| 2020 | 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020 |
| 2020 | A Baseline for Few-Shot Image Classification. Guneet Singh Dhillon, Pratik Chaudhari, Avinash Ravichandran, Stefano Soatto |
| 2020 | A Closer Look at Deep Policy Gradients. Andrew Ilyas, Logan Engstrom, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Madry |
| 2020 | A Closer Look at the Optimization Landscapes of Generative Adversarial Networks. Hugo Berard, Gauthier Gidel, Amjad Almahairi, Pascal Vincent, Simon Lacoste-Julien |
| 2020 | A Constructive Prediction of the Generalization Error Across Scales. Jonathan S. Rosenfeld, Amir Rosenfeld, Yonatan Belinkov, Nir Shavit |
| 2020 | A Fair Comparison of Graph Neural Networks for Graph Classification. Federico Errica, Marco Podda, Davide Bacciu, Alessio Micheli |
| 2020 | A Framework for robustness Certification of Smoothed Classifiers using F-Divergences. Krishnamurthy (Dj) Dvijotham, Jamie Hayes, Borja Balle, J. Zico Kolter, Chongli Qin, András György, Kai Xiao, Sven Gowal, Pushmeet Kohli |
| 2020 | A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case. Greg Ongie, Rebecca Willett, Daniel Soudry, Nathan Srebro |
| 2020 | A Generalized Training Approach for Multiagent Learning. Paul Muller, Shayegan Omidshafiei, Mark Rowland, Karl Tuyls, Julien Pérolat, Siqi Liu, Daniel Hennes, Luke Marris, Marc Lanctot, Edward Hughes, Zhe Wang, Guy Lever, Nicolas Heess, Thore Graepel, Rémi Munos |
| 2020 | A Latent Morphology Model for Open-Vocabulary Neural Machine Translation. Duygu Ataman, Wilker Aziz, Alexandra Birch |
| 2020 | A Learning-based Iterative Method for Solving Vehicle Routing Problems. Hao Lu, Xingwen Zhang, Shuang Yang |
| 2020 | A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms. Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Nan Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, Christopher J. Pal |
| 2020 | A Mutual Information Maximization Perspective of Language Representation Learning. Lingpeng Kong, Cyprien de Masson d'Autume, Lei Yu, Wang Ling, Zihang Dai, Dani Yogatama |
| 2020 | A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning. Soochan Lee, Junsoo Ha, Dongsu Zhang, Gunhee Kim |
| 2020 | A Probabilistic Formulation of Unsupervised Text Style Transfer. Junxian He, Xinyi Wang, Graham Neubig, Taylor Berg-Kirkpatrick |
| 2020 | A Signal Propagation Perspective for Pruning Neural Networks at Initialization. Namhoon Lee, Thalaiyasingam Ajanthan, Stephen Gould, Philip H. S. Torr |
| 2020 | A Stochastic Derivative Free Optimization Method with Momentum. Eduard Gorbunov, Adel Bibi, Ozan Sener, El Houcine Bergou, Peter Richtárik |
| 2020 | A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer Learning. Shahbaz Rezaei, Xin Liu |
| 2020 | A Theoretical Analysis of the Number of Shots in Few-Shot Learning. Tianshi Cao, Marc T. Law, Sanja Fidler |
| 2020 | A Theory of Usable Information under Computational Constraints. Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, Stefano Ermon |
| 2020 | A closer look at the approximation capabilities of neural networks. Kai Fong Ernest Chong |
| 2020 | A critical analysis of self-supervision, or what we can learn from a single image. Yuki Markus Asano, Christian Rupprecht, Andrea Vedaldi |
| 2020 | ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut |
| 2020 | AMRL: Aggregated Memory For Reinforcement Learning. Jacob Beck, Kamil Ciosek, Sam Devlin, Sebastian Tschiatschek, Cheng Zhang, Katja Hofmann |
| 2020 | Abductive Commonsense Reasoning. Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, Keisuke Sakaguchi, Ari Holtzman, Hannah Rashkin, Doug Downey, Wen-tau Yih, Yejin Choi |
| 2020 | Abstract Diagrammatic Reasoning with Multiplex Graph Networks. Duo Wang, Mateja Jamnik, Pietro Liò |
| 2020 | Accelerating SGD with momentum for over-parameterized learning. Chaoyue Liu, Mikhail Belkin |
| 2020 | Action Semantics Network: Considering the Effects of Actions in Multiagent Systems. Weixun Wang, Tianpei Yang, Yong Liu, Jianye Hao, Xiaotian Hao, Yujing Hu, Yingfeng Chen, Changjie Fan, Yang Gao |
| 2020 | Actor-Critic Provably Finds Nash Equilibria of Linear-Quadratic Mean-Field Games. Zuyue Fu, Zhuoran Yang, Yongxin Chen, Zhaoran Wang |
| 2020 | Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation. Xinjie Fan, Yizhe Zhang, Zhendong Wang, Mingyuan Zhou |
| 2020 | Adaptive Structural Fingerprints for Graph Attention Networks. Kai Zhang, Yaokang Zhu, Jun Wang, Jie Zhang |
| 2020 | Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks. Yuhang Li, Xin Dong, Wei Wang |
| 2020 | Adjustable Real-time Style Transfer. Mohammad Babaeizadeh, Golnaz Ghiasi |
| 2020 | AdvectiveNet: An Eulerian-Lagrangian Fluidic Reservoir for Point Cloud Processing. Xingzhe He, Helen Lu Cao, Bo Zhu |
| 2020 | Adversarial AutoAugment. Xinyu Zhang, Qiang Wang, Jian Zhang, Zhao Zhong |
| 2020 | Adversarial Lipschitz Regularization. Dávid Terjék |
| 2020 | Adversarial Policies: Attacking Deep Reinforcement Learning. Adam Gleave, Michael Dennis, Cody Wild, Neel Kant, Sergey Levine, Stuart Russell |
| 2020 | Adversarial Training and Provable Defenses: Bridging the Gap. Mislav Balunovic, Martin T. Vechev |
| 2020 | Adversarially Robust Representations with Smooth Encoders. A. Taylan Cemgil, Sumedh Ghaisas, Krishnamurthy (Dj) Dvijotham, Pushmeet Kohli |
| 2020 | Adversarially robust transfer learning. Ali Shafahi, Parsa Saadatpanah, Chen Zhu, Amin Ghiasi, Christoph Studer, David W. Jacobs, Tom Goldstein |
| 2020 | Ae-OT: a New Generative Model based on Extended Semi-discrete Optimal transport. Dongsheng An, Yang Guo, Na Lei, Zhongxuan Luo, Shing-Tung Yau, Xianfeng Gu |
