title stringlengths 19 143 | url stringlengths 41 43 | detail_url stringlengths 41 43 | authors stringlengths 9 347 | tags stringclasses 3
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Temporally-Extended ε-Greedy Exploration | https://openreview.net/forum?id=ONBPHFZ7zG4 | https://openreview.net/forum?id=ONBPHFZ7zG4 | Will Dabney,Georg Ostrovski,Andre Barreto | ICLR 2021,Poster | Recent work on exploration in reinforcement learning (RL) has led to a series of increasingly complex solutions to the problem. This increase in complexity often comes at the expense of generality. Recent empirical studies suggest that, when applied to a broader set of domains, some sophisticated exploration methods ar... | https://openreview.net/pdf/be288b1cdd527108548adea1d4d8319ce8a8eae8.pdf |
Learning Associative Inference Using Fast Weight Memory | https://openreview.net/forum?id=TuK6agbdt27 | https://openreview.net/forum?id=TuK6agbdt27 | Imanol Schlag,Tsendsuren Munkhdalai,Jürgen Schmidhuber | ICLR 2021,Poster | Humans can quickly associate stimuli to solve problems in novel contexts. Our novel neural network model learns state representations of facts that can be composed to perform such associative inference. To this end, we augment the LSTM model with an associative memory, dubbed \textit{Fast Weight Memory} (FWM). Through ... | https://openreview.net/pdf/96ccad214bb6dc5b347aa32436f14fdd5391d21b.pdf |
Multiscale Score Matching for Out-of-Distribution Detection | https://openreview.net/forum?id=xoHdgbQJohv | https://openreview.net/forum?id=xoHdgbQJohv | Ahsan Mahmood,Junier Oliva,Martin Andreas Styner | ICLR 2021,Poster | We present a new methodology for detecting out-of-distribution (OOD) images by utilizing norms of the score estimates at multiple noise scales. A score is defined to be the gradient of the log density with respect to the input data. Our methodology is completely unsupervised and follows a straight forward training sche... | https://openreview.net/pdf/639279c160eb93e79cf2ee33db8f9dc5b040f345.pdf |
Learning to Sample with Local and Global Contexts in Experience Replay Buffer | https://openreview.net/forum?id=gJYlaqL8i8 | https://openreview.net/forum?id=gJYlaqL8i8 | Youngmin Oh,Kimin Lee,Jinwoo Shin,Eunho Yang,Sung Ju Hwang | ICLR 2021,Poster | Experience replay, which enables the agents to remember and reuse experience from the past, has played a significant role in the success of off-policy reinforcement learning (RL). To utilize the experience replay efficiently, the existing sampling methods allow selecting out more meaningful experiences by imposing prio... | https://openreview.net/pdf/92ef8e632b99778a17bd8e0187962812d2cd42c5.pdf |
Parameter-Based Value Functions | https://openreview.net/forum?id=tV6oBfuyLTQ | https://openreview.net/forum?id=tV6oBfuyLTQ | Francesco Faccio,Louis Kirsch,Jürgen Schmidhuber | ICLR 2021,Poster | Traditional off-policy actor-critic Reinforcement Learning (RL) algorithms learn value functions of a single target policy. However, when value functions are updated to track the learned policy, they forget potentially useful information about old policies. We introduce a class of value functions called Parameter-Based... | https://openreview.net/pdf/c79ef13431e3c5decbd9f2ba989bc20a847b37be.pdf |
New Bounds For Distributed Mean Estimation and Variance Reduction | https://openreview.net/forum?id=t86MwoUCCNe | https://openreview.net/forum?id=t86MwoUCCNe | Peter Davies,Vijaykrishna Gurunanthan,Niusha Moshrefi,Saleh Ashkboos,Dan Alistarh | ICLR 2021,Poster | We consider the problem of distributed mean estimation (DME), in which $n$ machines are each given a local $d$-dimensional vector $\mathbf x_v \in \mathbb R^d$, and must cooperate to estimate the mean of their inputs $\mathbf \mu = \frac 1n\sum_{v = 1}^n \mathbf x_v$, while minimizing total communication cost. DME is ... | https://openreview.net/pdf/02618eb8b76a664b33780ceb32a0450c69a54d1c.pdf |
Learning to Set Waypoints for Audio-Visual Navigation | https://openreview.net/forum?id=cR91FAodFMe | https://openreview.net/forum?id=cR91FAodFMe | Changan Chen,Sagnik Majumder,Ziad Al-Halah,Ruohan Gao,Santhosh Kumar Ramakrishnan,Kristen Grauman | ICLR 2021,Poster | In audio-visual navigation, an agent intelligently travels through a complex, unmapped 3D environment using both sights and sounds to find a sound source (e.g., a phone ringing in another room). Existing models learn to act at a fixed granularity of agent motion and rely on simple recurrent aggregations of the audio ob... | https://openreview.net/pdf/fa0a991905ae30b2fa74ca7b101b3acabd532c13.pdf |
Disambiguating Symbolic Expressions in Informal Documents | https://openreview.net/forum?id=K5j7D81ABvt | https://openreview.net/forum?id=K5j7D81ABvt | Dennis Müller,Cezary Kaliszyk | ICLR 2021,Poster | We propose the task of \emph{disambiguating} symbolic expressions in informal STEM documents in the form of \LaTeX files -- that is, determining their precise semantics and abstract syntax tree -- as a neural machine translation task. We discuss the distinct challenges involved and present a dataset with roughly 33,000... | https://openreview.net/pdf/006f5f9df1ed650389c8a89fd0087c3a9cb81605.pdf |
Colorization Transformer | https://openreview.net/forum?id=5NA1PinlGFu | https://openreview.net/forum?id=5NA1PinlGFu | Manoj Kumar,Dirk Weissenborn,Nal Kalchbrenner | ICLR 2021,Poster | We present the Colorization Transformer, a novel approach for diverse high fidelity image colorization based on self-attention. Given a grayscale image, the colorization proceeds in three steps. We first use a conditional autoregressive transformer to produce a low resolution coarse coloring of the grayscale image. Our... | https://openreview.net/pdf/f2f5d9057587995de8d113d1ba35dd7d8b98f48e.pdf |
Theoretical bounds on estimation error for meta-learning | https://openreview.net/forum?id=SZ3wtsXfzQR | https://openreview.net/forum?id=SZ3wtsXfzQR | James Lucas,Mengye Ren,Irene Raissa KAMENI KAMENI,Toniann Pitassi,Richard Zemel | ICLR 2021,Poster | Machine learning models have traditionally been developed under the assumption that the training and test distributions match exactly. However, recent success in few-shot learning and related problems are encouraging signs that these models can be adapted to more realistic settings where train and test distributions di... | https://openreview.net/pdf/f6e0a3923ea91b65f312ccf597276c427be18097.pdf |
Variational Information Bottleneck for Effective Low-Resource Fine-Tuning | https://openreview.net/forum?id=kvhzKz-_DMF | https://openreview.net/forum?id=kvhzKz-_DMF | Rabeeh Karimi mahabadi,Yonatan Belinkov,James Henderson | ICLR 2021,Poster | While large-scale pretrained language models have obtained impressive results when fine-tuned on a wide variety of tasks, they still often suffer from overfitting in low-resource scenarios. Since such models are general-purpose feature extractors, many of these features are inevitably irrelevant for a given target task... | https://openreview.net/pdf/62f3ae7c05e30f870e3a6435b704afbd5c5290ba.pdf |
TropEx: An Algorithm for Extracting Linear Terms in Deep Neural Networks | https://openreview.net/forum?id=IqtonxWI0V3 | https://openreview.net/forum?id=IqtonxWI0V3 | Martin Trimmel,Henning Petzka,Cristian Sminchisescu | ICLR 2021,Poster | Deep neural networks with rectified linear (ReLU) activations are piecewise linear functions, where hyperplanes partition the input space into an astronomically high number of linear regions. Previous work focused on counting linear regions to measure the network's expressive power and on analyzing geometric properties... | https://openreview.net/pdf/6f5f94ea1f9082d97859b79f1358b2a25baa8fcd.pdf |
Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections | https://openreview.net/forum?id=dx4b7lm8jMM | https://openreview.net/forum?id=dx4b7lm8jMM | Csaba Toth,Patric Bonnier,Harald Oberhauser | ICLR 2021,Poster | Sequential data such as time series, video, or text can be challenging to analyse as the ordered structure gives rise to complex dependencies. At the heart of this is non-commutativity, in the sense that reordering the elements of a sequence can completely change its meaning. We use a classical mathematical object -- t... | https://openreview.net/pdf/bc313164adf3017b7e94a07aecbd830b43e5c49a.pdf |
Representation learning for improved interpretability and classification accuracy of clinical factors from EEG | https://openreview.net/forum?id=TVjLza1t4hI | https://openreview.net/forum?id=TVjLza1t4hI | Garrett Honke,Irina Higgins,Nina Thigpen,Vladimir Miskovic,Katie Link,Sunny Duan,Pramod Gupta,Julia Klawohn,Greg Hajcak | ICLR 2021,Poster | Despite extensive standardization, diagnostic interviews for mental health disorders encompass substantial subjective judgment. Previous studies have demonstrated that EEG-based neural measures can function as reliable objective correlates of depression, or even predictors of depression and its course. However, their c... | https://openreview.net/pdf/2932815e2ab354b7f926e4803d4ba6847916d44d.pdf |
Language-Agnostic Representation Learning of Source Code from Structure and Context | https://openreview.net/forum?id=Xh5eMZVONGF | https://openreview.net/forum?id=Xh5eMZVONGF | Daniel Zügner,Tobias Kirschstein,Michele Catasta,Jure Leskovec,Stephan Günnemann | ICLR 2021,Poster | Source code (Context) and its parsed abstract syntax tree (AST; Structure) are two complementary representations of the same computer program. Traditionally, designers of machine learning models have relied predominantly either on Structure or Context. We propose a new model, which jointly learns on Context and Structu... | https://openreview.net/pdf/69c9ae01f0f1b9a15ea1b21d87cdf95dff32a6f5.pdf |
Generalized Multimodal ELBO | https://openreview.net/forum?id=5Y21V0RDBV | https://openreview.net/forum?id=5Y21V0RDBV | Thomas M. Sutter,Imant Daunhawer,Julia E Vogt | ICLR 2021,Poster | Multiple data types naturally co-occur when describing real-world phenomena and learning from them is a long-standing goal in machine learning research. However, existing self-supervised generative models approximating an ELBO are not able to fulfill all desired requirements of multimodal models: their posterior approx... | https://openreview.net/pdf/2cfd5fea6a35d4586487da796743d75dacc7118c.pdf |
Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose? | https://openreview.net/forum?id=p5uylG94S68 | https://openreview.net/forum?id=p5uylG94S68 | Balázs Kégl,Gabriel Hurtado,Albert Thomas | ICLR 2021,Poster | We contribute to micro-data model-based reinforcement learning (MBRL) by rigorously comparing popular generative models using a fixed (random shooting) control agent. We find that on an environment that requires multimodal posterior predictives, mixture density nets outperform all other models by a large margin. When m... | https://openreview.net/pdf/04313ea0678f51bf6e97525219f5b92003b041b9.pdf |
Set Prediction without Imposing Structure as Conditional Density Estimation | https://openreview.net/forum?id=04ArenGOz3 | https://openreview.net/forum?id=04ArenGOz3 | David W Zhang,Gertjan J. Burghouts,Cees G. M. Snoek | ICLR 2021,Poster | Set prediction is about learning to predict a collection of unordered variables with unknown interrelations. Training such models with set losses imposes the structure of a metric space over sets. We focus on stochastic and underdefined cases, where an incorrectly chosen loss function leads to implausible predictions. ... | https://openreview.net/pdf/04c489674227569994e57717321c907597b1355c.pdf |
Learning Value Functions in Deep Policy Gradients using Residual Variance | https://openreview.net/forum?id=NX1He-aFO_F | https://openreview.net/forum?id=NX1He-aFO_F | Yannis Flet-Berliac,reda ouhamma,odalric-ambrym maillard,Philippe Preux | ICLR 2021,Poster | Policy gradient algorithms have proven to be successful in diverse decision making and control tasks. However, these methods suffer from high sample complexity and instability issues. In this paper, we address these challenges by providing a different approach for training the critic in the actor-critic framework. Our ... | https://openreview.net/pdf/d19c38b4919b1481e2aa3972a928c866f4502b44.pdf |
IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression | https://openreview.net/forum?id=MBOyiNnYthd | https://openreview.net/forum?id=MBOyiNnYthd | Rianne van den Berg,Alexey A. Gritsenko,Mostafa Dehghani,Casper Kaae Sønderby,Tim Salimans | ICLR 2021,Poster | In this paper we analyse and improve integer discrete flows for lossless compression. Integer discrete flows are a recently proposed class of models that learn invertible transformations for integer-valued random variables. Their discrete nature makes them particularly suitable for lossless compression with entropy cod... | https://openreview.net/pdf/049fd6f43de5700220bd49a24b2ae38e78c3782c.pdf |
Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders | https://openreview.net/forum?id=agHLCOBM5jP | https://openreview.net/forum?id=agHLCOBM5jP | Mangal Prakash,Alexander Krull,Florian Jug | ICLR 2021,Poster | Deep Learning based methods have emerged as the indisputable leaders for virtually all image restoration tasks. Especially in the domain of microscopy images, various content-aware image restoration (CARE) approaches are now used to improve the interpretability of acquired data. Naturally, there are limitations to what... | https://openreview.net/pdf/2afe972808ebb66f3926468902039c366b274c59.pdf |
Is Attention Better Than Matrix Decomposition? | https://openreview.net/forum?id=1FvkSpWosOl | https://openreview.net/forum?id=1FvkSpWosOl | Zhengyang Geng,Meng-Hao Guo,Hongxu Chen,Xia Li,Ke Wei,Zhouchen Lin | ICLR 2021,Poster | As an essential ingredient of modern deep learning, attention mechanism, especially self-attention, plays a vital role in the global correlation discovery. However, is hand-crafted attention irreplaceable when modeling the global context? Our intriguing finding is that self-attention is not better than the matrix decom... | https://openreview.net/pdf/1cb5acc6fe475a215dd1192beec6158b8a4da5dc.pdf |
Improving Transformation Invariance in Contrastive Representation Learning | https://openreview.net/forum?id=NomEDgIEBwE | https://openreview.net/forum?id=NomEDgIEBwE | Adam Foster,Rattana Pukdee,Tom Rainforth | ICLR 2021,Poster | We propose methods to strengthen the invariance properties of representations obtained by contrastive learning. While existing approaches implicitly induce a degree of invariance as representations are learned, we look to more directly enforce invariance in the encoding process. To this end, we first introduce a traini... | https://openreview.net/pdf/401efbc12f590198cf9a4094f6a0ce66e21be5e9.pdf |
On the Origin of Implicit Regularization in Stochastic Gradient Descent | https://openreview.net/forum?id=rq_Qr0c1Hyo | https://openreview.net/forum?id=rq_Qr0c1Hyo | Samuel L Smith,Benoit Dherin,David Barrett,Soham De | ICLR 2021,Poster | For infinitesimal learning rates, stochastic gradient descent (SGD) follows the path of gradient flow on the full batch loss function. However moderately large learning rates can achieve higher test accuracies, and this generalization benefit is not explained by convergence bounds, since the learning rate which maximiz... | https://openreview.net/pdf/e5f4bcf96d3ed905ac91e4ea6e3993321ecda830.pdf |
Transient Non-stationarity and Generalisation in Deep Reinforcement Learning | https://openreview.net/forum?id=Qun8fv4qSby | https://openreview.net/forum?id=Qun8fv4qSby | Maximilian Igl,Gregory Farquhar,Jelena Luketina,Wendelin Boehmer,Shimon Whiteson | ICLR 2021,Poster | Non-stationarity can arise in Reinforcement Learning (RL) even in stationary environments. For example, most RL algorithms collect new data throughout training, using a non-stationary behaviour policy. Due to the transience of this non-stationarity, it is often not explicitly addressed in deep RL and a single neural ne... | https://openreview.net/pdf/ea444807010b334cd2b90645f1cfa31bd38f3ef7.pdf |
