of the widely known methods for model compression. In essence, KD trains a smaller student model based on a larger teacher model and tries to retain the teacher model's level of performance as much as possible. However, the existing KD methods suffer from the following limitations. First, since the student model is small in absolute size, it inherently lacks model complexity. Second, the absence of an initial guide for the student model makes it difficult for the student to imitate the teacher model to its fullest. Conventional KD methods yield low performance due to these limitations.
In this paper, we propose Pea-KD (Parameter-efficient and accurate Knowledge Distillation), a novel approach to KD. Pea-KD consists of two main parts: Shuffled Parameter Sharing (SPS) and Pretraining with Teacher's Predictions (PTP). Using this combination, we are capable of alleviating the KD's limitations. SPS is a new parameter sharing method that allows greater model complexity for the student model. PTP is a KD-specialized initialization method, which can act as a good initial guide for the student. When combined, this method yields a significant increase in student model's performance. Experiments conducted on different datasets and tasks show that the proposed approach improves the student model's performance by 4.4% on average in four GLUE tasks, outperforming existing KD baselines by significant margins.
| Reject |
ICLR.cc/2023/Conference | ThinkSum: Probabilistic reasoning over sets using large language models | Large language models (LLMs) have a substantial capacity for high-level analogical reasoning: reproducing patterns in linear text that occur in their training data (zero-shot evaluation) or in the provided context (few-shot in-context learning). However, recent studies show that even the largest LLMs fail in scenarios that require reasoning over multiple objects or facts or making sequences of logical deductions. We propose a two-stage probabilistic inference paradigm, ThinkSum, that reasons over sets of objects or facts in a structured manner. In the first stage (Think -- 'fast' retrieval of associations), a LLM is queried in parallel over a set of phrases extracted from the prompt or an auxiliary model call. In the second stage (Sum -- 'slow' probabilistic inference or reasoning), the results of these queries are aggregated to make the final prediction. We demonstrate the advantages of ThinkSum on the BIG-bench suite of evaluation tasks, achieving improvements over the state of the art using GPT-family models on ten difficult tasks, often with far smaller model variants. We compare and contrast ThinkSum with other proposed modifications to direct prompting of LLMs, such as variants of chain-of-thought prompting. We argue that because the probabilistic inference in ThinkSum is performed outside of calls to the LLM, ThinkSum is less sensitive to prompt design, yields more interpretable predictions, and can be flexibly combined with latent variable models to extract structured knowledge from LLMs. | Reject |
ICLR.cc/2021/Conference | Rethinking Parameter Counting: Effective Dimensionality Revisited | Neural networks appear to have mysterious generalization properties when using parameter counting as a proxy for complexity. Indeed, neural networks often have many more parameters than there are data points, yet still provide good generalization performance. Moreover, when we measure generalization as a function of parameters, we see double descent behaviour, where the test error decreases, increases, and then again decreases. We show that many of these properties become understandable when viewed through the lens of effective dimensionality, which measures the dimensionality of the parameter space determined by the data. We relate effective dimensionality to posterior contraction in Bayesian deep learning, model selection, width-depth tradeoffs, double descent, and functional diversity in loss surfaces, leading to a richer understanding of the interplay between parameters and functions in deep models. We also show that effective dimensionality compares favourably to alternative norm- and flatness- based generalization measures. | Reject |
ICLR.cc/2020/Conference | PNEN: Pyramid Non-Local Enhanced Networks | Existing neural networks proposed for low-level image processing tasks are usually implemented by stacking convolution layers with limited kernel size. Every convolution layer merely involves in context information from a small local neighborhood. More contextual features can be explored as more convolution layers are adopted. However it is difficult and costly to take full advantage of long-range dependencies. We employ non-local operation to build up connection between every pixel and all remain pixels. Moreover a novel \emph{Pyramid Non-local Block} is devised to robustly estimate pairwise similarity coefficients between different scales of content patterns. Considering computation burden and memory consumption, we exploit embedding feature maps with coarser resolution to represent content patterns with larger spatial scale. Through elaborately combining the pyramid non-local blocks and dilated residual blocks, we set up a \emph{Pyramid Non-local Enhanced Network} for edge-preserving image smoothing. It achieves state-of-the-art performance in imitating three classical image smoothing algorithms. Additionally, the pyramid non-local block can be directly incorporated into existing convolution neural networks for other image processing tasks. We integrate it into two state-of-the-art methods for image denoising and single image super-resolution respectively, achieving consistently improved performance. | Withdrawn |
ICLR.cc/2023/Conference | Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels | Recent studies on contrastive learning have achieved remarkable performance solely by leveraging few labels in the context of medical image segmentation. Existing methods mainly focus on instance discrimination and invariant mapping (i.e., pulling positive samples closer and negative samples apart in the feature space). However, they face three common pitfalls: (1) tailness: medical image data usually follows an implicit long-tail class distribution. Blindly leveraging all pixels in training hence can lead to the data imbalance issues, and cause deteriorated performance; (2) consistency: it remains unclear whether a segmentation model has learned meaningful and yet consistent anatomical features due to the intra-class variations between different anatomical features; and (3) diversity: the intra-slice correlations within the entire dataset have received significantly less attention. This motivates us to seek a principled approach for strategically making use of the dataset itself to discover similar yet distinct samples from different anatomical views. In this paper, we introduce a novel semi-supervised medical image segmentation framework termed Mine yOur owN Anatomy (MONA), and make three contributions. First, prior work argues that every pixel equally matters to the model training; we observe empirically that this alone is unlikely to define meaningful anatomical features, mainly due to lacking the supervision signal. We show two simple solutions towards learning invariances -- through the use of stronger data augmentations and nearest neighbors. Second, we construct a set of objectives that encourage the model to be capable of decomposing medical images into a collection of anatomical features in an unsupervised manner. Lastly, our extensive results on three benchmark datasets with different labeled settings validate the effectiveness of our proposed MONA which achieves new state-of-the-art under different labeled settings. Perhaps most impressively, MONA trained with 10% labeled -- for the first time -- outperforms the supervised counterpart on all three datasets. MONA makes minimal assumptions on domain expertise, and hence constitutes a practical and versatile solution in medical image analysis. Codes will be made available to the public. | Withdrawn |