| 2020 | An Exponential Learning Rate Schedule for Deep Learning. Zhiyuan Li, Sanjeev Arora |
| 2020 | An Inductive Bias for Distances: Neural Nets that Respect the Triangle Inequality. Silviu Pitis, Harris Chan, Kiarash Jamali, Jimmy Ba |
| 2020 | Analysis of Video Feature Learning in Two-Stream CNNs on the Example of Zebrafish Swim Bout Classification. Bennet Breier, Arno Onken |
| 2020 | And the Bit Goes Down: Revisiting the Quantization of Neural Networks. Pierre Stock, Armand Joulin, Rémi Gribonval, Benjamin Graham, Hervé Jégou |
| 2020 | Are Pre-trained Language Models Aware of Phrases? Simple but Strong Baselines for Grammar Induction. Taeuk Kim, Jihun Choi, Daniel Edmiston, Sang-goo Lee |
| 2020 | Are Transformers universal approximators of sequence-to-sequence functions? Chulhee Yun, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar |
| 2020 | AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures. Michael S. Ryoo, A. J. Piergiovanni, Mingxing Tan, Anelia Angelova |
| 2020 | Asymptotics of Wide Networks from Feynman Diagrams. Ethan Dyer, Guy Gur-Ari |
| 2020 | At Stability's Edge: How to Adjust Hyperparameters to Preserve Minima Selection in Asynchronous Training of Neural Networks? Niv Giladi, Mor Shpigel Nacson, Elad Hoffer, Daniel Soudry |
| 2020 | AtomNAS: Fine-Grained End-to-End Neural Architecture Search. Jieru Mei, Yingwei Li, Xiaochen Lian, Xiaojie Jin, Linjie Yang, Alan L. Yuille, Jianchao Yang |
| 2020 | AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty. Dan Hendrycks, Norman Mu, Ekin Dogus Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan |
| 2020 | Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space. AkshatKumar Nigam, Pascal Friederich, Mario Krenn, Alán Aspuru-Guzik |
| 2020 | Augmenting Non-Collaborative Dialog Systems with Explicit Semantic and Strategic Dialog History. Yiheng Zhou, Yulia Tsvetkov, Alan W. Black, Zhou Yu |
| 2020 | AutoQ: Automated Kernel-Wise Neural Network Quantization. Qian Lou, Feng Guo, Minje Kim, Lantao Liu, Lei Jiang |
| 2020 | Automated Relational Meta-learning. Huaxiu Yao, Xian Wu, Zhiqiang Tao, Yaliang Li, Bolin Ding, Ruirui Li, Zhenhui Li |
| 2020 | Automated curriculum generation through setter-solver interactions. Sébastien Racanière, Andrew K. Lampinen, Adam Santoro, David P. Reichert, Vlad Firoiu, Timothy P. Lillicrap |
| 2020 | Automatically Discovering and Learning New Visual Categories with Ranking Statistics. Kai Han, Sylvestre-Alvise Rebuffi, Sébastien Ehrhardt, Andrea Vedaldi, Andrew Zisserman |
| 2020 | B-Spline CNNs on Lie groups. Erik J. Bekkers |
| 2020 | BERTScore: Evaluating Text Generation with BERT. Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, Yoav Artzi |
| 2020 | BackPACK: Packing more into Backprop. Felix Dangel, Frederik Kunstner, Philipp Hennig |
| 2020 | Batch-shaping for learning conditional channel gated networks. Babak Ehteshami Bejnordi, Tijmen Blankevoort, Max Welling |
| 2020 | BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning. Yeming Wen, Dustin Tran, Jimmy Ba |
| 2020 | BayesOpt Adversarial Attack. Binxin Ru, Adam D. Cobb, Arno Blaas, Yarin Gal |
| 2020 | Bayesian Meta Sampling for Fast Uncertainty Adaptation. Zhenyi Wang, Yang Zhao, Ping Yu, Ruiyi Zhang, Changyou Chen |
| 2020 | Behaviour Suite for Reinforcement Learning. Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvári, Satinder Singh, Benjamin Van Roy, Richard S. Sutton, David Silver, Hado van Hasselt |
| 2020 | Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks. Yu Bai, Jason D. Lee |
| 2020 | BinaryDuo: Reducing Gradient Mismatch in Binary Activation Network by Coupling Binary Activations. Hyungjun Kim, Kyungsu Kim, Jinseok Kim, Jae-Joon Kim |
| 2020 | Biologically inspired sleep algorithm for increased generalization and adversarial robustness in deep neural networks. Timothy Tadros, Giri P. Krishnan, Ramyaa Ramyaa, Maxim Bazhenov |
| 2020 | Black-Box Adversarial Attack with Transferable Model-based Embedding. Zhichao Huang, Tong Zhang |
| 2020 | Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning. Ali Mousavi, Lihong Li, Qiang Liu, Denny Zhou |
| 2020 | BlockSwap: Fisher-guided Block Substitution for Network Compression on a Budget. Jack Turner, Elliot J. Crowley, Michael F. P. O'Boyle, Amos Storkey, Gavin Gray |
| 2020 | Bounds on Over-Parameterization for Guaranteed Existence of Descent Paths in Shallow ReLU Networks. Arsalan Sharif-Nassab, Saber Salehkaleybar, S. Jamaloddin Golestani |
| 2020 | Breaking Certified Defenses: Semantic Adversarial Examples with Spoofed robustness Certificates. Amin Ghiasi, Ali Shafahi, Tom Goldstein |
| 2020 | Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness. Pu Zhao, Pin-Yu Chen, Payel Das, Karthikeyan Natesan Ramamurthy, Xue Lin |
| 2020 | Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints. Mengtian Li, Ersin Yumer, Deva Ramanan |
| 2020 | Building Deep Equivariant Capsule Networks. Sai Raam Venkataraman, S. Balasubramanian, R. Raghunatha Sarma |
| 2020 | CAQL: Continuous Action Q-Learning. Moonkyung Ryu, Yinlam Chow, Ross Anderson, Christian Tjandraatmadja, Craig Boutilier |
| 2020 | CATER: A diagnostic dataset for Compositional Actions & TEmporal Reasoning. Rohit Girdhar, Deva Ramanan |
| 2020 | CLEVRER: Collision Events for Video Representation and Reasoning. Kexin Yi, Chuang Gan, Yunzhu Li, Pushmeet Kohli, Jiajun Wu, Antonio Torralba, Joshua B. Tenenbaum |
| 2020 | CLN2INV: Learning Loop Invariants with Continuous Logic Networks. Gabriel Ryan, Justin Wong, Jianan Yao, Ronghui Gu, Suman Jana |
| 2020 | CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning. Jiachen Yang, Alireza Nakhaei, David Isele, Kikuo Fujimura, Hongyuan Zha |