Lossless Compression of Structured Convolutional Models via Lifting | https://openreview.net/forum?id=oxnp2q-PGL4 | https://openreview.net/forum?id=oxnp2q-PGL4 | Gustav Sourek,Filip Zelezny,Ondrej Kuzelka | ICLR 2021,Poster | Lifting is an efficient technique to scale up graphical models generalized to relational domains by exploiting the underlying symmetries. Concurrently, neural models are continuously expanding from grid-like tensor data into structured representations, such as various attributed graphs and relational databases. To addr... | https://openreview.net/pdf/6ca46d0a2419236e20aac30bbf133f4c81154953.pdf |
Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective | https://openreview.net/forum?id=-qh0M9XWxnv | https://openreview.net/forum?id=-qh0M9XWxnv | Muhammet Balcilar,Guillaume Renton,Pierre Héroux,Benoit Gaüzère,Sébastien Adam,Paul Honeine | ICLR 2021,Poster | In the recent literature of Graph Neural Networks (GNN), the expressive power of models has been studied through their capability to distinguish if two given graphs are isomorphic or not. Since the graph isomorphism problem is NP-intermediate, and Weisfeiler-Lehman (WL) test can give sufficient but not enough evidence ... | https://openreview.net/pdf/859c9ee357c81e0b9a1cb989b1e23b8b42d741f1.pdf |
A unifying view on implicit bias in training linear neural networks | https://openreview.net/forum?id=ZsZM-4iMQkH | https://openreview.net/forum?id=ZsZM-4iMQkH | Chulhee Yun,Shankar Krishnan,Hossein Mobahi | ICLR 2021,Poster | We study the implicit bias of gradient flow (i.e., gradient descent with infinitesimal step size) on linear neural network training. We propose a tensor formulation of neural networks that includes fully-connected, diagonal, and convolutional networks as special cases, and investigate the linear version of the formulat... | https://openreview.net/pdf/7592938b320208bd563349d1ea3385dd9e80cbe6.pdf |
Balancing Constraints and Rewards with Meta-Gradient D4PG | https://openreview.net/forum?id=TQt98Ya7UMP | https://openreview.net/forum?id=TQt98Ya7UMP | Dan A. Calian,Daniel J Mankowitz,Tom Zahavy,Zhongwen Xu,Junhyuk Oh,Nir Levine,Timothy Mann | ICLR 2021,Poster | Deploying Reinforcement Learning (RL) agents to solve real-world applications often requires satisfying complex system constraints. Often the constraint thresholds are incorrectly set due to the complex nature of a system or the inability to verify the thresholds offline (e.g, no simulator or reasonable offline evaluat... | https://openreview.net/pdf/c2bc1eac3b05c897508a2b6cf4f096a98dbcc8e2.pdf |
Robust Curriculum Learning: from clean label detection to noisy label self-correction | https://openreview.net/forum?id=lmTWnm3coJJ | https://openreview.net/forum?id=lmTWnm3coJJ | Tianyi Zhou,Shengjie Wang,Jeff Bilmes | ICLR 2021,Poster | Neural network training can easily overfit noisy labels resulting in poor generalization performance. Existing methods address this problem by (1) filtering out the noisy data and only using the clean data for training or (2) relabeling the noisy data by the model during training or by another model trained only on a c... | https://openreview.net/pdf/06ca7281bb3ba57d591dedb4b5127373e0c1d429.pdf |
Clairvoyance: A Pipeline Toolkit for Medical Time Series | https://openreview.net/forum?id=xnC8YwKUE3k | https://openreview.net/forum?id=xnC8YwKUE3k | Daniel Jarrett,Jinsung Yoon,Ioana Bica,Zhaozhi Qian,Ari Ercole,Mihaela van der Schaar | ICLR 2021,Poster | Time-series learning is the bread and butter of data-driven *clinical decision support*, and the recent explosion in ML research has demonstrated great potential in various healthcare settings. At the same time, medical time-series problems in the wild are challenging due to their highly *composite* nature: They entail... | https://openreview.net/pdf/c4f52313ee7aa37bb754ae2f6524cc0aeb47ce43.pdf |
Plan-Based Relaxed Reward Shaping for Goal-Directed Tasks | https://openreview.net/forum?id=w2Z2OwVNeK | https://openreview.net/forum?id=w2Z2OwVNeK | Ingmar Schubert,Ozgur S Oguz,Marc Toussaint | ICLR 2021,Poster | In high-dimensional state spaces, the usefulness of Reinforcement Learning (RL) is limited by the problem of exploration. This issue has been addressed using potential-based reward shaping (PB-RS) previously. In the present work, we introduce Final-Volume-Preserving Reward Shaping (FV-RS). FV-RS relaxes the strict opti... | https://openreview.net/pdf/6ab6b9e3a9fe5a364f986aaff177de866990899b.pdf |
Improving VAEs' Robustness to Adversarial Attack | https://openreview.net/forum?id=-Hs_otp2RB | https://openreview.net/forum?id=-Hs_otp2RB | Matthew JF Willetts,Alexander Camuto,Tom Rainforth,S Roberts,Christopher C Holmes | ICLR 2021,Poster | Variational autoencoders (VAEs) have recently been shown to be vulnerable to adversarial attacks, wherein they are fooled into reconstructing a chosen target image. However, how to defend against such attacks remains an open problem. We make significant advances in addressing this issue by introducing methods for produ... | https://openreview.net/pdf/99d30d8f3d5b1463f05554f92526d389e651b1db.pdf |
Differentiable Segmentation of Sequences | https://openreview.net/forum?id=4T489T4yav | https://openreview.net/forum?id=4T489T4yav | Erik Scharwächter,Jonathan Lennartz,Emmanuel Müller | ICLR 2021,Poster | Segmented models are widely used to describe non-stationary sequential data with discrete change points. Their estimation usually requires solving a mixed discrete-continuous optimization problem, where the segmentation is the discrete part and all other model parameters are continuous. A number of estimation algorithm... | https://openreview.net/pdf/211648c2242f789fd76f662801f326094db7433d.pdf |
GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing | https://openreview.net/forum?id=kyaIeYj4zZ | https://openreview.net/forum?id=kyaIeYj4zZ | Tao Yu,Chien-Sheng Wu,Xi Victoria Lin,bailin wang,Yi Chern Tan,Xinyi Yang,Dragomir Radev,richard socher,Caiming Xiong | ICLR 2021,Poster | We present GraPPa, an effective pre-training approach for table semantic parsing that learns a compositional inductive bias in the joint representations of textual and tabular data. We construct synthetic question-SQL pairs over high-quality tables via a synchronous context-free grammar (SCFG). We pre-train our model o... | https://openreview.net/pdf/41a8d65642880c0853bfa9f37d81b4fc15cba53e.pdf |
Sliced Kernelized Stein Discrepancy | https://openreview.net/forum?id=t0TaKv0Gx6Z | https://openreview.net/forum?id=t0TaKv0Gx6Z | Wenbo Gong,Yingzhen Li,José Miguel Hernández-Lobato | ICLR 2021,Poster | Kernelized Stein discrepancy (KSD), though being extensively used in goodness-of-fit tests and model learning, suffers from the curse-of-dimensionality. We address this issue by proposing the sliced Stein discrepancy and its scalable and kernelized variants, which employs kernel-based test functions defined on the opti... | https://openreview.net/pdf/39d9fa2661eb33fc05f7d9de6fddb979108767c4.pdf |
Variational Intrinsic Control Revisited | https://openreview.net/forum?id=P0p33rgyoE | https://openreview.net/forum?id=P0p33rgyoE | Taehwan Kwon | ICLR 2021,Poster | In this paper, we revisit variational intrinsic control (VIC), an unsupervised reinforcement learning method for finding the largest set of intrinsic options available to an agent. In the original work by Gregor et al. (2016), two VIC algorithms were proposed: one that represents the options explicitly, and the other t... | https://openreview.net/pdf/8841dcedad713be63398c9001418c334c7479b4e.pdf |
