title stringlengths 12 151 | url stringlengths 41 43 | detail_url stringlengths 41 43 | authors stringlengths 6 562 | tags stringclasses 3
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Learning Towards The Largest Margins | https://openreview.net/forum?id=hqkhcFHOeKD | https://openreview.net/forum?id=hqkhcFHOeKD | Xiong Zhou,Xianming Liu,Deming Zhai,Junjun Jiang,Xin Gao,Xiangyang Ji | ICLR 2022,Poster | One of the main challenges for feature representation in deep learning-based classification is the design of appropriate loss functions that exhibit strong discriminative power. The classical softmax loss does not explicitly encourage discriminative learning of features. A popular direction of research is to incorporat... | https://openreview.net/pdf/05f12453b1762c08d54507567f592f91d86425be.pdf |
Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations? | https://openreview.net/forum?id=28ib9tf6zhr | https://openreview.net/forum?id=28ib9tf6zhr | Yonggan Fu,Shunyao Zhang,Shang Wu,Cheng Wan,Yingyan Lin | ICLR 2022,Poster | Vision transformers (ViTs) have recently set off a new wave in neural architecture design thanks to their record-breaking performance in various vision tasks. In parallel, to fulfill the goal of deploying ViTs into real-world vision applications, their robustness against potential malicious attacks has gained increasin... | https://openreview.net/pdf/4c7b8d2f80c4ea1bfe11754da2e7c69fc5183754.pdf |
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation | https://openreview.net/forum?id=Q5uh1Nvv5dm | https://openreview.net/forum?id=Q5uh1Nvv5dm | David Berthelot,Rebecca Roelofs,Kihyuk Sohn,Nicholas Carlini,Alexey Kurakin | ICLR 2022,Poster | We extend semi-supervised learning to the problem of domain adaptation to learn significantly higher-accuracy models that train on one data distribution and test on a different one. With the goal of generality, we introduce AdaMatch, a unified solution for unsupervised domain adaptation (UDA), semi-supervised learning ... | https://openreview.net/pdf/8dd30c7eff2e4f152d2d24368c232baec4e5e974.pdf |
Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound | https://openreview.net/forum?id=l_amHf1oaK | https://openreview.net/forum?id=l_amHf1oaK | Claudio Ferrari,Mark Niklas Mueller,Nikola Jovanović,Martin Vechev | ICLR 2022,Poster | State-of-the-art neural network verifiers are fundamentally based on one of two paradigms: either encoding the whole verification problem via tight multi-neuron convex relaxations or applying a Branch-and-Bound (BaB) procedure leveraging imprecise but fast bounding methods on a large number of easier subproblems. The f... | https://openreview.net/pdf/fcc20218f5754386cf64f4156a1f41039038b5da.pdf |
Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Quality | https://openreview.net/forum?id=VFBjuF8HEp | https://openreview.net/forum?id=VFBjuF8HEp | Daniel Watson,William Chan,Jonathan Ho,Mohammad Norouzi | ICLR 2022,Poster | Diffusion models have emerged as an expressive family of generative models rivaling GANs in sample quality and autoregressive models in likelihood scores. Standard diffusion models typically require hundreds of forward passes through the model to generate a single high-fidelity sample. We introduce Differentiable Diffu... | https://openreview.net/pdf/56f0145dd15f32bd53f6dba7efde74914a88f663.pdf |
Distribution Compression in Near-Linear Time | https://openreview.net/forum?id=lzupY5zjaU9 | https://openreview.net/forum?id=lzupY5zjaU9 | Abhishek Shetty,Raaz Dwivedi,Lester Mackey | ICLR 2022,Poster | In distribution compression, one aims to accurately summarize a probability distribution $\mathbb{P}$ using a small number of representative points. Near-optimal thinning procedures achieve this goal by sampling $n$ points from a Markov chain and identifying $\sqrt{n}$ points with $\widetilde{\mathcal{O}}(1/\sqrt{n})$ ... | https://openreview.net/pdf/484f68f97f561be1f3272522336a9a0b1fa84bbc.pdf |
Capturing Structural Locality in Non-parametric Language Models | https://openreview.net/forum?id=nnU3IUMJmN | https://openreview.net/forum?id=nnU3IUMJmN | Frank F. Xu,Junxian He,Graham Neubig,Vincent Josua Hellendoorn | ICLR 2022,Poster | Structural locality is a ubiquitous feature of real-world datasets, wherein data points are organized into local hierarchies. Some examples include topical clusters in text or project hierarchies in source code repositories. In this paper, we explore utilizing this structural locality within non-parametric language mod... | https://openreview.net/pdf/05677eb0d7fca88dd7c4c6cbefa73f6ae430ad68.pdf |
Audio Lottery: Speech Recognition Made Ultra-Lightweight, Noise-Robust, and Transferable | https://openreview.net/forum?id=9Nk6AJkVYB | https://openreview.net/forum?id=9Nk6AJkVYB | Shaojin Ding,Tianlong Chen,Zhangyang Wang | ICLR 2022,Poster | Lightweight speech recognition models have seen explosive demands owing to a growing amount of speech-interactive features on mobile devices. Since designing such systems from scratch is non-trivial, practitioners typically choose to compress large (pre-trained) speech models. Recently, lottery ticket hypothesis reveal... | https://openreview.net/pdf/3d42ff881f8ec8954935d0f8bbcb2a21d71106ea.pdf |
Learning to Map for Active Semantic Goal Navigation | https://openreview.net/forum?id=swrMQttr6wN | https://openreview.net/forum?id=swrMQttr6wN | Georgios Georgakis,Bernadette Bucher,Karl Schmeckpeper,Siddharth Singh,Kostas Daniilidis | ICLR 2022,Poster | We consider the problem of object goal navigation in unseen environments. Solving this problem requires learning of contextual semantic priors, a challenging endeavour given the spatial and semantic variability of indoor environments. Current methods learn to implicitly encode these priors through goal-oriented navigat... | https://openreview.net/pdf/8097afd8a3e6d7c824f59390ca5a9cee0530bbd1.pdf |
Benchmarking the Spectrum of Agent Capabilities | https://openreview.net/forum?id=1W0z96MFEoH | https://openreview.net/forum?id=1W0z96MFEoH | Danijar Hafner | ICLR 2022,Poster | Evaluating the general abilities of intelligent agents requires complex simulation environments. Existing benchmarks typically evaluate only one narrow task per environment, requiring researchers to perform expensive training runs on many different environments. We introduce Crafter, an open world survival game with vi... | https://openreview.net/pdf/116a18888b3fb460e882ec2b844128223e3b17ca.pdf |
Mind the Gap: Domain Gap Control for Single Shot Domain Adaptation for Generative Adversarial Networks | https://openreview.net/forum?id=vqGi8Kp0wM | https://openreview.net/forum?id=vqGi8Kp0wM | Peihao Zhu,Rameen Abdal,John Femiani,Peter Wonka | ICLR 2022,Poster | We present a new method for one shot domain adaptation. The input to our method is trained GAN that can produce images in domain A and a single reference image I_B from domain B. The proposed algorithm can translate any output of the trained GAN from domain A to domain B. There are two main advantages of our method com... | https://openreview.net/pdf/2f6e593f100fa850ecde50e059aa6b2e73a3f6fe.pdf |