| 2020 | Can gradient clipping mitigate label noise? Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar |
| 2020 | Capsules with Inverted Dot-Product Attention Routing. Yao-Hung Hubert Tsai, Nitish Srivastava, Hanlin Goh, Ruslan Salakhutdinov |
| 2020 | Causal Discovery with Reinforcement Learning. Shengyu Zhu, Ignavier Ng, Zhitang Chen |
| 2020 | Certified Defenses for Adversarial Patches. Ping-Yeh Chiang, Renkun Ni, Ahmed Abdelkader, Chen Zhu, Christoph Studer, Tom Goldstein |
| 2020 | Certified Robustness for Top-k Predictions against Adversarial Perturbations via Randomized Smoothing. Jinyuan Jia, Xiaoyu Cao, Binghui Wang, Neil Zhenqiang Gong |
| 2020 | Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation. Byung Hoon Ahn, Prannoy Pilligundla, Amir Yazdanbakhsh, Hadi Esmaeilzadeh |
| 2020 | Classification-Based Anomaly Detection for General Data. Liron Bergman, Yedid Hoshen |
| 2020 | Co-Attentive Equivariant Neural Networks: Focusing Equivariance On Transformations Co-Occurring in Data. David W. Romero, Mark Hoogendoorn |
| 2020 | CoPhy: Counterfactual Learning of Physical Dynamics. Fabien Baradel, Natalia Neverova, Julien Mille, Greg Mori, Christian Wolf |
| 2020 | Coherent Gradients: An Approach to Understanding Generalization in Gradient Descent-based Optimization. Satrajit Chatterjee |
| 2020 | Combining Q-Learning and Search with Amortized Value Estimates. Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Tobias Pfaff, Theophane Weber, Lars Buesing, Peter W. Battaglia |
| 2020 | Comparing Rewinding and Fine-tuning in Neural Network Pruning. Alex Renda, Jonathan Frankle, Michael Carbin |
| 2020 | Composing Task-Agnostic Policies with Deep Reinforcement Learning. Ahmed Hussain Qureshi, Jacob J. Johnson, Yuzhe Qin, Taylor Henderson, Byron Boots, Michael C. Yip |
| 2020 | Composition-based Multi-Relational Graph Convolutional Networks. Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, Partha P. Talukdar |
| 2020 | Compositional Language Continual Learning. Yuanpeng Li, Liang Zhao, Kenneth Church, Mohamed Elhoseiny |
| 2020 | Compositional languages emerge in a neural iterated learning model. Yi Ren, Shangmin Guo, Matthieu Labeau, Shay B. Cohen, Simon Kirby |
| 2020 | Compression based bound for non-compressed network: unified generalization error analysis of large compressible deep neural network. Taiji Suzuki, Hiroshi Abe, Tomoaki Nishimura |
| 2020 | Compressive Transformers for Long-Range Sequence Modelling. Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Chloe Hillier, Timothy P. Lillicrap |
| 2020 | Computation Reallocation for Object Detection. Feng Liang, Chen Lin, Ronghao Guo, Ming Sun, Wei Wu, Junjie Yan, Wanli Ouyang |
| 2020 | Conditional Learning of Fair Representations. Han Zhao, Amanda Coston, Tameem Adel, Geoffrey J. Gordon |
| 2020 | Conservative Uncertainty Estimation By Fitting Prior Networks. Kamil Ciosek, Vincent Fortuin, Ryota Tomioka, Katja Hofmann, Richard E. Turner |
| 2020 | Consistency Regularization for Generative Adversarial Networks. Han Zhang, Zizhao Zhang, Augustus Odena, Honglak Lee |
| 2020 | Continual Learning with Adaptive Weights (CLAW). Tameem Adel, Han Zhao, Richard E. Turner |
| 2020 | Continual Learning with Bayesian Neural Networks for Non-Stationary Data. Richard Kurle, Botond Cseke, Alexej Klushyn, Patrick van der Smagt, Stephan Günnemann |
| 2020 | Continual learning with hypernetworks. Johannes von Oswald, Christian Henning, João Sacramento, Benjamin F. Grewe |
| 2020 | Contrastive Learning of Structured World Models. Thomas N. Kipf, Elise van der Pol, Max Welling |
| 2020 | Contrastive Representation Distillation. Yonglong Tian, Dilip Krishnan, Phillip Isola |
| 2020 | Controlling generative models with continuous factors of variations. Antoine Plumerault, Hervé Le Borgne, Céline Hudelot |
| 2020 | Convergence of Gradient Methods on Bilinear Zero-Sum Games. Guojun Zhang, Yaoliang Yu |
| 2020 | Convolutional Conditional Neural Processes. Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong, James Requeima, Yann Dubois, Richard E. Turner |
| 2020 | Counterfactuals uncover the modular structure of deep generative models. Michel Besserve, Arash Mehrjou, Rémy Sun, Bernhard Schölkopf |
| 2020 | Critical initialisation in continuous approximations of binary neural networks. George Stamatescu, Federica Gerace, Carlo Lucibello, Ian G. Fuss, Langford B. White |
| 2020 | Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation. Hung-Yu Tseng, Hsin-Ying Lee, Jia-Bin Huang, Ming-Hsuan Yang |
| 2020 | Cross-Lingual Ability of Multilingual BERT: An Empirical Study. Karthikeyan K, Zihan Wang, Stephen Mayhew, Dan Roth |
| 2020 | Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework. Zirui Wang, Jiateng Xie, Ruochen Xu, Yiming Yang, Graham Neubig, Jaime G. Carbonell |
| 2020 | Curriculum Loss: Robust Learning and Generalization against Label Corruption. Yueming Lyu, Ivor W. Tsang |
| 2020 | Curvature Graph Network. Ze Ye, Kin Sum Liu, Tengfei Ma, Jie Gao, Chao Chen |
| 2020 | Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning. Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, Andrew Gordon Wilson |
| 2020 | DBA: Distributed Backdoor Attacks against Federated Learning. Chulin Xie, Keli Huang, Pin-Yu Chen, Bo Li |
| 2020 | DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames. Erik Wijmans, Abhishek Kadian, Ari Morcos, Stefan Lee, Irfan Essa, Devi Parikh, Manolis Savva, Dhruv Batra |
| 2020 | DDSP: Differentiable Digital Signal Processing. Jesse H. Engel, Lamtharn Hantrakul, Chenjie Gu, Adam Roberts |
| 2020 | Data-Independent Neural Pruning via Coresets. Ben Mussay, Margarita Osadchy, Vladimir Braverman, Samson Zhou, Dan Feldman |