HyperDynamics: Meta-Learning Object and Agent Dynamics with Hypernetworks | https://openreview.net/forum?id=pHXfe1cOmA | https://openreview.net/forum?id=pHXfe1cOmA | Zhou Xian,Shamit Lal,Hsiao-Yu Tung,Emmanouil Antonios Platanios,Katerina Fragkiadaki | ICLR 2021,Poster | We propose HyperDynamics, a dynamics meta-learning framework that conditions on an agent’s interactions with the environment and optionally its visual observations, and generates the parameters of neural dynamics models based on inferred properties of the dynamical system. Physical and visual properties of the environm... | https://openreview.net/pdf/08774c9cdcc696092021d165f4b6e807b414198c.pdf |
Towards Resolving the Implicit Bias of Gradient Descent for Matrix Factorization: Greedy Low-Rank Learning | https://openreview.net/forum?id=AHOs7Sm5H7R | https://openreview.net/forum?id=AHOs7Sm5H7R | Zhiyuan Li,Yuping Luo,Kaifeng Lyu | ICLR 2021,Poster | Matrix factorization is a simple and natural test-bed to investigate the implicit regularization of gradient descent. Gunasekar et al. (2017) conjectured that gradient flow with infinitesimal initialization converges to the solution that minimizes the nuclear norm, but a series of recent papers argued that the language... | https://openreview.net/pdf/e29b53584bc9017cb15b9394735cd51b56c32446.pdf |
Private Post-GAN Boosting | https://openreview.net/forum?id=6isfR3JCbi | https://openreview.net/forum?id=6isfR3JCbi | Marcel Neunhoeffer,Steven Wu,Cynthia Dwork | ICLR 2021,Poster | Differentially private GANs have proven to be a promising approach for generating realistic synthetic data without compromising the privacy of individuals. Due to the privacy-protective noise introduced in the training, the convergence of GANs becomes even more elusive, which often leads to poor utility in the output ... | https://openreview.net/pdf/9af34be61229e9ded84048009befadeb57d1957d.pdf |
Characterizing signal propagation to close the performance gap in unnormalized ResNets | https://openreview.net/forum?id=IX3Nnir2omJ | https://openreview.net/forum?id=IX3Nnir2omJ | Andrew Brock,Soham De,Samuel L Smith | ICLR 2021,Poster | Batch Normalization is a key component in almost all state-of-the-art image classifiers, but it also introduces practical challenges: it breaks the independence between training examples within a batch, can incur compute and memory overhead, and often results in unexpected bugs. Building on recent theoretical analyses ... | https://openreview.net/pdf/796f0f646a7dc728f2d8d89bc6d55288c9457889.pdf |
Prototypical Contrastive Learning of Unsupervised Representations | https://openreview.net/forum?id=KmykpuSrjcq | https://openreview.net/forum?id=KmykpuSrjcq | Junnan Li,Pan Zhou,Caiming Xiong,Steven Hoi | ICLR 2021,Poster | This paper presents Prototypical Contrastive Learning (PCL), an unsupervised representation learning method that bridges contrastive learning with clustering. PCL not only learns low-level features for the task of instance discrimination, but more importantly, it implicitly encodes semantic structures of the data into ... | https://openreview.net/pdf/601011be0933cda056049e8fd0b25a10bcfd4515.pdf |
Hyperbolic Neural Networks++ | https://openreview.net/forum?id=Ec85b0tUwbA | https://openreview.net/forum?id=Ec85b0tUwbA | Ryohei Shimizu,YUSUKE Mukuta,Tatsuya Harada | ICLR 2021,Poster | Hyperbolic spaces, which have the capacity to embed tree structures without distortion owing to their exponential volume growth, have recently been applied to machine learning to better capture the hierarchical nature of data. In this study, we generalize the fundamental components of neural networks in a single hyperb... | https://openreview.net/pdf/83447b5937824f2d585bcbca44769d242615f9f5.pdf |
Lipschitz Recurrent Neural Networks | https://openreview.net/forum?id=-N7PBXqOUJZ | https://openreview.net/forum?id=-N7PBXqOUJZ | N. Benjamin Erichson,Omri Azencot,Alejandro Queiruga,Liam Hodgkinson,Michael W. Mahoney | ICLR 2021,Poster | Viewing recurrent neural networks (RNNs) as continuous-time dynamical systems, we propose a recurrent unit that describes the hidden state's evolution with two parts: a well-understood linear component plus a Lipschitz nonlinearity. This particular functional form facilitates stability analysis of the long-term behavio... | https://openreview.net/pdf/fab880544ab1da571de32581b8939abf93ce475f.pdf |
A statistical theory of cold posteriors in deep neural networks | https://openreview.net/forum?id=Rd138pWXMvG | https://openreview.net/forum?id=Rd138pWXMvG | Laurence Aitchison | ICLR 2021,Poster | To get Bayesian neural networks to perform comparably to standard neural networks it is usually necessary to artificially reduce uncertainty using a tempered or cold posterior. This is extremely concerning: if the prior is accurate, Bayes inference/decision theory is optimal, and any artificial changes to the posterior... | https://openreview.net/pdf/ad6b61823bafd130bfd5c821fd1ceb7913a54d2d.pdf |
Boost then Convolve: Gradient Boosting Meets Graph Neural Networks | https://openreview.net/forum?id=ebS5NUfoMKL | https://openreview.net/forum?id=ebS5NUfoMKL | Sergei Ivanov,Liudmila Prokhorenkova | ICLR 2021,Poster | Graph neural networks (GNNs) are powerful models that have been successful in various graph representation learning tasks. Whereas gradient boosted decision trees (GBDT) often outperform other machine learning methods when faced with heterogeneous tabular data. But what approach should be used for graphs with tabular n... | https://openreview.net/pdf/e8b53ad374bcf1f4207b1153a22ea94fb05e3311.pdf |
Genetic Soft Updates for Policy Evolution in Deep Reinforcement Learning | https://openreview.net/forum?id=TGFO0DbD_pk | https://openreview.net/forum?id=TGFO0DbD_pk | Enrico Marchesini,Davide Corsi,Alessandro Farinelli | ICLR 2021,Poster | The combination of Evolutionary Algorithms (EAs) and Deep Reinforcement Learning (DRL) has been recently proposed to merge the benefits of both solutions. Existing mixed approaches, however, have been successfully applied only to actor-critic methods and present significant overhead. We address these issues by introduc... | https://openreview.net/pdf/2a012533ff0b6880941f619b1e03b63abd1414c6.pdf |
Spatially Structured Recurrent Modules | https://openreview.net/forum?id=5l9zj5G7vDY | https://openreview.net/forum?id=5l9zj5G7vDY | Nasim Rahaman,Anirudh Goyal,Muhammad Waleed Gondal,Manuel Wuthrich,Stefan Bauer,Yash Sharma,Yoshua Bengio,Bernhard Schölkopf | ICLR 2021,Poster | Capturing the structure of a data-generating process by means of appropriate inductive biases can help in learning models that generalise well and are robust to changes in the input distribution. While methods that harness spatial and temporal structures find broad application, recent work has demonstrated the potentia... | https://openreview.net/pdf/3590e3dd48376daa86d4fee6c6cb3c8b051d03b9.pdf |
On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines | https://openreview.net/forum?id=nzpLWnVAyah | https://openreview.net/forum?id=nzpLWnVAyah | Marius Mosbach,Maksym Andriushchenko,Dietrich Klakow | ICLR 2021,Poster | Fine-tuning pre-trained transformer-based language models such as BERT has become a common practice dominating leaderboards across various NLP benchmarks. Despite the strong empirical performance of fine-tuned models, fine-tuning is an unstable process: training the same model with multiple random seeds can result in a... | https://openreview.net/pdf/ecb1af8e8fc55b9e071db6ef6b56163a21f00a44.pdf |
End-to-End Egospheric Spatial Memory | https://openreview.net/forum?id=rRFIni1CYmy | https://openreview.net/forum?id=rRFIni1CYmy | Daniel James Lenton,Stephen James,Ronald Clark,Andrew Davison | ICLR 2021,Poster | Spatial memory, or the ability to remember and recall specific locations and objects, is central to autonomous agents' ability to carry out tasks in real environments. However, most existing artificial memory modules are not very adept at storing spatial information. We propose a parameter-free module, Egospheric Spati... | https://openreview.net/pdf/5c9e921c94b83d510872e5e048479c56c66cad04.pdf |