On Evaluation Metrics for Graph Generative Models | https://openreview.net/forum?id=EnwCZixjSh | https://openreview.net/forum?id=EnwCZixjSh | Rylee Thompson,Boris Knyazev,Elahe Ghalebi,Jungtaek Kim,Graham W. Taylor | ICLR 2022,Poster | In image generation, generative models can be evaluated naturally by visually inspecting model outputs. However, this is not always the case for graph generative models (GGMs), making their evaluation challenging. Currently, the standard process for evaluating GGMs suffers from three critical limitations: i) it does no... | https://openreview.net/pdf/fcb94055fd54a7db263aab7d0f85b591c34e713e.pdf |
Selective Ensembles for Consistent Predictions | https://openreview.net/forum?id=HfUyCRBeQc | https://openreview.net/forum?id=HfUyCRBeQc | Emily Black,Klas Leino,Matt Fredrikson | ICLR 2022,Poster | Recent work has shown that models trained to the same objective, and which achieve similar measures of accuracy on consistent test data, may nonetheless behave very differently on individual predictions. This inconsistency is undesirable in high-stakes contexts, such as medical diagnosis and finance. We show that this ... | https://openreview.net/pdf/aef96c65d43466af59147df0d990f0b94efbef7a.pdf |
Graph Condensation for Graph Neural Networks | https://openreview.net/forum?id=WLEx3Jo4QaB | https://openreview.net/forum?id=WLEx3Jo4QaB | Wei Jin,Lingxiao Zhao,Shichang Zhang,Yozen Liu,Jiliang Tang,Neil Shah | ICLR 2022,Poster | Given the prevalence of large-scale graphs in real-world applications, the storage and time for training neural models have raised increasing concerns. To alleviate the concerns, we propose and study the problem of graph condensation for graph neural networks (GNNs). Specifically, we aim to condense the large, origina... | https://openreview.net/pdf/fb904d1d840eb264e6ab2e160ff7322153a1fbb0.pdf |
DIVA: Dataset Derivative of a Learning Task | https://openreview.net/forum?id=bVvMOtLMiw | https://openreview.net/forum?id=bVvMOtLMiw | Yonatan Dukler,Alessandro Achille,Giovanni Paolini,Avinash Ravichandran,Marzia Polito,Stefano Soatto | ICLR 2022,Poster | We present a method to compute the derivative of a learning task with respect to a dataset. A learning task is a function from a training set to the validation error, which can be represented by a trained deep neural network (DNN). The ``dataset derivative'' is a linear operator, computed around the trained model, that... | https://openreview.net/pdf/c20ae574c689fe5fbecb96f791b3e678973e0053.pdf |
Towards General Function Approximation in Zero-Sum Markov Games | https://openreview.net/forum?id=sA4qIu3zv6v | https://openreview.net/forum?id=sA4qIu3zv6v | Baihe Huang,Jason D. Lee,Zhaoran Wang,Zhuoran Yang | ICLR 2022,Poster | This paper considers two-player zero-sum finite-horizon Markov games with simultaneous moves. The study focuses on the challenging settings where the value
function or the model is parameterized by general function classes. Provably efficient
algorithms for both decoupled and coordinated settings are developed. In the ... | https://openreview.net/pdf/89164a5698b4ced1396254451108620fc52d5bc1.pdf |
Exposing the Implicit Energy Networks behind Masked Language Models via Metropolis--Hastings | https://openreview.net/forum?id=6PvWo1kEvlT | https://openreview.net/forum?id=6PvWo1kEvlT | Kartik Goyal,Chris Dyer,Taylor Berg-Kirkpatrick | ICLR 2022,Poster | While recent work has shown that scores from models trained by the ubiquitous masked language modeling (MLM) objective effectively discriminate probable from improbable sequences, it is still an open question if these MLMs specify a principled probability distribution over the space of possible sequences. In this paper... | https://openreview.net/pdf/dfdc7212f0c035baaec71e0d9d64317aec15492b.pdf |
ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods | https://openreview.net/forum?id=EZNOb_uNpJk | https://openreview.net/forum?id=EZNOb_uNpJk | Victor Schmidt,Alexandra Luccioni,Mélisande Teng,Tianyu Zhang,Alexia Reynaud,Sunand Raghupathi,Gautier Cosne,Adrien Juraver,Vahe Vardanyan,Alex Hernández-García,Yoshua Bengio | ICLR 2022,Poster | Climate change is a major threat to humanity and the actions required to prevent its catastrophic consequences include changes in both policy-making and individual behaviour. However, taking action requires understanding its seemingly abstract and distant consequences. Projecting the potential impacts of extreme climat... | https://openreview.net/pdf/ca121d72177c0fb77244bde0b2958681a89d4b98.pdf |
A Comparison of Hamming Errors of Representative Variable Selection Methods | https://openreview.net/forum?id=nhN-fqxmNGx | https://openreview.net/forum?id=nhN-fqxmNGx | Tracy Ke,Longlin Wang | ICLR 2022,Poster | Lasso is a celebrated method for variable selection in linear models, but it faces challenges when the covariates are moderately or strongly correlated. This motivates alternative approaches such as using a non-convex penalty, adding a ridge regularization, or conducting a post-Lasso thresholding. In this paper, we com... | https://openreview.net/pdf/ae8e44624ed225194ef2c6ef294ae6d5067515b8.pdf |
A Program to Build E(N)-Equivariant Steerable CNNs | https://openreview.net/forum?id=WE4qe9xlnQw | https://openreview.net/forum?id=WE4qe9xlnQw | Gabriele Cesa,Leon Lang,Maurice Weiler | ICLR 2022,Poster | Equivariance is becoming an increasingly popular design choice to build data efficient neural networks by exploiting prior knowledge about the symmetries of the problem at hand. Euclidean steerable CNNs are one of the most common classes of equivariant networks. While the constraints these architectures need to satisfy... | https://openreview.net/pdf/6d634b6f1eabc70593f897e223c78025e3029b52.pdf |
Minimax Optimization with Smooth Algorithmic Adversaries | https://openreview.net/forum?id=UdxJ2fJx7N0 | https://openreview.net/forum?id=UdxJ2fJx7N0 | Tanner Fiez,Chi Jin,Praneeth Netrapalli,Lillian J Ratliff | ICLR 2022,Poster | This paper considers minimax optimization $\min_x \max_y f(x, y)$ in the challenging setting where $f$ can be both nonconvex in $x$ and nonconcave in $y$. Though such optimization problems arise in many machine learning paradigms including training generative adversarial networks (GANs) and adversarially robust models,... | https://openreview.net/pdf/6f978c34600cf6fcf440c6e1bf8d1f93e0afce3d.pdf |
On Distributed Adaptive Optimization with Gradient Compression | https://openreview.net/forum?id=CI-xXX9dg9l | https://openreview.net/forum?id=CI-xXX9dg9l | Xiaoyun Li,Belhal Karimi,Ping Li | ICLR 2022,Poster | We study COMP-AMS, a distributed optimization framework based on gradient averaging and adaptive AMSGrad algorithm. Gradient compression with error feedback is applied to reduce the communication cost in the gradient transmission process. Our convergence analysis of COMP-AMS shows that such compressed gradient averagin... | https://openreview.net/pdf/84313c8e0bf7b65d71addc3b16aba48f161f4092.pdf |
Leveraging unlabeled data to predict out-of-distribution performance | https://openreview.net/forum?id=o_HsiMPYh_x | https://openreview.net/forum?id=o_HsiMPYh_x | Saurabh Garg,Sivaraman Balakrishnan,Zachary Chase Lipton,Behnam Neyshabur,Hanie Sedghi | ICLR 2022,Poster | Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions
that may cause performance drops. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Aver... | https://openreview.net/pdf/f94008d1c0cfc4177d8617db211b62b1f85906ea.pdf |