ICLR.cc/2023/Conference | Guiding Safe Exploration with Weakest Preconditions | In reinforcement learning for safety-critical settings, it is often desirable for the agent to obey safety constraints at all points in time, including during training. We present a novel neurosymbolic approach called SPICE to solve this safe exploration problem. SPICE uses an online shielding layer based on symbolic weakest preconditions to achieve a more precise safety analysis than existing tools without unduly impacting the training process. We evaluate the approach on a suite of continuous control benchmarks and show that it can achieve comparable performance to existing safe learning techniques while incurring fewer safety violations. Additionally, we present theoretical results showing that SPICE converges to the optimal safe policy under reasonable assumptions. | Accept: poster |
ICLR.cc/2023/Conference | The Role of ImageNet Classes in Fréchet Inception Distance | Fréchet Inception Distance (FID) is the primary metric for ranking models in data-driven generative modeling. While remarkably successful, the metric is known to sometimes disagree with human judgement. We investigate a root cause of these discrepancies, and visualize what FID "looks at" in generated images. We show that the feature space that FID is (typically) computed in is so close to the ImageNet classifications that aligning the histograms of Top-$N$ classifications between sets of generated and real images can reduce FID substantially — without actually improving the quality of results. Thus, we conclude that FID is prone to intentional or accidental distortions. As a practical example of an accidental distortion, we discuss a case where an ImageNet pre-trained FastGAN achieves a FID comparable to StyleGAN2, while being worse in terms of human evaluation. | Accept: notable-top-25% |
ICLR.cc/2020/Conference | TrojanNet: Exposing the Danger of Trojan Horse Attack on Neural Networks | The complexity of large-scale neural networks can lead to poor understanding of their internal details. We show that this opaqueness provides an opportunity for adversaries to embed unintended functionalities into the network in the form of Trojan horse attacks. Our novel framework hides the existence of a malicious network within a benign transport network. Our attack is flexible, easy to execute, and difficult to detect. We prove theoretically that the malicious network's detection is computationally infeasible and demonstrate empirically that the transport network does not compromise its disguise. Our attack exposes an important, previously unknown loophole that unveils a new direction in machine learning security. | Reject |
ICLR.cc/2023/Conference | Using the Training History to Detect and Prevent Overfitting in Deep Learning Models | Overfitting of deep learning models on training data leads to poor generalizability on unseen data. Overfitting can be (1) prevented (e.g., using dropout or early stopping) or (2) detected in a trained model (e.g., using correlation-based methods). We propose a method that can both detect and prevent overfitting based on the training history (i.e., validation losses). Our method first trains a time series classifier on training histories of overfit models. This classifier is then used to detect if a trained model is overfit. In addition, our trained classifier can be used to prevent overfitting by identifying the optimal point to stop a model's training. We evaluate our method on its ability to identify and prevent overfitting in real-world samples (collected from papers published in the last 5 years at top AI venues). We compare our method against correlation-based detection methods and the most commonly used prevention method (i.e., early stopping). Our method achieves an F1 score of 0.91 which is at least 5% higher than the current best-performing non-intrusive overfitting detection method. In addition, our method can find the optimal stopping point and avoid overfitting at least 32% earlier than early stopping and achieve at least the same accuracy (often better) as early stopping. | Reject |
ICLR.cc/2021/Conference | Teaching Temporal Logics to Neural Networks | We study two fundamental questions in neuro-symbolic computing: can deep learning tackle challenging problems in logics end-to-end, and can neural networks learn the semantics of logics. In this work we focus on linear-time temporal logic (LTL), as it is widely used in verification. We train a Transformer on the problem to directly predict a solution, i.e. a trace, to a given LTL formula. The training data is generated with classical solvers, which, however, only provide one of many possible solutions to each formula. We demonstrate that it is sufficient to train on those particular solutions to formulas, and that Transformers can predict solutions even to formulas from benchmarks from the literature on which the classical solver timed out. Transformers also generalize to the semantics of the logics: while they often deviate from the solutions found by the classical solvers, they still predict correct solutions to most formulas. | Accept (Poster) |
ICLR.cc/2020/Conference | CLAREL: classification via retrieval loss for zero-shot learning | We address the problem of learning fine-grained cross-modal representations. We propose an instance-based deep metric learning approach in joint visual and textual space. The key novelty of this paper is that it shows that using per-image semantic supervision leads to substantial improvement in zero-shot performance over using class-only supervision. On top of that, we provide a probabilistic justification for a metric rescaling approach that solves a very common problem in the generalized zero-shot learning setting, i.e., classifying test images from unseen classes as one of the classes seen during training. We evaluate our approach on two fine-grained zero-shot learning datasets: CUB and FLOWERS. We find that on the generalized zero-shot classification task CLAREL consistently outperforms the existing approaches on both datasets. | Reject |
ICLR.cc/2023/Conference | BigVGAN: A Universal Neural Vocoder with Large-Scale Training | Despite recent progress in generative adversarial network (GAN)-based vocoders, where the model generates raw waveform conditioned on acoustic features, it is challenging to synthesize high-fidelity audio for numerous speakers across various recording environments. In this work, we present BigVGAN, a universal vocoder that generalizes well for various out-of-distribution scenarios without fine-tuning. We introduce periodic activation function and anti-aliased representation into the GAN generator, which brings the desired inductive bias for audio synthesis and significantly improves audio quality. In addition, we train our GAN vocoder at the largest scale up to 112M parameters, which is unprecedented in the literature. We identify and address the failure modes in large-scale GAN training for audio, while maintaining high-fidelity output without over-regularization. Our BigVGAN, trained only on clean speech (LibriTTS), achieves the state-of-the-art performance for various zero-shot (out-of-distribution) conditions, including unseen speakers, languages, recording environments, singing voices, music, and instrumental audio. We release our code and model at: https://github.com/NVIDIA/BigVGAN | Accept: poster |
ICLR.cc/2020/Conference | Progressive Upsampling Audio Synthesis via Effective Adversarial Training | This paper proposes a novel generative model called PUGAN, which progressively synthesizes high-quality audio in a raw waveform. PUGAN leverages on the recently proposed idea of progressive generation of higher-resolution images by stacking multiple encode-decoder architectures. To effectively apply it to raw audio generation, we propose two novel modules: (1) a neural upsampling layer and (2) a sinc convolutional layer. Compared to the existing state-of-the-art model called WaveGAN, which uses a single decoder architecture, our model generates audio signals and converts them in a higher resolution in a progressive manner, while using a significantly smaller number of parameters, e.g., 20x smaller for 44.1kHz output, than an existing technique called WaveGAN. Our experiments show that the audio signals can be generated in real-time with the comparable quality to that of WaveGAN with respect to the inception scores and the human evaluation. | Reject |