LEAF: A Learnable Frontend for Audio Classification | https://openreview.net/forum?id=jM76BCb6F9m | https://openreview.net/forum?id=jM76BCb6F9m | Neil Zeghidour,Olivier Teboul,Félix de Chaumont Quitry,Marco Tagliasacchi | ICLR 2021,Poster | Mel-filterbanks are fixed, engineered audio features which emulate human perception and have been used through the history of audio understanding up to today. However, their undeniable qualities are counterbalanced by the fundamental limitations of handmade representations. In this work we show that we can train a sing... | https://openreview.net/pdf/426d58043e09ff47db27ab72f40e8db575a46f7b.pdf |
Simple Augmentation Goes a Long Way: ADRL for DNN Quantization | https://openreview.net/forum?id=Qr0aRliE_Hb | https://openreview.net/forum?id=Qr0aRliE_Hb | Lin Ning,Guoyang Chen,Weifeng Zhang,Xipeng Shen | ICLR 2021,Poster | Mixed precision quantization improves DNN performance by assigning different layers with different bit-width values. Searching for the optimal bit-width for each layer, however, remains a challenge. Deep Reinforcement Learning (DRL) shows some recent promise. It however suffers instability due to function approximation... | https://openreview.net/pdf/4f1af14f420632aa60f163e48701a935fae3a547.pdf |
The inductive bias of ReLU networks on orthogonally separable data | https://openreview.net/forum?id=krz7T0xU9Z_ | https://openreview.net/forum?id=krz7T0xU9Z_ | Mary Phuong,Christoph H Lampert | ICLR 2021,Poster | We study the inductive bias of two-layer ReLU networks trained by gradient flow. We identify a class of easy-to-learn (`orthogonally separable') datasets, and characterise the solution that ReLU networks trained on such datasets converge to. Irrespective of network width, the solution turns out to be a combination of t... | https://openreview.net/pdf/a68e4ef7c465175fddb6ba540763c62f8708c9e3.pdf |
Monte-Carlo Planning and Learning with Language Action Value Estimates | https://openreview.net/forum?id=7_G8JySGecm | https://openreview.net/forum?id=7_G8JySGecm | Youngsoo Jang,Seokin Seo,Jongmin Lee,Kee-Eung Kim | ICLR 2021,Poster | Interactive Fiction (IF) games provide a useful testbed for language-based reinforcement learning agents, posing significant challenges of natural language understanding, commonsense reasoning, and non-myopic planning in the combinatorial search space. Agents based on standard planning algorithms struggle to play IF ga... | https://openreview.net/pdf/255385188b591f81f5ec4cb8c99ea2b92467f6be.pdf |
Learning Energy-Based Models by Diffusion Recovery Likelihood | https://openreview.net/forum?id=v_1Soh8QUNc | https://openreview.net/forum?id=v_1Soh8QUNc | Ruiqi Gao,Yang Song,Ben Poole,Ying Nian Wu,Diederik P Kingma | ICLR 2021,Poster | While energy-based models (EBMs) exhibit a number of desirable properties, training and sampling on high-dimensional datasets remains challenging. Inspired by recent progress on diffusion probabilistic models, we present a diffusion recovery likelihood method to tractably learn and sample from a sequence of EBMs traine... | https://openreview.net/pdf/bb74e78ec73a15dcfd250d8dac827fa7009897b2.pdf |
Capturing Label Characteristics in VAEs | https://openreview.net/forum?id=wQRlSUZ5V7B | https://openreview.net/forum?id=wQRlSUZ5V7B | Tom Joy,Sebastian Schmon,Philip Torr,Siddharth N,Tom Rainforth | ICLR 2021,Poster | We present a principled approach to incorporating labels in variational autoencoders (VAEs) that captures the rich characteristic information associated with those labels. While prior work has typically conflated these by learning latent variables that directly correspond to label values, we argue this is contrary to t... | https://openreview.net/pdf/f58d5a4d19e174d578190ec9687a1904e52596b6.pdf |
Linear Mode Connectivity in Multitask and Continual Learning | https://openreview.net/forum?id=Fmg_fQYUejf | https://openreview.net/forum?id=Fmg_fQYUejf | Seyed Iman Mirzadeh,Mehrdad Farajtabar,Dilan Gorur,Razvan Pascanu,Hassan Ghasemzadeh | ICLR 2021,Poster | Continual (sequential) training and multitask (simultaneous) training are often attempting to solve the same overall objective: to find a solution that performs well on all considered tasks. The main difference is in the training regimes, where continual learning can only have access to one task at a time, which for ne... | https://openreview.net/pdf/258e0f0ad7124932b50cc607ded20cd020bfccf8.pdf |
Computational Separation Between Convolutional and Fully-Connected Networks | https://openreview.net/forum?id=hkMoYYEkBoI | https://openreview.net/forum?id=hkMoYYEkBoI | eran malach,Shai Shalev-Shwartz | ICLR 2021,Poster | Convolutional neural networks (CNN) exhibit unmatched performance in a multitude of computer vision tasks. However, the advantage of using convolutional networks over fully-connected networks is not understood from a theoretical perspective. In this work, we show how convolutional networks can leverage locality in the ... | https://openreview.net/pdf/f6530436996abef24697ac8461be780c738d0b41.pdf |
Rethinking Embedding Coupling in Pre-trained Language Models | https://openreview.net/forum?id=xpFFI_NtgpW | https://openreview.net/forum?id=xpFFI_NtgpW | Hyung Won Chung,Thibault Fevry,Henry Tsai,Melvin Johnson,Sebastian Ruder | ICLR 2021,Poster | We re-evaluate the standard practice of sharing weights between input and output embeddings in state-of-the-art pre-trained language models. We show that decoupled embeddings provide increased modeling flexibility, allowing us to significantly improve the efficiency of parameter allocation in the input embedding of mul... | https://openreview.net/pdf/adedfbb0966285d46a1b5e7fb42ed8f57385af9e.pdf |
Physics-aware, probabilistic model order reduction with guaranteed stability | https://openreview.net/forum?id=vyY0jnWG-tK | https://openreview.net/forum?id=vyY0jnWG-tK | Sebastian Kaltenbach,Phaedon Stelios Koutsourelakis | ICLR 2021,Poster | Given (small amounts of) time-series' data from a high-dimensional, fine-grained, multiscale dynamical system, we propose a generative framework for learning an effective, lower-dimensional, coarse-grained dynamical model that is predictive of the fine-grained system's long-term evolution but also of its behavior unde... | https://openreview.net/pdf/0dbc13eb90ca0605840fb7ee708d76db95df9cbd.pdf |
Disentangling 3D Prototypical Networks for Few-Shot Concept Learning | https://openreview.net/forum?id=-Lr-u0b42he | https://openreview.net/forum?id=-Lr-u0b42he | Mihir Prabhudesai,Shamit Lal,Darshan Patil,Hsiao-Yu Tung,Adam W Harley,Katerina Fragkiadaki | ICLR 2021,Poster | We present neural architectures that disentangle RGB-D images into objects’ shapes and styles and a map of the background scene, and explore their applications for few-shot 3D object detection and few-shot concept classification. Our networks incorporate architectural biases that reflect the image formation process, 3D... | https://openreview.net/pdf/b42e4e31403f7d4fdb789fb870cace1f71e6bb86.pdf |
LiftPool: Bidirectional ConvNet Pooling | https://openreview.net/forum?id=kE3vd639uRW | https://openreview.net/forum?id=kE3vd639uRW | Jiaojiao Zhao,Cees G. M. Snoek | ICLR 2021,Poster | Pooling is a critical operation in convolutional neural networks for increasing receptive fields and improving robustness to input variations. Most existing pooling operations downsample the feature maps, which is a lossy process. Moreover, they are not invertible: upsampling a downscaled feature map can not recover... | https://openreview.net/pdf/723c52d5e33d391f50b4913e512241a208596a0c.pdf |
Latent Convergent Cross Mapping | https://openreview.net/forum?id=4TSiOTkKe5P | https://openreview.net/forum?id=4TSiOTkKe5P | Edward De Brouwer,Adam Arany,Jaak Simm,Yves Moreau | ICLR 2021,Poster | Discovering causal structures of temporal processes is a major tool of scientific inquiry because it helps us better understand and explain the mechanisms driving a phenomenon of interest, thereby facilitating analysis, reasoning, and synthesis for such systems.