VC dimension of partially quantized neural networks in the overparametrized regime | https://openreview.net/forum?id=7udZAsEzd60 | https://openreview.net/forum?id=7udZAsEzd60 | Yutong Wang,Clayton Scott | ICLR 2022,Poster | Vapnik-Chervonenkis (VC) theory has so far been unable to explain the small generalization error of overparametrized neural networks. Indeed, existing applications of VC theory to large networks obtain upper bounds on VC dimension that are proportional to the number of weights, and for a large class of networks, these ... | https://openreview.net/pdf/9760187606b3496a5f4a0fe752a22416bb4a2e21.pdf |
Optimal Representations for Covariate Shift | https://openreview.net/forum?id=Rf58LPCwJj0 | https://openreview.net/forum?id=Rf58LPCwJj0 | Yangjun Ruan,Yann Dubois,Chris J. Maddison | ICLR 2022,Poster | Machine learning systems often experience a distribution shift between training and testing. In this paper, we introduce a simple variational objective whose optima are exactly the set of all representations on which risk minimizers are guaranteed to be robust to any distribution shift that preserves the Bayes predicto... | https://openreview.net/pdf/ddc6369b11aed2bc1a72bc2f493bb2ebd0f65be7.pdf |
Fortuitous Forgetting in Connectionist Networks | https://openreview.net/forum?id=ei3SY1_zYsE | https://openreview.net/forum?id=ei3SY1_zYsE | Hattie Zhou,Ankit Vani,Hugo Larochelle,Aaron Courville | ICLR 2022,Poster | Forgetting is often seen as an unwanted characteristic in both human and machine learning. However, we propose that forgetting can in fact be favorable to learning. We introduce forget-and-relearn as a powerful paradigm for shaping the learning trajectories of artificial neural networks. In this process, the forgetting... | https://openreview.net/pdf/ca4d5fd0fac40867b797ca356f4056c7cb11fc6a.pdf |
EigenGame Unloaded: When playing games is better than optimizing | https://openreview.net/forum?id=So6YAqnqgMj | https://openreview.net/forum?id=So6YAqnqgMj | Ian Gemp,Brian McWilliams,Claire Vernade,Thore Graepel | ICLR 2022,Poster | We build on the recently proposed EigenGame that views eigendecomposition as a competitive game. EigenGame's updates are biased if computed using minibatches of data, which hinders convergence and more sophisticated parallelism in the stochastic setting. In this work, we propose an unbiased stochastic update that is as... | https://openreview.net/pdf/cedcb096f43d8f1b1e43c8969cf5b1dd7e83d5ae.pdf |
Contextualized Scene Imagination for Generative Commonsense Reasoning | https://openreview.net/forum?id=Oh1r2wApbPv | https://openreview.net/forum?id=Oh1r2wApbPv | PeiFeng Wang,Jonathan Zamora,Junfeng Liu,Filip Ilievski,Muhao Chen,Xiang Ren | ICLR 2022,Poster | Humans use natural language to compose common concepts from their environment into plausible, day-to-day scene descriptions. However, such generative commonsense reasoning (GCSR) skills are lacking in state-of-the-art text generation methods. Descriptive sentences about arbitrary concepts generated by neural text gener... | https://openreview.net/pdf/a66e1b12b2211131a44463611c8c272c21decbfb.pdf |
Scene Transformer: A unified architecture for predicting future trajectories of multiple agents | https://openreview.net/forum?id=Wm3EA5OlHsG | https://openreview.net/forum?id=Wm3EA5OlHsG | Jiquan Ngiam,Vijay Vasudevan,Benjamin Caine,Zhengdong Zhang,Hao-Tien Lewis Chiang,Jeffrey Ling,Rebecca Roelofs,Alex Bewley,Chenxi Liu,Ashish Venugopal,David J Weiss,Benjamin Sapp,Zhifeng Chen,Jonathon Shlens | ICLR 2022,Poster | Predicting the motion of multiple agents is necessary for planning in dynamic environments. This task is challenging for autonomous driving since agents (e.g., vehicles and pedestrians) and their associated behaviors may be diverse and influence one another. Most prior work have focused on predicting independent future... | https://openreview.net/pdf/92f191f2cdcf1389ed2d3dce901833dc5fc6deaf.pdf |
DISSECT: Disentangled Simultaneous Explanations via Concept Traversals | https://openreview.net/forum?id=qY79G8jGsep | https://openreview.net/forum?id=qY79G8jGsep | Asma Ghandeharioun,Been Kim,Chun-Liang Li,Brendan Jou,Brian Eoff,Rosalind Picard | ICLR 2022,Poster | Explaining deep learning model inferences is a promising venue for scientific understanding, improving safety, uncovering hidden biases, evaluating fairness, and beyond, as argued by many scholars. One of the principal benefits of counterfactual explanations is allowing users to explore "what-if" scenarios through what... | https://openreview.net/pdf/8e8a8d5dafd24c9cba49d3671b2ee34d0decdecf.pdf |
Heteroscedastic Temporal Variational Autoencoder For Irregularly Sampled Time Series | https://openreview.net/forum?id=Az7opqbQE-3 | https://openreview.net/forum?id=Az7opqbQE-3 | Satya Narayan Shukla,Benjamin Marlin | ICLR 2022,Poster | Irregularly sampled time series commonly occur in several domains where they present a significant challenge to standard deep learning models. In this paper, we propose a new deep learning framework for probabilistic interpolation of irregularly sampled time series that we call the Heteroscedastic Temporal Variational ... | https://openreview.net/pdf/4a602866528e0ae9511889c65b61991ad9ddfd8b.pdf |
A Neural Tangent Kernel Perspective of Infinite Tree Ensembles | https://openreview.net/forum?id=vUH85MOXO7h | https://openreview.net/forum?id=vUH85MOXO7h | Ryuichi Kanoh,Mahito Sugiyama | ICLR 2022,Poster | In practical situations, the tree ensemble is one of the most popular models along with neural networks. A soft tree is a variant of a decision tree. Instead of using a greedy method for searching splitting rules, the soft tree is trained using a gradient method in which the entire splitting operation is formulated in ... | https://openreview.net/pdf/39b3d2b8700abc51932e7eea69ff8d0868dc2be8.pdf |
AlphaZero-based Proof Cost Network to Aid Game Solving | https://openreview.net/forum?id=nKWjE4QF1hB | https://openreview.net/forum?id=nKWjE4QF1hB | Ti-Rong Wu,Chung-Chin Shih,Ting Han Wei,Meng-Yu Tsai,Wei-Yuan Hsu,I-Chen Wu | ICLR 2022,Poster | The AlphaZero algorithm learns and plays games without hand-crafted expert knowledge. However, since its objective is to play well, we hypothesize that a better objective can be defined for the related but separate task of solving games. This paper proposes a novel approach to solving problems by modifying the training... | https://openreview.net/pdf/b5c23474ea991857d67e3e750bb82c36a669b2e9.pdf |
Bayesian Framework for Gradient Leakage | https://openreview.net/forum?id=f2lrIbGx3x7 | https://openreview.net/forum?id=f2lrIbGx3x7 | Mislav Balunovic,Dimitar Iliev Dimitrov,Robin Staab,Martin Vechev | ICLR 2022,Poster | Federated learning is an established method for training machine learning models without sharing training data. However, recent work has shown that it cannot guarantee data privacy as shared gradients can still leak sensitive information. To formalize the problem of gradient leakage, we propose a theoretical framework ... | https://openreview.net/pdf/4e51a98c83f488bc5362a078c71216dab544be00.pdf |
Universalizing Weak Supervision | https://openreview.net/forum?id=YpPiNigTzMT | https://openreview.net/forum?id=YpPiNigTzMT | Changho Shin,Winfred Li,Harit Vishwakarma,Nicholas Carl Roberts,Frederic Sala | ICLR 2022,Poster | Weak supervision (WS) frameworks are a popular way to bypass hand-labeling large datasets for training data-hungry models.