ICLR.cc/2021/Conference | ACT: Asymptotic Conditional Transport | We propose conditional transport (CT) as a new divergence to measure the difference between two probability distributions. The CT divergence consists of the expected cost of a forward CT, which constructs a navigator to stochastically transport a data point of one distribution to the other distribution, and that of a backward CT which reverses the transport direction. To apply it to the distributions whose probability density functions are unknown but random samples are accessible, we further introduce asymptotic CT (ACT), whose estimation only requires access to mini-batch based discrete empirical distributions. Equipped with two navigators that amortize the computation of conditional transport plans, the ACT divergence comes with unbiased sample gradients that are straightforward to compute, making it amenable to mini-batch stochastic gradient descent based optimization. When applied to train a generative model, the ACT divergence is shown to strike a good balance between mode covering and seeking behaviors and strongly resist mode collapse. To model high-dimensional data, we show that it is sufficient to modify the adversarial game of an existing generative adversarial network (GAN) to a game played by a generator, a forward navigator, and a backward navigator, which try to minimize a distribution-to-distribution transport cost by optimizing both the distribution of the generator and conditional transport plans specified by the navigators, versus a critic that does the opposite by inflating the point-to-point transport cost. On a wide variety of benchmark datasets for generative modeling, substituting the default statistical distance of an existing GAN with the ACT divergence is shown to consistently improve the performance. | Reject |
ICLR.cc/2021/Conference | Domain Knowledge in Exploration Noise in AlphaZero | The AlphaZero algorithm has achieved remarkable success in a variety of sequential, perfect information games including Go, Shogi and Chess. In the original paper the only hyperparameter that is changed from game to game is the $\alpha$ parameter governing a search prior. In this paper we investigate the properties of this hyperparameter. First, we build a formal intuition for its behavior on a toy example meant to isolate the influence of $\alpha$. Then, by comparing performance of AlphaZero agents with different $\alpha$ values on Connect 4, we show that the performance of AlphaZero improves considerably with a good choice of $\alpha$. This all highlights the importance of $\alpha$ as an interpretable hyperparameter which allows for cross-game tuning that more opaque hyperparameters like model architecture may not. | Withdrawn |
ICLR.cc/2022/Conference | WaveMix: Multi-Resolution Token Mixing for Images | Even though vision transformers (ViTs) have provided state-of-the-art results on image classification, their requirements of large data, model size, and GPU usage have put them out of reach of most practitioners of computer vision. We present WaveMix as an alternative to self-attention mechanisms in ViT and convolutional neural networks to significantly reduce computational costs and memory footprint without compromising on image classification accuracy. WaveMix uses a multi-level two-dimensional discrete wavelet transform for mixing tokens and aggregating multi-resolution pixel information over long distances, which gives it the following advantages. Firstly, unlike the self-attention mechanism of ViT, WaveMix does not unroll the image. Thus, it has the right inductive bias to utilize the 2-D structure of an image, which reduces the demand for large training data. Additionally, the quadratic complexity with respect to sequence length is also eliminated. Secondly, due to its multi-resolution token-mixing, WaveMix also requires much fewer layers than a CNN does for comparable accuracy. Preliminary results from our experiments on supervised learning using CIFAR-10 dataset show that a four-layer WaveMix model can be 37% more accurate than a ViT with a comparable number of parameters, while consuming only 3% of the latter's GPU RAM and memory. This model also performs better than efficient transformers and models not based on attention, such as, FNet, and MLP Mixer. Scaling up the WaveMix model to achieve a top-1 accuracy of over 85% on CIFAR-10 could be done on a 16 GB GPU, while consuming only 6% of the GPU RAM used by the largest ViT which could fit in that GPU. Our work suggests that research on model structures that exploit the right inductive bias is far from over, and that such models can enable the training of computer vision models in settings with limited GPU resources. | Withdrawn |
ICLR.cc/2023/Conference | Mixed Federated Learning: Joint Decentralized and Centralized Learning | Federated learning (FL) enables learning from decentralized privacy-sensitive data, with computations on raw data confined to take place at edge clients. This paper introduces mixed FL, which incorporates an additional loss term calculated at the coordinating server (while maintaining FL’s private data restrictions). For example, additional datacenter data can be leveraged to jointly learn from centralized (datacenter) and decentralized (federated) training data and better match an expected inference data distribution.Mixed FL also enables offloading some intensive computations (e.g., embedding regularization) to the server, greatly reducing communication and client computation load. For these and other mixed FL use cases, we present three algorithms: PARALLEL TRAINING, 1-WAY GRADIENT TRANSFER, and 2-WAY GRADIENT TRANSFER. We perform extensive experiments of the algorithms on three tasks, demonstrating that mixed FL can blend training data to achieve an oracle’s accuracy on an inference distribution, and can reduce communication and computation overhead by more than 90%. Finally, we state convergence bounds for all algorithms, and give intuition on the mixed FL problems best suited to each. The theory confirms our empirical observations of how the algorithms perform under different mixed FL problem settings. | Reject |
ICLR.cc/2023/Conference | Understanding Multi-Task Scaling in Machine Translation | In this work, we provide a large-scale empirical study of the scaling properties of multilingual (multitask) neural machine translation models. We examine how increases in the model size affect the model performance and investigate the role of the individual task weights on the scaling behavior. We find that these weights only affect the multiplicative factor of the scaling law and in particular, the scaling exponent is unaffected by them. Through a novel joint scaling law formulation, we compute the effective number of parameters allocated to each task and examine the role of language similarity in the scaling behavior of our models. We find minimal evidence that language similarity has any impact. In contrast, ``direction'' of the multilinguality plays a big role, with models translating from multiple languages into English having a larger number of effective parameters per task than their reversed counterparts. Finally, we leverage our observations to predict the performance of multilingual models trained with any language weighting at any scale, greatly reducing efforts required for task balancing in large multitask models. Our findings apply to both in-domain and out-of-domain test sets and to multiple evaluation metrics, such as ChrF and BLEURT. | Reject |
ICLR.cc/2020/Conference | STABILITY AND CONVERGENCE THEORY FOR LEARNING RESNET: A FULL CHARACTERIZATION | ResNet structure has achieved great success since its debut. In this paper, we study the stability of learning ResNet. Specifically, we consider the ResNet block $h_l = \phi(h_{l-1}+\tau\cdot g(h_{l-1}))$ where $\phi(\cdot)$ is ReLU activation and $\tau$ is a scalar. We show that for standard initialization used in practice, $\tau =1/\Omega(\sqrt{L})$ is a sharp value in characterizing the stability of forward/backward process of ResNet, where $L$ is the number of residual blocks. Specifically, stability is guaranteed for $\tau\le 1/\Omega(\sqrt{L})$ while conversely forward process explodes when $\tau>L^{-\frac{1}{2}+c}$ for a positive constant $c$. Moreover, if ResNet is properly over-parameterized, we show for $\tau \le 1/\tilde{\Omega}(\sqrt{L})$ gradient descent is guaranteed to find the global minima \footnote{We use $\tilde{\Omega}(\cdot)$ to hide logarithmic factor.}, which significantly enlarges the range of $\tau\le 1/\tilde{\Omega}(L)$ that admits global convergence in previous work. We also demonstrate that the over-parameterization requirement of ResNet only weakly depends on the depth, which corroborates the advantage of ResNet over vanilla feedforward network. Empirically, with $\tau\le1/\sqrt{L}$, deep ResNet can be easily trained even without normalization layer. Moreover, adding $\tau=1/\sqrt{L}$ can also improve the performance of ResNet with normalization layer. | Reject |