However, accurately inferring causal structures within a... | https://openreview.net/pdf/973e4e487f91472cfee202c1353ca7932b83a942.pdf |
You Only Need Adversarial Supervision for Semantic Image Synthesis | https://openreview.net/forum?id=yvQKLaqNE6M | https://openreview.net/forum?id=yvQKLaqNE6M | Edgar Schönfeld,Vadim Sushko,Dan Zhang,Juergen Gall,Bernt Schiele,Anna Khoreva | ICLR 2021,Poster | Despite their recent successes, GAN models for semantic image synthesis still suffer from poor image quality when trained with only adversarial supervision. Historically, additionally employing the VGG-based perceptual loss has helped to overcome this issue, significantly improving the synthesis quality, but at the sam... | https://openreview.net/pdf/296a08e6901d8e9191af10b50555200a0efb3fc4.pdf |
A Diffusion Theory For Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat Minima | https://openreview.net/forum?id=wXgk_iCiYGo | https://openreview.net/forum?id=wXgk_iCiYGo | Zeke Xie,Issei Sato,Masashi Sugiyama | ICLR 2021,Poster | Stochastic Gradient Descent (SGD) and its variants are mainstream methods for training deep networks in practice. SGD is known to find a flat minimum that often generalizes well. However, it is mathematically unclear how deep learning can select a flat minimum among so many minima. To answer the question quantitatively... | https://openreview.net/pdf/8d09cb383c404f3ef7a8782e7e20297845235b60.pdf |
Robust Learning of Fixed-Structure Bayesian Networks in Nearly-Linear Time | https://openreview.net/forum?id=euDnVs0Ynts | https://openreview.net/forum?id=euDnVs0Ynts | Yu Cheng,Honghao Lin | ICLR 2021,Poster | We study the problem of learning Bayesian networks where an $\epsilon$-fraction of the samples are adversarially corrupted. We focus on the fully-observable case where the underlying graph structure is known. In this work, we present the first nearly-linear time algorithm for this problem with a dimension-independent... | https://openreview.net/pdf/01c090bb63e775869f6bc2d003ebf3cd5e79df67.pdf |
Activation-level uncertainty in deep neural networks | https://openreview.net/forum?id=UvBPbpvHRj- | https://openreview.net/forum?id=UvBPbpvHRj- | Pablo Morales-Alvarez,Daniel Hernández-Lobato,Rafael Molina,José Miguel Hernández-Lobato | ICLR 2021,Poster | Current approaches for uncertainty estimation in deep learning often produce too confident results. Bayesian Neural Networks (BNNs) model uncertainty in the space of weights, which is usually high-dimensional and limits the quality of variational approximations. The more recent functional BNNs (fBNNs) address this only... | https://openreview.net/pdf/3675d798eb4cc1b53b84850025e0a9edaee1ddcb.pdf |
SkipW: Resource Adaptable RNN with Strict Upper Computational Limit | https://openreview.net/forum?id=2CjEVW-RGOJ | https://openreview.net/forum?id=2CjEVW-RGOJ | Tsiry Mayet,Anne Lambert,Pascal Leguyadec,Francoise Le Bolzer,François Schnitzler | ICLR 2021,Poster | We introduce Skip-Window, a method to allow recurrent neural networks (RNNs) to trade off accuracy for computational cost during the analysis of a sequence. Similarly to existing approaches, Skip-Window extends existing RNN cells by adding a mechanism to encourage the model to process fewer inputs. Unlike existing appr... | https://openreview.net/pdf/6c45c14eaa50cfd7a61ea01da21211148f40eccf.pdf |
Wasserstein-2 Generative Networks | https://openreview.net/forum?id=bEoxzW_EXsa | https://openreview.net/forum?id=bEoxzW_EXsa | Alexander Korotin,Vage Egiazarian,Arip Asadulaev,Alexander Safin,Evgeny Burnaev | ICLR 2021,Poster | We propose a novel end-to-end non-minimax algorithm for training optimal transport mappings for the quadratic cost (Wasserstein-2 distance). The algorithm uses input convex neural networks and a cycle-consistency regularization to approximate Wasserstein-2 distance. In contrast to popular entropic and quadratic regular... | https://openreview.net/pdf/dbe3a9934dc8bb605cdc8c67d7e68c0a54cf4d38.pdf |
Group Equivariant Stand-Alone Self-Attention For Vision | https://openreview.net/forum?id=JkfYjnOEo6M | https://openreview.net/forum?id=JkfYjnOEo6M | David W. Romero,Jean-Baptiste Cordonnier | ICLR 2021,Poster | We provide a general self-attention formulation to impose group equivariance to arbitrary symmetry groups. This is achieved by defining positional encodings that are invariant to the action of the group considered. Since the group acts on the positional encoding directly, group equivariant self-attention networks (GSA-... | https://openreview.net/pdf/d8bac9d42bd7732afa503ae4fe5f83e1ace88bb2.pdf |
Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization | https://openreview.net/forum?id=3tFAs5E-Pe | https://openreview.net/forum?id=3tFAs5E-Pe | Alexander Korotin,Lingxiao Li,Justin Solomon,Evgeny Burnaev | ICLR 2021,Poster | Wasserstein barycenters provide a geometric notion of the weighted average of probability measures based on optimal transport. In this paper, we present a scalable algorithm to compute Wasserstein-2 barycenters given sample access to the input measures, which are not restricted to being discrete. While past approaches ... | https://openreview.net/pdf/e0ff5cb89ad8da4cac3b85587213f35c465757fc.pdf |
RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs | https://openreview.net/forum?id=tGZu6DlbreV | https://openreview.net/forum?id=tGZu6DlbreV | Meng Qu,Junkun Chen,Louis-Pascal Xhonneux,Yoshua Bengio,Jian Tang | ICLR 2021,Poster | This paper studies learning logic rules for reasoning on knowledge graphs. Logic rules provide interpretable explanations when used for prediction as well as being able to generalize to other tasks, and hence are critical to learn. Existing methods either suffer from the problem of searching in a large search space (e.... | https://openreview.net/pdf/847ad1169fb024508870737fba6927e2e34b9271.pdf |
Selective Classification Can Magnify Disparities Across Groups | https://openreview.net/forum?id=N0M_4BkQ05i | https://openreview.net/forum?id=N0M_4BkQ05i | Erik Jones,Shiori Sagawa,Pang Wei Koh,Ananya Kumar,Percy Liang | ICLR 2021,Poster | Selective classification, in which models can abstain on uncertain predictions, is a natural approach to improving accuracy in settings where errors are costly but abstentions are manageable. In this paper, we find that while selective classification can improve average accuracies, it can simultaneously magnify existin... | https://openreview.net/pdf/b9ac6534faf7141a9138e3cfcfed7dbada0a6f36.pdf |
FedMix: Approximation of Mixup under Mean Augmented Federated Learning | https://openreview.net/forum?id=Ogga20D2HO- | https://openreview.net/forum?id=Ogga20D2HO- | Tehrim Yoon,Sumin Shin,Sung Ju Hwang,Eunho Yang | ICLR 2021,Poster | Federated learning (FL) allows edge devices to collectively learn a model without directly sharing data within each device, thus preserving privacy and eliminating the need to store data globally. While there are promising results under the assumption of independent and identically distributed (iid) local data, current... | https://openreview.net/pdf/0258da18459084a22b881d20dbd411e7184bb3d3.pdf |
In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness | https://openreview.net/forum?id=jznizqvr15J | https://openreview.net/forum?id=jznizqvr15J | Sang Michael Xie,Ananya Kumar,Robbie Jones,Fereshte Khani,Tengyu Ma,Percy Liang | ICLR 2021,Poster | Consider a prediction setting with few in-distribution labeled examples and many unlabeled examples both in- and out-of-distribution (OOD). The goal is to learn a model which performs well both in-distribution and OOD. In these settings, auxiliary information is often cheaply available for every input. How should we be... | https://openreview.net/pdf/b003dea7a8dcfbb18d462cb7ce96b56a1a484fc6.pdf |