These approaches synthesize multiple noisy but cheaply-acquired estimates of labels into a set of high-quality pseudo-labels for downstream training. However, the synthesis technique is specific to... | https://openreview.net/pdf/a2adc08eeb52dcddf2563c7bb42940946813b522.pdf |
Maximum n-times Coverage for Vaccine Design | https://openreview.net/forum?id=ULfq0qR25dY | https://openreview.net/forum?id=ULfq0qR25dY | Ge Liu,Alexander Dimitrakakis,Brandon Carter,David Gifford | ICLR 2022,Poster | We introduce the maximum $n$-times coverage problem that selects $k$ overlays to maximize the summed coverage of weighted elements, where each element must be covered at least $n$ times. We also define the min-cost $n$-times coverage problem where the objective is to select the minimum set of overlays such that the sum... | https://openreview.net/pdf/9d61f13ecd3d02a7e3ed6243e5e82f05c5f456cf.pdf |
KL Guided Domain Adaptation | https://openreview.net/forum?id=0JzqUlIVVDd | https://openreview.net/forum?id=0JzqUlIVVDd | A. Tuan Nguyen,Toan Tran,Yarin Gal,Philip Torr,Atilim Gunes Baydin | ICLR 2022,Poster | Domain adaptation is an important problem and often needed for real-world applications. In this problem, instead of i.i.d. training and testing datapoints, we assume that the source (training) data and the target (testing) data have different distributions. With that setting, the empirical risk minimization training pr... | https://openreview.net/pdf/943a05167d50e4a4de4e6c043f7c7e6374502f72.pdf |
From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness | https://openreview.net/forum?id=Mspk_WYKoEH | https://openreview.net/forum?id=Mspk_WYKoEH | Lingxiao Zhao,Wei Jin,Leman Akoglu,Neil Shah | ICLR 2022,Poster | Message Passing Neural Networks (MPNNs) are a common type of Graph Neural Network (GNN), in which each node’s representation is computed recursively by aggregating representations (“messages”) from its immediate neighbors akin to a star-shaped pattern. MPNNs are appealing for being efficient and scalable, however their... | https://openreview.net/pdf/cc341ac588b917bee10fc4d5bb31b4a119b6108b.pdf |
NETWORK INSENSITIVITY TO PARAMETER NOISE VIA PARAMETER ATTACK DURING TRAINING | https://openreview.net/forum?id=-8sBpe7rDiV | https://openreview.net/forum?id=-8sBpe7rDiV | Julian Büchel,Fynn Firouz Faber,Dylan Richard Muir | ICLR 2022,Poster | Neuromorphic neural network processors, in the form of compute-in-memory crossbar arrays of memristors, or in the form of subthreshold analog and mixed-signal ASICs, promise enormous advantages in compute density and energy efficiency for NN-based ML tasks. However, these technologies are prone to computational non-ide... | https://openreview.net/pdf/b7b77ce8535702dba33084aa20eb08cae53193f4.pdf |
Gradient Importance Learning for Incomplete Observations | https://openreview.net/forum?id=fXHl76nO2AZ | https://openreview.net/forum?id=fXHl76nO2AZ | Qitong Gao,Dong Wang,Joshua David Amason,Siyang Yuan,Chenyang Tao,Ricardo Henao,Majda Hadziahmetovic,Lawrence Carin,Miroslav Pajic | ICLR 2022,Poster | Though recent works have developed methods that can generate estimates (or imputations) of the missing entries in a dataset to facilitate downstream analysis, most depend on assumptions that may not align with real-world applications and could suffer from poor performance in subsequent tasks such as classification. Thi... | https://openreview.net/pdf/77f82d36ef5cbde5647d6e9f7fb7dd38ce4e2a91.pdf |
Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset | https://openreview.net/forum?id=v6s3HVjPerv | https://openreview.net/forum?id=v6s3HVjPerv | Leon Sixt,Martin Schuessler,Oana-Iuliana Popescu,Philipp Weiß,Tim Landgraf | ICLR 2022,Poster | A variety of methods exist to explain image classification models. However, whether they provide any benefit to users over simply comparing various inputs and the model’s respective predictions remains unclear. We conducted a user study (N=240) to test how such a baseline explanation technique performs against concept-... | https://openreview.net/pdf/49e3023b785924a7159ee756c546ac2ec523e8ea.pdf |
Understanding the Variance Collapse of SVGD in High Dimensions | https://openreview.net/forum?id=Qycd9j5Qp9J | https://openreview.net/forum?id=Qycd9j5Qp9J | Jimmy Ba,Murat A Erdogdu,Marzyeh Ghassemi,Shengyang Sun,Taiji Suzuki,Denny Wu,Tianzong Zhang | ICLR 2022,Poster | Stein variational gradient descent (SVGD) is a deterministic inference algorithm that evolves a set of particles to fit a target distribution. Despite its computational efficiency, SVGD often underestimates the variance of the target distribution in high dimensions. In this work we attempt to explain the variance colla... | https://openreview.net/pdf/71e77dab5447ab6226d0f2e58132575f2217dc3b.pdf |
Generalisation in Lifelong Reinforcement Learning through Logical Composition | https://openreview.net/forum?id=ZOcX-eybqoL | https://openreview.net/forum?id=ZOcX-eybqoL | Geraud Nangue Tasse,Steven James,Benjamin Rosman | ICLR 2022,Poster | We leverage logical composition in reinforcement learning to create a framework that enables an agent to autonomously determine whether a new task can be immediately solved using its existing abilities, or whether a task-specific skill should be learned. In the latter case, the proposed algorithm also enables the agent... | https://openreview.net/pdf/89cb79a9b9bb6a9a833a7a8ae73c8c5a87792970.pdf |
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions | https://openreview.net/forum?id=gSdSJoenupI | https://openreview.net/forum?id=gSdSJoenupI | Zhaoqi Leng,Mingxing Tan,Chenxi Liu,Ekin Dogus Cubuk,Jay Shi,Shuyang Cheng,Dragomir Anguelov | ICLR 2022,Poster | Cross-entropy loss and focal loss are the most common choices when training deep neural networks for classification problems. Generally speaking, however, a good loss function can take on much more flexible forms, and should be tailored for different tasks and datasets. Motivated by how functions can be approximated vi... | https://openreview.net/pdf/d1430448cff98fb37273293f39735ba9c6a4313a.pdf |
Improving Non-Autoregressive Translation Models Without Distillation | https://openreview.net/forum?id=I2Hw58KHp8O | https://openreview.net/forum?id=I2Hw58KHp8O | Xiao Shi Huang,Felipe Perez,Maksims Volkovs | ICLR 2022,Poster | Transformer-based autoregressive (AR) machine translation models have achieved significant performance improvements, nearing human-level accuracy on some languages. The AR framework translates one token at a time which can be time consuming, especially for long sequences. To accelerate inference, recent work has been e... | https://openreview.net/pdf/fe5e18c9939f10295c39693c81d77b03816cad63.pdf |
A Theory of Tournament Representations | https://openreview.net/forum?id=zzk231Ms1Ih | https://openreview.net/forum?id=zzk231Ms1Ih | Arun Rajkumar,Vishnu Veerathu,Abdul Bakey Mir | ICLR 2022,Poster | Real-world tournaments are almost always intransitive. Recent works have noted that parametric models which assume $d$ dimensional node representations can effectively model intransitive tournaments. However, nothing is known about the structure of the class of tournaments that arise out of any fixed $d$ dimensional r... | https://openreview.net/pdf/a7853d8c301f8a37bc858f4c428d73862dabff26.pdf |
Convergent and Efficient Deep Q Learning Algorithm | https://openreview.net/forum?id=OJm3HZuj4r7 | https://openreview.net/forum?id=OJm3HZuj4r7 | Zhikang T. Wang,Masahito Ueda | ICLR 2022,Poster | Despite the empirical success of the deep Q network (DQN) reinforcement learning algorithm and its variants, DQN is still not well understood and it does not guarantee convergence. In this work, we show that DQN can indeed diverge and cease to operate in realistic settings. Although there exist gradient-based convergen... | https://openreview.net/pdf/d999c3cb704da4722ea5330b5dd48600eb9c4ef4.pdf |
Trigger Hunting with a Topological Prior for Trojan Detection | https://openreview.net/forum?id=TXsjU8BaibT | https://openreview.net/forum?id=TXsjU8BaibT | Xiaoling Hu,Xiao Lin,Michael Cogswell,Yi Yao,Susmit Jha,Chao Chen | ICLR 2022,Poster | Despite their success and popularity, deep neural networks (DNNs) are vulnerable when facing backdoor attacks. This impedes their wider adoption, especially in mission critical applications. This paper tackles the problem of Trojan detection, namely, identifying Trojaned models – models trained with poisoned data. One ... | https://openreview.net/pdf/4db1d42d467c296c5ec7fa3f38e37dcb5c140e84.pdf |
Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL | https://openreview.net/forum?id=JM2kFbJvvI | https://openreview.net/forum?id=JM2kFbJvvI | Yanchao Sun,Ruijie Zheng,Yongyuan Liang,Furong Huang | ICLR 2022,Poster | Evaluating the worst-case performance of a reinforcement learning (RL) agent under the strongest/optimal adversarial perturbations on state observations (within some constraints) is crucial for understanding the robustness of RL agents. However, finding the optimal adversary is challenging, in terms of both whether we ... | https://openreview.net/pdf/b11335ea1d1d4ca95531723261e11735e0550bc4.pdf |
Chunked Autoregressive GAN for Conditional Waveform Synthesis | https://openreview.net/forum?id=v3aeIsY_vVX | https://openreview.net/forum?id=v3aeIsY_vVX | Max Morrison,Rithesh Kumar,Kundan Kumar,Prem Seetharaman,Aaron Courville,Yoshua Bengio | ICLR 2022,Poster | Conditional waveform synthesis models learn a distribution of audio waveforms given conditioning such as text, mel-spectrograms, or MIDI. These systems employ deep generative models that model the waveform via either sequential (autoregressive) or parallel (non-autoregressive) sampling. Generative adversarial networks ... | https://openreview.net/pdf/070239829c83980ec499e2eff346d48eafe3ecb5.pdf |
COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks | https://openreview.net/forum?id=psh0oeMSBiF | https://openreview.net/forum?id=psh0oeMSBiF | Fan Wu,Linyi Li,Huan Zhang,Bhavya Kailkhura,Krishnaram Kenthapadi,Ding Zhao,Bo Li | ICLR 2022,Poster | As reinforcement learning (RL) has achieved near human-level performance in a variety of tasks, its robustness has raised great attention. While a vast body of research has explored test-time (evasion) attacks in RL and corresponding defenses, its robustness against training-time (poisoning) attacks remains largely una... | https://openreview.net/pdf/0a24a116cb24a1e99cd715566dae243e36472472.pdf |
ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning | https://openreview.net/forum?id=Vzh1BFUCiIX | https://openreview.net/forum?id=Vzh1BFUCiIX | Vamsi Aribandi,Yi Tay,Tal Schuster,Jinfeng Rao,Huaixiu Steven Zheng,Sanket Vaibhav Mehta,Honglei Zhuang,Vinh Q. Tran,Dara Bahri,Jianmo Ni,Jai Gupta,Kai Hui,Sebastian Ruder,Donald Metzler | ICLR 2022,Poster | Despite the recent success of multi-task learning and transfer learning for natural language processing (NLP), few works have systematically studied the effect of scaling up the number of tasks during pre-training. Towards this goal, this paper introduces ExMix (Extreme Mixture): a massive collection of 107 supervised ... | https://openreview.net/pdf/b64da5c159b90bf56d174fc67459b74928711232.pdf |
Provable Adaptation across Multiway Domains via Representation Learning | https://openreview.net/forum?id=gRCCdgpVZf | https://openreview.net/forum?id=gRCCdgpVZf | Zhili Feng,Shaobo Han,Simon Shaolei Du | ICLR 2022,Poster | This paper studies zero-shot domain adaptation where each domain is indexed on a multi-dimensional array, and we only have data from a small subset of domains. Our goal is to produce predictors that perform well on \emph{unseen} domains. We propose a model which consists of a domain-invariant latent representation laye... | https://openreview.net/pdf/097cce8a39240bc2a614483e1cb4e0314237f10a.pdf |
Efficient Token Mixing for Transformers via Adaptive Fourier Neural Operators | https://openreview.net/forum?id=EXHG-A3jlM | https://openreview.net/forum?id=EXHG-A3jlM | John Guibas,Morteza Mardani,Zongyi Li,Andrew Tao,Anima Anandkumar,Bryan Catanzaro | ICLR 2022,Poster | Vision transformers have delivered tremendous success in representation learning. This is primarily due to effective token mixing through self attention. However, this scales quadratically with the number of pixels, which becomes infeasible for high-resolution inputs. To cope with this challenge, we propose Adaptive Fo... | https://openreview.net/pdf/bec7c123720932f2545dfb12e85bab8ac5cca6ff.pdf |
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels | https://openreview.net/forum?id=xENf4QUL4LW | https://openreview.net/forum?id=xENf4QUL4LW | Xiaobo Xia,Tongliang Liu,Bo Han,Mingming Gong,Jun Yu,Gang Niu,Masashi Sugiyama | ICLR 2022,Poster | In learning with noisy labels, the sample selection approach is very popular, which regards small-loss data as correctly labeled data during training. However, losses are generated on-the-fly based on the model being trained with noisy labels, and thus large-loss data are likely but not certain to be incorrect. There ar... | https://openreview.net/pdf/0ebab5bba4b36eec025abfd2e21f947e05d6e662.pdf |
Data-Driven Offline Optimization for Architecting Hardware Accelerators | https://openreview.net/forum?id=GsH-K1VIyy | https://openreview.net/forum?id=GsH-K1VIyy | Aviral Kumar,Amir Yazdanbakhsh,Milad Hashemi,Kevin Swersky,Sergey Levine | ICLR 2022,Poster | To attain higher efficiency, the industry has gradually reformed towards application-specific hardware accelerators. While such a paradigm shift is already starting to show promising results, designers need to spend considerable manual effort and perform large number of time-consuming simulations to find accelerators t... | https://openreview.net/pdf/62fa3ad6648729230b552447a872cf6777743905.pdf |
Multi-Agent MDP Homomorphic Networks | https://openreview.net/forum?id=H7HDG--DJF0 | https://openreview.net/forum?id=H7HDG--DJF0 | Elise van der Pol,Herke van Hoof,Frans A Oliehoek,Max Welling | ICLR 2022,Poster | This paper introduces Multi-Agent MDP Homomorphic Networks, a class of networks that allows distributed execution using only local information, yet is able to share experience between global symmetries in the joint state-action space of cooperative multi-agent systems. In cooperative multi-agent systems, complex symmet... | https://openreview.net/pdf/3a8f28592a8f20859b54c37f57cb659f7b0664fa.pdf |
Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields | https://openreview.net/forum?id=yhCp5RcZD7 | https://openreview.net/forum?id=yhCp5RcZD7 | Wang Yifan,Lukas Rahmann,Olga Sorkine-hornung | ICLR 2022,Poster | We present implicit displacement fields, a novel representation for detailed 3D geometry. Inspired by a classic surface deformation technique, displacement mapping, our method represents a complex surface as a smooth base surface plus a displacement along the base's normal directions, resulting in a frequency-based sha... | https://openreview.net/pdf/55c1560b8382311a7f02b90aaba2fa21e4475e9d.pdf |
Modeling Label Space Interactions in Multi-label Classification using Box Embeddings | https://openreview.net/forum?id=tyTH9kOxcvh | https://openreview.net/forum?id=tyTH9kOxcvh | Dhruvesh Patel,Pavitra Dangati,Jay-Yoon Lee,Michael Boratko,Andrew McCallum | ICLR 2022,Poster | Multi-label classification is a challenging structured prediction task in which a set of output class labels are predicted for each input. Real-world datasets often have natural or latent taxonomic relationships between labels, making it desirable for models to employ label representations capable of capturing such tax... | https://openreview.net/pdf/f5671d43125692a6533d9c7a1996335b8a1cd482.pdf |
It Takes Two to Tango: Mixup for Deep Metric Learning | https://openreview.net/forum?id=ZKy2X3dgPA | https://openreview.net/forum?id=ZKy2X3dgPA | Shashanka Venkataramanan,Bill Psomas,Ewa Kijak,laurent amsaleg,Konstantinos Karantzalos,Yannis Avrithis | ICLR 2022,Poster | Metric learning involves learning a discriminative representation such that embeddings of similar classes are encouraged to be close, while embeddings of dissimilar classes are pushed far apart. State-of-the-art methods focus mostly on sophisticated loss functions or mining strategies. On the one hand, metric learning ... | https://openreview.net/pdf/1b4683c706bc39fb7b56b3982f8c10166b29773d.pdf |
Data Efficient Language-Supervised Zero-Shot Recognition with Optimal Transport Distillation | https://openreview.net/forum?id=G89-1yZLFHk | https://openreview.net/forum?id=G89-1yZLFHk | Bichen Wu,Ruizhe Cheng,Peizhao Zhang,Tianren Gao,Joseph E. Gonzalez,Peter Vajda | ICLR 2022,Poster | Traditional computer vision models are trained to predict a fixed set of predefined categories. Recently, natural language has been shown to be a broader and richer source of supervision that provides finer descriptions to visual concepts than supervised "gold" labels. Previous works, such as CLIP, use InfoNCE loss to ... | https://openreview.net/pdf/4692c27fcf85afed7f22e02ea4a1c14104fce2a4.pdf |
A Statistical Framework for Efficient Out of Distribution Detection in Deep Neural Networks | https://openreview.net/forum?id=Oy9WeuZD51 | https://openreview.net/forum?id=Oy9WeuZD51 | Matan Haroush,Tzviel Frostig,Ruth Heller,Daniel Soudry | ICLR 2022,Poster | Background.
Commonly, Deep Neural Networks (DNNs) generalize well on samples drawn from a distribution similar to that of the training set. However, DNNs' predictions are brittle and unreliable when the test samples are drawn from a dissimilar distribution.
