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Anti-Concentrated Confidence Bonuses For Scalable Exploration | https://openreview.net/forum?id=RXQ-FPbQYVn | https://openreview.net/forum?id=RXQ-FPbQYVn | Jordan T. Ash,Cyril Zhang,Surbhi Goel,Akshay Krishnamurthy,Sham M. Kakade | ICLR 2022,Poster | Intrinsic rewards play a central role in handling the exploration-exploitation tradeoff when designing sequential decision-making algorithms, in both foundational theory and state-of-the-art deep reinforcement learning. The LinUCB algorithm, a centerpiece of the stochastic linear bandits literature, prescribes an elliptical bonus which addresses the challenge of leveraging shared information in large action spaces. This bonus scheme cannot be directly transferred to high-dimensional exploration problems, however, due to the computational cost of maintaining the inverse covariance matrix of action features. We introduce anti-concentrated confidence bounds for efficiently approximating the elliptical bonus, using an ensemble of regressors trained to predict random noise from policy network-derived features. Using this approximation, we obtain stochastic linear bandit algorithms which obtain $\tilde O(d \sqrt{T})$ regret bounds for $\mathsf{poly}(d)$ fixed actions. We develop a practical variant that is competitive with contemporary intrinsic reward heuristics on Atari benchmarks. | https://openreview.net/pdf/d7951ff75473361f77560f2a0e4763704e2580cd.pdf |
Sqrt(d) Dimension Dependence of Langevin Monte Carlo | https://openreview.net/forum?id=5-2mX9_U5i | https://openreview.net/forum?id=5-2mX9_U5i | Ruilin Li,Hongyuan Zha,Molei Tao | ICLR 2022,Poster | This article considers the popular MCMC method of unadjusted Langevin Monte Carlo (LMC) and provides a non-asymptotic analysis of its sampling error in 2-Wasserstein distance. The proof is based on a refinement of mean-square analysis in Li et al. (2019), and this refined framework automates the analysis of a large class of sampling algorithms based on discretizations of contractive SDEs. Using this framework, we establish an $\tilde{O}(\sqrt{d}/\epsilon)$ mixing time bound for LMC, without warm start, under the common log-smooth and log-strongly-convex conditions, plus a growth condition on the 3rd-order derivative of the potential of target measures. This bound improves the best previously known $\tilde{O}(d/\epsilon)$ result and is optimal (in terms of order) in both dimension $d$ and accuracy tolerance $\epsilon$ for target measures satisfying the aforementioned assumptions. Our theoretical analysis is further validated by numerical experiments. | https://openreview.net/pdf/e0edbcfc68c6fe7fb5ce570f96df1a072918442e.pdf |
Relational Surrogate Loss Learning | https://openreview.net/forum?id=dZPgfwaTaXv | https://openreview.net/forum?id=dZPgfwaTaXv | Tao Huang,Zekang Li,Hua Lu,Yong Shan,Shusheng Yang,Yang Feng,Fei Wang,Shan You,Chang Xu | ICLR 2022,Poster | Evaluation metrics in machine learning are often hardly taken as loss functions, as they could be non-differentiable and non-decomposable, e.g., average precision and F1 score. This paper aims to address this problem by revisiting the surrogate loss learning, where a deep neural network is employed to approximate the evaluation metrics. Instead of pursuing an exact recovery of the evaluation metric through a deep neural network, we are reminded of the purpose of the existence of these evaluation metrics, which is to distinguish whether one model is better or worse than another. In this paper, we show that directly maintaining the relation of models between surrogate losses and metrics suffices, and propose a rank correlation-based optimization method to maximize this relation and learn surrogate losses. Compared to previous works, our method is much easier to optimize and enjoys significant efficiency and performance gains. Extensive experiments show that our method achieves improvements on various tasks including image classification and neural machine translation, and even outperforms state-of-the-art methods on human pose estimation and machine reading comprehension tasks. Code is available at: https://github.com/hunto/ReLoss. | https://openreview.net/pdf/da75d64e0685a0086938d2d63a0b5ef70e48324a.pdf |
Structure-Aware Transformer Policy for Inhomogeneous Multi-Task Reinforcement Learning | https://openreview.net/forum?id=fy_XRVHqly | https://openreview.net/forum?id=fy_XRVHqly | Sunghoon Hong,Deunsol Yoon,Kee-Eung Kim | ICLR 2022,Poster | Modular Reinforcement Learning, where the agent is assumed to be morphologically structured as a graph, for example composed of limbs and joints, aims to learn a policy that is transferable to a structurally similar but different agent. Compared to traditional Multi-Task Reinforcement Learning, this promising approach allows us to cope with inhomogeneous tasks where the state and action space dimensions differ across tasks. Graph Neural Networks are a natural model for representing the pertinent policies, but a recent work has shown that their multi-hop message passing mechanism is not ideal for conveying important information to other modules and thus a transformer model without morphological information was proposed. In this work, we argue that the morphological information is still very useful and propose a transformer policy model that effectively encodes such information. Specifically, we encode the morphological information in terms of the traversal-based positional embedding and the graph-based relational embedding. We empirically show that the morphological information is crucial for modular reinforcement learning, substantially outperforming prior state-of-the-art methods on multi-task learning as well as transfer learning settings with different state and action space dimensions. | https://openreview.net/pdf/111d5058b0200075159b27c0969addf7f1a2a871.pdf |
Toward Efficient Low-Precision Training: Data Format Optimization and Hysteresis Quantization | https://openreview.net/forum?id=3HJOA-1hb0e | https://openreview.net/forum?id=3HJOA-1hb0e | Sunwoo Lee,Jeongwoo Park,Dongsuk Jeon | ICLR 2022,Poster | As the complexity and size of deep neural networks continue to increase, low-precision training has been extensively studied in the last few years to reduce hardware overhead. Training performance is largely affected by the numeric formats representing different values in low-precision training, but finding an optimal format typically requires numerous training runs, which is a very time-consuming process. In this paper, we propose a method to efficiently find an optimal format for activations and errors without actual training. We employ this method to determine an 8-bit format suitable for training various models. In addition, we propose hysteresis quantization to suppress undesired fluctuation in quantized weights during training. This scheme enables deeply quantized training using 4-bit weights, exhibiting only 0.2% degradation for ResNet-18 trained on ImageNet. | https://openreview.net/pdf/d108ae611f0e33dfa11c0140644d61b0b0774170.pdf |
Knowledge Infused Decoding | https://openreview.net/forum?id=upnDJ7itech | https://openreview.net/forum?id=upnDJ7itech | Ruibo Liu,Guoqing Zheng,Shashank Gupta,Radhika Gaonkar,Chongyang Gao,Soroush Vosoughi,Milad Shokouhi,Ahmed Hassan Awadallah | ICLR 2022,Poster | Pre-trained language models (LMs) have been shown to memorize a substantial amount of knowledge from the pre-training corpora; however, they are still limited in recalling factually correct knowledge given a certain context. Hence. they tend to suffer from counterfactual or hallucinatory generation when used in knowledge-intensive natural language generation (NLG) tasks. Recent remedies to this problem focus on modifying either the pre-training or task fine-tuning objectives to incorporate knowledge, which normally require additional costly training or architecture modification of LMs for practical applications.
We present Knowledge Infused Decoding (KID)---a novel decoding algorithm for generative LMs, which dynamically infuses external knowledge into each step of the LM decoding. Specifically, we maintain a local knowledge memory based on the current context, interacting with a dynamically created external knowledge trie, and continuously update the local memory as a knowledge-aware constraint to guide decoding via reinforcement learning. On six diverse knowledge-intensive NLG tasks, task-agnostic LMs (e.g., GPT-2 and BART) armed with KID outperform many task-optimized state-of-the-art models, and show particularly strong performance in few-shot scenarios over seven related knowledge-infusion techniques. Human evaluation confirms KID's ability to generate more relevant and factual language for the input context when compared with multiple baselines. Finally, KID also alleviates exposure bias and provides stable generation quality when generating longer sequences. | https://openreview.net/pdf/a95e766c5c2193002a679156d0b1d1b582238b50.pdf |
Parallel Training of GRU Networks with a Multi-Grid Solver for Long Sequences | https://openreview.net/forum?id=N1WI0vJLER | https://openreview.net/forum?id=N1WI0vJLER | Euhyun Moon,Eric C Cyr | ICLR 2022,Poster | Parallelizing Gated Recurrent Unit (GRU) is a challenging task, as the training procedure of GRU is inherently sequential. Prior efforts to parallelize GRU have largely focused on conventional parallelization strategies such as data-parallel and model-parallel training algorithms. However, when the given sequences are very long, existing approaches are still inevitably performance limited in terms of both training time and model accuracy. In this paper, we present a novel parallel training scheme (called parallel-in-time) for GRU based on a multigrid reduction in time (MGRIT) solver. MGRIT partitions a sequence into multiple shorter sub-sequences and trains the sub-sequences on different processors in parallel. The key to achieving speedup is a hierarchical correction of the hidden state to accelerate end-to-end communication in both the forward and backward propagation phases of gradient descent. Experimental results on the HMDB51 dataset, where each video is an image sequence, demonstrate that a new parallel training scheme of GRU achieves up to $6.5 \times$ speedup over a serial approach. As efficiency of our new parallelization strategy is associated with the sequence length, our parallel GRU algorithm achieves significant performance improvement as the length of sequence increases. Further, the proposed approach can be applied simultaneously with batch and other forms of model parallelism. | https://openreview.net/pdf/8571a323c3024175c5f0d27227e6c74f594b1290.pdf |
QUERY EFFICIENT DECISION BASED SPARSE ATTACKS AGAINST BLACK-BOX DEEP LEARNING MODELS | https://openreview.net/forum?id=73MEhZ0anV | https://openreview.net/forum?id=73MEhZ0anV | Viet Vo,Ehsan M Abbasnejad,Damith Ranasinghe | ICLR 2022,Poster | Despite our best efforts, deep learning models remain highly vulnerable to even tiny adversarial perturbations applied to the inputs. The ability to extract information from solely the output of a machine learning model to craft adversarial perturbations to black-box models is a practical threat against real-world systems, such as Machine Learning as a Service (MLaaS), particularly $sparse~attacks$. The realization of sparse attacks in black-box settings demonstrates that machine learning models are more vulnerable than we believe. Because, these attacks aim to $minimize~the~number~of~perturbed~pixels$—measured by $l_0$ norm—required to mislead a model by $solely$ observing the decision ($the~predicted~label$) returned to a model query; the so-called $decision-based~setting$. But, such an attack leads to an NP-hard optimization problem. We develop an evolution-based algorithm—$SparseEvo$—for the problem and evaluate it against both convolutional deep neural networks and $vision~transformers$. Notably, vision transformers are yet to be investigated under a decision-based attack setting. SparseEvo requires significantly fewer queries than the state-of-the-art sparse attack $Pointwise$ for both untargeted and targeted attacks. The attack algorithm, although conceptually simple, is competitive with only a limited query budget against the state-of-the-art gradient-based $white-box$ attacks in standard computer vision tasks such as $ImageNet$. Importantly, the query efficient SparseEvo, along with decision-based attacks, in general, raises new questions regarding the safety of deployed systems and poses new directions to study and understand the robustness of machine learning models. | https://openreview.net/pdf/02ebabe963b750dd8f1c95cf41ba5e78dd63b8f9.pdf |
Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations | https://openreview.net/forum?id=gJLEXy3ySpu | https://openreview.net/forum?id=gJLEXy3ySpu | Jinyuan Jia,Binghui Wang,Xiaoyu Cao,Hongbin Liu,Neil Zhenqiang Gong | ICLR 2022,Poster | Top-$k$ predictions are used in many real-world applications such as machine learning as a service, recommender systems, and web searches. $\ell_0$-norm adversarial perturbation characterizes an attack that arbitrarily modifies some features of an input such that a classifier makes an incorrect prediction for the perturbed input. $\ell_0$-norm adversarial perturbation is easy to interpret and can be implemented in the physical world. Therefore, certifying robustness of top-$k$ predictions against $\ell_0$-norm adversarial perturbation is important. However, existing studies either focused on certifying $\ell_0$-norm robustness of top-$1$ predictions or $\ell_2$-norm robustness of top-$k$ predictions. In this work, we aim to bridge the gap. Our approach is based on randomized smoothing, which builds a provably robust classifier from an arbitrary classifier via randomizing an input. Our major theoretical contribution is an almost tight $\ell_0$-norm certified robustness guarantee for top-$k$ predictions. We empirically evaluate our method on CIFAR10 and ImageNet. For instance, our method can build a classifier that achieves a certified top-3 accuracy of 69.2\% on ImageNet when an attacker can arbitrarily perturb 5 pixels of a testing image. | https://openreview.net/pdf/468bb7a69cab231837989615dedd6b3b33a6c683.pdf |
Proving the Lottery Ticket Hypothesis for Convolutional Neural Networks | https://openreview.net/forum?id=Vjki79-619- | https://openreview.net/forum?id=Vjki79-619- | Arthur da Cunha,Emanuele Natale,Laurent Viennot | ICLR 2022,Poster | The lottery ticket hypothesis states that a randomly-initialized neural network contains a small subnetwork which, when trained in isolation, can compete with the performance of the original network. Recent theoretical works proved an even stronger version: every sufficiently overparameterized (dense) neural network contains a subnetwork that, even without training, achieves accuracy comparable to that of the trained large network. These works left as an open problem to extend the result to convolutional neural networks (CNNs).