Sample-Efficient Automated Deep Reinforcement Learning | https://openreview.net/forum?id=hSjxQ3B7GWq | https://openreview.net/forum?id=hSjxQ3B7GWq | Jörg K.H. Franke,Gregor Koehler,André Biedenkapp,Frank Hutter | ICLR 2021,Poster | Despite significant progress in challenging problems across various domains, applying state-of-the-art deep reinforcement learning (RL) algorithms remains challenging due to their sensitivity to the choice of hyperparameters. This sensitivity can partly be attributed to the non-stationarity of the RL problem, potential... | https://openreview.net/pdf/50e735ee784190b4976fe22036a75b2ac2feee2b.pdf |
A Temporal Kernel Approach for Deep Learning with Continuous-time Information | https://openreview.net/forum?id=whE31dn74cL | https://openreview.net/forum?id=whE31dn74cL | Da Xu,Chuanwei Ruan,Evren Korpeoglu,Sushant Kumar,Kannan Achan | ICLR 2021,Poster | Sequential deep learning models such as RNN, causal CNN and attention mechanism do not readily consume continuous-time information. Discretizing the temporal data, as we show, causes inconsistency even for simple continuous-time processes. Current approaches often handle time in a heuristic manner to be consistent with... | https://openreview.net/pdf/40fc3e707f1a7db2333d5459c3b472809d4e33c1.pdf |
Convex Regularization behind Neural Reconstruction | https://openreview.net/forum?id=VErQxgyrbfn | https://openreview.net/forum?id=VErQxgyrbfn | Arda Sahiner,Morteza Mardani,Batu Ozturkler,Mert Pilanci,John M. Pauly | ICLR 2021,Poster | Neural networks have shown tremendous potential for reconstructing high-resolution images in inverse problems. The non-convex and opaque nature of neural networks, however, hinders their utility in sensitive applications such as medical imaging. To cope with this challenge, this paper advocates a convex duality framewo... | https://openreview.net/pdf/cd9dfc05e045919a65b1eb93e132822e42d873e4.pdf |
Vector-output ReLU Neural Network Problems are Copositive Programs: Convex Analysis of Two Layer Networks and Polynomial-time Algorithms | https://openreview.net/forum?id=fGF8qAqpXXG | https://openreview.net/forum?id=fGF8qAqpXXG | Arda Sahiner,Tolga Ergen,John M. Pauly,Mert Pilanci | ICLR 2021,Poster | We describe the convex semi-infinite dual of the two-layer vector-output ReLU neural network training problem. This semi-infinite dual admits a finite dimensional representation, but its support is over a convex set which is difficult to characterize. In particular, we demonstrate that the non-convex neural network tra... | https://openreview.net/pdf/0222bcef2a87d75e3670c0707c8b848554ecbe31.pdf |
Learning Better Structured Representations Using Low-rank Adaptive Label Smoothing | https://openreview.net/forum?id=5NsEIflpbSv | https://openreview.net/forum?id=5NsEIflpbSv | Asish Ghoshal,Xilun Chen,Sonal Gupta,Luke Zettlemoyer,Yashar Mehdad | ICLR 2021,Poster | Training with soft targets instead of hard targets has been shown to improve performance and calibration of deep neural networks. Label smoothing is a popular way of computing soft targets, where one-hot encoding of a class is smoothed with a uniform distribution. Owing to its simplicity, label smoothing has found wide... | https://openreview.net/pdf/4538feaf2c0ace4bc3472484186d4cda25dc7c01.pdf |
Training GANs with Stronger Augmentations via Contrastive Discriminator | https://openreview.net/forum?id=eo6U4CAwVmg | https://openreview.net/forum?id=eo6U4CAwVmg | Jongheon Jeong,Jinwoo Shin | ICLR 2021,Poster | Recent works in Generative Adversarial Networks (GANs) are actively revisiting various data augmentation techniques as an effective way to prevent discriminator overfitting. It is still unclear, however, that which augmentations could actually improve GANs, and in particular, how to apply a wider range of augmentations... | https://openreview.net/pdf/2d308c93802630f8c000471788307eb87a9027fd.pdf |
Private Image Reconstruction from System Side Channels Using Generative Models | https://openreview.net/forum?id=y06VOYLcQXa | https://openreview.net/forum?id=y06VOYLcQXa | Yuanyuan Yuan,Shuai Wang,Junping Zhang | ICLR 2021,Poster | System side channels denote effects imposed on the underlying system and hardware when running a program, such as its accessed CPU cache lines. Side channel analysis (SCA) allows attackers to infer program secrets based on observed side channel signals. Given the ever-growing adoption of machine learning as a service (... | https://openreview.net/pdf/73fc8942e64baa03de7625e340fa3c6d84db3589.pdf |
Learning to Make Decisions via Submodular Regularization | https://openreview.net/forum?id=ac288vnG_7U | https://openreview.net/forum?id=ac288vnG_7U | Ayya Alieva,Aiden Aceves,Jialin Song,Stephen Mayo,Yisong Yue,Yuxin Chen | ICLR 2021,Poster | Many sequential decision making tasks can be viewed as combinatorial optimization problems over a large number of actions. When the cost of evaluating an action is high, even a greedy algorithm, which iteratively picks the best action given the history, is prohibitive to run. In this paper, we aim to learn a greedy heu... | https://openreview.net/pdf/1c1034956d2f523aa299974f4f639d1b8ecb0026.pdf |
The Recurrent Neural Tangent Kernel | https://openreview.net/forum?id=3T9iFICe0Y9 | https://openreview.net/forum?id=3T9iFICe0Y9 | Sina Alemohammad,Zichao Wang,Randall Balestriero,Richard Baraniuk | ICLR 2021,Poster | The study of deep neural networks (DNNs) in the infinite-width limit, via the so-called neural tangent kernel (NTK) approach, has provided new insights into the dynamics of learning, generalization, and the impact of initialization. One key DNN architecture remains to be kernelized, namely, the recurrent neural network... | https://openreview.net/pdf/0ede6a7293a24c88d58e7542b3c44d97270a2a0c.pdf |
Evaluation of Similarity-based Explanations | https://openreview.net/forum?id=9uvhpyQwzM_ | https://openreview.net/forum?id=9uvhpyQwzM_ | Kazuaki Hanawa,Sho Yokoi,Satoshi Hara,Kentaro Inui | ICLR 2021,Poster | Explaining the predictions made by complex machine learning models helps users to understand and accept the predicted outputs with confidence. One promising way is to use similarity-based explanation that provides similar instances as evidence to support model predictions. Several relevance metrics are used for this pu... | https://openreview.net/pdf/ede4daa61cd87856ebce2c047d94f9fdc6149edf.pdf |
Adaptive Procedural Task Generation for Hard-Exploration Problems | https://openreview.net/forum?id=8xLkv08d70T | https://openreview.net/forum?id=8xLkv08d70T | Kuan Fang,Yuke Zhu,Silvio Savarese,L. Fei-Fei | ICLR 2021,Poster | We introduce Adaptive Procedural Task Generation (APT-Gen), an approach to progressively generate a sequence of tasks as curricula to facilitate reinforcement learning in hard-exploration problems. At the heart of our approach, a task generator learns to create tasks from a parameterized task space via a black-box proc... | https://openreview.net/pdf/24bbbe680bd44c907aab36d5e18bae82a7a5a48f.pdf |
Linear Last-iterate Convergence in Constrained Saddle-point Optimization | https://openreview.net/forum?id=dx11_7vm5_r | https://openreview.net/forum?id=dx11_7vm5_r | Chen-Yu Wei,Chung-Wei Lee,Mengxiao Zhang,Haipeng Luo | ICLR 2021,Poster | Optimistic Gradient Descent Ascent (OGDA) and Optimistic Multiplicative Weights Update (OMWU) for saddle-point optimization have received growing attention due to their favorable last-iterate convergence. However, their behaviors for simple bilinear games over the probability simplex are still not fully understood --- ... | https://openreview.net/pdf/80ab11841a700c095d09408aebe0552dc6c2c21f.pdf |
On Graph Neural Networks versus Graph-Augmented MLPs | https://openreview.net/forum?id=tiqI7w64JG2 | https://openreview.net/forum?id=tiqI7w64JG2 | Lei Chen,Zhengdao Chen,Joan Bruna | ICLR 2021,Poster | From the perspectives of expressive power and learning, this work compares multi-layer Graph Neural Networks (GNNs) with a simplified alternative that we call Graph-Augmented Multi-Layer Perceptrons (GA-MLPs), which first augments node features with certain multi-hop operators on the graph and then applies learnable no... | https://openreview.net/pdf/974857db041de4f514814723ec84f8c39aa35126.pdf |