This is a major concern for deployment in real-world applicati... | https://openreview.net/pdf/8ab4fc0f10bb1b17497961ee8ff9912af8ed2cc3.pdf |
FedBABU: Toward Enhanced Representation for Federated Image Classification | https://openreview.net/forum?id=HuaYQfggn5u | https://openreview.net/forum?id=HuaYQfggn5u | Jaehoon Oh,SangMook Kim,Se-Young Yun | ICLR 2022,Poster | Federated learning has evolved to improve a single global model under data heterogeneity (as a curse) or to develop multiple personalized models using data heterogeneity (as a blessing). However, little research has considered both directions simultaneously. In this paper, we first investigate the relationship between ... | https://openreview.net/pdf/09e0b377fa4e3200e80d267b3e1df94235e10a45.pdf |
Should I Run Offline Reinforcement Learning or Behavioral Cloning? | https://openreview.net/forum?id=AP1MKT37rJ | https://openreview.net/forum?id=AP1MKT37rJ | Aviral Kumar,Joey Hong,Anikait Singh,Sergey Levine | ICLR 2022,Poster | Offline reinforcement learning (RL) algorithms can acquire effective policies by utilizing only previously collected experience, without any online interaction. While it is widely understood that offline RL is able to extract good policies even from highly suboptimal data, in practice offline RL is often used with dat... | https://openreview.net/pdf/ab91050974b19858a9a241236b4d69019903de0e.pdf |
Learning State Representations via Retracing in Reinforcement Learning | https://openreview.net/forum?id=CLpxpXqqBV | https://openreview.net/forum?id=CLpxpXqqBV | Changmin Yu,Dong Li,Jianye HAO,Jun Wang,Neil Burgess | ICLR 2022,Poster | We propose learning via retracing, a novel self-supervised approach for learning the state representation (and the associated dynamics model) for reinforcement learning tasks. In addition to the predictive (reconstruction) supervision in the forward direction, we propose to include "retraced" transitions for representa... | https://openreview.net/pdf/04d24e2870546f3dcff312162e1b4006ecd641b7.pdf |
Open-World Semi-Supervised Learning | https://openreview.net/forum?id=O-r8LOR-CCA | https://openreview.net/forum?id=O-r8LOR-CCA | Kaidi Cao,Maria Brbic,Jure Leskovec | ICLR 2022,Poster | A fundamental limitation of applying semi-supervised learning in real-world settings is the assumption that unlabeled test data contains only classes previously encountered in the labeled training data. However, this assumption rarely holds for data in-the-wild, where instances belonging to novel classes may appear at ... | https://openreview.net/pdf/e5ffbb438b307d601bd7794c87fae3c23950a63f.pdf |
Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent | https://openreview.net/forum?id=af1eUDdUVz | https://openreview.net/forum?id=af1eUDdUVz | Oliver Bryniarski,Nabeel Hingun,Pedro Pachuca,Vincent Wang,Nicholas Carlini | ICLR 2022,Poster | Evading adversarial example detection defenses requires finding adversarial examples that must simultaneously (a) be misclassified by the model and (b) be detected as non-adversarial. We find that existing attacks that attempt to satisfy multiple simultaneous constraints often over-optimize against one constraint at th... | https://openreview.net/pdf/3d2eb96b012475581aa80cda16373c217e28c087.pdf |
Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-off | https://openreview.net/forum?id=Azh9QBQ4tR7 | https://openreview.net/forum?id=Azh9QBQ4tR7 | Rahul Rade,Seyed-Mohsen Moosavi-Dezfooli | ICLR 2022,Poster | While adversarial training has become the de facto approach for training robust classifiers, it leads to a drop in accuracy. This has led to prior works postulating that accuracy is inherently at odds with robustness. Yet, the phenomenon remains inexplicable. In this paper, we closely examine the changes induced in the... | https://openreview.net/pdf/c2a72787c4e6f0d24586b17eab7ca97027346386.pdf |
Expressivity of Emergent Languages is a Trade-off between Contextual Complexity and Unpredictability | https://openreview.net/forum?id=WxuE_JWxjkW | https://openreview.net/forum?id=WxuE_JWxjkW | Shangmin Guo,Yi Ren,Kory Wallace Mathewson,Simon Kirby,Stefano V Albrecht,Kenny Smith | ICLR 2022,Poster | Researchers are using deep learning models to explore the emergence of language in various language games, where agents interact and develop an emergent language to solve tasks. We focus on the factors that determine the expressivity of emergent languages, which reflects the amount of information about input spaces tho... | https://openreview.net/pdf/be46689741877d2b59dc56c09443500af7dd2941.pdf |
Fast AdvProp | https://openreview.net/forum?id=hcoswsDHNAW | https://openreview.net/forum?id=hcoswsDHNAW | Jieru Mei,Yucheng Han,Yutong Bai,Yixiao Zhang,Yingwei Li,Xianhang Li,Alan Yuille,Cihang Xie | ICLR 2022,Poster | Adversarial Propagation (AdvProp) is an effective way to improve recognition models, leveraging adversarial examples. Nonetheless, AdvProp suffers from the extremely slow training speed, mainly because: a) extra forward and backward passes are required for generating adversarial examples; b) both original samples and t... | https://openreview.net/pdf/12e365a996eeb801b2173df149f6f8bc69ec02fa.pdf |
Triangle and Four Cycle Counting with Predictions in Graph Streams | https://openreview.net/forum?id=8in_5gN9I0 | https://openreview.net/forum?id=8in_5gN9I0 | Justin Y Chen,Talya Eden,Piotr Indyk,Honghao Lin,Shyam Narayanan,Ronitt Rubinfeld,Sandeep Silwal,Tal Wagner,David Woodruff,Michael Zhang | ICLR 2022,Poster | We propose data-driven one-pass streaming algorithms for estimating the number of triangles and four cycles, two fundamental problems in graph analytics that are widely studied in the graph data stream literature. Recently, Hsu et al. (2019) and Jiang et al. (2020) applied machine learning techniques in other data stre... | https://openreview.net/pdf/25b70c42018200ce5f79c1f1dfc16f4c95ff9304.pdf |
Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning | https://openreview.net/forum?id=js62_xuLDDv | https://openreview.net/forum?id=js62_xuLDDv | Natalie Dullerud,Karsten Roth,Kimia Hamidieh,Nicolas Papernot,Marzyeh Ghassemi | ICLR 2022,Poster | Deep metric learning (DML) enables learning with less supervision through its emphasis on the similarity structure of representations. There has been much work on improving generalization of DML in settings like zero-shot retrieval, but little is known about its implications for fairness. In this paper, we are the fir... | https://openreview.net/pdf/f404cf882e197b2c86f3e62a769c3cbf9024a9b5.pdf |
NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs | https://openreview.net/forum?id=xMJWUKJnFSw | https://openreview.net/forum?id=xMJWUKJnFSw | Mikhail Galkin,Etienne Denis,Jiapeng Wu,William L. Hamilton | ICLR 2022,Poster | Conventional representation learning algorithms for knowledge graphs (KG) map each entity to a unique embedding vector.
Such a shallow lookup results in a linear growth of memory consumption for storing the embedding matrix and incurs high computational costs of working with real-world KGs.