ICLR.cc/2022/Conference | GPT-Critic: Offline Reinforcement Learning for End-to-End Task-Oriented Dialogue Systems | Training a task-oriented dialogue agent can be naturally formulated as offline reinforcement learning (RL) problem, where the agent aims to learn a conversational strategy to achieve user goals, only from a dialogue corpus. It is very challenging in terms of RL since the natural language action space is astronomical, while feasible (syntactically and semantically correct) actions are very sparse. Thus, standard RL methods easily fail and generate responses diverging from human language, even when fine-tuning a powerful pre-trained language model. In this paper, we introduce GPT-Critic, an offline RL method for task-oriented dialogue. GPT-Critic is built upon GPT-2, fine-tuning the language model through behavior cloning of the critic-guided self-generated sentences. GPT-Critic is essentially free from the issue of diverging from human language since it learns from the sentences sampled from the pre-trained language model. In the experiments, we demonstrate that our algorithm outperforms the state-of-the-art in the task-oriented dialogue benchmarks including MultiWOZ 2.0 and ConvLab. | Accept (Poster) |
ICLR.cc/2023/Conference | ProtoVAE: Using Prototypical Networks for Unsupervised Disentanglement | Generative modeling and self-supervised learning have in recent years made great strides towards learning from data in a completely \emph{unsupervised} way. There is still, however, an open area of investigation into guiding the neural network to learn useful or good representations. The problem of unsupervised \textit{Disentanglement} is of particular importance as it offers to learn interpretable representations, with disjoint subsets of the representation encoding different, meaningful factors of variation. Recent work has theoretically grounded the factors of variation, via the lens of group theory, as disentangled actions of the symmetry subgroups which transform only the correspond subspaces of the disentangled representation. We use this mathematical formalism instead to impose constraints on the representations learned by a unsupervised generative neural network, such that transformations of the representation correspond to the actions of a unique symmetry subgroup. To this end, we introduce a novel model, ProtoVAE, that leverages a deep metric learning Prototypical network trained via self-supervision to constrain the latent space of a Variational Autoencoder to decompose into independent subspaces. Further, we actively change or \textit{intervene} in the latent space during training to enforce each dimension of the representation to uniquely and consistently transform the data corresponding to some symmetry subgroup. We demonstrate and evaluate our proposed model on the benchmark DSprites and 3DShapes datasets and compare with other state of the art disentanglement methods via qualitative traversals in the latent space, as well as quantitative disentanglement metrics. We further qualitatively demonstrate the effectiveness of our model on the real-world datasets CelebA which consistently encodes the different factors. | Reject |
ICLR.cc/2022/Conference | Spatial Frequency Sensitivity Regularization for Robustness | The ability to generalize to out-of-distribution data is a major challenge for modern deep neural networks. Recent work has shown that deep neural networks latch on to superficial Fourier statistics of the training data and fail to generalize when these statistics change, such as when images are subject to common corruptions. In this paper, we study the frequency characteristics of deep neural networks in order to improve their robustness. We first propose a general measure of a model's $\textit{\textbf{spatial frequency sensitivity}}$ based on its input-Jacobian represented in the Fourier-basis. When applied to deep neural networks, we find that standard minibatch training consistently leads to increased sensitivity towards particular spatial frequencies independent of network architecture. We further propose a family of $\textit{\textbf{spatial frequency regularizers}}$ based on our proposed measure to induce specific spatial frequency sensitivities in a model. In experiments on datasets with out-of-distribution test images arising from various common image corruptions, we find that deep neural networks trained with our proposed regularizers obtain significantly improved classification accuracy while maintaining high accuracy on in-distribution clean test images. | Reject |
ICLR.cc/2022/Conference | Accelerating Stochastic Simulation with Interactive Neural Processes | Stochastic simulations such as large-scale, spatiotemporal, age-structured epidemic models are computationally expensive at fine-grained resolution. We propose Interactive Neural Process (INP), a Bayesian active learning framework to proactively learn a deep learning surrogate model and accelerate simulation. Our framework is based on the novel integration of neural process, deep sequence model and active learning. In particular, we develop a novel spatiotemporal neural process model to mimic the simulator dynamics. Our model automatically infers the latent process which describes the intrinsic uncertainty of the simulator. This also gives rise to a new acquisition function based on the latent information gain. We design Bayesian active learning algorithms to iteratively query the simulator, gather more data, and continuously improve the model. We perform theoretical analysis and demonstrate that our approach reduces sample complexity compared with random sampling in high dimension. Empirically, we demonstrate our framework can faithfully imitate the behavior of a complex infectious disease simulator with a small number of examples, enabling rapid simulation and scenario exploration. | Withdrawn |
ICLR.cc/2022/Conference | Semi-supervised Long-tailed Recognition using Alternate Sampling | Main challenges in long-tailed recognition come from the imbalanced data distribution and sample scarcity in its tail classes. While techniques have been proposed to achieve a more balanced training loss and to improve tail classes data variations with synthesized samples, we resort to leverage readily available unlabeled data to boost recognition accuracy. The idea leads to a new recognition setting, namely semi-supervised long-tailed recognition. We argue this setting better resembles the real-world data collection and annotation process and hence can help close the gap to real-world scenarios. To address the semi-supervised long-tailed recognition problem, we present an alternate sampling framework combining the intuitions from successful methods in these two research areas. The classifier and feature embedding are learned separately and updated iteratively. The class-balanced sampling strategy has been implemented to train the classifier in a way not affected by the pseudo labels' quality on the unlabeled data. A consistency loss has been introduced to limit the impact from unlabeled data while leveraging them to update the feature embedding. We demonstrate significant accuracy improvements over other competitive methods on two datasets. | Reject |