| 2020 | Data-dependent Gaussian Prior Objective for Language Generation. Zuchao Li, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Zhuosheng Zhang, Hai Zhao |
| 2020 | DeFINE: Deep Factorized Input Token Embeddings for Neural Sequence Modeling. Sachin Mehta, Rik Koncel-Kedziorski, Mohammad Rastegari, Hannaneh Hajishirzi |
| 2020 | Decentralized Deep Learning with Arbitrary Communication Compression. Anastasia Koloskova, Tao Lin, Sebastian U. Stich, Martin Jaggi |
| 2020 | Decoding As Dynamic Programming For Recurrent Autoregressive Models. Najam Zaidi, Trevor Cohn, Gholamreza Haffari |
| 2020 | Decoupling Representation and Classifier for Long-Tailed Recognition. Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, Yannis Kalantidis |
| 2020 | Deep 3D Pan via local adaptive "t-shaped" convolutions with global and local adaptive dilations. Juan Luis Gonzalez Bello, Munchurl Kim |
| 2020 | Deep Audio Priors Emerge From Harmonic Convolutional Networks. Zhoutong Zhang, Yunyun Wang, Chuang Gan, Jiajun Wu, Joshua B. Tenenbaum, Antonio Torralba, William T. Freeman |
| 2020 | Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal |
| 2020 | Deep Double Descent: Where Bigger Models and More Data Hurt. Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever |
| 2020 | Deep Graph Matching Consensus. Matthias Fey, Jan Eric Lenssen, Christopher Morris, Jonathan Masci, Nils M. Kriege |
| 2020 | Deep Imitative Models for Flexible Inference, Planning, and Control. Nicholas Rhinehart, Rowan McAllister, Sergey Levine |
| 2020 | Deep Learning For Symbolic Mathematics. Guillaume Lample, François Charton |
| 2020 | Deep Learning of Determinantal Point Processes via Proper Spectral Sub-gradient. Tianshu Yu, Yikang Li, Baoxin Li |
| 2020 | Deep Network Classification by Scattering and Homotopy Dictionary Learning. John Zarka, Louis Thiry, Tomás Angles, Stéphane Mallat |
| 2020 | Deep Orientation Uncertainty Learning based on a Bingham Loss. Igor Gilitschenski, Roshni Sahoo, Wilko Schwarting, Alexander Amini, Sertac Karaman, Daniela Rus |
| 2020 | Deep Semi-Supervised Anomaly Detection. Lukas Ruff, Robert A. Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, Klaus-Robert Müller, Marius Kloft |
| 2020 | Deep Symbolic Superoptimization Without Human Knowledge. Hui Shi, Yang Zhang, Xinyun Chen, Yuandong Tian, Jishen Zhao |
| 2020 | Deep neuroethology of a virtual rodent. Josh Merel, Diego Aldarondo, Jesse Marshall, Yuval Tassa, Greg Wayne, Bence Olveczky |
| 2020 | Deep probabilistic subsampling for task-adaptive compressed sensing. Iris A. M. Huijben, Bastiaan S. Veeling, Ruud J. G. van Sloun |
| 2020 | DeepHoyer: Learning Sparser Neural Network with Differentiable Scale-Invariant Sparsity Measures. Huanrui Yang, Wei Wen, Hai Li |
| 2020 | DeepSphere: a graph-based spherical CNN. Michaël Defferrard, Martino Milani, Frédérick Gusset, Nathanaël Perraudin |
| 2020 | DeepV2D: Video to Depth with Differentiable Structure from Motion. Zachary Teed, Jia Deng |
| 2020 | Defending Against Physically Realizable Attacks on Image Classification. Tong Wu, Liang Tong, Yevgeniy Vorobeychik |
| 2020 | Deformable Kernels: Adapting Effective Receptive Fields for Object Deformation. Hang Gao, Xizhou Zhu, Stephen Lin, Jifeng Dai |
| 2020 | Demystifying Inter-Class Disentanglement. Aviv Gabbay, Yedid Hoshen |
| 2020 | Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators. Reinhard Heckel, Mahdi Soltanolkotabi |
| 2020 | Depth-Adaptive Transformer. Maha Elbayad, Jiatao Gu, Edouard Grave, Michael Auli |
| 2020 | Depth-Width Trade-offs for ReLU Networks via Sharkovsky's Theorem. Vaggos Chatziafratis, Sai Ganesh Nagarajan, Ioannis Panageas, Xiao Wang |
| 2020 | Detecting Extrapolation with Local Ensembles. David Madras, James Atwood, Alexander D'Amour |
| 2020 | Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions. Yao Qin, Nicholas Frosst, Sara Sabour, Colin Raffel, Garrison W. Cottrell, Geoffrey E. Hinton |
| 2020 | DiffTaichi: Differentiable Programming for Physical Simulation. Yuanming Hu, Luke Anderson, Tzu-Mao Li, Qi Sun, Nathan Carr, Jonathan Ragan-Kelley, Frédo Durand |
| 2020 | Difference-Seeking Generative Adversarial Network-Unseen Sample Generation. Yi Lin Sung, Sung-Hsien Hsieh, Soo-Chang Pei, Chun-Shien Lu |
| 2020 | Differentiable Reasoning over a Virtual Knowledge Base. Bhuwan Dhingra, Manzil Zaheer, Vidhisha Balachandran, Graham Neubig, Ruslan Salakhutdinov, William W. Cohen |
| 2020 | Differentiable learning of numerical rules in knowledge graphs. Po-Wei Wang, Daria Stepanova, Csaba Domokos, J. Zico Kolter |
| 2020 | Differentially Private Meta-Learning. Jeffrey Li, Mikhail Khodak, Sebastian Caldas, Ameet Talwalkar |
| 2020 | Differentiation of Blackbox Combinatorial Solvers. Marin Vlastelica Pogancic, Anselm Paulus, Vít Musil, Georg Martius, Michal Rolínek |
| 2020 | Directional Message Passing for Molecular Graphs. Johannes Klicpera, Janek Groß, Stephan Günnemann |
| 2020 | Disagreement-Regularized Imitation Learning. Kianté Brantley, Wen Sun, Mikael Henaff |
| 2020 | Discovering Motor Programs by Recomposing Demonstrations. Tanmay Shankar, Shubham Tulsiani, Lerrel Pinto, Abhinav Gupta |
| 2020 | Discrepancy Ratio: Evaluating Model Performance When Even Experts Disagree on the Truth. Igor Lovchinsky, Alon Daks, Israel Malkin, Pouya Samangouei, Ardavan Saeedi, Yang Liu, Swami Sankaranarayanan, Tomer Gafner, Ben Sternlieb, Patrick Maher, Nathan Silberman |
| 2020 | Discriminative Particle Filter Reinforcement Learning for Complex Partial observations. Xiao Ma, Péter Karkus, David Hsu, Wee Sun Lee, Nan Ye |
| 2020 | Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN). Peter Sorrenson, Carsten Rother, Ullrich Köthe |