Drawing parallels with subw... | https://openreview.net/pdf/6eb641d163812ce838dbad1b8e7fddebb2c72c12.pdf |
Pix2seq: A Language Modeling Framework for Object Detection | https://openreview.net/forum?id=e42KbIw6Wb | https://openreview.net/forum?id=e42KbIw6Wb | Ting Chen,Saurabh Saxena,Lala Li,David J. Fleet,Geoffrey Hinton | ICLR 2022,Poster | We present Pix2Seq, a simple and generic framework for object detection. Unlike existing approaches that explicitly integrate prior knowledge about the task, we cast object detection as a language modeling task conditioned on the observed pixel inputs. Object descriptions (e.g., bounding boxes and class labels) are exp... | https://openreview.net/pdf/1f7291d96e3b195bdf0664dfb0f5313b0eab7a04.pdf |
Particle Stochastic Dual Coordinate Ascent: Exponential convergent algorithm for mean field neural network optimization | https://openreview.net/forum?id=PQQp7AJwz3 | https://openreview.net/forum?id=PQQp7AJwz3 | Kazusato Oko,Taiji Suzuki,Atsushi Nitanda,Denny Wu | ICLR 2022,Poster | We introduce Particle-SDCA, a gradient-based optimization algorithm for two-layer neural networks in the mean field regime that achieves exponential convergence rate in regularized empirical risk minimization. The proposed algorithm can be regarded as an infinite dimensional extension of Stochastic Dual Coordinate Asce... | https://openreview.net/pdf/b6a0af59072ab41c5553c6952e5a786b25d0adde.pdf |
The Effects of Invertibility on the Representational Complexity of Encoders in Variational Autoencoders | https://openreview.net/forum?id=7_JR7WpwKV1 | https://openreview.net/forum?id=7_JR7WpwKV1 | Divyansh Pareek,Andrej Risteski | ICLR 2022,Poster | Training and using modern neural-network based latent-variable generative models (like Variational Autoencoders) often require simultaneously training a generative direction along with an inferential (encoding) direction, which approximates the posterior distribution over the latent variables. Thus, the question arises... | https://openreview.net/pdf/4116475bedc76111284bad627cb9a8fbaec2059b.pdf |
Tracking the risk of a deployed model and detecting harmful distribution shifts | https://openreview.net/forum?id=Ro_zAjZppv | https://openreview.net/forum?id=Ro_zAjZppv | Aleksandr Podkopaev,Aaditya Ramdas | ICLR 2022,Poster | When deployed in the real world, machine learning models inevitably encounter changes in the data distribution, and certain---but not all---distribution shifts could result in significant performance degradation. In practice, it may make sense to ignore benign shifts, under which the performance of a deployed model doe... | https://openreview.net/pdf/f763a5271b61d98bca4127ab14ce483150d152c4.pdf |
Towards Understanding the Robustness Against Evasion Attack on Categorical Data | https://openreview.net/forum?id=BmJV7kyAmg | https://openreview.net/forum?id=BmJV7kyAmg | Hongyan Bao,Yufei Han,Yujun Zhou,Yun Shen,Xiangliang Zhang | ICLR 2022,Poster | Characterizing and assessing the adversarial vulnerability of classification models with categorical input has been a practically important, while rarely explored research problem. Our work echoes the challenge by first unveiling the impact factors of adversarial vulnerability of classification models with categorical ... | https://openreview.net/pdf/b599972b615dea56e3cd777bb3c09e18b73ba736.pdf |
Learning Curves for SGD on Structured Features | https://openreview.net/forum?id=WPI2vbkAl3Q | https://openreview.net/forum?id=WPI2vbkAl3Q | Blake Bordelon,Cengiz Pehlevan | ICLR 2022,Poster | The generalization performance of a machine learning algorithm such as a neural network depends in a non-trivial way on the structure of the data distribution. To analyze the influence of data structure on test loss dynamics, we study an exactly solveable model of stochastic gradient descent (SGD) on the square loss wh... | https://openreview.net/pdf/05e1bd43845bd2321a0ab8593b8960931a65e24e.pdf |
NASViT: Neural Architecture Search for Efficient Vision Transformers with Gradient Conflict aware Supernet Training | https://openreview.net/forum?id=Qaw16njk6L | https://openreview.net/forum?id=Qaw16njk6L | Chengyue Gong,Dilin Wang,Meng Li,Xinlei Chen,Zhicheng Yan,Yuandong Tian,qiang liu,Vikas Chandra | ICLR 2022,Poster | Designing accurate and efficient vision transformers (ViTs) is a highly important but challenging task. Supernet-based one-shot neural architecture search (NAS) enables fast architecture optimization and has achieved state-of-the-art (SOTA) results on convolutional neural networks (CNNs). However, directly applying the... | https://openreview.net/pdf/a6df48abb7e0bb493e7c343c46beb7b365cdc788.pdf |
Graphon based Clustering and Testing of Networks: Algorithms and Theory | https://openreview.net/forum?id=sTNHCrIKDQc | https://openreview.net/forum?id=sTNHCrIKDQc | Mahalakshmi Sabanayagam,Leena Chennuru Vankadara,Debarghya Ghoshdastidar | ICLR 2022,Poster | Network-valued data are encountered in a wide range of applications, and pose challenges in learning due to their complex structure and absence of vertex correspondence. Typical examples of such problems include classification or grouping of protein structures and social networks. Various methods, ranging from graph ke... | https://openreview.net/pdf/bc3a82e090f7f3cfaa9a92ef69181887e0348ede.pdf |
Network Augmentation for Tiny Deep Learning | https://openreview.net/forum?id=TYw3-OlrRm- | https://openreview.net/forum?id=TYw3-OlrRm- | Han Cai,Chuang Gan,Ji Lin,Song Han | ICLR 2022,Poster | We introduce Network Augmentation (NetAug), a new training method for improving the performance of tiny neural networks. Existing regularization techniques (e.g., data augmentation, dropout) have shown much success on large neural networks by adding noise to overcome over-fitting. However, we found these techniques hur... | https://openreview.net/pdf/484496875b902e745fc4d6514abb817e7be477c2.pdf |
Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations | https://openreview.net/forum?id=o-1v9hdSult | https://openreview.net/forum?id=o-1v9hdSult | Sarath Sreedharan,Utkarsh Soni,Mudit Verma,Siddharth Srivastava,Subbarao Kambhampati | ICLR 2022,Poster | As increasingly complex AI systems are introduced into our daily lives, it becomes important for such systems to be capable of explaining the rationale for their decisions and allowing users to contest these decisions. A significant hurdle to allowing for such explanatory dialogue could be the {\em vocabulary mismatch}... | https://openreview.net/pdf/2558c3735ba361f65aac84ecf8e9f4624e87dec8.pdf |
Distributional Reinforcement Learning with Monotonic Splines | https://openreview.net/forum?id=C8Ltz08PtBp | https://openreview.net/forum?id=C8Ltz08PtBp | Yudong Luo,Guiliang Liu,Haonan Duan,Oliver Schulte,Pascal Poupart | ICLR 2022,Poster | Distributional Reinforcement Learning (RL) differs from traditional RL by estimating the distribution over returns to capture the intrinsic uncertainty of MDPs. One key challenge in distributional RL lies in how to parameterize the quantile function when minimizing the Wasserstein metric of temporal differences. Existi... | https://openreview.net/pdf/376a906de470631ee01098610befe6addc3d72de.pdf |
Toward Faithful Case-based Reasoning through Learning Prototypes in a Nearest Neighbor-friendly Space. | https://openreview.net/forum?id=R79ZGjHhv6p | https://openreview.net/forum?id=R79ZGjHhv6p | Seyed Omid Davoudi,Majid Komeili | ICLR 2022,Poster | Recent advances in machine learning have brought opportunities for the ever-increasing use of AI in the real world. This has created concerns about the black-box nature of many of the most recent machine learning approaches. In this work, we propose an interpretable neural network that leverages metric and prototype le... | https://openreview.net/pdf/6d0714a184aa752df631ed2df558e8cfee0d4bb9.pdf |
Augmented Sliced Wasserstein Distances | https://openreview.net/forum?id=iMqTLyfwnOO | https://openreview.net/forum?id=iMqTLyfwnOO | Xiongjie Chen,Yongxin Yang,Yunpeng Li | ICLR 2022,Poster | While theoretically appealing, the application of the Wasserstein distance to large-scale machine learning problems has been hampered by its prohibitive computational cost. The sliced Wasserstein distance and its variants improve the computational efficiency through the random projection, yet they suffer from low accur... | https://openreview.net/pdf/d09a765a0ca6e8fe66e61db6af5518d089814c41.pdf |