Solving Compositional Reinforcement Learning Problems via Task Reduction | https://openreview.net/forum?id=9SS69KwomAM | https://openreview.net/forum?id=9SS69KwomAM | Yunfei Li,Yilin Wu,Huazhe Xu,Xiaolong Wang,Yi Wu | ICLR 2021,Poster | We propose a novel learning paradigm, Self-Imitation via Reduction (SIR), for solving compositional reinforcement learning problems. SIR is based on two core ideas: task reduction and self-imitation. Task reduction tackles a hard-to-solve task by actively reducing it to an easier task whose solution is known by the RL ... | https://openreview.net/pdf/77f78b692f36356e5e5bbddd012a3367bd821b29.pdf |
Conditional Generative Modeling via Learning the Latent Space | https://openreview.net/forum?id=VJnrYcnRc6 | https://openreview.net/forum?id=VJnrYcnRc6 | Sameera Ramasinghe,Kanchana Nisal Ranasinghe,Salman Khan,Nick Barnes,Stephen Gould | ICLR 2021,Poster | Although deep learning has achieved appealing results on several machine learning tasks, most of the models are deterministic at inference, limiting their application to single-modal settings. We propose a novel general-purpose framework for conditional generation in multimodal spaces, that uses latent variables to mod... | https://openreview.net/pdf/ad10b1238b8c96783d156228bbe0a955123a991c.pdf |
DialoGraph: Incorporating Interpretable Strategy-Graph Networks into Negotiation Dialogues | https://openreview.net/forum?id=kDnal_bbb-E | https://openreview.net/forum?id=kDnal_bbb-E | Rishabh Joshi,Vidhisha Balachandran,Shikhar Vashishth,Alan Black,Yulia Tsvetkov | ICLR 2021,Poster | To successfully negotiate a deal, it is not enough to communicate fluently: pragmatic planning of persuasive negotiation strategies is essential. While modern dialogue agents excel at generating fluent sentences, they still lack pragmatic grounding and cannot reason strategically. We present DialoGraph, a negotiation s... | https://openreview.net/pdf/1f09e2eb0a2962d022f2fc8411de57bb2f420a25.pdf |
WaNet - Imperceptible Warping-based Backdoor Attack | https://openreview.net/forum?id=eEn8KTtJOx | https://openreview.net/forum?id=eEn8KTtJOx | Tuan Anh Nguyen,Anh Tuan Tran | ICLR 2021,Poster | With the thriving of deep learning and the widespread practice of using pre-trained networks, backdoor attacks have become an increasing security threat drawing many research interests in recent years. A third-party model can be poisoned in training to work well in normal conditions but behave maliciously when a trigge... | https://openreview.net/pdf/db3277f5b47619abfe13880772b864960e98f643.pdf |
Nonseparable Symplectic Neural Networks | https://openreview.net/forum?id=B5VvQrI49Pa | https://openreview.net/forum?id=B5VvQrI49Pa | Shiying Xiong,Yunjin Tong,Xingzhe He,Shuqi Yang,Cheng Yang,Bo Zhu | ICLR 2021,Poster | Predicting the behaviors of Hamiltonian systems has been drawing increasing attention in scientific machine learning. However, the vast majority of the literature was focused on predicting separable Hamiltonian systems with their kinematic and potential energy terms being explicitly decoupled, while building data-drive... | https://openreview.net/pdf/c9ab2e0778f4de8dcfb0a34ffd1c09aa50ceb3b8.pdf |
Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization | https://openreview.net/forum?id=lvRTC669EY_ | https://openreview.net/forum?id=lvRTC669EY_ | Zhenggang Tang,Chao Yu,Boyuan Chen,Huazhe Xu,Xiaolong Wang,Fei Fang,Simon Shaolei Du,Yu Wang,Yi Wu | ICLR 2021,Poster | We propose a simple, general and effective technique, Reward Randomization for discovering diverse strategic policies in complex multi-agent games. Combining reward randomization and policy gradient, we derive a new algorithm, Reward-Randomized Policy Gradient (RPG). RPG is able to discover a set of multiple distinctiv... | https://openreview.net/pdf/2062fdf1e8a1dbc3c1d293239ad291f853463ba8.pdf |
Multi-timescale Representation Learning in LSTM Language Models | https://openreview.net/forum?id=9ITXiTrAoT | https://openreview.net/forum?id=9ITXiTrAoT | Shivangi Mahto,Vy Ai Vo,Javier S. Turek,Alexander Huth | ICLR 2021,Poster | Language models must capture statistical dependencies between words at timescales ranging from very short to very long. Earlier work has demonstrated that dependencies in natural language tend to decay with distance between words according to a power law. However, it is unclear how this knowledge can be used for analyz... | https://openreview.net/pdf/6faff0f37219bcee41b257a3d80d7eeb3df0e2d6.pdf |
Explaining the Efficacy of Counterfactually Augmented Data | https://openreview.net/forum?id=HHiiQKWsOcV | https://openreview.net/forum?id=HHiiQKWsOcV | Divyansh Kaushik,Amrith Setlur,Eduard H Hovy,Zachary Chase Lipton | ICLR 2021,Poster | In attempts to produce machine learning models less reliant on spurious patterns in NLP datasets, researchers have recently proposed curating counterfactually augmented data (CAD) via a human-in-the-loop process in which given some documents and their (initial) labels, humans must revise the text to make a counterfactu... | https://openreview.net/pdf/73361dc2c4d80cb501745448d7de1e3c99d2f2a8.pdf |
Revisiting Locally Supervised Learning: an Alternative to End-to-end Training | https://openreview.net/forum?id=fAbkE6ant2 | https://openreview.net/forum?id=fAbkE6ant2 | Yulin Wang,Zanlin Ni,Shiji Song,Le Yang,Gao Huang | ICLR 2021,Poster | Due to the need to store the intermediate activations for back-propagation, end-to-end (E2E) training of deep networks usually suffers from high GPUs memory footprint. This paper aims to address this problem by revisiting the locally supervised learning, where a network is split into gradient-isolated modules and train... | https://openreview.net/pdf/ae46b2e0daac3e1e7af2c0b30ca3ed05b9675f66.pdf |
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? | https://openreview.net/forum?id=fgd7we_uZa6 | https://openreview.net/forum?id=fgd7we_uZa6 | Zixiang Chen,Yuan Cao,Difan Zou,Quanquan Gu | ICLR 2021,Poster | A recent line of research on deep learning focuses on the extremely over-parameterized setting, and shows that when the network width is larger than a high degree polynomial of the training sample size $n$ and the inverse of the target error $\epsilon^{-1}$, deep neural networks learned by (stochastic) gradient descent... | https://openreview.net/pdf/7d4b4fabf3654c85ec7bc9a41516a3fe17bbccd8.pdf |
Blending MPC & Value Function Approximation for Efficient Reinforcement Learning | https://openreview.net/forum?id=RqCC_00Bg7V | https://openreview.net/forum?id=RqCC_00Bg7V | Mohak Bhardwaj,Sanjiban Choudhury,Byron Boots | ICLR 2021,Poster | Model-Predictive Control (MPC) is a powerful tool for controlling complex, real-world systems that uses a model to make predictions about future behavior. For each state encountered, MPC solves an online optimization problem to choose a control action that will minimize future cost. This is a surprisingly effective str... | https://openreview.net/pdf/50c99bb8be8ec7784b7ca8b4a8b59da987b66045.pdf |
Probabilistic Numeric Convolutional Neural Networks | https://openreview.net/forum?id=T1XmO8ScKim | https://openreview.net/forum?id=T1XmO8ScKim | Marc Anton Finzi,Roberto Bondesan,Max Welling | ICLR 2021,Poster | Continuous input signals like images and time series that are irregularly sampled or have missing values are challenging for existing deep learning methods. Coherently defined feature representations must depend on the values in unobserved regions of the input. Drawing from the work in probabilistic numerics, we propos... | https://openreview.net/pdf/132819644044c301e530ea14a0a17e7e4d6756d7.pdf |
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