| 2020 | Disentangling Factors of Variations Using Few Labels. Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem |
| 2020 | Disentangling neural mechanisms for perceptual grouping. Junkyung Kim, Drew Linsley, Kalpit Thakkar, Thomas Serre |
| 2020 | Distance-Based Learning from Errors for Confidence Calibration. Chen Xing, Sercan Ömer Arik, Zizhao Zhang, Tomas Pfister |
| 2020 | Distributed Bandit Learning: Near-Optimal Regret with Efficient Communication. Yuanhao Wang, Jiachen Hu, Xiaoyu Chen, Liwei Wang |
| 2020 | Distributionally Robust Neural Networks. Shiori Sagawa, Pang Wei Koh, Tatsunori B. Hashimoto, Percy Liang |
| 2020 | Diverse Trajectory Forecasting with Determinantal Point Processes. Ye Yuan, Kris M. Kitani |
| 2020 | DivideMix: Learning with Noisy Labels as Semi-supervised Learning. Junnan Li, Richard Socher, Steven C. H. Hoi |
| 2020 | Domain Adaptive Multibranch Networks. Róger Bermúdez-Chacón, Mathieu Salzmann, Pascal Fua |
| 2020 | Don't Use Large Mini-batches, Use Local SGD. Tao Lin, Sebastian U. Stich, Kumar Kshitij Patel, Martin Jaggi |
| 2020 | Double Neural Counterfactual Regret Minimization. Hui Li, Kailiang Hu, Shaohua Zhang, Yuan Qi, Le Song |
| 2020 | Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation. Ziyang Tang, Yihao Feng, Lihong Li, Dengyong Zhou, Qiang Liu |
| 2020 | Drawing Early-Bird Tickets: Toward More Efficient Training of Deep Networks. Haoran You, Chaojian Li, Pengfei Xu, Yonggan Fu, Yue Wang, Xiaohan Chen, Richard G. Baraniuk, Zhangyang Wang, Yingyan Lin |
| 2020 | Dream to Control: Learning Behaviors by Latent Imagination. Danijar Hafner, Timothy P. Lillicrap, Jimmy Ba, Mohammad Norouzi |
| 2020 | DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. Yu Rong, Wenbing Huang, Tingyang Xu, Junzhou Huang |
| 2020 | Duration-of-Stay Storage Assignment under Uncertainty. Michael Lingzhi Li, Elliott Wolf, Daniel Wintz |
| 2020 | Dynamic Model Pruning with Feedback. Tao Lin, Sebastian U. Stich, Luis Barba, Daniil Dmitriev, Martin Jaggi |
| 2020 | Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers. Junjie Liu, Zhe Xu, Runbin Shi, Ray C. C. Cheung, Hayden Kwok-Hay So |
| 2020 | Dynamic Time Lag Regression: Predicting What & When. Mandar Chandorkar, Cyril Furtlehner, Bala Poduval, Enrico Camporeale, Michèle Sebag |
| 2020 | Dynamical Distance Learning for Semi-Supervised and Unsupervised Skill Discovery. Kristian Hartikainen, Xinyang Geng, Tuomas Haarnoja, Sergey Levine |
| 2020 | Dynamically Pruned Message Passing Networks for Large-scale Knowledge Graph Reasoning. Xiaoran Xu, Wei Feng, Yunsheng Jiang, Xiaohui Xie, Zhiqing Sun, Zhi-Hong Deng |
| 2020 | Dynamics-Aware Embeddings. William F. Whitney, Rajat Agarwal, Kyunghyun Cho, Abhinav Gupta |
| 2020 | Dynamics-Aware Unsupervised Discovery of Skills. Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, Karol Hausman |
| 2020 | ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning |
| 2020 | EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness Against Adversarial Attacks. Sanchari Sen, Balaraman Ravindran, Anand Raghunathan |
| 2020 | ES-MAML: Simple Hessian-Free Meta Learning. Xingyou Song, Wenbo Gao, Yuxiang Yang, Krzysztof Choromanski, Aldo Pacchiano, Yunhao Tang |
| 2020 | Economy Statistical Recurrent Units For Inferring Nonlinear Granger Causality. Saurabh Khanna, Vincent Y. F. Tan |
| 2020 | Editable Neural Networks. Anton Sinitsin, Vsevolod Plokhotnyuk, Dmitry V. Pyrkin, Sergei Popov, Artem Babenko |
| 2020 | Effect of Activation Functions on the Training of Overparametrized Neural Nets. Abhishek Panigrahi, Abhishek Shetty, Navin Goyal |
| 2020 | Efficient Probabilistic Logic Reasoning with Graph Neural Networks. Yuyu Zhang, Xinshi Chen, Yuan Yang, Arun Ramamurthy, Bo Li, Yuan Qi, Le Song |
| 2020 | Efficient Riemannian Optimization on the Stiefel Manifold via the Cayley Transform. Jun Li, Fuxin Li, Sinisa Todorovic |
| 2020 | Efficient and Information-Preserving Future Frame Prediction and Beyond. Wei Yu, Yichao Lu, Steve Easterbrook, Sanja Fidler |
| 2020 | Emergence of functional and structural properties of the head direction system by optimization of recurrent neural networks. Christopher J. Cueva, Peter Y. Wang, Matthew Chin, Xue-Xin Wei |
| 2020 | Emergent Tool Use From Multi-Agent Autocurricula. Bowen Baker, Ingmar Kanitscheider, Todor M. Markov, Yi Wu, Glenn Powell, Bob McGrew, Igor Mordatch |
| 2020 | Empirical Bayes Transductive Meta-Learning with Synthetic Gradients. Shell Xu Hu, Pablo Garcia Moreno, Yang Xiao, Xi Shen, Guillaume Obozinski, Neil D. Lawrence, Andreas C. Damianou |
| 2020 | Empirical Studies on the Properties of Linear Regions in Deep Neural Networks. Xiao Zhang, Dongrui Wu |
| 2020 | Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation. Nitin Rathi, Gopalakrishnan Srinivasan, Priyadarshini Panda, Kaushik Roy |
| 2020 | Encoding word order in complex embeddings. Benyou Wang, Donghao Zhao, Christina Lioma, Qiuchi Li, Peng Zhang, Jakob Grue Simonsen |
| 2020 | End to End Trainable Active Contours via Differentiable Rendering. Shir Gur, Tal Shaharabany, Lior Wolf |
| 2020 | Energy-based models for atomic-resolution protein conformations. Yilun Du, Joshua Meier, Jerry Ma, Rob Fergus, Alexander Rives |
| 2020 | Enhancing Adversarial Defense by k-Winners-Take-All. Chang Xiao, Peilin Zhong, Changxi Zheng |
| 2020 | Enhancing Transformation-Based Defenses Against Adversarial Attacks with a Distribution Classifier. Connie Kou, Hwee Kuan Lee, Ee-Chien Chang, Teck Khim Ng |
| 2020 | Ensemble Distribution Distillation. Andrey Malinin, Bruno Mlodozeniec, Mark J. F. Gales |