In this work we provide such generalization by showing that, with high probability, it is possible to approximate any CNN by pruning a random CNN whose size is larger by a logarithmic factor. | https://openreview.net/pdf/883b1c57d46f2d43344902ddf405db1696b615f9.pdf |
Discovering Nonlinear PDEs from Scarce Data with Physics-encoded Learning | https://openreview.net/forum?id=Vog_3GXsgmb | https://openreview.net/forum?id=Vog_3GXsgmb | Chengping Rao,Pu Ren,Yang Liu,Hao Sun | ICLR 2022,Poster | There have been growing interests in leveraging experimental measurements to discover the underlying partial differential equations (PDEs) that govern complex physical phenomena. Although past research attempts have achieved great success in data-driven PDE discovery, the robustness of the existing methods cannot be guaranteed when dealing with low-quality measurement data. To overcome this challenge, we propose a novel physics-encoded discrete learning framework for discovering spatiotemporal PDEs from scarce and noisy data. The general idea is to (1) firstly introduce a novel deep convolutional-recurrent networks, which can encode prior physics knowledge (e.g., known terms, assumed PDE structure, initial/boundary conditions, etc.) while remaining flexible on representation capability, to accurately reconstruct high-fidelity data, and (2) then perform sparse regression with the reconstructed data to identify the analytical form of the governing PDEs. We validate our proposed framework on three high-dimensional PDE systems. The effectiveness and superiority of the proposed method over baselines are demonstrated. | https://openreview.net/pdf/82ec65c9323f68d0077918ee9391653298302272.pdf |
Rethinking Goal-Conditioned Supervised Learning and Its Connection to Offline RL | https://openreview.net/forum?id=KJztlfGPdwW | https://openreview.net/forum?id=KJztlfGPdwW | Rui Yang,Yiming Lu,Wenzhe Li,Hao Sun,Meng Fang,Yali Du,Xiu Li,Lei Han,Chongjie Zhang | ICLR 2022,Poster | Solving goal-conditioned tasks with sparse rewards using self-supervised learning is promising because of its simplicity and stability over current reinforcement learning (RL) algorithms. A recent work, called Goal-Conditioned Supervised Learning (GCSL), provides a new learning framework by iteratively relabeling and imitating self-generated experiences. In this paper, we revisit the theoretical property of GCSL --- optimizing a lower bound of the goal reaching objective, and extend GCSL as a novel offline goal-conditioned RL algorithm. The proposed method is named Weighted GCSL (WGCSL), in which we introduce an advanced compound weight consisting of three parts (1) discounted weight for goal relabeling, (2) goal-conditioned exponential advantage weight, and (3) best-advantage weight. Theoretically, WGCSL is proved to optimize an equivalent lower bound of the goal-conditioned RL objective and generates monotonically improved policies via an iterated scheme. The monotonic property holds for any behavior policies, and therefore WGCSL can be applied to both online and offline settings. To evaluate algorithms in the offline goal-conditioned RL setting, we provide a benchmark including a range of point and simulated robot domains. Experiments in the introduced benchmark demonstrate that WGCSL can consistently outperform GCSL and existing state-of-the-art offline methods in the fully offline goal-conditioned setting. | https://openreview.net/pdf/4480bd8b649d86ec422acd68ae606cd7f8fa1d6d.pdf |
Topologically Regularized Data Embeddings | https://openreview.net/forum?id=P1QUVhOtEFP | https://openreview.net/forum?id=P1QUVhOtEFP | Robin Vandaele,Bo Kang,Jefrey Lijffijt,Tijl De Bie,Yvan Saeys | ICLR 2022,Poster | Unsupervised feature learning often finds low-dimensional embeddings that capture the structure of complex data. For tasks for which prior expert topological knowledge is available, incorporating this into the learned representation may lead to higher quality embeddings. For example, this may help one to embed the data into a given number of clusters, or to accommodate for noise that prevents one from deriving the distribution of the data over the model directly, which can then be learned more effectively. However, a general tool for integrating different prior topological knowledge into embeddings is lacking. Although differentiable topology layers have been recently developed that can (re)shape embeddings into prespecified topological models, they have two important limitations for representation learning, which we address in this paper. First, the currently suggested topological losses fail to represent simple models such as clusters and flares in a natural manner. Second, these losses neglect all original structural (such as neighborhood) information in the data that is useful for learning. We overcome these limitations by introducing a new set of topological losses, and proposing their usage as a way for topologically regularizing data embeddings to naturally represent a prespecified model. We include thorough experiments on synthetic and real data that highlight the usefulness and versatility of this approach, with applications ranging from modeling high-dimensional single-cell data, to graph embedding. | https://openreview.net/pdf/8b4e6211ed28114e194a05b0d878e41273cc19f5.pdf |
PF-GNN: Differentiable particle filtering based approximation of universal graph representations | https://openreview.net/forum?id=oh4TirnfSem | https://openreview.net/forum?id=oh4TirnfSem | Mohammed Haroon Dupty,Yanfei Dong,Wee Sun Lee | ICLR 2022,Poster | Message passing Graph Neural Networks (GNNs) are known to be limited in expressive power by the 1-WL color-refinement test for graph isomorphism. Other more expressive models either are computationally expensive or need preprocessing to extract structural features from the graph. In this work, we propose to make GNNs universal by guiding the learning process with exact isomorphism solver techniques which operate on the paradigm of $\textit{Individualization and refinement}$ (IR), a method to artificially introduce asymmetry and further refine the coloring when 1-WL stops. Isomorphism solvers generate a search-tree of colorings whose leaves uniquely identify the graph. However, the tree grows exponentially large and needs hand-crafted pruning techniques which are not desirable from a learning perspective. We take a probabilistic view and approximate the search tree of colorings ( i.e. embeddings) by sampling multiple paths from root to leaves of the search-tree. To learn more discriminative representations, we guide the sampling process with $\textit{particle filter}$ updates, a principled approach for sequential state estimation. Our algorithm is end-to-end differentiable, can be applied with any GNN as backbone and learns richer graph representations with only linear increase in runtime. Experimental evaluation shows that our approach consistently outperforms leading GNN models on both synthetic benchmarks for isomorphism detection as well as real-world datasets. | https://openreview.net/pdf/0118eb807320ffa7b7d1699e78d45f3a138d256c.pdf |
Nonlinear ICA Using Volume-Preserving Transformations | https://openreview.net/forum?id=AMpki9kp8Cn | https://openreview.net/forum?id=AMpki9kp8Cn | Xiaojiang Yang,Yi Wang,Jiacheng Sun,Xing Zhang,Shifeng Zhang,Zhenguo Li,Junchi Yan | ICLR 2022,Poster | Nonlinear ICA is a fundamental problem in machine learning, aiming to identify the underlying independent components (sources) from data which is assumed to be a nonlinear function (mixing function) of these sources. Recent works prove that if the sources have some particular structures (e.g. temporal structure), they are theoretically identifiable even if the mixing function is arbitrary. However, in many cases such restrictions on the sources are difficult to satisfy or even verify, hence it inhibits the applicability of the proposed methods. Different from these works, we propose a general framework for nonlinear ICA, in which the mixing function is assumed to be a volume-preserving transformation, and meanwhile the conditions on the sources can be much looser. We provide an insightful proof of the identifiability of the proposed framework. We implement the framework by volume-preserving Flow-based models, and verify our theory by experiments on artificial data and synthesized images. Moreover, results on real-world images indicate that our framework can disentangle interpretable features. | https://openreview.net/pdf/3a5f3218a8dcb5454a7f2124915513a251c47534.pdf |
Online Ad Hoc Teamwork under Partial Observability | https://openreview.net/forum?id=18Ys0-PzyPI | https://openreview.net/forum?id=18Ys0-PzyPI | Pengjie Gu,Mengchen Zhao,Jianye Hao,Bo An | ICLR 2022,Poster | Autonomous agents often need to work together as a team to accomplish complex cooperative tasks. Due to privacy and other realistic constraints, agents might need to collaborate with previously unknown teammates on the fly. This problem is known as ad hoc teamwork, which remains a core research challenge. Prior works usually rely heavily on strong assumptions like full observability, fixed and predefined teammates' types. This paper relaxes these assumptions with a novel reinforcement learning framework called ODITS, which allows the autonomous agent to adapt to arbitrary teammates in an online fashion. Instead of limiting teammates into a finite set of predefined types, ODITS automatically learns latent variables of teammates' behaviors to infer how to cooperate with new teammates effectively. To overcome partial observability, we introduce an information-based regularizer to derive proxy representations of the learned variables from local observations. Extensive experimental results show that ODITS significantly outperforms various baselines in widely used ad hoc teamwork tasks. | https://openreview.net/pdf/69ebe98356c87ca06fb980028a882b8bf4ea9078.pdf |
Continual Normalization: Rethinking Batch Normalization for Online Continual Learning | https://openreview.net/forum?id=vwLLQ-HwqhZ | https://openreview.net/forum?id=vwLLQ-HwqhZ | Quang Pham,Chenghao Liu,Steven HOI | ICLR 2022,Poster | Existing continual learning methods use Batch Normalization (BN) to facilitate training and improve generalization across tasks. However, the non-i.i.d and non-stationary nature of continual learning data, especially in the online setting, amplify the discrepancy between training and testing in BN and hinder the performance of older tasks. In this work, we study the cross-task normalization effect of BN in online continual learning where BN normalizes the testing data using moments biased towards the current task, resulting in higher catastrophic forgetting. This limitation motivates us to propose a simple yet effective method that we call Continual Normalization (CN) to facilitate training similar to BN while mitigating its negative effect. Extensive experiments on different continual learning algorithms and online scenarios show that CN is a direct replacement for BN and can provide substantial performance improvements. Our implementation will be made publicly available upon acceptance. | https://openreview.net/pdf/10665207380ab7045b3c071ec3558f28f179a53a.pdf |
Equivariant Graph Mechanics Networks with Constraints | https://openreview.net/forum?id=SHbhHHfePhP | https://openreview.net/forum?id=SHbhHHfePhP | Wenbing Huang,Jiaqi Han,Yu Rong,Tingyang Xu,Fuchun Sun,Junzhou Huang | ICLR 2022,Poster | Learning to reason about relations and dynamics over multiple interacting objects is a challenging topic in machine learning. The challenges mainly stem from that the interacting systems are exponentially-compositional, symmetrical, and commonly geometrically-constrained.
Current methods, particularly the ones based on equivariant Graph Neural Networks (GNNs), have targeted on the first two challenges but remain immature for constrained systems.
In this paper, we propose Graph Mechanics Network (GMN) which is combinatorially efficient, equivariant and constraint-aware. The core of GMN is that it represents, by generalized coordinates, the forward kinematics information (positions and velocities) of a structural object. In this manner, the geometrical constraints are implicitly and naturally encoded in the forward kinematics. Moreover, to allow equivariant message passing in GMN, we have developed a general form of orthogonality-equivariant functions, given that the dynamics of constrained systems are more complicated than the unconstrained counterparts. Theoretically, the proposed equivariant formulation is proved to be universally expressive under certain conditions. Extensive experiments support the advantages of GMN compared to the state-of-the-art GNNs in terms of prediction accuracy, constraint satisfaction and data efficiency on the simulated systems consisting of particles, sticks and hinges, as well as two real-world datasets for molecular dynamics prediction and human motion capture. | https://openreview.net/pdf/d257472583625e833ddf5bdf3abbbee9da8421da.pdf |
Towards Continual Knowledge Learning of Language Models | https://openreview.net/forum?id=vfsRB5MImo9 | https://openreview.net/forum?id=vfsRB5MImo9 | Joel Jang,Seonghyeon Ye,Sohee Yang,Joongbo Shin,Janghoon Han,Gyeonghun KIM,Stanley Jungkyu Choi,Minjoon Seo | ICLR 2022,Poster | Large Language Models (LMs) are known to encode world knowledge in their parameters as they pretrain on a vast amount of web corpus, which is often utilized for performing knowledge-dependent downstream tasks such as question answering, fact-checking, and open dialogue. In real-world scenarios, the world knowledge stored in the LMs can quickly become outdated as the world changes, but it is non-trivial to avoid catastrophic forgetting and reliably acquire new knowledge while preserving invariant knowledge. To push the community towards better maintenance of ever-changing LMs, we formulate a new continual learning (CL) problem called Continual Knowledge Learning (CKL). We construct a new benchmark and metric to quantify the retention of time-invariant world knowledge, the update of outdated knowledge, and the acquisition of new knowledge. We adopt applicable recent methods from literature to create several strong baselines. Through extensive experiments, we find that CKL exhibits unique challenges that are not addressed in previous CL setups, where parameter expansion is necessary to reliably retain and learn knowledge simultaneously. By highlighting the critical causes of knowledge forgetting, we show that CKL is a challenging and important problem that helps us better understand and train ever-changing LMs. | https://openreview.net/pdf/873039fe2b300e8ad8b1dd3a34054bad1386f1a1.pdf |
Surreal-GAN:Semi-Supervised Representation Learning via GAN for uncovering heterogeneous disease-related imaging patterns | https://openreview.net/forum?id=nf3A0WZsXS5 | https://openreview.net/forum?id=nf3A0WZsXS5 | Zhijian Yang,Junhao Wen,Christos Davatzikos | ICLR 2022,Poster | A plethora of machine learning methods have been applied to imaging data, enabling the construction of clinically relevant imaging signatures of neurological and neuropsychiatric diseases. Oftentimes, such methods don't explicitly model the heterogeneity of disease effects, or approach it via nonlinear models that are not interpretable. Moreover, unsupervised methods may parse heterogeneity that is driven by nuisance confounding factors that affect brain structure or function, rather than heterogeneity relevant to a pathology of interest. On the other hand, semi-supervised clustering methods seek to derive a dichotomous subtype membership, ignoring the truth that disease heterogeneity spatially and temporally extends along a continuum. To address the aforementioned limitations, herein, we propose a novel method, termed Surreal-GAN (Semi-SUpeRvised ReprEsentAtion Learning via GAN). Using cross-sectional imaging data, Surreal-GAN dissects underlying disease-related heterogeneity under the principle of semi-supervised clustering (cluster mappings from normal control to patient), proposes a continuously dimensional representation, and infers the disease severity of patients at individual level along each dimension. The model first learns a transformation function from normal control (CN) domain to the patient (PT) domain with latent variables controlling transformation directions. An inverse mapping function together with regularization on function continuity, pattern orthogonality and monotonicity was also imposed to make sure that the transformation function captures necessarily meaningful imaging patterns with clinical significance. We first validated the model through extensive semi-synthetic experiments, and then demonstrate its potential in capturing biologically plausible imaging patterns in Alzheimer's disease (AD). | https://openreview.net/pdf/169e0c29b5ff15ab03db77fc148c513fa8a71257.pdf |
SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning | https://openreview.net/forum?id=TfhfZLQ2EJO | https://openreview.net/forum?id=TfhfZLQ2EJO | Jongjin Park,Younggyo Seo,Jinwoo Shin,Honglak Lee,Pieter Abbeel,Kimin Lee | ICLR 2022,Poster | Preference-based reinforcement learning (RL) has shown potential for teaching agents to perform the target tasks without a costly, pre-defined reward function by learning the reward with a supervisor’s preference between the two agent behaviors. However, preference-based learning often requires a large amount of human feedback, making it difficult to apply this approach to various applications. This data-efficiency problem, on the other hand, has been typically addressed by using unlabeled samples or data augmentation techniques in the context of supervised learning. Motivated by the recent success of these approaches, we present SURF, a semi-supervised reward learning framework that utilizes a large amount of unlabeled samples with data augmentation. In order to leverage unlabeled samples for reward learning, we infer pseudo-labels of the unlabeled samples based on the confidence of the preference predictor. To further improve the label-efficiency of reward learning, we introduce a new data augmentation that temporally crops consecutive subsequences from the original behaviors. Our experiments demonstrate that our approach significantly improves the feedback-efficiency of the state-of-the-art preference-based method on a variety of locomotion and robotic manipulation tasks. | https://openreview.net/pdf/2fe39fd19cc40472a98221890fb4cfd5f924e6c7.pdf |
Convergent Graph Solvers | https://openreview.net/forum?id=ItkxLQU01lD | https://openreview.net/forum?id=ItkxLQU01lD | Junyoung Park,Jinhyun Choo,Jinkyoo Park | ICLR 2022,Poster | We propose the convergent graph solver (CGS), a deep learning method that learns iterative mappings to predict the properties of a graph system at its stationary state (fixed point) with guaranteed convergence. The forward propagation of CGS proceeds in three steps: (1) constructing the input-dependent linear contracting iterative maps, (2) computing the fixed points of the iterative maps, and (3) decoding the fixed points to estimate the properties. The contractivity of the constructed linear maps guarantees the existence and uniqueness of the fixed points following the Banach fixed point theorem. To train CGS efficiently, we also derive a tractable analytical expression for its gradient by leveraging the implicit function theorem. We evaluate the performance of CGS by applying it to various network-analytic and graph benchmark problems. The results indicate that CGS has competitive capabilities for predicting the stationary properties of graph systems, irrespective of whether the target systems are linear or non-linear. CGS also shows high performance for graph classification problems where the existence or the meaning of a fixed point is hard to be clearly defined, which highlights the potential of CGS as a general graph neural network architecture. | https://openreview.net/pdf/fb18728414ffbbdaa143c3e893d041eb10d254ce.pdf |
Spread Spurious Attribute: Improving Worst-group Accuracy with Spurious Attribute Estimation | https://openreview.net/forum?id=_F9xpOrqyX9 | https://openreview.net/forum?id=_F9xpOrqyX9 | Junhyun Nam,Jaehyung Kim,Jaeho Lee,Jinwoo Shin | ICLR 2022,Poster | The paradigm of worst-group loss minimization has shown its promise in avoiding to learn spurious correlations, but requires costly additional supervision on spurious attributes. To resolve this, recent works focus on developing weaker forms of supervision---e.g., hyperparameters discovered with a small number of validation samples with spurious attribute annotation---but none of the methods retain comparable performance to methods using full supervision on the spurious attribute. In this paper, instead of searching for weaker supervisions, we ask: Given access to a fixed number of samples with spurious attribute annotations, what is the best achievable worst-group loss if we ''fully exploit'' them? To this end, we propose a pseudo-attribute-based algorithm, coined Spread Spurious Attribute (SSA), for improving the worst-group accuracy. In particular, we leverage samples both with and without spurious attribute annotations to train a model to predict the spurious attribute, then use the pseudo-attribute predicted by the trained model as supervision on the spurious attribute to train a new robust model having minimal worst-group loss. Our experiments on various benchmark datasets show that our algorithm consistently outperforms the baseline methods using the same number of validation samples with spurious attribute annotations. We also demonstrate that the proposed SSA can achieve comparable performances to methods using full (100%) spurious attribute supervision, by using a much smaller number of annotated samples---from 0.6% and up to 1.5%, depending on the dataset. | https://openreview.net/pdf/1ef5907fce7a34e12970b51ba7aa96afb5e46997.pdf |
Learning Scenario Representation for Solving Two-stage Stochastic Integer Programs | https://openreview.net/forum?id=06Wy2BtxXrz | https://openreview.net/forum?id=06Wy2BtxXrz | Yaoxin Wu,Wen Song,Zhiguang Cao,Jie Zhang | ICLR 2022,Poster | Many practical combinatorial optimization problems under uncertainty can be modeled as stochastic integer programs (SIPs), which are extremely challenging to solve due to the high complexity. To solve two-stage SIPs efficiently, we propose a conditional variational autoencoder (CVAE) based method to learn scenario representation for a class of SIP instances. Specifically, we design a graph convolutional network based encoder to embed each scenario with the deterministic part of its instance (i.e. context) into a low-dimensional latent space, from which a decoder reconstructs the scenario from its latent representation conditioned on the context. Such a design effectively captures the dependencies of the scenarios on their corresponding instances. We apply the trained encoder to two tasks in typical SIP solving, i.e. scenario reduction and objective prediction. Experiments on two SIP problems show that the learned latent representation significantly boosts the solving performance to attain high-quality solutions in short computational time, and generalizes fairly well to problems of larger sizes or with more scenarios. | https://openreview.net/pdf/87cf7ed439965040c8013759c94576533321c4b5.pdf |
Generalization Through the Lens of Leave-One-Out Error | https://openreview.net/forum?id=7grkzyj89A_ | https://openreview.net/forum?id=7grkzyj89A_ | Gregor Bachmann,Thomas Hofmann,Aurelien Lucchi | ICLR 2022,Poster | Despite the tremendous empirical success of deep learning models to solve various learning tasks, our theoretical understanding of their generalization ability is very limited. Classical generalization bounds based on tools such as the VC dimension or Rademacher complexity, are so far unsuitable for deep models and it is doubtful that these techniques can yield tight bounds even in the most idealistic settings~\citep{nagarajan2019uniform}. In this work, we instead revisit the concept of leave-one-out (LOO) error to measure the generalization ability of deep models in the so-called kernel regime. While popular in statistics, the LOO error has been largely overlooked in the context of deep learning. By building upon the recently established connection between neural networks and kernel learning, we leverage the closed-form expression for the leave-one-out error, giving us access to an efficient proxy for the test error. We show both theoretically and empirically that the leave-one-out error is capable of capturing various phenomena in generalization theory, such as double descent, random labels or transfer learning.