ICLR.cc/2022/Conference | Self-Supervision is All You Need for Solving Rubik's Cube | While combinatorial problems are of great academic and practical importance, previous approaches like explicit heuristics and reinforcement learning have been complex and costly. To address this, we developed a simple and robust method to train a Deep Neural Network (DNN) through self-supervised learning for solving a goal-predefined combinatorial problem. Assuming that more optimal moves occur more frequently as a path of random moves connecting two problem states, the DNN can approximate an optimal solver by learning to predict the last move of a random scramble based on the problem state. Tested on 1,000 scrambled Rubik's Cube instances, a Transformer-based model could solve all of them near-optimally using a breadth-first search; with a maximum breadth of $10^3$, the mean solution length was $20.5$ moves. The proposed method may apply to other goal-predefined combinatorial problems, though it has a few constraints. | Withdrawn |
ICLR.cc/2021/Conference | Mathematical Reasoning via Self-supervised Skip-tree Training | We demonstrate that self-supervised language modeling applied to mathematical formulas enables logical reasoning. To measure the logical reasoning abilities of language models, we formulate several evaluation (downstream) tasks, such as inferring types, suggesting missing assumptions and completing equalities. For training language models for formal mathematics, we propose a novel skip-tree task. We find that models trained on the skip-tree task show surprisingly strong mathematical reasoning abilities, and outperform models trained on standard skip-sequence tasks. We also analyze the models' ability to formulate new conjectures by measuring how often the predictions are provable and useful in other proofs. | Accept (Spotlight) |
ICLR.cc/2018/Conference | Discovery of Predictive Representations With a Network of General Value Functions | The ability of an agent to {\em discover} its own learning objectives has long been considered a key ingredient for artificial general intelligence. Breakthroughs in autonomous decision making and reinforcement learning have primarily been in domains where the agent's goal is outlined and clear: such as playing a game to win, or driving safely. Several studies have demonstrated that learning extramural sub-tasks and auxiliary predictions can improve (1) single human-specified task learning, (2) transfer of learning, (3) and the agent's learned representation of the world. In all these examples, the agent was instructed what to learn about. We investigate a framework for discovery: curating a large collection of predictions, which are used to construct the agent's representation of the world. Specifically, our system maintains a large collection of predictions, continually pruning and replacing predictions. We highlight the importance of considering stability rather than convergence for such a system, and develop an adaptive, regularized algorithm towards that aim. We provide several experiments in computational micro-worlds demonstrating that this simple approach can be effective for discovering useful predictions autonomously. | Reject |
ICLR.cc/2019/Conference | Meta-Learning with Individualized Feature Space for Few-Shot Classification | Meta-learning provides a promising learning framework to address few-shot classification tasks. In existing meta-learning methods, the meta-learner is designed to learn about model optimization, parameter initialization, or similarity metric. Differently, in this paper, we propose to learn how to create an individualized feature embedding specific to a given query image for better classifying, i.e., given a query image, a specific feature embedding tailored for its characteristics is created accordingly, leading to an individualized feature space in which the query image can be more accurately classified. Specifically, we introduce a kernel generator as meta-learner to learn to construct feature embedding for query images. The kernel generator acquires meta-knowledge of generating adequate convolutional kernels for different query images during training, which can generalize to unseen categories without fine-tuning. In two standard few-shot classification data sets, i.e. Omniglot, and \emph{mini}ImageNet, our method shows highly competitive performance. | Reject |
ICLR.cc/2023/Conference | SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient | Many deep learning applications benefit from using large models with billions of parameters. Training these models is notoriously expensive due to the need for specialized HPC clusters. In this work, we consider alternative setups for training large models: using cheap ``preemptible'' instances or pooling existing resources from multiple regions. We analyze the performance of existing model-parallel algorithms in these conditions and find configurations where training larger models becomes less communication-intensive. Based on these findings, we propose SWARM Parallelism (Stochastically Wired Adaptively Rebalanced Model Parallelism), a model-parallel training algorithm designed for poorly connected, heterogeneous and unreliable devices. SWARM creates temporary randomized pipelines between nodes that are rebalanced in case of failure. We empirically validate our findings and compare SWARM Parallelism with existing large-scale training approaches. Finally, we combine our insights with compression strategies to train a large Transformer language model with 1B shared parameters ($\approx$13B before sharing) on preemptible T4 GPUs with less than 200 Mb/s network. | Reject |
ICLR.cc/2021/Conference | MSFM: Multi-Scale Fusion Module for Object Detection | Feature fusion is beneficial to object detection tasks in two folds. On one hand, detail and position information can be combined with semantic information when high and low-resolution features from shallow and deep layers are fused. On the other hand, objects can be detected in different scales, which improves the robustness of the framework. In this work, we present a Multi-Scale Fusion Module (MSFM) that extracts both detail and semantical information from a single input but at different scales within the same layer. Specifically, the input of the module will be resized into different scales on which position and semantic information will be processed, and then they will be rescaled back and combined with the module input. The MSFM is lightweight and can be used as a drop-in layer to many existing object detection frameworks. Experiments show that MSFM can bring +2.5% mAP improvement with only 2.4M extra parameters on Faster R-CNN with ResNet-50 FPN backbone on COCO Object Detection minival set, outperforming that with ResNet-101 FPN backbone without the module which obtains +2.0% mAP with 19.0M extra parameters. The best resulting model achieves a 45.7% mAP on test-dev set. Code will be available. | Reject |
ICLR.cc/2021/Conference | Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks | Neural networks (NNs) whose subnetworks implement reusable functions are expected to offer numerous advantages, including compositionality through efficient recombination of functional building blocks, interpretability, preventing catastrophic interference, etc. Understanding if and how NNs are modular could provide insights into how to improve them. Current inspection methods, however, fail to link modules to their functionality. In this paper, we present a novel method based on learning binary weight masks to identify individual weights and subnets responsible for specific functions. Using this powerful tool, we contribute an extensive study of emerging modularity in NNs that covers several standard architectures and datasets. We demonstrate how common NNs fail to reuse submodules and offer new insights into the related issue of systematic generalization on language tasks. | Accept (Poster) |