| 2020 | Environmental drivers of systematicity and generalization in a situated agent. Felix Hill, Andrew K. Lampinen, Rosalia Schneider, Stephen Clark, Matthew M. Botvinick, James L. McClelland, Adam Santoro |
| 2020 | Episodic Reinforcement Learning with Associative Memory. Guangxiang Zhu, Zichuan Lin, Guangwen Yang, Chongjie Zhang |
| 2020 | Escaping Saddle Points Faster with Stochastic Momentum. Jun-Kun Wang, Chi-Heng Lin, Jacob D. Abernethy |
| 2020 | Estimating Gradients for Discrete Random Variables by Sampling without Replacement. Wouter Kool, Herke van Hoof, Max Welling |
| 2020 | Estimating counterfactual treatment outcomes over time through adversarially balanced representations. Ioana Bica, Ahmed M. Alaa, James Jordon, Mihaela van der Schaar |
| 2020 | Evaluating The Search Phase of Neural Architecture Search. Kaicheng Yu, Christian Sciuto, Martin Jaggi, Claudiu Musat, Mathieu Salzmann |
| 2020 | Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning. Qian Long, Zihan Zhou, Abhinav Gupta, Fei Fang, Yi Wu, Xiaolong Wang |
| 2020 | Expected Information Maximization: Using the I-Projection for Mixture Density Estimation. Philipp Becker, Oleg Arenz, Gerhard Neumann |
| 2020 | Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution. Nikaash Puri, Sukriti Verma, Piyush Gupta, Dhruv Kayastha, Shripad V. Deshmukh, Balaji Krishnamurthy, Sameer Singh |
| 2020 | Explanation by Progressive Exaggeration. Sumedha Singla, Brian Pollack, Junxiang Chen, Kayhan Batmanghelich |
| 2020 | Exploration in Reinforcement Learning with Deep Covering Options. Yuu Jinnai, Jee Won Park, Marlos C. Machado, George Dimitri Konidaris |
| 2020 | Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning. Akanksha Atrey, Kaleigh Clary, David D. Jensen |
| 2020 | Exploring Model-based Planning with Policy Networks. Tingwu Wang, Jimmy Ba |
| 2020 | Extreme Classification via Adversarial Softmax Approximation. Robert Bamler, Stephan Mandt |
| 2020 | Extreme Tensoring for Low-Memory Preconditioning. Xinyi Chen, Naman Agarwal, Elad Hazan, Cyril Zhang, Yi Zhang |
| 2020 | FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary. Yingzhen Yang, Jiahui Yu, Nebojsa Jojic, Jun Huan, Thomas S. Huang |
| 2020 | FSPool: Learning Set Representations with Featurewise Sort Pooling. Yan Zhang, Jonathon S. Hare, Adam Prügel-Bennett |
| 2020 | Fair Resource Allocation in Federated Learning. Tian Li, Maziar Sanjabi, Ahmad Beirami, Virginia Smith |
| 2020 | Fantastic Generalization Measures and Where to Find Them. Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, Samy Bengio |
| 2020 | Fast Neural Network Adaptation via Parameter Remapping and Architecture Search. Jiemin Fang, Yuzhu Sun, Kangjian Peng, Qian Zhang, Yuan Li, Wenyu Liu, Xinggang Wang |
| 2020 | Fast Task Inference with Variational Intrinsic Successor Features. Steven Hansen, Will Dabney, André Barreto, David Warde-Farley, Tom Van de Wiele, Volodymyr Mnih |
| 2020 | Fast is better than free: Revisiting adversarial training. Eric Wong, Leslie Rice, J. Zico Kolter |
| 2020 | FasterSeg: Searching for Faster Real-time Semantic Segmentation. Wuyang Chen, Xinyu Gong, Xianming Liu, Qian Zhang, Yuan Li, Zhangyang Wang |
| 2020 | Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection. Michael Tsang, Dehua Cheng, Hanpeng Liu, Xue Feng, Eric Zhou, Yan Liu |
| 2020 | Federated Adversarial Domain Adaptation. Xingchao Peng, Zijun Huang, Yizhe Zhu, Kate Saenko |
| 2020 | Federated Learning with Matched Averaging. Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris S. Papailiopoulos, Yasaman Khazaeni |
| 2020 | Few-Shot Learning on graphs via super-Classes based on Graph spectral Measures. Jatin Chauhan, Deepak Nathani, Manohar Kaul |
| 2020 | Few-shot Text Classification with Distributional Signatures. Yujia Bao, Menghua Wu, Shiyu Chang, Regina Barzilay |
| 2020 | Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents. Christian Rupprecht, Cyril Ibrahim, Christopher J. Pal |
| 2020 | Finite Depth and Width Corrections to the Neural Tangent Kernel. Boris Hanin, Mihai Nica |
| 2020 | Fooling Detection Alone is Not Enough: Adversarial Attack against Multiple Object Tracking. Yunhan Jia, Yantao Lu, Junjie Shen, Qi Alfred Chen, Hao Chan, Zhenyu Zhong, Tao Wei |
| 2020 | Four Things Everyone Should Know to Improve Batch Normalization. Cecilia Summers, Michael J. Dinneen |
| 2020 | FreeLB: Enhanced Adversarial Training for Natural Language Understanding. Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Tom Goldstein, Jingjing Liu |
| 2020 | Frequency-based Search-control in Dyna. Yangchen Pan, Jincheng Mei, Amir-massoud Farahmand |
| 2020 | From Inference to Generation: End-to-end Fully Self-supervised Generation of Human Face from Speech. Hyeong-Seok Choi, Changdae Park, Kyogu Lee |
| 2020 | From Variational to Deterministic Autoencoders. Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari, Michael J. Black, Bernhard Schölkopf |
| 2020 | Functional Regularisation for Continual Learning with Gaussian Processes. Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews, Razvan Pascanu, Yee Whye Teh |
| 2020 | Functional vs. parametric equivalence of ReLU networks. Mary Phuong, Christoph H. Lampert |
| 2020 | GAT: Generative Adversarial Training for Adversarial Example Detection and Robust Classification. Xuwang Yin, Soheil Kolouri, Gustavo K. Rohde |
| 2020 | GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations. Martin Engelcke, Adam R. Kosiorek, Oiwi Parker Jones, Ingmar Posner |
| 2020 | GLAD: Learning Sparse Graph Recovery. Harsh Shrivastava, Xinshi Chen, Binghong Chen, Guanghui Lan, Srinivas Aluru, Han Liu, Le Song |