Our work therefore demonstrates that the leave-one-out error provides a tractable way to estimate the generalization ability of deep neural networks in the kernel regime, opening the door to potential, new research directions in the field of generalization. | https://openreview.net/pdf/16d55edf6606d7cd0494df4bb7f9a39233c8c550.pdf |
Self-Supervised Inference in State-Space Models | https://openreview.net/forum?id=VPjw9KPWRSK | https://openreview.net/forum?id=VPjw9KPWRSK | David Ruhe,Patrick Forré | ICLR 2022,Poster | We perform approximate inference in state-space models with nonlinear state transitions. Without parameterizing a generative model, we apply Bayesian update formulas using a local linearity approximation parameterized by neural networks. It comes accompanied by a maximum likelihood objective that requires no supervision via uncorrupt observations or ground truth latent states. The optimization backpropagates through a recursion similar to the classical Kalman filter and smoother. Additionally, using an approximate conditional independence, we can perform smoothing without having to parameterize a separate model. In scientific applications, domain knowledge can give a linear approximation of the latent transition maps, which we can easily incorporate into our model. Usage of such domain knowledge is reflected in excellent results (despite our model's simplicity) on the chaotic Lorenz system compared to fully supervised and variational inference methods. Finally, we show competitive results on an audio denoising experiment.
| https://openreview.net/pdf/0b43e4ea11108c37cd4f7f5548e1d635e571c000.pdf |
On the Role of Neural Collapse in Transfer Learning | https://openreview.net/forum?id=SwIp410B6aQ | https://openreview.net/forum?id=SwIp410B6aQ | Tomer Galanti,András György,Marcus Hutter | ICLR 2022,Poster | We study the ability of foundation models to learn representations for classification that are transferable to new, unseen classes. Recent results in the literature show that representations learned by a single classifier over many classes are competitive on few-shot learning problems with representations learned by special-purpose algorithms designed for such problems. In this paper, we provide an explanation for this behavior based on the recently observed phenomenon that the features learned by overparameterized classification networks show an interesting clustering property, called neural collapse. We demonstrate both theoretically and empirically that neural collapse generalizes to new samples from the training classes, and -- more importantly -- to new classes as well, allowing foundation models to provide feature maps that work well in transfer learning and, specifically, in the few-shot setting. | https://openreview.net/pdf/92ef43eae5d5f2d0950bab4169ae4d3e49bbe534.pdf |
Information-theoretic Online Memory Selection for Continual Learning | https://openreview.net/forum?id=IpctgL7khPp | https://openreview.net/forum?id=IpctgL7khPp | Shengyang Sun,Daniele Calandriello,Huiyi Hu,Ang Li,Michalis Titsias | ICLR 2022,Poster | A challenging problem in task-free continual learning is the online selection of a representative replay memory from data streams. In this work, we investigate the online memory selection problem from an information-theoretic perspective. To gather the most information, we propose the \textit{surprise} and the \textit{learnability} criteria to pick informative points and to avoid outliers. We present a Bayesian model to compute the criteria efficiently by exploiting rank-one matrix structures. We demonstrate that these criteria encourage selecting informative points in a greedy algorithm for online memory selection. Furthermore, by identifying the importance of \textit{the timing to update the memory}, we introduce a stochastic information-theoretic reservoir sampler (InfoRS), which conducts sampling among selective points with high information. Compared to reservoir sampling, InfoRS demonstrates improved robustness against data imbalance. Finally, empirical performances over continual learning benchmarks manifest its efficiency and efficacy. | https://openreview.net/pdf/c23f5db3dfaa1687d370340809b6da378029c78e.pdf |
Dealing with Non-Stationarity in MARL via Trust-Region Decomposition | https://openreview.net/forum?id=XHUxf5aRB3s | https://openreview.net/forum?id=XHUxf5aRB3s | Wenhao Li,Xiangfeng Wang,Bo Jin,Junjie Sheng,Hongyuan Zha | ICLR 2022,Poster | Non-stationarity is one thorny issue in cooperative multi-agent reinforcement learning (MARL). One of the reasons is the policy changes of agents during the learning process. Some existing works have discussed various consequences caused by non-stationarity with several kinds of measurement indicators. This makes the objectives or goals of existing algorithms are inevitably inconsistent and disparate. In this paper, we introduce a novel notion, the $\delta$-$stationarity$ measurement, to explicitly measure the non-stationarity of a policy sequence, which can be further proved to be bounded by the KL-divergence of consecutive joint policies. A straightforward but highly non-trivial way is to control the joint policies' divergence, which is difficult to estimate accurately by imposing the trust-region constraint on the joint policy. Although it has lower computational complexity to decompose the joint policy and impose trust-region constraints on the factorized policies, simple policy factorization like mean-field approximation will lead to more considerable policy divergence, which can be considered as the trust-region decomposition dilemma. We model the joint policy as a pairwise Markov random field and propose a trust-region decomposition network (TRD-Net) based on message passing to estimate the joint policy divergence more accurately. The Multi-Agent Mirror descent policy algorithm with Trust region decomposition, called MAMT, is established by adjusting the trust-region of the local policies adaptively in an end-to-end manner. MAMT can approximately constrain the consecutive joint policies' divergence to satisfy $\delta$-stationarity and alleviate the non-stationarity problem. Our method can bring noticeable and stable performance improvement compared with baselines in cooperative tasks of different complexity. | https://openreview.net/pdf/533ea53e5b32c81e14b06b4528f54a68836c63a0.pdf |
Information Bottleneck: Exact Analysis of (Quantized) Neural Networks | https://openreview.net/forum?id=kF9DZQQrU0w | https://openreview.net/forum?id=kF9DZQQrU0w | Stephan Sloth Lorenzen,Christian Igel,Mads Nielsen | ICLR 2022,Poster | The information bottleneck (IB) principle has been suggested as a way to analyze deep neural networks. The learning dynamics are studied by inspecting the mutual information (MI) between the hidden layers and the input and output. Notably, separate fitting and compression phases during training have been reported. This led to some controversy including claims that the observations are not reproducible and strongly dependent on the type of activation function used as well as on the way the MI is estimated. Our study confirms that different ways of binning when computing the MI lead to qualitatively different results, either supporting or refusing IB conjectures.
To resolve the controversy, we study the IB principle in settings where MI is non-trivial and can be computed exactly. We monitor the dynamics of quantized neural networks, that is, we discretize the whole deep learning system so that no approximation is required when computing the MI. This allows us to quantify the information flow without measurement errors.
In this setting, we observed a fitting phase for all layers and a compression phase for the output layer in all experiments; the compression in the hidden layers was dependent on the type of activation function. Our study shows that the initial IB results were not artifacts of binning when computing the MI. However, the critical claim that the compression phase may not be observed for some networks also holds true. | https://openreview.net/pdf/b617eacd043db498a3da393c1fd34223f0f25f55.pdf |
GLASS: GNN with Labeling Tricks for Subgraph Representation Learning | https://openreview.net/forum?id=XLxhEjKNbXj | https://openreview.net/forum?id=XLxhEjKNbXj | Xiyuan Wang,Muhan Zhang | ICLR 2022,Poster | Despite the remarkable achievements of Graph Neural Networks (GNNs) on graph representation learning, few works have tried to use them to predict properties of subgraphs in the whole graph. The existing state-of-the-art method SubGNN introduces an overly complicated subgraph-level GNN model which synthesizes three artificial channels each of which has two carefully designed subgraph-level message passing modules, yet only slightly outperforms a plain GNN which performs node-level message passing and then pools node embeddings within the subgraph. By analyzing SubGNN and plain GNNs, we find that the key for subgraph representation learning might be to distinguish nodes inside and outside the subgraph. With this insight, we propose an expressive and scalable labeling trick, namely max-zero-one, to enhance plain GNNs for subgraph tasks. The resulting model is called GLASS (GNN with LAbeling trickS for Subgraph). We theoretically characterize GLASS's expressive power. Compared with SubGNN, GLASS is more expressive, more scalable, and easier to implement. Experiments on eight benchmark datasets show that GLASS outperforms the strongest baseline by $14.8\%$ on average. And ablation analysis shows that our max-zero-one labeling trick can boost the performance of a plain GNN by up to $105\%$ in maximum, which illustrates the effectiveness of labeling trick on subgraph tasks. Furthermore, training a GLASS model only takes $37\%$ time needed for a SubGNN on average. | https://openreview.net/pdf/68ad0d714b6345734d34cf7edafb258497151385.pdf |
MoReL: Multi-omics Relational Learning | https://openreview.net/forum?id=DnG75_KyHjX | https://openreview.net/forum?id=DnG75_KyHjX | Arman Hasanzadeh,Ehsan Hajiramezanali,Nick Duffield,Xiaoning Qian | ICLR 2022,Poster | Multi-omics data analysis has the potential to discover hidden molecular interactions, revealing potential regulatory and/or signal transduction pathways for cellular processes of interest when studying life and disease systems. One of critical challenges when dealing with real-world multi-omics data is that they may manifest heterogeneous structures and data quality as often existing data may be collected from different subjects under different conditions for each type of omics data. We propose a novel deep Bayesian generative model to efficiently infer a multi-partite graph encoding molecular interactions across such heterogeneous views, using a fused Gromov-Wasserstein (FGW) regularization between latent representations of corresponding views for integrative analysis. With such an optimal transport regularization in the deep Bayesian generative model, it not only allows incorporating view-specific side information, either with graph-structured or unstructured data in different views, but also increases the model flexibility with the distribution-based regularization. This allows efficient alignment of heterogeneous latent variable distributions to derive reliable interaction predictions compared to the existing point-based graph embedding methods. Our experiments on several real-world datasets demonstrate enhanced performance of MoReL in inferring meaningful interactions compared to existing baselines. | https://openreview.net/pdf/081ce5efc31fe21daad00db739aa3979506ff0d3.pdf |
Provable Learning-based Algorithm For Sparse Recovery | https://openreview.net/forum?id=BwPaPxwgyQb | https://openreview.net/forum?id=BwPaPxwgyQb | Xinshi Chen,Haoran Sun,Le Song | ICLR 2022,Poster | Recovering sparse parameters from observational data is a fundamental problem in machine learning with wide applications. Many classic algorithms can solve this problem with theoretical guarantees, but their performances rely on choosing the correct hyperparameters. Besides, hand-designed algorithms do not fully exploit the particular problem distribution of interest. In this work, we propose a deep learning method for algorithm learning called PLISA (Provable Learning-based Iterative Sparse recovery Algorithm). PLISA is designed by unrolling a classic path-following algorithm for sparse recovery, with some components being more flexible and learnable. We theoretically show the improved recovery accuracy achievable by PLISA. Furthermore, we analyze the empirical Rademacher complexity of PLISA to characterize its generalization ability to solve new problems outside the training set. This paper contains novel theoretical contributions to the area of learning-based algorithms in the sense that (i) PLISA is generically applicable to a broad class of sparse estimation problems, (ii) generalization analysis has received less attention so far, and (iii) our analysis makes novel connections between the generalization ability and algorithmic properties such as stability and convergence of the unrolled algorithm, which leads to a tighter bound that can explain the empirical observations. The techniques could potentially be applied to analyze other learning-based algorithms in the literature. | https://openreview.net/pdf/5b72c15b934bbc248f48b3fadda48f2ffbdfaece.pdf |
Defending Against Image Corruptions Through Adversarial Augmentations | https://openreview.net/forum?id=jJOjjiZHy3h | https://openreview.net/forum?id=jJOjjiZHy3h | Dan Andrei Calian,Florian Stimberg,Olivia Wiles,Sylvestre-Alvise Rebuffi,András György,Timothy A Mann,Sven Gowal | ICLR 2022,Poster | Modern neural networks excel at image classification, yet they remain vulnerable to common image corruptions such as blur, speckle noise or fog. Recent methods that focus on this problem, such as AugMix and DeepAugment, introduce defenses that operate in expectation over a distribution of image corruptions. In contrast, the literature on Lp-norm bounded perturbations focuses on defenses against worst-case corruptions. In this work, we reconcile both approaches by proposing AdversarialAugment, a technique which optimizes the parameters of image-to-image models to generate adversarially corrupted augmented images. We theoretically motivate our method and give sufficient conditions for the consistency of its idealized version as well as that of DeepAugment. Our classifiers improve upon the state-of-the-art on common image corruption benchmarks conducted in expectation on CIFAR-10-C and improve worst-case performance against Lp-norm bounded perturbations on both CIFAR-10 and ImageNet. | https://openreview.net/pdf/22ad1c6524e3463a96b8f63f8935a1ed975fbede.pdf |
Attacking deep networks with surrogate-based adversarial black-box methods is easy | https://openreview.net/forum?id=Zf4ZdI4OQPV | https://openreview.net/forum?id=Zf4ZdI4OQPV | Nicholas A. Lord,Romain Mueller,Luca Bertinetto | ICLR 2022,Poster | A recent line of work on black-box adversarial attacks has revived the use of transfer from surrogate models by integrating it into query-based search. However, we find that existing approaches of this type underperform their potential, and can be overly complicated besides. Here, we provide a short and simple algorithm which achieves state-of-the-art results through a search which uses the surrogate network's class-score gradients, with no need for other priors or heuristics. The guiding assumption of the algorithm is that the studied networks are in a fundamental sense learning similar functions, and that a transfer attack from one to the other should thus be fairly "easy". This assumption is validated by the extremely low query counts and failure rates achieved: e.g. an untargeted attack on a VGG-16 ImageNet network using a ResNet-152 as the surrogate yields a median query count of 6 at a success rate of 99.9%. Code is available at https://github.com/fiveai/GFCS. | https://openreview.net/pdf/a443a40817b896b8fa11383453c012d785af6a17.pdf |
Autoregressive Diffusion Models | https://openreview.net/forum?id=Lm8T39vLDTE | https://openreview.net/forum?id=Lm8T39vLDTE | Emiel Hoogeboom,Alexey A. Gritsenko,Jasmijn Bastings,Ben Poole,Rianne van den Berg,Tim Salimans | ICLR 2022,Poster | We introduce Autoregressive Diffusion Models (ARDMs), a model class encompassing and generalizing order-agnostic autoregressive models (Uria et al., 2014) and absorbing discrete diffusion (Austin et al., 2021), which we show are special cases of ARDMs under mild assumptions. ARDMs are simple to implement and easy to train. Unlike standard ARMs, they do not require causal masking of model representations, and can be trained using an efficient objective similar to modern probabilistic diffusion models that scales favourably to highly-dimensional data. At test time, ARDMs support parallel generation which can be adapted to fit any given generation budget. We find that ARDMs require significantly fewer steps than discrete diffusion models to attain the same performance. Finally, we apply ARDMs to lossless compression, and show that they are uniquely suited to this task. Contrary to existing approaches based on bits-back coding, ARDMs obtain compelling results not only on complete datasets, but also on compressing single data points. Moreover, this can be done using a modest number of network calls for (de)compression due to the model's adaptable parallel generation. | https://openreview.net/pdf/1e4e9d350d86450e306f14f662b9cdf718fce184.pdf |