ICLR.cc/2021/Conference | Selective Sensing: A Data-driven Nonuniform Subsampling Approach for Computation-free On-Sensor Data Dimensionality Reduction | Designing an on-sensor data dimensionality reduction scheme for efficient signal sensing has always been a challenging task. Compressive sensing is a state-of-the-art sensing technique used for on-sensor data dimensionality reduction. However, the undesired computational complexity involved in the sensing stage of compressive sensing limits its practical application in resource-constrained sensor devices or high-data-rate sensor devices dealing with high-dimensional signals. In this paper, we propose a selective sensing framework that adopts the novel concept of data-driven nonuniform subsampling to reduce the dimensionality of acquired signals while retaining the information of interest in a computation-free fashion. Selective sensing adopts a co-optimization methodology to co-train a selective sensing operator with a subsequent information decoding neural network. We take image as the sensing modality and reconstruction as the information decoding task to demonstrate the 1st proof-of-concept of selective sensing. The experiment results on CIFAR10, Set5 and Set14 datasets show that selective sensing can achieve an average reconstruction accuracy improvement in terms of PSNR/SSIM by 3.73dB/0.07 and 9.43dB/0.16 over compressive sensing and uniform subsampling counterparts across the compression ratios of 4-32x, respectively. Source code is available at https://figshare.com/s/519a923fae8f386d7f5b | Reject |
ICLR.cc/2023/Conference | Thresholded Lexicographic Ordered Multi-Objective Reinforcement Learning | Lexicographic multi-objective problems, which impose a lexicographic importance order over the objectives, arise in many real-life scenarios. Existing Reinforcement Learning work directly addressing lexicographic tasks has been scarce. The few proposed approaches were all noted to be heuristics without theoretical guarantees as the Bellman equation is not applicable to them. Additionally, the practical applicability of these prior approaches also suffers from various issues such as not being able to reach the goal state. While some of these issues have been known before, in this work we investigate further shortcomings, and propose fixes for improving practical performance in many cases. We also present a policy optimization approach using our Lexicographic Projection Optimization (LPO) algorithm that has the potential to address these theoretical and practical concerns. Finally, we demonstrate our proposed algorithms on benchmark problems. | Reject |
ICLR.cc/2022/Conference | A Sampling-Free Approximation of Gaussian Variational Auto-Encoders | We propose a sampling-free approximate formulation of Gaussian variational auto-encoders. Instead of computing the loss via stochastic sampling, we propagate the Gaussian distributions from the latent space into the output space. As computing the exact likelihood probability is intractable, we propose to locally approximate the decoder network by its Taylor series. We demonstrate that this approximation allows us to approximate the Gaussian variational auto-encoder training objective in closed form. We evaluate the proposed method on the CelebA, the 3D Chairs, and the MNIST data sets. We find that our sampling-free approximation performs better than its sampling counterpart on the Frechet inception distance and on par on the estimated marginal likelihood. | Reject |
ICLR.cc/2021/Conference | MultiModalQA: complex question answering over text, tables and images | When answering complex questions, people can seamlessly combine information from visual, textual and tabular sources.
While interest in models that reason over multiple pieces of evidence has surged in recent years, there has been relatively little work on question answering models that reason across multiple modalities.
In this paper, we present MultiModalQA (MMQA): a challenging question answering dataset that requires joint reasoning over text, tables and images.
We create MMQA using a new framework for generating complex multi-modal questions at scale, harvesting tables from Wikipedia, and attaching images and text paragraphs using entities that appear in each table. We then define a formal language that allows us to take questions that can be answered from a single modality, and combine them to generate cross-modal questions. Last, crowdsourcing workers take these automatically generated questions and rephrase them into more fluent language.
We create 29,918 questions through this procedure, and empirically demonstrate the necessity of a multi-modal multi-hop approach to solve our task: our multi-hop model, ImplicitDecomp, achieves an average F1 of 51.7 over cross-modal questions, substantially outperforming a strong baseline that achieves 38.2 F1, but still lags significantly behind human performance, which is at 90.1 F1. | Accept (Poster) |
ICLR.cc/2023/Conference | Heterogeneous Loss Function with Aggressive Rejection for Contaminated data in anomaly detection | A training clean dataset, which consists of only normal data, is crucial for detecting anomalous data. However, a clean dataset is challenging to produce in practice. Here, heterogeneous loss function with aggressive rejection is proposed, which strengthens robustness against contamination. Aggressive rejection constrains training on the intersection of normal and abnormal distributions to handle the potential anomalies. Heterogeneous loss function utilizes an adaptive mini-batch stochastic choice of an order of asymptotic polynomial of GA loss, which dynamically optimizes the gradient for the intersection further. Through the proposed method, mean square error based models can outperform various robust loss functions and generate comparable performance with robust models for contaminated data on three image datasets. | Withdrawn |
ICLR.cc/2019/Conference | NOODL: Provable Online Dictionary Learning and Sparse Coding | We consider the dictionary learning problem, where the aim is to model the given data as a linear combination of a few columns of a matrix known as a dictionary, where the sparse weights forming the linear combination are known as coefficients. Since the dictionary and coefficients, parameterizing the linear model are unknown, the corresponding optimization is inherently non-convex. This was a major challenge until recently, when provable algorithms for dictionary learning were proposed. Yet, these provide guarantees only on the recovery of the dictionary, without explicit recovery guarantees on the coefficients. Moreover, any estimation error in the dictionary adversely impacts the ability to successfully localize and estimate the coefficients. This potentially limits the utility of existing provable dictionary learning methods in applications where coefficient recovery is of interest. To this end, we develop NOODL: a simple Neurally plausible alternating Optimization-based Online Dictionary Learning algorithm, which recovers both the dictionary and coefficients exactly at a geometric rate, when initialized appropriately. Our algorithm, NOODL, is also scalable and amenable for large scale distributed implementations in neural architectures, by which we mean that it only involves simple linear and non-linear operations. Finally, we corroborate these theoretical results via experimental evaluation of the proposed algorithm with the current state-of-the-art techniques. | Accept (Poster) |
ICLR.cc/2018/Conference | Video Action Segmentation with Hybrid Temporal Networks | Action segmentation as a milestone towards building automatic systems to understand untrimmed videos has received considerable attention in the recent years. It is typically being modeled as a sequence labeling problem but contains intrinsic and sufficient differences than text parsing or speech processing. In this paper, we introduce a novel hybrid temporal convolutional and recurrent network (TricorNet), which has an encoder-decoder architecture: the encoder consists of a hierarchy of temporal convolutional kernels that capture the local motion changes of different actions; the decoder is a hierarchy of recurrent neural networks that are able to learn and memorize long-term action dependencies after the encoding stage. Our model is simple but extremely effective in terms of video sequence labeling. The experimental results on three public action segmentation datasets have shown that the proposed model achieves superior performance over the state of the art. | Reject |