Relational Learning with Variational Bayes | https://openreview.net/forum?id=Az-7gJc6lpr | https://openreview.net/forum?id=Az-7gJc6lpr | Kuang-Hung Liu | ICLR 2022,Poster | In psychology, relational learning refers to the ability to recognize and respond to relationship among objects irrespective of the nature of those objects. Relational learning has long been recognized as a hallmark of human cognition and a key question in artificial intelligence research. In this work, we propose an u... | https://openreview.net/pdf/9d3dfe42360aa203adb14bacece6acbb08064ac0.pdf |
Provably Robust Adversarial Examples | https://openreview.net/forum?id=UMfhoMtIaP5 | https://openreview.net/forum?id=UMfhoMtIaP5 | Dimitar Iliev Dimitrov,Gagandeep Singh,Timon Gehr,Martin Vechev | ICLR 2022,Poster | We introduce the concept of provably robust adversarial examples for deep neural networks – connected input regions constructed from standard adversarial examples which are guaranteed to be robust to a set of real-world perturbations (such as changes in pixel intensity and geometric transformations). We present a novel... | https://openreview.net/pdf/3b8eb27fbc166f48033673d3fadc49a86ef0b79f.pdf |
Joint Shapley values: a measure of joint feature importance | https://openreview.net/forum?id=vcUmUvQCloe | https://openreview.net/forum?id=vcUmUvQCloe | Chris Harris,Richard Pymar,Colin Rowat | ICLR 2022,Poster | The Shapley value is one of the most widely used measures of feature importance partly as it measures a feature's average effect on a model's prediction. We introduce joint Shapley values, which directly extend Shapley's axioms and intuitions: joint Shapley values measure a set of features' average effect on a model's... | https://openreview.net/pdf/7d8a95bb048b3b204b4a1c9a95e93486a12439a1.pdf |
Low-Budget Active Learning via Wasserstein Distance: An Integer Programming Approach | https://openreview.net/forum?id=v8OlxjGn23S | https://openreview.net/forum?id=v8OlxjGn23S | Rafid Mahmood,Sanja Fidler,Marc T Law | ICLR 2022,Poster | Active learning is the process of training a model with limited labeled data by selecting a core subset of an unlabeled data pool to label. The large scale of data sets used in deep learning forces most sample selection strategies to employ efficient heuristics. This paper introduces an integer optimization problem for... | https://openreview.net/pdf/9dac127c30d4567d8dde179f21749b9ca5494686.pdf |
Efficient Self-supervised Vision Transformers for Representation Learning | https://openreview.net/forum?id=fVu3o-YUGQK | https://openreview.net/forum?id=fVu3o-YUGQK | Chunyuan Li,Jianwei Yang,Pengchuan Zhang,Mei Gao,Bin Xiao,Xiyang Dai,Lu Yuan,Jianfeng Gao | ICLR 2022,Poster | This paper investigates two techniques for developing efficient self-supervised vision transformers (EsViT) for visual representation learning. First, we show through a comprehensive empirical study that multi-stage architectures with sparse self-attentions can significantly reduce modeling complexity but with a cost o... | https://openreview.net/pdf/e7b63dccef8ad598db1c36a2386c8d8a63058e8e.pdf |
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain | https://openreview.net/forum?id=9RUHPlladgh | https://openreview.net/forum?id=9RUHPlladgh | Lukas Schott,Julius Von Kügelgen,Frederik Träuble,Peter Vincent Gehler,Chris Russell,Matthias Bethge,Bernhard Schölkopf,Francesco Locatello,Wieland Brendel | ICLR 2022,Poster | An important component for generalization in machine learning is to uncover underlying latent factors of variation as well as the mechanism through which each factor acts in the world.
In this paper, we test whether 17 unsupervised, weakly supervised, and fully supervised representation learning approaches correctly in... | https://openreview.net/pdf/775e024ab2e9ce40e6b2f7608d5b1eb2c1136e75.pdf |
Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions | https://openreview.net/forum?id=e2Lle5cij9D | https://openreview.net/forum?id=e2Lle5cij9D | Arda Sahiner,Tolga Ergen,Batu Ozturkler,Burak Bartan,John M. Pauly,Morteza Mardani,Mert Pilanci | ICLR 2022,Poster | Generative Adversarial Networks (GANs) are commonly used for modeling complex distributions of data. Both the generators and discriminators of GANs are often modeled by neural networks, posing a non-transparent optimization problem which is non-convex and non-concave over the generator and discriminator, respectively. ... | https://openreview.net/pdf/733796fc142ddb063afc1a0818ecba208aef1465.pdf |
Memory Augmented Optimizers for Deep Learning | https://openreview.net/forum?id=NRX9QZ6yqt | https://openreview.net/forum?id=NRX9QZ6yqt | Paul-Aymeric Martin McRae,Prasanna Parthasarathi,Mido Assran,Sarath Chandar | ICLR 2022,Poster | Popular approaches for minimizing loss in data-driven learning often involve an abstraction or an explicit retention of the history of gradients for efficient parameter updates.
The aggregated history of gradients nudges the parameter updates in the right direction even when the gradients at any given step are not inf... | https://openreview.net/pdf/874e2c95385be68f564d4d96107e652253f10706.pdf |
Orchestrated Value Mapping for Reinforcement Learning | https://openreview.net/forum?id=c87d0TS4yX | https://openreview.net/forum?id=c87d0TS4yX | Mehdi Fatemi,Arash Tavakoli | ICLR 2022,Poster | We present a general convergent class of reinforcement learning algorithms that is founded on two distinct principles: (1) mapping value estimates to a different space using arbitrary functions from a broad class, and (2) linearly decomposing the reward signal into multiple channels. The first principle enables incorpo... | https://openreview.net/pdf/9ef3cef089b9f45f5bdb93fddb0ed8ccfa9e3268.pdf |
Learning to Generalize across Domains on Single Test Samples | https://openreview.net/forum?id=CIaQKbTBwtU | https://openreview.net/forum?id=CIaQKbTBwtU | Zehao Xiao,Xiantong Zhen,Ling Shao,Cees G. M. Snoek | ICLR 2022,Poster | We strive to learn a model from a set of source domains that generalizes well to unseen target domains. The main challenge in such a domain generalization scenario is the unavailability of any target domain data during training, resulting in the learned model not being explicitly adapted to the unseen target domains. W... | https://openreview.net/pdf/4fcc67594340f12c1beb7e4f1ce64c7be6f70c0a.pdf |
Prototype memory and attention mechanisms for few shot image generation | https://openreview.net/forum?id=lY0-7bj0Vfz | https://openreview.net/forum?id=lY0-7bj0Vfz | Tianqin Li,Zijie Li,Andrew Luo,Harold Rockwell,Amir Barati Farimani,Tai Sing Lee | ICLR 2022,Poster | Recent discoveries indicate that the neural codes in the primary visual cortex (V1) of macaque monkeys are complex, diverse and sparse. This leads us to ponder the computational advantages and functional role of these “grandmother cells." Here, we propose that such cells can serve as prototype memory priors that bias a... | https://openreview.net/pdf/c2a4a72f1bd5890c4beeb93de11cac4746eae2c1.pdf |
TPU-GAN: Learning temporal coherence from dynamic point cloud sequences | https://openreview.net/forum?id=FEBFJ98FKx | https://openreview.net/forum?id=FEBFJ98FKx | Zijie Li,Tianqin Li,Amir Barati Farimani | ICLR 2022,Poster | Point cloud sequence is an important data representation that provides flexible shape and motion information. Prior work demonstrates that incorporating scene flow information into loss can make model learn temporally coherent feature spaces. However, it is prohibitively expensive to acquire point correspondence inform... | https://openreview.net/pdf/52569840ae5698d2203efde4f8f06d012fa7868a.pdf |
A First-Occupancy Representation for Reinforcement Learning | https://openreview.net/forum?id=JBAZe2yN6Ub | https://openreview.net/forum?id=JBAZe2yN6Ub | Ted Moskovitz,Spencer R Wilson,Maneesh Sahani | ICLR 2022,Poster | Both animals and artificial agents benefit from state representations that support rapid transfer of learning across tasks and which enable them to efficiently traverse their environments to reach rewarding states. The successor representation (SR), which measures the expected cumulative, discounted state occupancy un... | https://openreview.net/pdf/46abdff2d131f44012d855cdd93c0fa7034d601a.pdf |
Deep ReLU Networks Preserve Expected Length | https://openreview.net/forum?id=ci7LBzDn2Q | https://openreview.net/forum?id=ci7LBzDn2Q | Boris Hanin,Ryan Jeong,David Rolnick | ICLR 2022,Poster | Assessing the complexity of functions computed by a neural network helps us understand how the network will learn and generalize. One natural measure of complexity is how the network distorts length - if the network takes a unit-length curve as input, what is the length of the resulting curve of outputs? It has been wi... | https://openreview.net/pdf/726f7b1d7efcb38a8f1685099dbfc32c938b1267.pdf |
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