| 2020 | Gap-Aware Mitigation of Gradient Staleness. Saar Barkai, Ido Hakimi, Assaf Schuster |
| 2020 | GenDICE: Generalized Offline Estimation of Stationary Values. Ruiyi Zhang, Bo Dai, Lihong Li, Dale Schuurmans |
| 2020 | Generalization bounds for deep convolutional neural networks. Philip M. Long, Hanie Sedghi |
| 2020 | Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint. Jimmy Ba, Murat A. Erdogdu, Taiji Suzuki, Denny Wu, Tianzong Zhang |
| 2020 | Generalization through Memorization: Nearest Neighbor Language Models. Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, Mike Lewis |
| 2020 | Generalized Convolutional Forest Networks for Domain Generalization and Visual Recognition. Jongbin Ryu, Gitaek Kwon, Ming-Hsuan Yang, Jongwoo Lim |
| 2020 | Generative Models for Effective ML on Private, Decentralized Datasets. Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Agüera y Arcas |
| 2020 | Generative Ratio Matching Networks. Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton |
| 2020 | Geom-GCN: Geometric Graph Convolutional Networks. Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, Bo Yang |
| 2020 | Geometric Analysis of Nonconvex Optimization Landscapes for Overcomplete Learning. Qing Qu, Yuexiang Zhai, Xiao Li, Yuqian Zhang, Zhihui Zhu |
| 2020 | Geometric Insights into the Convergence of Nonlinear TD Learning. David Brandfonbrener, Joan Bruna |
| 2020 | Global Relational Models of Source Code. Vincent J. Hellendoorn, Charles Sutton, Rishabh Singh, Petros Maniatis, David Bieber |
| 2020 | Gradient $\ell_1$ Regularization for Quantization Robustness. Milad Alizadeh, Arash Behboodi, Mart van Baalen, Christos Louizos, Tijmen Blankevoort, Max Welling |
| 2020 | Gradient Descent Maximizes the Margin of Homogeneous Neural Networks. Kaifeng Lyu, Jian Li |
| 2020 | Gradient-Based Neural DAG Learning. Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, Simon Lacoste-Julien |
| 2020 | Gradientless Descent: High-Dimensional Zeroth-Order Optimization. Daniel Golovin, John Karro, Greg Kochanski, Chansoo Lee, Xingyou Song, Qiuyi (Richard) Zhang |
| 2020 | Gradients as Features for Deep Representation Learning. Fangzhou Mu, Yingyu Liang, Yin Li |
| 2020 | Graph Constrained Reinforcement Learning for Natural Language Action Spaces. Prithviraj Ammanabrolu, Matthew J. Hausknecht |
| 2020 | Graph Convolutional Reinforcement Learning. Jiechuan Jiang, Chen Dun, Tiejun Huang, Zongqing Lu |
| 2020 | Graph Neural Networks Exponentially Lose Expressive Power for Node Classification. Kenta Oono, Taiji Suzuki |
| 2020 | Graph inference learning for semi-supervised classification. Chunyan Xu, Zhen Cui, Xiaobin Hong, Tong Zhang, Jian Yang, Wei Liu |
| 2020 | GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation. Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, Jian Tang |
| 2020 | GraphSAINT: Graph Sampling Based Inductive Learning Method. Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor K. Prasanna |
| 2020 | GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding. Chenhui Deng, Zhiqiang Zhao, Yongyu Wang, Zhiru Zhang, Zhuo Feng |
| 2020 | Guiding Program Synthesis by Learning to Generate Examples. Larissa Laich, Pavol Bielik, Martin T. Vechev |
| 2020 | Hamiltonian Generative Networks. Peter Toth, Danilo J. Rezende, Andrew Jaegle, Sébastien Racanière, Aleksandar Botev, Irina Higgins |
| 2020 | Harnessing Structures for Value-Based Planning and Reinforcement Learning. Yuzhe Yang, Guo Zhang, Zhi Xu, Dina Katabi |
| 2020 | Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks. Sanjeev Arora, Simon S. Du, Zhiyuan Li, Ruslan Salakhutdinov, Ruosong Wang, Dingli Yu |
| 2020 | HiLLoC: lossless image compression with hierarchical latent variable models. James Townsend, Thomas Bird, Julius Kunze, David Barber |
| 2020 | Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation. Suraj Nair, Chelsea Finn |
| 2020 | High Fidelity Speech Synthesis with Adversarial Networks. Mikolaj Binkowski, Jeff Donahue, Sander Dieleman, Aidan Clark, Erich Elsen, Norman Casagrande, Luis C. Cobo, Karen Simonyan |
| 2020 | Higher-Order Function Networks for Learning Composable 3D Object Representations. Eric Mitchell, Selim Engin, Volkan Isler, Daniel D. Lee |
| 2020 | Hoppity: Learning Graph Transformations to Detect and Fix Bugs in Programs. Elizabeth Dinella, Hanjun Dai, Ziyang Li, Mayur Naik, Le Song, Ke Wang |
| 2020 | How much Position Information Do Convolutional Neural Networks Encode? Md. Amirul Islam, Sen Jia, Neil D. B. Bruce |
| 2020 | How to 0wn the NAS in Your Spare Time. Sanghyun Hong, Michael Davinroy, Yigitcan Kaya, Dana Dachman-Soled, Tudor Dumitras |
| 2020 | Hyper-SAGNN: a self-attention based graph neural network for hypergraphs. Ruochi Zhang, Yuesong Zou, Jian Ma |
| 2020 | Hypermodels for Exploration. Vikranth Dwaracherla, Xiuyuan Lu, Morteza Ibrahimi, Ian Osband, Zheng Wen, Benjamin Van Roy |
| 2020 | I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively. Haotao Wang, Tianlong Chen, Zhangyang Wang, Kede Ma |
| 2020 | IMPACT: Importance Weighted Asynchronous Architectures with Clipped Target Networks. Michael Luo, Jiahao Yao, Richard Liaw, Eric Liang, Ion Stoica |
| 2020 | Identifying through Flows for Recovering Latent Representations. Shen Li, Bryan Hooi, Gim Hee Lee |
| 2020 | Identity Crisis: Memorization and Generalization Under Extreme Overparameterization. Chiyuan Zhang, Samy Bengio, Moritz Hardt, Michael C. Mozer, Yoram Singer |
| 2020 | Image-guided Neural Object Rendering. Justus Thies, Michael Zollhöfer, Christian Theobalt, Marc Stamminger, Matthias Nießner |
| 2020 | Imitation Learning via Off-Policy Distribution Matching. Ilya Kostrikov, Ofir Nachum, Jonathan Tompson |