ICLR.cc/2022/Conference | Rethinking Positional Encoding | It is well noted that coordinate based MLPs benefit greatly -- in terms of preserving high-frequency information -- through the encoding of coordinate positions as an array of Fourier features. Hitherto, the rationale for the effectiveness of these positional encodings has been solely studied through a Fourier lens. In this paper, we strive to broaden this understanding by showing that alternative non-Fourier embedding functions can indeed be used for positional encoding. Moreover, we show that their performance is entirely determined by a trade-off between the stable rank of the embedded matrix and the distance preservation between embedded coordinates. We further establish that the now ubiquitous Fourier feature mapping of position is a special case that fulfills these conditions. Consequently, we present a more general theory to analyze positional encoding in terms of shifted basis functions. To this end, we develop the necessary theoretical formulae and empirically verify that our theoretical claims hold in practice. | Withdrawn |
ICLR.cc/2021/Conference | A Critique of Self-Expressive Deep Subspace Clustering | Subspace clustering is an unsupervised clustering technique designed to cluster data that is supported on a union of linear subspaces, with each subspace defining a cluster with dimension lower than the ambient space. Many existing formulations for this problem are based on exploiting the self-expressive property of linear subspaces, where any point within a subspace can be represented as linear combination of other points within the subspace. To extend this approach to data supported on a union of non-linear manifolds, numerous studies have proposed learning an embedding of the original data using a neural network which is regularized by a self-expressive loss function on the data in the embedded space to encourage a union of linear subspaces prior on the data in the embedded space. Here we show that there are a number of potential flaws with this approach which have not been adequately addressed in prior work. In particular, we show the model formulation is often ill-posed in that it can lead to a degenerate embedding of the data, which need not correspond to a union of subspaces at all and is poorly suited for clustering. We validate our theoretical results experimentally and also repeat prior experiments reported in the literature, where we conclude that a significant portion of the previously claimed performance benefits can be attributed to an ad-hoc post processing step rather than the deep subspace clustering model. | Accept (Poster) |
ICLR.cc/2020/Conference | Monotonic Multihead Attention | Simultaneous machine translation models start generating a target sequence before they have encoded or read the source sequence. Recent approach for this task either apply a fixed policy on transformer, or a learnable monotonic attention on a weaker recurrent neural network based structure. In this paper, we propose a new attention mechanism, Monotonic Multihead Attention (MMA), which introduced the monotonic attention mechanism to multihead attention. We also introduced two novel interpretable approaches for latency control that are specifically designed for multiple attentions. We apply MMA to the simultaneous machine translation task and demonstrate better latency-quality tradeoffs compared to MILk, the previous state-of-the-art approach.
| Accept (Poster) |
ICLR.cc/2023/Conference | From Points to Functions: Infinite-dimensional Representations in Diffusion Models | Diffusion-based generative models learn to iteratively transfer unstructured noise to a complex target distribution as opposed to Generative Adversarial Networks (GANs) or the decoder of Variational Autoencoders (VAEs) which produce samples from the target distribution in a single step. Thus, in diffusion models every sample is naturally connected to a random trajectory which is a solution to a learned stochastic differential equation (SDE). Generative models are only concerned with the final state of this trajectory that delivers samples from the desired distribution. \cite{abstreiter2021diffusion} showed that these stochastic trajectories can be seen as continuous filters that wash out information along the way. Consequently, it is reasonable to ask if there is an intermediate time step at which the preserved information is optimal for a given downstream task. In this work, we show that a combination of information content from different time steps gives a strictly better representation for the downstream task. We introduce an attention and recurrence based modules that ``learn to mix'' information content of various time-steps such that the resultant representation leads to superior performance in downstream tasks.
| Reject |
ICLR.cc/2018/Conference | Evolutionary Expectation Maximization for Generative Models with Binary Latents | We establish a theoretical link between evolutionary algorithms and variational parameter optimization of probabilistic generative models with binary hidden variables.
While the novel approach is independent of the actual generative model, here we use two such models to investigate its applicability and scalability: a noisy-OR Bayes Net (as a standard example of binary data) and Binary Sparse Coding (as a model for continuous data).
Learning of probabilistic generative models is first formulated as approximate maximum likelihood optimization using variational expectation maximization (EM).
We choose truncated posteriors as variational distributions in which discrete latent states serve as variational parameters. In the variational E-step,
the latent states are then
optimized according to a tractable free-energy objective. Given a data point, we can show that evolutionary algorithms can be used for the variational optimization loop by (A)~considering the bit-vectors of the latent states as genomes of individuals, and by (B)~defining the fitness of the
individuals as the (log) joint probabilities given by the used generative model.
As a proof of concept, we apply the novel evolutionary EM approach to the optimization of the parameters of noisy-OR Bayes nets and binary sparse coding on artificial and real data (natural image patches). Using point mutations and single-point cross-over for the evolutionary algorithm, we find that scalable variational EM algorithms are obtained which efficiently improve the data likelihood. In general we believe that, with the link established here, standard as well as recent results in the field of evolutionary optimization can be leveraged to address the difficult problem of parameter optimization in generative models. | Reject |
ICLR.cc/2023/Conference | GT-CausIn: a novel causal-based insight for traffic prediction | Traffic forecasting is an important issue of spatiotemporal series prediction. Among different methods, graph neural networks have achieved so far the most promising results, learning relations between graph nodes then becomes a crucial task. However, improvement space is very limited when these relations are learned in a node-to-node manner. The challenge stems from (1) obscure temporal dependencies between different stations, (2) difficulties in defining variables beyond the node level, and (3) no ready-made method to validate the learned relations. To confront these challenges, we define legitimate traffic variables to discover the causal structure of the traffic network. The causal relation is carefully checked with statistic tools and case analysis. We then present a novel model named Graph Spatial-Temporal Network Based on Causal Insight (GT-CausIn), where graph diffusion layers and temporal convolutional network (TCN) layers are integrated with causal knowledge to capture dependencies in spatiotemporal space. Experiments are carried out on two real-world traffic datasets: PEMS-BAY and METR-LA, which show that GT-CausIn significantly outperforms the state-of-the-art models. | Withdrawn |