Auto-scaling Vision Transformers without Training | https://openreview.net/forum?id=H94a1_Pyr-6 | https://openreview.net/forum?id=H94a1_Pyr-6 | Wuyang Chen,Wei Huang,Xianzhi Du,Xiaodan Song,Zhangyang Wang,Denny Zhou | ICLR 2022,Poster | This work targets automated designing and scaling of Vision Transformers (ViTs). The motivation comes from two pain spots: 1) the lack of efficient and principled methods for designing and scaling ViTs; 2) the tremendous computational cost of training ViT that is much heavier than its convolution counterpart. To tackle these issues, we propose As-ViT, an auto-scaling framework for ViTs without training, which automatically discovers and scales up ViTs in an efficient and principled manner. Specifically, we first design a "seed" ViT topology by leveraging a training-free search process. This extremely fast search is fulfilled by a comprehensive study of ViT's network complexity, yielding a strong Kendall-tau correlation with ground-truth accuracies. Second, starting from the "seed" topology, we automate the scaling rule for ViTs by growing widths/depths to different ViT layers. This results in a series of architectures with different numbers of parameters in a single run. Finally, based on the observation that ViTs can tolerate coarse tokenization in early training stages, we propose a progressive tokenization strategy to train ViTs faster and cheaper. As a unified framework, As-ViT achieves strong performance on classification (83.5% top1 on ImageNet-1k) and detection (52.7% mAP on COCO) without any manual crafting nor scaling of ViT architectures: the end-to-end model design and scaling process costs only 12 hours on one V100 GPU. Our code is available at https://github.com/VITA-Group/AsViT. | https://openreview.net/pdf/ef4c46fde37c64720cfb924254715a045c828420.pdf |
Fine-grained Differentiable Physics: A Yarn-level Model for Fabrics | https://openreview.net/forum?id=KPEFXR1HdIo | https://openreview.net/forum?id=KPEFXR1HdIo | Deshan Gong,Zhanxing Zhu,Andrew J. Bulpitt,He Wang | ICLR 2022,Poster | Differentiable physics modeling combines physics models with gradient-based learning to provide model explicability and data efficiency. It has been used to learn dynamics, solve inverse problems and facilitate design, and is at its inception of impact. Current successes have concentrated on general physics models such as rigid bodies, deformable sheets, etc, assuming relatively simple structures and forces. Their granularity is intrinsically coarse and therefore incapable of modelling complex physical phenomena. Fine-grained models are still to be developed to incorporate sophisticated material structures and force interactions with gradient-based learning. Following this motivation, we propose a new differentiable fabrics model for composite materials such as cloths, where we dive into the granularity of yarns and model individual yarn physics and yarn-to-yarn interactions. To this end, we propose several differentiable forces, whose counterparts in empirical physics are indifferentiable, to facilitate gradient-based learning. These forces, albeit applied to cloths, are ubiquitous in various physical systems. Through comprehensive evaluation and comparison, we demonstrate our model's $\textit{explicability}$ in learning meaningful physical parameters, $\textit{versatility}$ in incorporating complex physical structures and heterogeneous materials, $\textit{data-efficiency}$ in learning, and $\textit{high-fidelity}$ in capturing subtle dynamics. | https://openreview.net/pdf/6d1903facd585c8674990513d5c9f2112b98160d.pdf |
Revisiting flow generative models for Out-of-distribution detection | https://openreview.net/forum?id=6y2KBh-0Fd9 | https://openreview.net/forum?id=6y2KBh-0Fd9 | Dihong Jiang,Sun Sun,Yaoliang Yu | ICLR 2022,Poster | Deep generative models have been widely used in practical applications such as the detection of out-of-distribution (OOD) data. In this work, we aim to re-examine the potential of generative flow models in OOD detection. We first propose a simple combination of univariate one-sample statistical test (e.g., Kolmogorov-Smirnov) and random projections in the latent space of flow models to perform OOD detection. Then, we propose a two-sample version of our test to account for imperfect flow models. Quite distinctly, our method does not pose parametric assumptions on OOD data and is capable of exploiting any flow model. Experimentally, firstly we confirm the efficacy of our method against state-of-the-art baselines through extensive experiments on several image datasets; secondly we investigate the relationship between model accuracy (e.g., the generation quality) and the OOD detection performance, and found surprisingly that they are not always positively correlated; and thirdly we show that detection in the latent space of flow models generally outperforms detection in the sample space across various OOD datasets, hence highlighting the benefits of training a flow model. | https://openreview.net/pdf/beca73c1366c5e014f7cb574e09aa9de9598f794.pdf |
Missingness Bias in Model Debugging | https://openreview.net/forum?id=Te5ytkqsnl | https://openreview.net/forum?id=Te5ytkqsnl | Saachi Jain,Hadi Salman,Eric Wong,Pengchuan Zhang,Vibhav Vineet,Sai Vemprala,Aleksander Madry | ICLR 2022,Poster | Missingness, or the absence of features from an input, is a concept fundamental to many model debugging tools. However, in computer vision, pixels cannot simply be removed from an image. One thus tends to resort to heuristics such as blacking out pixels, which may in turn introduce bias into the debugging process. We study such biases and, in particular, show how transformer-based architectures can enable a more natural implementation of missingness, which side-steps these issues and improves the reliability of model debugging in practice.
| https://openreview.net/pdf/c17968a4f7476f9b48d38625f1f4024268d299a9.pdf |
Meta Learning Low Rank Covariance Factors for Energy Based Deterministic Uncertainty | https://openreview.net/forum?id=GQd7mXSPua | https://openreview.net/forum?id=GQd7mXSPua | Jeffrey Ryan Willette,Hae Beom Lee,Juho Lee,Sung Ju Hwang | ICLR 2022,Poster | Numerous recent works utilize bi-Lipschitz regularization of neural network layers to preserve relative distances between data instances in the feature spaces of each layer. This distance sensitivity with respect to the data aids in tasks such as uncertainty calibration and out-of-distribution (OOD) detection. In previous works, features extracted with a distance sensitive model are used to construct feature covariance matrices which are used in deterministic uncertainty estimation or OOD detection. However, in cases where there is a distribution over tasks, these methods result in covariances which are sub-optimal, as they may not leverage all of the meta information which can be shared among tasks. With the use of an attentive set encoder, we propose to meta learn either diagonal or diagonal plus low-rank factors to efficiently construct task specific covariance matrices. Additionally, we propose an inference procedure which utilizes scaled energy to achieve a final predictive distribution which is well calibrated under a distributional dataset shift. | https://openreview.net/pdf/62e7fc9d01f6386911b5a5f46df746fe07a8c819.pdf |
Conditional Object-Centric Learning from Video | https://openreview.net/forum?id=aD7uesX1GF_ | https://openreview.net/forum?id=aD7uesX1GF_ | Thomas Kipf,Gamaleldin Fathy Elsayed,Aravindh Mahendran,Austin Stone,Sara Sabour,Georg Heigold,Rico Jonschkowski,Alexey Dosovitskiy,Klaus Greff | ICLR 2022,Poster | Object-centric representations are a promising path toward more systematic generalization by providing flexible abstractions upon which compositional world models can be built. Recent work on simple 2D and 3D datasets has shown that models with object-centric inductive biases can learn to segment and represent meaningful objects from the statistical structure of the data alone without the need for any supervision. However, such fully-unsupervised methods still fail to scale to diverse realistic data, despite the use of increasingly complex inductive biases such as priors for the size of objects or the 3D geometry of the scene. In this paper, we instead take a weakly-supervised approach and focus on how 1) using the temporal dynamics of video data in the form of optical flow and 2) conditioning the model on simple object location cues can be used to enable segmenting and tracking objects in significantly more realistic synthetic data. We introduce a sequential extension to Slot Attention which we train to predict optical flow for realistic looking synthetic scenes and show that conditioning the initial state of this model on a small set of hints, such as center of mass of objects in the first frame, is sufficient to significantly improve instance segmentation. These benefits generalize beyond the training distribution to novel objects, novel backgrounds, and to longer video sequences. We also find that such initial-state-conditioning can be used during inference as a flexible interface to query the model for specific objects or parts of objects, which could pave the way for a range of weakly-supervised approaches and allow more effective interaction with trained models. | https://openreview.net/pdf/1316f4949f9f4e100ae886bd9d15e275c1b8a39b.pdf |
Scale Efficiently: Insights from Pretraining and Finetuning Transformers | https://openreview.net/forum?id=f2OYVDyfIB | https://openreview.net/forum?id=f2OYVDyfIB | Yi Tay,Mostafa Dehghani,Jinfeng Rao,William Fedus,Samira Abnar,Hyung Won Chung,Sharan Narang,Dani Yogatama,Ashish Vaswani,Donald Metzler | ICLR 2022,Poster | There remain many open questions pertaining to the scaling behaviour of Transformer architectures. These scaling decisions and findings can be critical, as training runs often come with an associated computational cost which have both financial and/or environmental impact. The goal of this paper is to present scaling insights from pretraining and finetuning Transformers. While Kaplan et al. presents a comprehensive study of the scaling behaviour of Transformer language models, the scope is only on the upstream (pretraining) loss. Therefore, it is still unclear if these set of findings transfer to downstream task within the context of the pretrain-finetune paradigm. The key findings of this paper are as follows: (1) we show that aside from only the model size, model shape matters for downstream fine-tuning, (2) scaling protocols operate differently at different compute regions, (3) widely adopted T5-base and T5-large sizes are Pareto-inefficient. To this end, we present improved scaling protocols whereby our redesigned models achieve similar downstream fine-tuning quality while having 50\% fewer parameters and training 40\% faster compared to the widely adopted T5-base model. We publicly release over 100 pretrained checkpoints of different T5 configurations to facilitate future research and analysis. | https://openreview.net/pdf/bd048936d58108f06fb49d610e86a9a5dcb9b281.pdf |
Vitruvion: A Generative Model of Parametric CAD Sketches | https://openreview.net/forum?id=Ow1C7s3UcY | https://openreview.net/forum?id=Ow1C7s3UcY | Ari Seff,Wenda Zhou,Nick Richardson,Ryan P Adams | ICLR 2022,Poster | Parametric computer-aided design (CAD) tools are the predominant way that engineers specify physical structures, from bicycle pedals to airplanes to printed circuit boards. The key characteristic of parametric CAD is that design intent is encoded not only via geometric primitives, but also by parameterized constraints between the elements. This relational specification can be viewed as the construction of a constraint program, allowing edits to coherently propagate to other parts of the design. Machine learning offers the intriguing possibility of accelerating the design process via generative modeling of these structures, enabling new tools such as autocompletion, constraint inference, and conditional synthesis. In this work, we present such an approach to generative modeling of parametric CAD sketches, which constitute the basic computational building blocks of modern mechanical design. Our model, trained on real-world designs from the SketchGraphs dataset, autoregressively synthesizes sketches as sequences of primitives, with initial coordinates, and constraints that reference back to the sampled primitives. As samples from the model match the constraint graph representation used in standard CAD software, they may be directly imported, solved, and edited according to downstream design tasks. In addition, we condition the model on various contexts, including partial sketches (primers) and images of hand-drawn sketches. Evaluation of the proposed approach demonstrates its ability to synthesize realistic CAD sketches and its potential to aid the mechanical design workflow. | https://openreview.net/pdf/38ea22fe4cd42ce014e63702972f486b3c0b3995.pdf |
Space-Time Graph Neural Networks | https://openreview.net/forum?id=XJiajt89Omg | https://openreview.net/forum?id=XJiajt89Omg | Samar Hadou,Charilaos I Kanatsoulis,Alejandro Ribeiro | ICLR 2022,Poster | We introduce space-time graph neural network (ST-GNN), a novel GNN architecture, tailored to jointly process the underlying space-time topology of time-varying network data. The cornerstone of our proposed architecture is the composition of time and graph convolutional filters followed by pointwise nonlinear activation functions. We introduce a generic definition of convolution operators that mimic the diffusion process of signals over its underlying support. On top of this definition, we propose space-time graph convolutions that are built upon a composition of time and graph shift operators. We prove that ST-GNNs with multivariate integral Lipschitz filters are stable to small perturbations in the underlying graphs as well as small perturbations in the time domain caused by time warping. Our analysis shows that small variations in the network topology and time evolution of a system does not significantly affect the performance of ST-GNNs. Numerical experiments with decentralized control systems showcase the effectiveness and stability of the proposed ST-GNNs. | https://openreview.net/pdf/45cb8a6a28cdfdbd34c4053a847215f8bfe894a0.pdf |
Scattering Networks on the Sphere for Scalable and Rotationally Equivariant Spherical CNNs | https://openreview.net/forum?id=bjy5Zb2fo2 | https://openreview.net/forum?id=bjy5Zb2fo2 | Jason McEwen,Christopher Wallis,Augustine N. Mavor-Parker | ICLR 2022,Poster | Convolutional neural networks (CNNs) constructed natively on the sphere have been developed recently and shown to be highly effective for the analysis of spherical data. While an efficient framework has been formulated, spherical CNNs are nevertheless highly computationally demanding; typically they cannot scale beyond spherical signals of thousands of pixels. We develop scattering networks constructed natively on the sphere that provide a powerful representational space for spherical data. Spherical scattering networks are computationally scalable and exhibit rotational equivariance, while their representational space is invariant to isometries and provides efficient and stable signal representations. By integrating scattering networks as an additional type of layer in the generalized spherical CNN framework, we show how they can be leveraged to scale spherical CNNs to the high-resolution data typical of many practical applications, with spherical signals of many tens of megapixels and beyond. | https://openreview.net/pdf/583873ca12cbac94a9df056b123925c4d9781ccd.pdf |
Bayesian Neural Network Priors Revisited | https://openreview.net/forum?id=xkjqJYqRJy | https://openreview.net/forum?id=xkjqJYqRJy | Vincent Fortuin,Adrià Garriga-Alonso,Sebastian W. Ober,Florian Wenzel,Gunnar Ratsch,Richard E Turner,Mark van der Wilk,Laurence Aitchison | ICLR 2022,Poster | Isotropic Gaussian priors are the de facto standard for modern Bayesian neural network inference. However, it is unclear whether these priors accurately reflect our true beliefs about the weight distributions or give optimal performance. To find better priors, we study summary statistics of neural network weights in networks trained using stochastic gradient descent (SGD). We find that convolutional neural network (CNN) and ResNet weights display strong spatial correlations, while fully connected networks (FCNNs) display heavy-tailed weight distributions. We show that building these observations into priors can lead to improved performance on a variety of image classification datasets. Surprisingly, these priors mitigate the cold posterior effect in FCNNs, but slightly increase the cold posterior effect in ResNets. | https://openreview.net/pdf/5c1c882b8e944c1b7c1eef378588e7c5b8144659.pdf |
Goal-Directed Planning via Hindsight Experience Replay | https://openreview.net/forum?id=6NePxZwfae | https://openreview.net/forum?id=6NePxZwfae | Lorenzo Moro,Amarildo Likmeta,Enrico Prati,Marcello Restelli | ICLR 2022,Poster | We consider the problem of goal-directed planning under a deterministic transition model. Monte Carlo Tree Search has shown remarkable performance in solving deterministic control problems. It has been extended from complex continuous domains through function approximators to bias the search of the planning tree in AlphaZero. Nonetheless, these algorithms still struggle with control problems with sparse rewards, such as goal-directed domains, where a positive reward is awarded only when reaching a goal state. In this work, we recast AlphaZero with Hindsight Experience Replay to tackle complex goal-directed planning tasks. We perform a thorough empirical evaluation in several simulated domains, including a novel application to a quantum compiling domain. | https://openreview.net/pdf/97fe32650bb5e2dbad89bb4c2de9b120fc5caea0.pdf |
Hybrid Random Features | https://openreview.net/forum?id=EMigfE6ZeS | https://openreview.net/forum?id=EMigfE6ZeS | Krzysztof Marcin Choromanski,Han Lin,Haoxian Chen,Arijit Sehanobish,Yuanzhe Ma,Deepali Jain,Jake Varley,Andy Zeng,Michael S Ryoo,Valerii Likhosherstov,Dmitry Kalashnikov,Vikas Sindhwani,Adrian Weller | ICLR 2022,Poster | We propose a new class of random feature methods for linearizing softmax and Gaussian kernels called hybrid random features (HRFs) that automatically adapt the quality of kernel estimation to provide most accurate approximation in the defined regions of interest. Special instantiations of HRFs lead to well-known methods such as trigonometric (Rahimi & Recht, 2007) or (recently introduced in the context of linear-attention Transformers) positive random features (Choromanski et al., 2021). By generalizing Bochner’s Theorem for softmax/Gaussian kernels and leveraging random features for compositional kernels, the HRF-mechanism provides strong theoretical guarantees - unbiased approximation and strictly smaller worst-case relative errors than its counterparts. We conduct exhaustive empirical evaluation of HRF ranging from pointwise kernel estimation experiments, through tests on data admitting clustering structure to benchmarking implicit-attention Transformers (also for downstream Robotics applications), demonstrating its quality in a wide spectrum of machine learning problems. | https://openreview.net/pdf/21d31a35dc86b24e3937c906608595058a93a24c.pdf |
Pretrained Language Model in Continual Learning: A Comparative Study | https://openreview.net/forum?id=figzpGMrdD | https://openreview.net/forum?id=figzpGMrdD | Tongtong Wu,Massimo Caccia,Zhuang Li,Yuan-Fang Li,Guilin Qi,Gholamreza Haffari | ICLR 2022,Poster | Continual learning (CL) is a setting in which a model learns from a stream of incoming data while avoiding to forget previously learned knowledge. Pre-trained language models (PLMs) have been successfully employed in continual learning of different natural language problems. With the rapid development of many continual learning methods and PLMs, understanding and disentangling their interactions become essential for continued improvement of continual learning performance. In this paper, we thoroughly compare the continual learning performance over the combination of 5 PLMs and 4 CL approaches on 3 benchmarks in 2 typical incremental settings. Our extensive experimental analyses reveal interesting performance differences across PLMs and across CL methods. Furthermore, our representativeness probing analyses dissect PLMs’ performance characteristics in a layer-wise and task-wise manner, uncovering the extent to which their inner layers suffer from forgetting, and the effect of different CL approaches on each layer. Finally, our observations and analyses open up a number of important research questions that will inform and guide the design of effective continual learning techniques. | https://openreview.net/pdf/eecf8ea887fe5b42c7b3b93b2fb938fed766f7e2.pdf |
Salient ImageNet: How to discover spurious features in Deep Learning? | https://openreview.net/forum?id=XVPqLyNxSyh | https://openreview.net/forum?id=XVPqLyNxSyh | Sahil Singla,Soheil Feizi | ICLR 2022,Poster | Deep neural networks can be unreliable in the real world especially when they heavily use {\it spurious} features for their predictions. Focusing on image classifications, we define {\it core features} as the set of visual features that are always a part of the object definition while {\it spurious features} are the ones that are likely to {\it co-occur} with the object but not a part of it (e.g., attribute ``fingers" for class ``band aid"). Traditional methods for discovering spurious features either require extensive human annotations (thus, not scalable), or are useful on specific models. In this work, we introduce a {\it general} framework to discover a subset of spurious and core visual features used in inferences of a general model and localize them on a large number of images with minimal human supervision. Our methodology is based on this key idea: to identify spurious or core \textit{visual features} used in model predictions, we identify spurious or core \textit{neural features} (penultimate layer neurons of a robust model) via limited human supervision (e.g., using top 5 activating images per feature). We then show that these neural feature annotations {\it generalize} extremely well to many more images {\it without} any human supervision. We use the activation maps for these neural features as the soft masks to highlight spurious or core visual features. Using this methodology, we introduce the {\it Salient Imagenet} dataset containing core and spurious masks for a large set of samples from Imagenet. Using this dataset, we show that several popular Imagenet models rely heavily on various spurious features in their predictions, indicating the standard accuracy alone is not sufficient to fully assess model' performance specially in safety-critical applications. Code is available at \url{https://github.com/singlasahil14/salient_imagenet}. | https://openreview.net/pdf/078e6e26ef046a2af14430f17a7b149613824cc7.pdf |
Differentiable DAG Sampling | https://openreview.net/forum?id=9wOQOgNe-w | https://openreview.net/forum?id=9wOQOgNe-w | Bertrand Charpentier,Simon Kibler,Stephan Günnemann | ICLR 2022,Poster | We propose a new differentiable probabilistic model over DAGs (DP-DAG). DP-DAG allows fast and differentiable DAG sampling suited to continuous optimization. To this end, DP-DAG samples a DAG by successively (1) sampling a linear ordering of the node and (2) sampling edges consistent with the sampled linear ordering. We further propose VI-DP-DAG, a new method for DAG learning from observational data which combines DP-DAG with variational inference. Hence,VI-DP-DAG approximates the posterior probability over DAG edges given the observed data. VI-DP-DAG is guaranteed to output a valid DAG at any time during training and does not require any complex augmented Lagrangian optimization scheme in contrast to existing differentiable DAG learning approaches. In our extensive experiments, we compare VI-DP-DAG to other differentiable DAG learning baselines on synthetic and real datasets. VI-DP-DAG significantly improves DAG structure and causal mechanism learning while training faster than competitors. | https://openreview.net/pdf/b8fd21af0b7c600db973be1927034685eec3b104.pdf |
Evaluating Model-Based Planning and Planner Amortization for Continuous Control | https://openreview.net/forum?id=SS8F6tFX3- | https://openreview.net/forum?id=SS8F6tFX3- | Arunkumar Byravan,Leonard Hasenclever,Piotr Trochim,Mehdi Mirza,Alessandro Davide Ialongo,Yuval Tassa,Jost Tobias Springenberg,Abbas Abdolmaleki,Nicolas Heess,Josh Merel,Martin Riedmiller | ICLR 2022,Poster | There is a widespread intuition that model-based control methods should be able to surpass the data efficiency of model-free approaches. In this paper we attempt to evaluate this intuition on various challenging locomotion tasks. We take a hybrid approach, combining model predictive control (MPC) with a learned model and model-free policy learning; the learned policy serves as a proposal for MPC. We show that MPC with learned proposals and models (trained on the fly or transferred from related tasks) can significantly improve performance and data efficiency with respect to model-free methods. However, we find that well-tuned model-free agents are strong baselines even for high DoF control problems. Finally, we show that it is possible to distil a model-based planner into a policy that amortizes the planning computation without any loss of performance. | https://openreview.net/pdf/ae6d13b114d9f64756fe8146f4c5b0e55037ad9a.pdf |
Hierarchical Few-Shot Imitation with Skill Transition Models | https://openreview.net/forum?id=xKZ4K0lTj_ | https://openreview.net/forum?id=xKZ4K0lTj_ | Kourosh Hakhamaneshi,Ruihan Zhao,Albert Zhan,Pieter Abbeel,Michael Laskin | ICLR 2022,Poster | A desirable property of autonomous agents is the ability to both solve long-horizon problems and generalize to unseen tasks. Recent advances in data-driven skill learning have shown that extracting behavioral priors from offline data can enable agents to solve challenging long-horizon tasks with reinforcement learning. However, generalization to tasks unseen during behavioral prior training remains an outstanding challenge. To this end, we present Few-shot Imitation with Skill Transition Models (FIST), an algorithm that extracts skills from offline data and utilizes them to generalize to unseen tasks given a few downstream demonstrations. FIST learns an inverse skill dynamics model, a distance function, and utilizes a semi-parametric approach for imitation. We show that FIST is capable of generalizing to new tasks and substantially outperforms prior baselines in navigation experiments requiring traversing unseen parts of a large maze and 7-DoF robotic arm experiments requiring manipulating previously unseen objects in a kitchen. | https://openreview.net/pdf/2b401efeb1537504e778bc1d6c08a29ac6cdf9f1.pdf |
End-to-End Learning of Probabilistic Hierarchies on Graphs | https://openreview.net/forum?id=g2LCQwG7Of | https://openreview.net/forum?id=g2LCQwG7Of | Daniel Zügner,Bertrand Charpentier,Morgane Ayle,Sascha Geringer,Stephan Günnemann | ICLR 2022,Poster | We propose a novel probabilistic model over hierarchies on graphs obtained by continuous relaxation of tree-based hierarchies. We draw connections to Markov chain theory, enabling us to perform hierarchical clustering by efficient end-to-end optimization of relaxed versions of quality metrics such as Dasgupta cost or Tree-Sampling Divergence (TSD).
We show that our model learns rich, high-quality hierarchies present in 11 real world graphs, including a large graph with 2.3M nodes. Our model consistently outperforms recent as well as strong traditional baselines such as average linkage.
Our model also obtains strong results on link prediction despite not being trained on this task, highlighting the quality of the hierarchies discovered by our model. | https://openreview.net/pdf/fe59d617689e48f429a561c55779a24886d3ecf4.pdf |
GeneDisco: A Benchmark for Experimental Design in Drug Discovery | https://openreview.net/forum?id=-w2oomO6qgc | https://openreview.net/forum?id=-w2oomO6qgc | Arash Mehrjou,Ashkan Soleymani,Andrew Jesson,Pascal Notin,Yarin Gal,Stefan Bauer,Patrick Schwab | ICLR 2022,Poster | In vitro cellular experimentation with genetic interventions, using for example CRISPR technologies, is an essential step in early-stage drug discovery and target validation that serves to assess initial hypotheses about causal associations between biological mechanisms and disease pathologies. With billions of potential hypotheses to test, the experimental design space for in vitro genetic experiments is extremely vast, and the available experimental capacity - even at the largest research institutions in the world - pales in relation to the size of this biological hypothesis space. Machine learning methods, such as active and reinforcement learning, could aid in optimally exploring the vast biological space by integrating prior knowledge from various information sources as well as extrapolating to yet unexplored areas of the experimental design space based on available data. However, there exist no standardised benchmarks and data sets for this challenging task and little research has been conducted in this area to date. Here, we introduce GeneDisco, a benchmark suite for evaluating active learning algorithms for experimental design in drug discovery. GeneDisco contains a curated set of multiple publicly available experimental data sets as well as open-source implementations of state-of-the-art active learning policies for experimental design and exploration. | https://openreview.net/pdf/46263f4b010fc363d4351c09cb675b69418c8680.pdf |
GraphENS: Neighbor-Aware Ego Network Synthesis for Class-Imbalanced Node Classification | https://openreview.net/forum?id=MXEl7i-iru | https://openreview.net/forum?id=MXEl7i-iru | Joonhyung Park,Jaeyun Song,Eunho Yang | ICLR 2022,Poster | In many real-world node classification scenarios, nodes are highly class-imbalanced, where graph neural networks (GNNs) can be readily biased to major class instances. Albeit existing class imbalance approaches in other domains can alleviate this issue to some extent, they do not consider the impact of message passing between nodes. In this paper, we hypothesize that overfitting to the neighbor sets of minor class due to message passing is a major challenge for class-imbalanced node classification. To tackle this issue, we propose GraphENS, a novel augmentation method that synthesizes the whole ego network for minor class (minor node and its one-hop neighbors) by combining two different ego networks based on their similarity. Additionally, we introduce a saliency-based node mixing method to exploit the abundant class-generic attributes of other nodes while blocking the injection of class-specific features. Our approach consistently outperforms the baselines over multiple node classification benchmark datasets and architectures. | https://openreview.net/pdf/2005a08039a74bf579bee253f6edc87920ecb3af.pdf |
Continuously Discovering Novel Strategies via Reward-Switching Policy Optimization | https://openreview.net/forum?id=hcQHRHKfN_ | https://openreview.net/forum?id=hcQHRHKfN_ | Zihan Zhou,Wei Fu,Bingliang Zhang,Yi Wu | ICLR 2022,Poster | We present Reward-Switching Policy Optimization (RSPO), a paradigm to discover diverse strategies in complex RL environments by iteratively finding novel policies that are both locally optimal and sufficiently different from existing ones. To encourage the learning policy to consistently converge towards a previously undiscovered local optimum, RSPO switches between extrinsic and intrinsic rewards via a trajectory-based novelty measurement during the optimization process. When a sampled trajectory is sufficiently distinct, RSPO performs standard policy optimization with extrinsic rewards. For trajectories with high likelihood under existing policies, RSPO utilizes an intrinsic diversity reward to promote exploration. Experiments show that RSPO is able to discover a wide spectrum of strategies in a variety of domains, ranging from single-agent navigation tasks and MuJoCo control to multi-agent stag-hunt games and the StarCraft II Multi-Agent Challenge. | https://openreview.net/pdf/a123c43ffc76a63f5bbe2eccdf4bba5a4233b9ed.pdf |
Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting | https://openreview.net/forum?id=wwDg3bbYBIq | https://openreview.net/forum?id=wwDg3bbYBIq | Hyunwook Lee,Seungmin Jin,Hyeshin Chu,Hongkyu Lim,Sungahn Ko | ICLR 2022,Poster | Traffic forecasting is a challenging problem due to complex road networks and sudden speed changes caused by various events on roads. Several models have been proposed to solve this challenging problem, with a focus on learning the spatio-temporal dependencies of roads. In this work, we propose a new perspective for converting the forecasting problem into a pattern-matching task, assuming that large traffic data can be represented by a set of patterns. To evaluate the validity of this new perspective, we design a novel traffic forecasting model called Pattern-Matching Memory Networks (PM-MemNet), which learns to match input data to representative patterns with a key-value memory structure. We first extract and cluster representative traffic patterns that serve as keys in the memory. Then, by matching the extracted keys and inputs, PM-MemNet acquires the necessary information on existing traffic patterns from the memory and uses it for forecasting. To model the spatio-temporal correlation of traffic, we proposed a novel memory architecture, GCMem, which integrates attention and graph convolution. The experimental results indicate that PM-MemNet is more accurate than state-of-the-art models, such as Graph WaveNet, with higher responsiveness. We also present a qualitative analysis describing how PM-MemNet works and achieves higher accuracy when road speed changes rapidly. | https://openreview.net/pdf/3d2d0f8c044b58b34cdb6d2a91fcf86fc3792cae.pdf |
Why Propagate Alone? Parallel Use of Labels and Features on Graphs | https://openreview.net/forum?id=VTNjxbFRKly | https://openreview.net/forum?id=VTNjxbFRKly | Yangkun Wang,Jiarui Jin,Weinan Zhang,Yang Yongyi,Jiuhai Chen,Quan Gan,Yong Yu,Zheng Zhang,Zengfeng Huang,David Wipf | ICLR 2022,Poster | One of the challenges of graph-based semi-supervised learning over ordinary supervised learning for classification tasks lies in label utilization. The direct use of ground-truth labels in graphs for training purposes can result in a parametric model learning trivial degenerate solutions (e.g., an identity mapping from input to output). In addressing this issue, a label trick has recently been proposed in the literature and applied to a wide range of graph neural network (GNN) architectures, achieving state-of-the-art results on various datasets. The essential idea is to randomly split the observed labels on the graph and use a fraction of them as input to the model (along with original node features), and predict the remaining fraction. Despite its success in enabling GNNs to propagate features and labels simultaneously, this approach has never been analyzed from a theoretical perspective, nor fully explored across certain natural use cases. In this paper, we demonstrate that under suitable settings, this stochastic trick can be reduced to a more interpretable deterministic form, allowing us to better explain its behavior, including an emergent regularization effect, and motivate broader application scenarios. Our experimental results corroborate these analyses while also demonstrating improved node classification performance applying the label trick in new domains. | https://openreview.net/pdf/b179d9c177f43181658e714875f794ec799afbb8.pdf |
Learning by Directional Gradient Descent | https://openreview.net/forum?id=5i7lJLuhTm | https://openreview.net/forum?id=5i7lJLuhTm | David Silver,Anirudh Goyal,Ivo Danihelka,Matteo Hessel,Hado van Hasselt | ICLR 2022,Poster | How should state be constructed from a sequence of observations, so as to best achieve some objective? Most deep learning methods update the parameters of the state representation by gradient descent. However, no prior method for computing the gradient is fully satisfactory, for example consuming too much memory, introducing too much variance, or adding too much bias. In this work, we propose a new learning algorithm that addresses these limitations. The basic idea is to update the parameters of the representation by using the directional derivative along a candidate direction, a quantity that may be computed online with the same computational cost as the representation itself. We consider several different choices of candidate direction, including random selection and approximations to the true gradient, and investigate their performance on several synthetic tasks.