ICLR.cc/2022/Conference | Open-World Semi-Supervised Learning | A fundamental limitation of applying semi-supervised learning in real-world settings is the assumption that unlabeled test data contains only classes previously encountered in the labeled training data. However, this assumption rarely holds for data in-the-wild, where instances belonging to novel classes may appear at testing time. Here, we introduce a novel open-world semi-supervised learning setting that formalizes the notion that novel classes may appear in the unlabeled test data. In this novel setting, the goal is to solve the class distribution mismatch problem between labeled and unlabeled data, where at the test time every input instance either needs to be classified into one of the existing classes or a new unseen class needs to be initialized and the instance assigned to it. To tackle this challenging problem, we propose ORCA, an end-to-end approach that assigns instances to previously seen classes or forms novel classes by grouping similar instances without assuming any prior knowledge. The key idea in ORCA is to utilize uncertainty adaptive margin to circumvent the bias towards seen classes caused by learning seen classes faster than the novel classes. In this way, ORCA gradually increases the discriminability of the model during the training and reduces the gap between intra-class variance of seen with respect to novel classes. Extensive experiments on image classification datasets and a single-cell dataset demonstrate that ORCA consistently outperforms alternative baselines, achieving 25% improvement on seen and 96% improvement on novel classes of the ImageNet dataset. | Accept (Poster) |
ICLR.cc/2023/Conference | A Weight Variation-Aware Training Method for Hardware Neuromorphic Chips | Hardware neuromorphic chips that mimic the biological nervous systems have recently attracted significant attention due to their ultra-low power and parallel computation. However, the inherent variability of nano-scale synaptic devices causes a weight perturbation and performance drop of neural networks. This paper proposes a training method to find weight with robustness to intrinsic device variability. A stochastic weight characteristic incurred by device inherent variability is considered during training. We investigate the impact of weight variation on both Spiking Neural Network (SNN) and standard Artificial Neural Network (ANN) with different architectures including fully connected, convolutional neural network (CNN), VGG, and ResNet on MNIST, CIFAR-10, and CIFAR-100. Experimental results show that a weight variation-aware training method (WVAT) can dramatically minimize the performance drop on weight variability by exploring a flat loss landscape. When there are weight perturbations, WVAT yields 85.21% accuracy of VGG-5 on CIFAR-10, reducing accuracy degradation by more than 1/10 compared with SGD. Finally, WVAT is easy to implement on various architectures with little computational overhead. | Reject |
ICLR.cc/2022/Conference | Inverse Contextual Bandits: Learning How Behavior Evolves over Time | Understanding a decision-maker's priorities by observing their behavior is critical for transparency and accountability in decision processes—such as in healthcare. Though conventional approaches to policy learning almost invariably assume stationarity in behavior, this is hardly true in practice: Medical practice is constantly evolving as clinical professionals fine-tune their knowledge over time. For instance, as the medical community's understanding of organ transplantations has progressed over the years, a pertinent question is: How have actual organ allocation policies been evolving? To give an answer, we desire a policy learning method that provides interpretable representations of decision-making, in particular capturing an agent's non-stationary knowledge of the world, as well as operating in an offline manner. First, we model the evolving behavior of decision-makers in terms of contextual bandits, and formalize the problem of Inverse Contextual Bandits ("ICB''). Second, we propose two concrete algorithms as solutions, learning parametric and non-parametric representations of an agent's behavior. Finally, using both real and simulated data for liver transplantations, we illustrate the applicability and explainability of our method, as well as benchmarking and validating the accuracy of our algorithms. | Reject |
ICLR.cc/2020/Conference | Learning to Reason: Distilling Hierarchy via Self-Supervision and Reinforcement Learning | We present a hierarchical planning and control framework that enables an agent to perform various tasks and adapt to a new task flexibly. Rather than learning an individual policy for each particular task, the proposed framework, DISH, distills a hierarchical policy from a set of tasks by self-supervision and reinforcement learning. The framework is based on the idea of latent variable models that represent high-dimensional observations using low-dimensional latent variables. The resulting policy consists of two levels of hierarchy: (i) a planning module that reasons a sequence of latent intentions that would lead to optimistic future and (ii) a feedback control policy, shared across the tasks, that executes the inferred intention. Because the reasoning is performed in low-dimensional latent space, the learned policy can immediately be used to solve or adapt to new tasks without additional training. We demonstrate the proposed framework can learn compact representations (3-dimensional latent states for a 90-dimensional humanoid system) while solving a small number of imitation tasks, and the resulting policy is directly applicable to other types of tasks, i.e., navigation in cluttered environments. | Reject |
ICLR.cc/2021/Conference | AC-VAE: Learning Semantic Representation with VAE for Adaptive Clustering | Unsupervised representation learning is essential in the field of machine learning, and accurate neighbor clusters of representation show great potential to support unsupervised image classification. This paper proposes a VAE (Variational Autoencoder) based network and a clustering method to achieve adaptive neighbor clustering to support the self-supervised classification. The proposed network encodes the image into the representation with boundary information, and the proposed cluster method takes advantage of the boundary information to deliver adaptive neighbor cluster results. Experimental evaluations show that the proposed method outperforms state-of-the-art representation learning methods in terms of neighbor clustering accuracy. Particularly, AC-VAE achieves 95\% and 82\% accuracy on CIFAR10 dataset when the average neighbor cluster sizes are 10 and 100. Furthermore, the neighbor cluster results are found converge within the clustering range ($\alpha\leq2$), and the converged neighbor clusters are used to support the self-supervised classification. The proposed method delivers classification results that are competitive with the state-of-the-art and reduces the super parameter $k$ in KNN (K-nearest neighbor), which is often used in self-supervised classification. | Reject |
ICLR.cc/2022/Conference | Learning Strides in Convolutional Neural Networks | Convolutional neural networks typically contain several downsampling operators, such as strided convolutions or pooling layers, that progressively reduce the resolution of intermediate representations. This provides some shift-invariance while reducing the computational complexity of the whole architecture. A critical hyperparameter of such layers is their stride: the integer factor of downsampling. As strides are not differentiable, finding the best configuration either requires cross-validation or discrete optimization (e.g. architecture search), which rapidly become prohibitive as the search space grows exponentially with the number of downsampling layers. Hence, exploring this search space by gradient descent would allow finding better configurations at a lower computational cost. This work introduces DiffStride, the first downsampling layer with learnable strides. Our layer learns the size of a cropping mask in the Fourier domain, that effectively performs resizing in a differentiable way. Experiments on audio and image classification show the generality and effectiveness of our solution: we use DiffStride as a drop-in replacement to standard downsampling layers and outperform them. In particular, we show that introducing our layer into a ResNet-18 architecture allows keeping consistent high performance on CIFAR10, CIFAR100 and ImageNet even when training starts from poor random stride configurations. Moreover, formulating strides as learnable variables allows us to introduce a regularization term that controls the computational complexity of the architecture. We show how this regularization allows trading off accuracy for efficiency on ImageNet. | Accept (Oral) |