| https://openreview.net/pdf/a9213856f2d7ed9866d90862df188f6d5456262b.pdf |
Maximum Entropy RL (Provably) Solves Some Robust RL Problems | https://openreview.net/forum?id=PtSAD3caaA2 | https://openreview.net/forum?id=PtSAD3caaA2 | Benjamin Eysenbach,Sergey Levine | ICLR 2022,Poster | Many potential applications of reinforcement learning (RL) require guarantees that the agent will perform well in the face of disturbances to the dynamics or reward function. In this paper, we prove theoretically that maximum entropy (MaxEnt) RL maximizes a lower bound on a robust RL objective, and thus can be used to learn policies that are robust to some disturbances in the dynamics and the reward function. While this capability of MaxEnt RL has been observed empirically in prior work, to the best of our knowledge our work provides the first rigorous proof and theoretical characterization of the MaxEnt RL robust set. While a number of prior robust RL algorithms have been designed to handle similar disturbances to the reward function or dynamics, these methods typically require additional moving parts and hyperparameters on top of a base RL algorithm. In contrast, our results suggest that MaxEnt RL by itself is robust to certain disturbances, without requiring any additional modifications. While this does not imply that MaxEnt RL is the best available robust RL method, MaxEnt RL is a simple robust RL method with appealing formal guarantees. | https://openreview.net/pdf/3fb82fdb60ea0802001d004d0b6951f5226c3ff0.pdf |
A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training | https://openreview.net/forum?id=XhF2VOMRHS | https://openreview.net/forum?id=XhF2VOMRHS | Yifei Wang,Yisen Wang,Jiansheng Yang,Zhouchen Lin | ICLR 2022,Poster | Adversarial Training (AT) is known as an effective approach to enhance the robustness of deep neural networks. Recently researchers notice that robust models with AT have good generative ability and can synthesize realistic images, while the reason behind it is yet under-explored. In this paper, we demystify this phenomenon by developing a unified probabilistic framework, called Contrastive Energy-based Models (CEM). On the one hand, we provide the first probabilistic characterization of AT through a unified understanding of robustness and generative ability. On the other hand, our unified framework can be extended to the unsupervised scenario, which interprets unsupervised contrastive learning as an important sampling of CEM. Based on these, we propose a principled method to develop adversarial learning and sampling methods. Experiments show that the sampling methods derived from our framework improve the sample quality in both supervised and unsupervised learning. Notably, our unsupervised adversarial sampling method achieves an Inception score of 9.61 on CIFAR-10, which is superior to previous energy-based models and comparable to state-of-the-art generative models. | https://openreview.net/pdf/d2b73abba174f5c4a4f7a8236fccc58a742e0bc5.pdf |
Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks | https://openreview.net/forum?id=e95i1IHcWj | https://openreview.net/forum?id=e95i1IHcWj | Haorui Wang,Haoteng Yin,Muhan Zhang,Pan Li | ICLR 2022,Poster | Graph neural networks (GNN) have shown great advantages in many graph-based learning tasks but often fail to predict accurately for a task-based on sets of nodes such as link/motif prediction and so on. Many works have recently proposed to address this problem by using random node features or node distance features. However, they suffer from either slow convergence, inaccurate prediction, or high complexity. In this work, we revisit GNNs that allow using positional features of nodes given by positional encoding (PE) techniques such as Laplacian Eigenmap, Deepwalk, etc. GNNs with PE often get criticized because they are not generalizable to unseen graphs (inductive) or stable. Here, we study these issues in a principled way and propose a provable solution, a class of GNN layers termed PEG with rigorous mathematical analysis. PEG uses separate channels to update the original node features and positional features. PEG imposes permutation equivariance w.r.t. the original node features and rotation equivariance w.r.t. the positional features simultaneously. Extensive link prediction experiments over 8 real-world networks demonstrate the advantages of PEG in generalization and scalability. Code is available at https://github.com/Graph-COM/PEG. | https://openreview.net/pdf/cf28a7040f308283c69b833cd6901be5151a8cbc.pdf |
BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation Models | https://openreview.net/forum?id=Mng8CQ9eBW | https://openreview.net/forum?id=Mng8CQ9eBW | Kangjie Chen,Yuxian Meng,Xiaofei Sun,Shangwei Guo,Tianwei Zhang,Jiwei Li,Chun Fan | ICLR 2022,Poster | Pre-trained Natural Language Processing (NLP) models, which can be adapted to a variety of downstream language tasks via fine-tuning, highly accelerate the learning progress of NLP models. However, NLP models have been shown to be vulnerable to backdoor attacks. Previous NLP backdoor attacks mainly focus on one specific task. This limitation makes existing solutions less applicable to different NLP models which have been widely used in various tasks.
In this work, we propose BadPre, the first backdoor attack against various downstream models built based on pre-trained NLP models. BadPre can launch trojan attacks against different language tasks with the same trigger.
The key insight of our approach is that downstream models can inherit the security characteristics from the pre-trained models. Specifically, we leverage data posing to the pre-trained NLP models and then inference the downstream models with sentences embedded triggers. Furthermore, to fool backdoor detectors, we design a novel adversarial attack method to generate a more robust trigger.
Experimental results indicate that our approach can effectively attack a wide range of downstream NLP tasks and exhibit significant robustness against backdoor detectors. | https://openreview.net/pdf/4ac63a977d9d85abecd09a2214e195931e835ab5.pdf |
Shallow and Deep Networks are Near-Optimal Approximators of Korobov Functions | https://openreview.net/forum?id=AV8FPoMTTa | https://openreview.net/forum?id=AV8FPoMTTa | Moise Blanchard,Mohammed Amine Bennouna | ICLR 2022,Poster | In this paper, we analyze the number of neurons and training parameters that a neural network needs to approximate multivariate functions of bounded second mixed derivatives --- Korobov functions. We prove upper bounds on these quantities for shallow and deep neural networks, drastically lessening the curse of dimensionality. Our bounds hold for general activation functions, including ReLU. We further prove that these bounds nearly match the minimal number of parameters any continuous function approximator needs to approximate Korobov functions, showing that neural networks are near-optimal function approximators. | https://openreview.net/pdf/b207b8552118608b3abd1048731747b6575620fc.pdf |
What Makes Better Augmentation Strategies? Augment Difficult but Not too Different | https://openreview.net/forum?id=Ucx3DQbC9GH | https://openreview.net/forum?id=Ucx3DQbC9GH | Jaehyung Kim,Dongyeop Kang,Sungsoo Ahn,Jinwoo Shin | ICLR 2022,Poster | The practice of data augmentation has been extensively used to boost the performance of deep neural networks for various NLP tasks. It is more effective when only a limited number of labeled samples is available, e.g., low-data or class-imbalanced regimes. Most current augmentation techniques rely on parameter tuning or inherent randomness; hence, their effectiveness largely varies on the tasks. To efficiently find the best augmentation strategy for each task, learning data augmentation policy is a promising solution, but the question of what makes a good augmentation in NLP tasks and how to design the reward function for learning a good policy remains under-explored. To answer this, we hypothesize that good data augmentation should construct more diverse and challenging samples for providing informative training signals, while avoiding the risk of losing the semantics of original samples. Therefore, we design a novel reward function for updating the augmentation policy to construct difficult but not too different samples (DND). Particularly, we jointly optimize a data augmentation policy while training the model, to construct the augmented samples with low confidence but a high semantic similarity with original ones. In addition, we introduce a sample re-weighting scheme to focus on difficult augmented samples after the original ones are learned confidently for more effective learning from the augmented ones. Our learning-based augmentation outperforms the recent state-of-the-art augmentation schemes on various text classification tasks and GLUE benchmark by successfully discovering the effective augmentations for each task. Remarkably, our method is more effective on the challenging low-data and class-imbalanced regimes, and the learned augmentation policy is well-transferable to the different tasks and models. | https://openreview.net/pdf/133b80ca3acfcd64abacb3126997fc8a6c4b95ea.pdf |
Generative Pseudo-Inverse Memory | https://openreview.net/forum?id=Harn4_EZBw | https://openreview.net/forum?id=Harn4_EZBw | Kha Pham,Hung Le,Man Ngo,Truyen Tran,Bao Ho,Svetha Venkatesh | ICLR 2022,Poster | We propose Generative Pseudo-Inverse Memory (GPM), a class of deep generative memory models that are fast to write in and read out. Memory operations are recast as seeking robust solutions of linear systems, which naturally lead to the use of matrix pseudo-inverses. The pseudo-inverses are iteratively approximated, with practical computation complexity of almost $O(1)$. We prove theoretically and verify empirically that our model can retrieve exactly what have been written to the memory under mild conditions. A key capability of GPM is iterative reading, during which the attractor dynamics towards fixed points are enabled, allowing the model to iteratively improve sample quality in denoising and generating. More impressively, GPM can store a large amount of data while maintaining key abilities of accurate retrieving of stored patterns, denoising of corrupted data and generating novel samples. Empirically we demonstrate the efficiency and versatility of GPM on a comprehensive suite of experiments involving binarized MNIST, binarized Omniglot, FashionMNIST, CIFAR10 & CIFAR100 and CelebA. | https://openreview.net/pdf/1ff8e1fcd5f0f7899be75a462bcde482ae98fbfd.pdf |
A Deep Variational Approach to Clustering Survival Data | https://openreview.net/forum?id=RQ428ZptQfU | https://openreview.net/forum?id=RQ428ZptQfU | Laura Manduchi,Ričards Marcinkevičs,Michela C. Massi,Thomas Weikert,Alexander Sauter,Verena Gotta,Timothy Müller,Flavio Vasella,Marian C. Neidert,Marc Pfister,Bram Stieltjes,Julia E Vogt | ICLR 2022,Poster | In this work, we study the problem of clustering survival data — a challenging and so far under-explored task. We introduce a novel semi-supervised probabilistic approach to cluster survival data by leveraging recent advances in stochastic gradient variational inference. In contrast to previous work, our proposed method employs a deep generative model to uncover the underlying distribution of both the explanatory variables and censored survival times. We compare our model to the related work on clustering and mixture models for survival data in comprehensive experiments on a wide range of synthetic, semi-synthetic, and real-world datasets, including medical imaging data. Our method performs better at identifying clusters and is competitive at predicting survival times. Relying on novel generative assumptions, the proposed model offers a holistic perspective on clustering survival data and holds a promise of discovering subpopulations whose survival is regulated by different generative mechanisms. | https://openreview.net/pdf/4e7d71ca800835719ffc2ca92d89f481f7c33f71.pdf |
GPT-Critic: Offline Reinforcement Learning for End-to-End Task-Oriented Dialogue Systems | https://openreview.net/forum?id=qaxhBG1UUaS | https://openreview.net/forum?id=qaxhBG1UUaS | Youngsoo Jang,Jongmin Lee,Kee-Eung Kim | ICLR 2022,Poster | 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. | https://openreview.net/pdf/c1d0299a479595ff9da923e14398de97f2382394.pdf |
Charformer: Fast Character Transformers via Gradient-based Subword Tokenization | https://openreview.net/forum?id=JtBRnrlOEFN | https://openreview.net/forum?id=JtBRnrlOEFN | Yi Tay,Vinh Q. Tran,Sebastian Ruder,Jai Gupta,Hyung Won Chung,Dara Bahri,Zhen Qin,Simon Baumgartner,Cong Yu,Donald Metzler | ICLR 2022,Poster | State-of-the-art models in natural language processing rely on separate rigid subword tokenization algorithms, which limit their generalization ability and adaptation to new settings. In this paper, we propose a new model inductive bias that learns a subword tokenization end-to-end as part of the model. To this end, we introduce a soft gradient-based subword tokenization module (GBST) that automatically learns latent subword representations from characters in a data-driven fashion. Concretely, GBST enumerates candidate subword blocks and learns to score them in a position-wise fashion using a block scoring network. We additionally introduce Charformer, a deep Transformer model that integrates GBST and operates on the character level. Via extensive experiments on English GLUE, multilingual, and noisy text datasets, we show that Charformer outperforms a series of competitive character-level baselines while generally performing on par and sometimes outperforming subword-based models. Additionally, Charformer is fast, improving the speed of vanilla character-level Transformers by up to while maintaining quality. We believe this work paves the way for highly performant token-free models that are trained completely end-to-end. | https://openreview.net/pdf/73e9030ce98662e38514127e7c9b665b4386ed44.pdf |
Regularized Autoencoders for Isometric Representation Learning | https://openreview.net/forum?id=mQxt8l7JL04 | https://openreview.net/forum?id=mQxt8l7JL04 | Yonghyeon Lee,Sangwoong Yoon,MinJun Son,Frank C. Park | ICLR 2022,Poster | The recent success of autoencoders for representation learning can be traced in large part to the addition of a regularization term.
Such regularized autoencoders ``constrain" the representation so as to prevent overfitting to the data while producing a parsimonious generative model. A regularized autoencoder should in principle learn not only the data manifold, but also a set of geometry-preserving coordinates for the latent representation space; by geometry-preserving we mean that the latent space representation should attempt to preserve actual distances and angles on the data manifold. In this paper we first formulate a hierarchy for geometry-preserving mappings (isometry, conformal mapping of degree $k$, area-preserving mappings). We then show that a conformal regularization term of degree zero -- i.e., one that attempts to preserve angles and relative distances, instead of angles and exact distances -- produces data representations that are superior to other existing methods. Applying our algorithm to an unsupervised information retrieval task for CelebA data with 40 annotations, we achieve 79\% precision at five retrieved images, an improvement of more than 10\% compared to recent related work. Code is available at https://github.com/Gabe-YHLee/IRVAE-public. | https://openreview.net/pdf/a3c7c1d2d5e61bde51506a5b40b5a02f66d0d8e3.pdf |
Knowledge Removal in Sampling-based Bayesian Inference | https://openreview.net/forum?id=dTqOcTUOQO | https://openreview.net/forum?id=dTqOcTUOQO | Shaopeng Fu,Fengxiang He,Dacheng Tao | ICLR 2022,Poster | The right to be forgotten has been legislated in many countries, but its enforcement in the AI industry would cause unbearable costs. When single data deletion requests come, companies may need to delete the whole models learned with massive resources. Existing works propose methods to remove knowledge learned from data for explicitly parameterized models, which however are not appliable to the sampling-based Bayesian inference, {\it i.e.}, Markov chain Monte Carlo (MCMC), as MCMC can only infer implicit distributions. In this paper, we propose the first machine unlearning algorithm for MCMC. We first convert the MCMC unlearning problem into an explicit optimization problem. Based on this problem conversion, an {\it MCMC influence function} is designed to provably characterize the learned knowledge from data, which then delivers the MCMC unlearning algorithm. Theoretical analysis shows that MCMC unlearning would not compromise the generalizability of the MCMC models. Experiments on Gaussian mixture models and Bayesian neural networks confirm the effectiveness of the proposed algorithm. The code is available at \url{https://github.com/fshp971/mcmc-unlearning}. | https://openreview.net/pdf/a42ad90a502167268f1ba4c67f57150bf59ccbc9.pdf |
Actor-critic is implicitly biased towards high entropy optimal policies | https://openreview.net/forum?id=vEZyTBRPP6o | https://openreview.net/forum?id=vEZyTBRPP6o | Yuzheng Hu,Ziwei Ji,Matus Telgarsky | ICLR 2022,Poster | We show that the simplest actor-critic method — a linear softmax policy updated with TD through interaction with a linear MDP, but featuring no explicit regularization or exploration — does not merely find an optimal policy, but moreover prefers high entropy optimal policies. To demonstrate the strength of this bias, the algorithm not only has no regularization, no projections, and no exploration like $\epsilon$-greedy, but is moreover trained on a single trajectory with no resets. The key consequence of the high entropy bias is that uniform mixing assumptions on the MDP, which exist in some form in all prior work, can be dropped: the implicit regularization of the high entropy bias is enough to ensure that all chains mix and an optimal policy is reached with high probability. As auxiliary contributions, this work decouples concerns between the actor and critic by writing the actor update as an explicit mirror descent, provides tools to uniformly bound mixing times within KL balls of policy space, and provides a projection-free TD analysis with its own implicit bias which can be run from an unmixed starting distribution.
| https://openreview.net/pdf/e3148627ceb1e142df2619915a089a8c90153ef0.pdf |
Igeood: An Information Geometry Approach to Out-of-Distribution Detection | https://openreview.net/forum?id=mfwdY3U_9ea | https://openreview.net/forum?id=mfwdY3U_9ea | Eduardo Dadalto Camara Gomes,Florence Alberge,Pierre Duhamel,Pablo Piantanida | ICLR 2022,Poster | Reliable out-of-distribution (OOD) detection is fundamental to implementing safer modern machine learning (ML) systems. In this paper, we introduce Igeood, an effective method for detecting OOD samples. Igeood applies to any pre-trained neural network, works under various degrees of access to the ML model, does not require OOD samples or assumptions on the OOD data but can also benefit (if available) from OOD samples. By building on the geodesic (Fisher-Rao) distance between the underlying data distributions, our discriminator can combine confidence scores from the logits outputs and the learned features of a deep neural network. Empirically, we show that Igeood outperforms competing state-of-the-art methods on a variety of network architectures and datasets. | https://openreview.net/pdf/63528e722b436ffe0f150c1de5ef3b5ad5c47352.pdf |
Bag of Instances Aggregation Boosts Self-supervised Distillation | https://openreview.net/forum?id=N0uJGWDw21d | https://openreview.net/forum?id=N0uJGWDw21d | Haohang Xu,Jiemin Fang,XIAOPENG ZHANG,Lingxi Xie,Xinggang Wang,Wenrui Dai,Hongkai Xiong,Qi Tian | ICLR 2022,Poster | Recent advances in self-supervised learning have experienced remarkable progress, especially for contrastive learning based methods, which regard each image as well as its augmentations as an individual class and try to distinguish them from all other images. However, due to the large quantity of exemplars, this kind of pretext task intrinsically suffers from slow convergence and is hard for optimization. This is especially true for small-scale models, in which we find the performance drops dramatically comparing with its supervised counterpart. In this paper, we propose a simple but effective distillation strategy for unsupervised learning. The highlight is that the relationship among similar samples counts and can be seamlessly transferred to the student to boost the performance. Our method, termed as BINGO, which is short for Bag of InstaNces aGgregatiOn, targets at transferring the relationship learned by the teacher to the student. Here bag of instances indicates a set of similar samples constructed by the teacher and are grouped within a bag, and the goal of distillation is to aggregate compact representations over the student with respect to instances in a bag. Notably, BINGO achieves new state-of-the-art performance on small-scale models, i.e., 65.5% and 68.9% top-1 accuracies with linear evaluation on ImageNet, using ResNet-18 and ResNet-34 as the backbones respectively, surpassing baselines (52.5% and 57.4% top-1 accuracies) by a significant margin. The code is available at https://github.com/haohang96/bingo. | https://openreview.net/pdf/43c928d51f32bf6433e4fb0642c8ef6ee1a4af36.pdf |
Stability Regularization for Discrete Representation Learning | https://openreview.net/forum?id=6tmjoym9LR6 | https://openreview.net/forum?id=6tmjoym9LR6 | Adeel Pervez,Efstratios Gavves | ICLR 2022,Poster | We present a method for training neural network models with discrete stochastic variables.
The core of the method is \emph{stability regularization}, which is a regularization procedure based on the idea of noise stability developed in Gaussian isoperimetric theory in the analysis of Gaussian functions.
Stability regularization is method to make the output of continuous functions of Gaussian random variables close to discrete, that is binary or categorical, without the need for significant manual tuning.
The method allows control over the extent to which a Gaussian function's output is close to discrete, thus allowing for continued flow of gradient.
The method can be used standalone or in combination with existing continuous relaxation methods.
We validate the method in a broad range of experiments using discrete variables including neural relational inference, generative modeling, clustering and conditional computing. | https://openreview.net/pdf/599ab5c6c8513865531af017802b3e9a891d86b0.pdf |
Unrolling PALM for Sparse Semi-Blind Source Separation | https://openreview.net/forum?id=aBVxf5NaaRt | https://openreview.net/forum?id=aBVxf5NaaRt | Mohammad Fahes,Christophe Kervazo,Jérôme Bobin,Florence Tupin | ICLR 2022,Poster | Sparse Blind Source Separation (BSS) has become a well established tool for a wide range of applications – for instance, in astrophysics and remote sensing. Classical sparse BSS methods, such as the Proximal Alternating Linearized Minimization (PALM) algorithm, nevertheless often suffer from a difficult hyper-parameter choice, which undermines their results. To bypass this pitfall, we propose in this work to build on the thriving field of algorithm unfolding/unrolling. Unrolling PALM enables to leverage the data-driven knowledge stemming from realistic simulations or ground-truth data by learning both PALM hyper-parameters and variables. In contrast to most existing unrolled algorithms, which assume a fixed known dictionary during the training and testing phases, this article further emphasizes on the ability to deal with variable mixing matrices (a.k.a. dictionaries). The proposed Learned PALM (LPALM) algorithm thus enables to perform semi-blind source separation, which is key to increase the generalization of the learnt model in real-world applications. We illustrate the relevance of LPALM in astrophysical multispectral imaging: the algorithm not only needs up to $10^4−10^5$ times less iterations than PALM, but also improves the separation quality, while avoiding the cumbersome hyper-parameter and initialization choice of PALM. We further show that LPALM outperforms other unrolled source separation methods in the semi-blind setting. | https://openreview.net/pdf/a94df1bb2104d5b98e23e4936bda23189f807583.pdf |
Fast Generic Interaction Detection for Model Interpretability and Compression | https://openreview.net/forum?id=fQTlgI2qZqE | https://openreview.net/forum?id=fQTlgI2qZqE | Tianjian Zhang,Feng Yin,Zhi-Quan Luo | ICLR 2022,Poster | The ability of discovering feature interactions in a black-box model is vital to explainable deep learning. We propose a principled, global interaction detection method by casting our target as a multi-arm bandits problem and solving it swiftly with the UCB algorithm. This adaptive method is free of ad-hoc assumptions and among the cutting-edge methods with outstanding detection accuracy and stability. Based on the detection outcome, a lightweight and interpretable deep learning model (called ParaACE) is further built using the alternating conditional expectation (ACE) method. Our proposed ParaACE improves the prediction performance by 26 % and reduces the model size by 100+ times as compared to its Teacher model over various datasets. Furthermore, we show the great potential of our method for scientific discovery through interpreting various real datasets in the economics and smart medicine sectors. The code is available at https://github.com/zhangtj1996/ParaACE. | https://openreview.net/pdf/f1581535b01f857847507915f4721abcb1e41529.pdf |
Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift | https://openreview.net/forum?id=cGDAkQo1C0p | https://openreview.net/forum?id=cGDAkQo1C0p | Taesung Kim,Jinhee Kim,Yunwon Tae,Cheonbok Park,Jang-Ho Choi,Jaegul Choo | ICLR 2022,Poster | Statistical properties such as mean and variance often change over time in time series, i.e., time-series data suffer from a distribution shift problem. This change in temporal distribution is one of the main challenges that prevent accurate time-series forecasting. To address this issue, we propose a simple yet effective normalization method called reversible instance normalization (RevIN), a generally-applicable normalization-and-denormalization method with learnable affine transformation. The proposed method is symmetrically structured to remove and restore the statistical information of a time-series instance, leading to significant performance improvements in time-series forecasting, as shown in Fig. 1. We demonstrate the effectiveness of RevIN via extensive quantitative and qualitative analyses on various real-world datasets, addressing the distribution shift problem. | https://openreview.net/pdf/1d6c993e5092c7d7d1690b7adb1c2ae08d71f9dc.pdf |
On the Pitfalls of Analyzing Individual Neurons in Language Models | https://openreview.net/forum?id=8uz0EWPQIMu | https://openreview.net/forum?id=8uz0EWPQIMu | Omer Antverg,Yonatan Belinkov | ICLR 2022,Poster | While many studies have shown that linguistic information is encoded in hidden word representations, few have studied individual neurons, to show how and in which neurons it is encoded.
Among these, the common approach is to use an external probe to rank neurons according to their relevance to some linguistic attribute, and to evaluate the obtained ranking using the same probe that produced it.
We show two pitfalls in this methodology:
1. It confounds distinct factors: probe quality and ranking quality.
We separate them and draw conclusions on each.
2. It focuses on encoded information, rather than information that is used by the model.
We show that these are not the same.
We compare two recent ranking methods and a simple one we introduce, and evaluate them with regard to both of these aspects. | https://openreview.net/pdf/48b5f2da77098455f23b9b9c4bd9b7a6cd9712e8.pdf |
Query Embedding on Hyper-Relational Knowledge Graphs | https://openreview.net/forum?id=4rLw09TgRw9 | https://openreview.net/forum?id=4rLw09TgRw9 | Dimitrios Alivanistos,Max Berrendorf,Michael Cochez,Mikhail Galkin | ICLR 2022,Poster | Multi-hop logical reasoning is an established problem in the field of representation learning on knowledge graphs (KGs). It subsumes both one-hop link prediction as well as other more complex types of logical queries. Existing algorithms operate only on classical, triple-based graphs, whereas modern KGs often employ a hyper-relational modeling paradigm. In this paradigm, typed edges may have several key-value pairs known as qualifiers that provide fine-grained context for facts. In queries, this context modifies the meaning of relations, and usually reduces the answer set. Hyper-relational queries are often observed in real-world KG applications, and existing approaches for approximate query answering cannot make use of qualifier pairs. In this work, we bridge this gap and extend the multi-hop reasoning problem to hyper-relational KGs allowing to tackle this new type of complex queries. Building upon recent advancements in Graph Neural Networks and query embedding techniques, we study how to embed and answer hyper-relational conjunctive queries. Besides that, we propose a method to answer such queries and demonstrate in our experiments that qualifiers improve query answering on a diverse set of query patterns. | https://openreview.net/pdf/d5bd37f34db1f59820dc265c98290e913e627460.pdf |
Neural Solvers for Fast and Accurate Numerical Optimal Control | https://openreview.net/forum?id=m8bypnj7Yl5 | https://openreview.net/forum?id=m8bypnj7Yl5 | Federico Berto,Stefano Massaroli,Michael Poli,Jinkyoo Park | ICLR 2022,Poster | Synthesizing optimal controllers for dynamical systems often involves solving optimization problems with hard real-time constraints. These constraints determine the class of numerical methods that can be applied: computationally expensive but accurate numerical routines are replaced by fast and inaccurate methods, trading inference time for solution accuracy. This paper provides techniques to improve the quality of optimized control policies given a fixed computational budget. We achieve the above via a hypersolvers approach, which hybridizes a differential equation solver and a neural network. The performance is evaluated in direct and receding-horizon optimal control tasks in both low and high dimensions, where the proposed approach shows consistent Pareto improvements in solution accuracy and control performance. | https://openreview.net/pdf/c4f0fbf5ef323ba4d9446eec32b783a9366d37cf.pdf |
PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series | https://openreview.net/forum?id=Ix_mh42xq5w | https://openreview.net/forum?id=Ix_mh42xq5w | Paul Jeha,Michael Bohlke-Schneider,Pedro Mercado,Shubham Kapoor,Rajbir Singh Nirwan,Valentin Flunkert,Jan Gasthaus,Tim Januschowski | ICLR 2022,Poster | Realistic synthetic time series data of sufficient length enables practical applications in time series modeling tasks, such as forecasting, but remains a challenge. In this paper we present PSA-GAN, a generative adversarial network (GAN) that generates long time series samples of high quality using progressive growing of GANs and self-attention. We show that PSA-GAN can be used to reduce the error in several downstream forecasting tasks over baselines that only use real data. We also introduce a Frechet-Inception Distance-like score for time series, Context-FID, assessing the quality of synthetic time series samples. We find that Context-FID is indicative for downstream performance. Therefore, Context-FID could be a useful tool to develop time series GAN models. | https://openreview.net/pdf/22703570f03d2a1280efaf132cfd17e250dd590c.pdf |
ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind | https://openreview.net/forum?id=2t7CkQXNpuq | https://openreview.net/forum?id=2t7CkQXNpuq | Yuanfei Wang,fangwei zhong,Jing Xu,Yizhou Wang | ICLR 2022,Poster | Being able to predict the mental states of others is a key factor to effective social interaction. It is also crucial for distributed multi-agent systems, where agents are required to communicate and cooperate. In this paper, we introduce such an important social-cognitive skill, i.e. Theory of Mind (ToM), to build socially intelligent agents who are able to communicate and cooperate effectively to accomplish challenging tasks. With ToM, each agent is capable of inferring the mental states and intentions of others according to its (local) observation. Based on the inferred states, the agents decide "when'' and with "whom'' to share their intentions. With the information observed, inferred, and received, the agents decide their sub-goals and reach a consensus among the team. In the end, the low-level executors independently take primitive actions to accomplish the sub-goals. We demonstrate the idea in two typical target-oriented multi-agent tasks: cooperative navigation and multi-sensor target coverage. The experiments show that the proposed model not only outperforms the state-of-the-art methods on reward and communication efficiency, but also shows good generalization across different scales of the environment.
| https://openreview.net/pdf/a18a759fefcf7c83cb3cd488541d2df743059f5b.pdf |
Better Supervisory Signals by Observing Learning Paths | https://openreview.net/forum?id=Iog0djAdbHj | https://openreview.net/forum?id=Iog0djAdbHj | Yi Ren,Shangmin Guo,Danica J. Sutherland | ICLR 2022,Poster | Better-supervised models might have better performance. In this paper, we first clarify what makes for good supervision for a classification problem, and then explain two existing label refining methods, label smoothing and knowledge distillation, in terms of our proposed criterion. To further answer why and how better supervision emerges, we observe the learning path, i.e., the trajectory of the model's predictions during training, for each training sample. We find that the model can spontaneously refine "bad" labels through a "zig-zag" learning path, which occurs on both toy and real datasets. Observing the learning path not only provides a new perspective for understanding knowledge distillation, overfitting, and learning dynamics, but also reveals that the supervisory signal of a teacher network can be very unstable near the best points in training on real tasks. Inspired by this, we propose a new knowledge distillation scheme, Filter-KD, which improves downstream classification performance in various settings. | https://openreview.net/pdf/e1c609447b4fe82d58e7ebc489aaed7002dd8e7e.pdf |
TAda! Temporally-Adaptive Convolutions for Video Understanding | https://openreview.net/forum?id=izj68lUcBpt | https://openreview.net/forum?id=izj68lUcBpt | Ziyuan Huang,Shiwei Zhang,Liang Pan,Zhiwu Qing,Mingqian Tang,Ziwei Liu,Marcelo H Ang Jr | ICLR 2022,Poster | Spatial convolutions are widely used in numerous deep video models. It fundamentally assumes spatio-temporal invariance, i.e., using shared weights for every location in different frames. This work presents Temporally-Adaptive Convolutions (TAdaConv) for video understanding, which shows that adaptive weight calibration along the temporal dimension is an efficient way to facilitate modelling complex temporal dynamics in videos. Specifically, TAdaConv empowers the spatial convolutions with temporal modelling abilities by calibrating the convolution weights for each frame according to its local and global temporal context. Compared to previous temporal modelling operations, TAdaConv is more efficient as it operates over the convolution kernels instead of the features, whose dimension is an order of magnitude smaller than the spatial resolutions. Further, the kernel calibration brings an increased model capacity. We construct TAda2D and TAdaConvNeXt networks by replacing the 2D convolutions in ResNet and ConvNeXt with TAdaConv, which leads to at least on par or better performance compared to state-of-the-art approaches on multiple video action recognition and localization benchmarks. We also demonstrate that as a readily plug-in operation with negligible computation overhead, TAdaConv can effectively improve many existing video models with a convincing margin. | https://openreview.net/pdf/e6be2c061809752df033e546c9b00585b1c261f3.pdf |
Learning a subspace of policies for online adaptation in Reinforcement Learning | https://openreview.net/forum?id=4Muj-t_4o4 | https://openreview.net/forum?id=4Muj-t_4o4 | Jean-Baptiste Gaya,Laure Soulier,Ludovic Denoyer | ICLR 2022,Poster | Deep Reinforcement Learning (RL) is mainly studied in a setting where the training and the testing environments are similar. But in many practical applications, these environments may differ. For instance, in control systems, the robot(s) on which a policy is learned might differ from the robot(s) on which a policy will run. It can be caused by different internal factors (e.g., calibration issues, system attrition, defective modules) or also by external changes (e.g., weather conditions). There is a need to develop RL methods that generalize well to variations of the training conditions. In this article, we consider the simplest yet hard to tackle generalization setting where the test environment is unknown at train time, forcing the agent to adapt to the system's new dynamics. This online adaptation process can be computationally expensive (e.g., fine-tuning) and cannot rely on meta-RL techniques since there is just a single train environment. To do so, we propose an approach where we learn a subspace of policies within the parameter space. This subspace contains an infinite number of policies that are trained to solve the training environment while having different parameter values. As a consequence, two policies in that subspace process information differently and exhibit different behaviors when facing variations of the train environment. Our experiments carried out over a large variety of benchmarks compare our approach with baselines, including diversity-based methods. In comparison, our approach is simple to tune, does not need any extra component (e.g., discriminator) and learns policies able to gather a high reward on unseen environments. | https://openreview.net/pdf/2845a7c71512ebc0d2961233b0ec523e86dd65a8.pdf |
ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity | https://openreview.net/forum?id=2sDQwC_hmnM | https://openreview.net/forum?id=2sDQwC_hmnM | Xinchi Qiu,Javier Fernandez-Marques,Pedro PB Gusmao,Yan Gao,Titouan Parcollet,Nicholas Donald Lane | ICLR 2022,Poster | When the available hardware cannot meet the memory and compute requirements to efficiently train high performing machine learning models, a compromise in either the training quality or the model complexity is needed. In Federated Learning (FL), nodes are orders of magnitude more constrained than traditional server-grade hardware and are often battery powered, severely limiting the sophistication of models that can be trained under this paradigm. While most research has focused on designing better aggregation strategies to improve convergence rates and in alleviating the communication costs of FL, fewer efforts have been devoted to accelerating on-device training. Such stage, which repeats hundreds of times (i.e. every round) and can involve thousands of devices, accounts for the majority of the time required to train federated models and, the totality of the energy consumption at the client side. In this work, we present the first study on the unique aspects that arise when introducing sparsity at training time in FL workloads. We then propose ZeroFL, a framework that relies on highly sparse operations to accelerate on-device training. Models trained with ZeroFL and 95% sparsity achieve up to 2.3% higher accuracy compared to competitive baselines obtained from adapting a state-of-the-art sparse training framework to the FL setting. | https://openreview.net/pdf/51f094e72c1306f6546a372f9bb52b32060ebcf4.pdf |
Gaussian Mixture Convolution Networks | https://openreview.net/forum?id=Oxeka7Z7Hor | https://openreview.net/forum?id=Oxeka7Z7Hor | Adam Celarek,Pedro Hermosilla,Bernhard Kerbl,Timo Ropinski,Michael Wimmer | ICLR 2022,Poster | This paper proposes a novel method for deep learning based on the analytical convolution of multidimensional Gaussian mixtures.
In contrast to tensors, these do not suffer from the curse of dimensionality and allow for a compact representation, as data is only stored where details exist.
Convolution kernels and data are Gaussian mixtures with unconstrained weights, positions, and covariance matrices.
Similar to discrete convolutional networks, each convolution step produces several feature channels, represented by independent Gaussian mixtures.
Since traditional transfer functions like ReLUs do not produce Gaussian mixtures, we propose using a fitting of these functions instead.
This fitting step also acts as a pooling layer if the number of Gaussian components is reduced appropriately.
We demonstrate that networks based on this architecture reach competitive accuracy on Gaussian mixtures fitted to the MNIST and ModelNet data sets. | https://openreview.net/pdf/19208a1745f081633db29c386c4625dd681c9411.pdf |
How Does SimSiam Avoid Collapse Without Negative Samples? A Unified Understanding with Self-supervised Contrastive Learning | https://openreview.net/forum?id=bwq6O4Cwdl | https://openreview.net/forum?id=bwq6O4Cwdl | Chaoning Zhang,Kang Zhang,Chenshuang Zhang,Trung X. Pham,Chang D. Yoo,In So Kweon | ICLR 2022,Poster | To avoid collapse in self-supervised learning (SSL), a contrastive loss is widely used but often requires a large number of negative samples. Without negative samples yet achieving competitive performance, a recent work~\citep{chen2021exploring} has attracted significant attention for providing a minimalist simple Siamese (SimSiam) method to avoid collapse. However, the reason for how it avoids collapse without negative samples remains not fully clear and our investigation starts by revisiting the explanatory claims in the original SimSiam. After refuting their claims, we introduce vector decomposition for analyzing the collapse based on the gradient analysis of the $l_2$-normalized representation vector. This yields a unified perspective on how negative samples and SimSiam alleviate collapse. Such a unified perspective comes timely for understanding the recent progress in SSL. | https://openreview.net/pdf/83121a24e1afcc8d9cc5b1489f38d0c54a411bf7.pdf |
Attention-based Interpretability with Concept Transformers | https://openreview.net/forum?id=kAa9eDS0RdO | https://openreview.net/forum?id=kAa9eDS0RdO | Mattia Rigotti,Christoph Miksovic,Ioana Giurgiu,Thomas Gschwind,Paolo Scotton | ICLR 2022,Poster | Attention is a mechanism that has been instrumental in driving remarkable performance gains of deep neural network models in a host of visual, NLP and multimodal tasks.
One additional notable aspect of attention is that it conveniently exposes the ``reasoning'' behind each particular output generated by the model.
Specifically, attention scores over input regions or intermediate features have been interpreted as a measure of the contribution of the attended element to the model inference.
While the debate in regard to the interpretability of attention is still not settled, researchers have pointed out the existence of architectures and scenarios that afford a meaningful interpretation of the attention mechanism.
Here we propose the generalization of attention from low-level input features to high-level concepts as a mechanism to ensure the interpretability of attention scores within a given application domain.
In particular, we design the ConceptTransformer, a deep learning module that exposes explanations of the output of a model in which it is embedded in terms of attention over user-defined high-level concepts.
Such explanations are \emph{plausible} (i.e.\ convincing to the human user) and \emph{faithful} (i.e.\ truly reflective of the reasoning process of the model).
Plausibility of such explanations is obtained by construction by training the attention heads to conform with known relations between inputs, concepts and outputs dictated by domain knowledge.
Faithfulness is achieved by design by enforcing a linear relation between the transformer value vectors that represent the concepts and their contribution to the classification log-probabilities.
We validate our ConceptTransformer module on established explainability benchmarks and show how it can be used to infuse domain knowledge into classifiers to improve accuracy, and conversely to extract concept-based explanations of classification outputs. Code to reproduce our results is available at: \url{https://github.com/ibm/concept_transformer}. | https://openreview.net/pdf/d910731148e5b8279d1974d45e83aada94c35e55.pdf |
Inductive Relation Prediction Using Analogy Subgraph Embeddings | https://openreview.net/forum?id=PTRo58zPt3P | https://openreview.net/forum?id=PTRo58zPt3P | Jiarui Jin,Yangkun Wang,Kounianhua Du,Weinan Zhang,Zheng Zhang,David Wipf,Yong Yu,Quan Gan | ICLR 2022,Poster | Prevailing methods for relation prediction in heterogeneous graphs aim at learning latent representations (i.e., embeddings) of observed nodes and relations, and thus are limited to the transductive setting where the relation types must be known during training. Here, we propose ANalogy SubGraphEmbeddingLearning (GraphANGEL), a novel relation prediction framework that predicts relations5between each node pair based on the subgraphs containing the pair, as well as other (analogy) subgraphs with the same graph patterns. Each graph pattern explicitly represents a specific logical rule, which contributes to an inductive bias that facilitates generalization to unseen relations and leads to more explainable predictive models. Moreover, our method also removes the limited neighborhood constraint of graph neural networks. Our model consistently outperforms existing models on heterogeneous graph based recommendation as well as knowledge graph completion. We also empirically demonstrate our model’s capability in generalizing to new relations while producing explainable heat maps of attention scores across the discovered logic. | https://openreview.net/pdf/7ef097299fb4b886196be6fa5e836e944a2a770a.pdf |
Reinforcement Learning in Presence of Discrete Markovian Context Evolution | https://openreview.net/forum?id=CmsfC7u054S | https://openreview.net/forum?id=CmsfC7u054S | Hang Ren,Aivar Sootla,Taher Jafferjee,Junxiao Shen,Jun Wang,Haitham Bou Ammar | ICLR 2022,Poster | We consider a context-dependent Reinforcement Learning (RL) setting, which is characterized by: a) an unknown finite number of not directly observable contexts; b) abrupt (discontinuous) context changes occurring during an episode; and c) Markovian context evolution. We argue that this challenging case is often met in applications and we tackle it using a Bayesian model-based approach and variational inference. We adapt a sticky Hierarchical Dirichlet Process (HDP) prior for model learning, which is arguably best-suited for infinite Markov chain modeling. We then derive a context distillation procedure, which identifies and removes spurious contexts in an unsupervised fashion. We argue that the combination of these two components allows inferring the number of contexts from data thus dealing with the context cardinality assumption. We then find the representation of the optimal policy enabling efficient policy learning using off-the-shelf RL algorithms. Finally, we demonstrate empirically (using gym environments cart-pole swing-up, drone, intersection) that our approach succeeds where state-of-the-art methods of other frameworks fail and elaborate on the reasons for such failures. | https://openreview.net/pdf/53bda3136fe85f6fe3bed9a024f40991920a62fc.pdf |
Optimal Transport for Long-Tailed Recognition with Learnable Cost Matrix | https://openreview.net/forum?id=t98k9ePQQpn | https://openreview.net/forum?id=t98k9ePQQpn | Hanyu Peng,Mingming Sun,Ping Li | ICLR 2022,Poster | 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. | https://openreview.net/pdf/d835dec4f40d7fbc936204b98e3ddea446bc757c.pdf |
PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior | https://openreview.net/forum?id=_BNiN4IjC5 | https://openreview.net/forum?id=_BNiN4IjC5 | Sang-gil Lee,Heeseung Kim,Chaehun Shin,Xu Tan,Chang Liu,Qi Meng,Tao Qin,Wei Chen,Sungroh Yoon,Tie-Yan Liu | ICLR 2022,Poster | Denoising diffusion probabilistic models have been recently proposed to generate high-quality samples by estimating the gradient of the data density. The framework assumes the prior noise as a standard Gaussian distribution, whereas the corresponding data distribution may be more complicated than the standard Gaussian distribution, which potentially introduces inefficiency in denoising the prior noise into the data sample because of the discrepancy between the data and the prior. In this paper, we propose PriorGrad to improve the efficiency of the conditional diffusion model (for example, a vocoder using a mel-spectrogram as the condition) by applying an adaptive prior derived from the data statistics based on the conditional information. We formulate the training and sampling procedures of PriorGrad and demonstrate the advantages of an adaptive prior through a theoretical analysis. Focusing on the audio domain, we consider the recently proposed diffusion-based audio generative models based on both the spectral and time domains and show that PriorGrad achieves faster convergence and superior performance, leading to an improved perceptual quality and tolerance to a smaller network capacity, and thereby demonstrating the efficiency of a data-dependent adaptive prior. | https://openreview.net/pdf/0e602657a19a26315b3e07434feb9b84217302e6.pdf |
Target-Side Input Augmentation for Sequence to Sequence Generation | https://openreview.net/forum?id=pz1euXohm4H | https://openreview.net/forum?id=pz1euXohm4H | Shufang Xie,Ang Lv,Yingce Xia,Lijun Wu,Tao Qin,Tie-Yan Liu,Rui Yan | ICLR 2022,Poster | Autoregressive sequence generation, a prevalent task in machine learning and natural language processing, generates every target token conditioned on both a source input and previously generated target tokens. Previous data augmentation methods, which have been shown to be effective for the task, mainly enhance source inputs (e.g., injecting noise into the source sequence by random swapping or masking, back translation, etc.) while overlooking the target-side augmentation. In this work, we propose a target-side augmentation method for sequence generation. In training, we use the decoder output probability distributions as soft indicators, which are multiplied with target token embeddings, to build pseudo tokens. These soft pseudo tokens are then used as target tokens to enhance the training. We conduct comprehensive experiments on various sequence generation tasks, including dialog generation, machine translation, and abstractive summarization. Without using any extra labeled data or introducing additional model parameters, our method significantly outperforms strong baselines. The code is available at https://github.com/TARGET-SIDE-DATA-AUG/TSDASG. | https://openreview.net/pdf/422cd72cb7110055373e0c085cdbbdc9b40d0811.pdf |
UniFormer: Unified Transformer for Efficient Spatial-Temporal Representation Learning | https://openreview.net/forum?id=nBU_u6DLvoK | https://openreview.net/forum?id=nBU_u6DLvoK | Kunchang Li,Yali Wang,Gao Peng,Guanglu Song,Yu Liu,Hongsheng Li,Yu Qiao | ICLR 2022,Poster | It is a challenging task to learn rich and multi-scale spatiotemporal semantics from high-dimensional videos, due to large local redundancy and complex global dependency between video frames. The recent advances in this research have been mainly driven by 3D convolutional neural networks and vision transformers. Although 3D convolution can efficiently aggregate local context to suppress local redundancy from a small 3D neighborhood, it lacks the capability to capture global dependency because of the limited receptive field. Alternatively, vision transformers can effectively capture long-range dependency by self-attention mechanism, while having the limitation on reducing local redundancy with blind similarity comparison among all the tokens in each layer. Based on these observations, we propose a novel Unified transFormer (UniFormer) which seamlessly integrates merits of 3D convolution and spatiotemporal self-attention in a concise transformer format, and achieves a preferable balance between computation and accuracy. Different from traditional transformers, our relation aggregator can tackle both spatiotemporal redundancy and dependency, by learning local and global token affinity respectively in shallow and deep layers. We conduct extensive experiments on the popular video benchmarks, e.g., Kinetics-400, Kinetics-600, and Something-Something V1&V2. With only ImageNet-1K pretraining, our UniFormer achieves 82.9%/84.8% top-1 accuracy on Kinetics-400/Kinetics-600, while requiring 10x fewer GFLOPs than other state-of-the-art methods. For Something-Something V1 and V2, our UniFormer achieves new state-of-the-art performances of 60.9% and 71.2% top-1 accuracy respectively. Code is available at https://github.com/Sense-X/UniFormer. | https://openreview.net/pdf/4e646685f068cfb87b0f47952a01638be74eaacc.pdf |
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies | https://openreview.net/forum?id=DYypjaRdph2 | https://openreview.net/forum?id=DYypjaRdph2 | Alex Chan,Alicia Curth,Mihaela van der Schaar | ICLR 2022,Poster | Human decision making is well known to be imperfect and the ability to analyse such processes individually is crucial when attempting to aid or improve a decision-maker's ability to perform a task, e.g. to alert them to potential biases or oversights on their part. To do so, it is necessary to develop interpretable representations of how agents make decisions and how this process changes over time as the agent learns online in reaction to the accrued experience. To then understand the decision-making processes underlying a set of observed trajectories, we cast the policy inference problem as the inverse to this online learning problem. By interpreting actions within a potential outcomes framework, we introduce a meaningful mapping based on agents choosing an action they believe to have the greatest treatment effect. We introduce a practical algorithm for retrospectively estimating such perceived effects, alongside the process through which agents update them, using a novel architecture built upon an expressive family of deep state-space models. Through application to the analysis of UNOS organ donation acceptance decisions, we demonstrate that our approach can bring valuable insights into the factors that govern decision processes and how they change over time. | https://openreview.net/pdf/dc7c6c1afc5a4d35d2f621c9684cf86f140adf79.pdf |
Multi-Mode Deep Matrix and Tensor Factorization | https://openreview.net/forum?id=6YVIk0sAkF_ | https://openreview.net/forum?id=6YVIk0sAkF_ | Jicong Fan | ICLR 2022,Poster | Recently, deep linear and nonlinear matrix factorizations gain increasing attention in the area of machine learning. Existing deep nonlinear matrix factorization methods can only exploit partial nonlinearity of the data and are not effective in handling matrices of which the number of rows is comparable to the number of columns. On the other hand, there is still a gap between deep learning and tensor decomposition. This paper presents a framework of multi-mode deep matrix and tensor factorizations to explore and exploit the full nonlinearity of the data in matrices and tensors. We use the factorization methods to solve matrix and tensor completion problems and prove that our methods have tighter generalization error bounds than conventional matrix and tensor factorization methods. The experiments on synthetic data and real datasets showed that the proposed methods have much higher recovery accuracy than many baselines. | https://openreview.net/pdf/79b1b1ca98990989bacbd3c32a685e6ce2323668.pdf |
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