| 2020 | Implementation Matters in Deep RL: A Case Study on PPO and TRPO. Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Madry |
| 2020 | Implementing Inductive bias for different navigation tasks through diverse RNN attrractors. Tie Xu, Omri Barak |
| 2020 | Implicit Bias of Gradient Descent based Adversarial Training on Separable Data. Yan Li, Ethan X. Fang, Huan Xu, Tuo Zhao |
| 2020 | Improved Sample Complexities for Deep Neural Networks and Robust Classification via an All-Layer Margin. Colin Wei, Tengyu Ma |
| 2020 | Improved memory in recurrent neural networks with sequential non-normal dynamics. A. Emin Orhan, Xaq Pitkow |
| 2020 | Improving Adversarial Robustness Requires Revisiting Misclassified Examples. Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, Quanquan Gu |
| 2020 | Improving Generalization in Meta Reinforcement Learning using Learned Objectives. Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber |
| 2020 | Improving Neural Language Generation with Spectrum Control. Lingxiao Wang, Jing Huang, Kevin Huang, Ziniu Hu, Guangtao Wang, Quanquan Gu |
| 2020 | In Search for a SAT-friendly Binarized Neural Network Architecture. Nina Narodytska, Hongce Zhang, Aarti Gupta, Toby Walsh |
| 2020 | Incorporating BERT into Neural Machine Translation. Jinhua Zhu, Yingce Xia, Lijun Wu, Di He, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu |
| 2020 | Inductive Matrix Completion Based on Graph Neural Networks. Muhan Zhang, Yixin Chen |
| 2020 | Inductive and Unsupervised Representation Learning on Graph Structured Objects. Lichen Wang, Bo Zong, Qianqian Ma, Wei Cheng, Jingchao Ni, Wenchao Yu, Yanchi Liu, Dongjin Song, Haifeng Chen, Yun Fu |
| 2020 | Inductive representation learning on temporal graphs. Da Xu, Chuanwei Ruan, Evren Körpeoglu, Sushant Kumar, Kannan Achan |
| 2020 | Infinite-Horizon Differentiable Model Predictive Control. Sebastian East, Marco Gallieri, Jonathan Masci, Jan Koutník, Mark Cannon |
| 2020 | Infinite-horizon Off-Policy Policy Evaluation with Multiple Behavior Policies. Xinyun Chen, Lu Wang, Yizhe Hang, Heng Ge, Hongyuan Zha |
| 2020 | Influence-Based Multi-Agent Exploration. Tonghan Wang, Jianhao Wang, Yi Wu, Chongjie Zhang |
| 2020 | InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, Jian Tang |
| 2020 | Information Geometry of Orthogonal Initializations and Training. Piotr Aleksander Sokól, Il Memming Park |
| 2020 | Input Complexity and Out-of-distribution Detection with Likelihood-based Generative Models. Joan Serrà, David Álvarez, Vicenç Gómez, Olga Slizovskaia, José F. Núñez, Jordi Luque |
| 2020 | Intensity-Free Learning of Temporal Point Processes. Oleksandr Shchur, Marin Bilos, Stephan Günnemann |
| 2020 | Interpretable Complex-Valued Neural Networks for Privacy Protection. Liyao Xiang, Hao Zhang, Haotian Ma, Yifan Zhang, Jie Ren, Quanshi Zhang |
| 2020 | Intriguing Properties of Adversarial Training at Scale. Cihang Xie, Alan L. Yuille |
| 2020 | Intrinsic Motivation for Encouraging Synergistic Behavior. Rohan Chitnis, Shubham Tulsiani, Saurabh Gupta, Abhinav Gupta |
| 2020 | Intrinsically Motivated Discovery of Diverse Patterns in Self-Organizing Systems. Chris Reinke, Mayalen Etcheverry, Pierre-Yves Oudeyer |
| 2020 | Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? Simon S. Du, Sham M. Kakade, Ruosong Wang, Lin F. Yang |
| 2020 | Iterative energy-based projection on a normal data manifold for anomaly localization. David Dehaene, Oriel Frigo, Sébastien Combrexelle, Pierre Eline |
| 2020 | Jacobian Adversarially Regularized Networks for Robustness. Alvin Chan, Yi Tay, Yew-Soon Ong, Jie Fu |
| 2020 | Jelly Bean World: A Testbed for Never-Ending Learning. Emmanouil Antonios Platanios, Abulhair Saparov, Tom M. Mitchell |
| 2020 | Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps. Tri Dao, Nimit Sharad Sohoni, Albert Gu, Matthew Eichhorn, Amit Blonder, Megan Leszczynski, Atri Rudra, Christopher Ré |
| 2020 | Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning. Noah Y. Siegel, Jost Tobias Springenberg, Felix Berkenkamp, Abbas Abdolmaleki, Michael Neunert, Thomas Lampe, Roland Hafner, Nicolas Heess, Martin A. Riedmiller |
| 2020 | Kernel of CycleGAN as a principal homogeneous space. Nikita Moriakov, Jonas Adler, Jonas Teuwen |
| 2020 | Kernelized Wasserstein Natural Gradient. Michael Arbel, Arthur Gretton, Wuchen Li, Guido Montúfar |
| 2020 | Knowledge Consistency between Neural Networks and Beyond. Ruofan Liang, Tianlin Li, Longfei Li, Jing Wang, Quanshi Zhang |
| 2020 | LAMOL: LAnguage MOdeling for Lifelong Language Learning. Fan-Keng Sun, Cheng-Hao Ho, Hung-yi Lee |
| 2020 | Lagrangian Fluid Simulation with Continuous Convolutions. Benjamin Ummenhofer, Lukas Prantl, Nils Thuerey, Vladlen Koltun |
| 2020 | LambdaNet: Probabilistic Type Inference using Graph Neural Networks. Jiayi Wei, Maruth Goyal, Greg Durrett, Isil Dillig |
| 2020 | Language GANs Falling Short. Massimo Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joelle Pineau, Laurent Charlin |
| 2020 | Large Batch Optimization for Deep Learning: Training BERT in 76 minutes. Yang You, Jing Li, Sashank J. Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, Cho-Jui Hsieh |
| 2020 | Latent Normalizing Flows for Many-to-Many Cross-Domain Mappings. Shweta Mahajan, Iryna Gurevych, Stefan Roth |
| 2020 | Lazy-CFR: fast and near-optimal regret minimization for extensive games with imperfect information. Yichi Zhou, Tongzheng Ren, Jialian Li, Dong Yan, Jun Zhu |
| 2020 | Learn to Explain Efficiently via Neural Logic Inductive Learning. Yuan Yang, Le Song |
| 2020 | Learned Step Size quantization. Steven K. Esser, Jeffrey L. McKinstry, Deepika Bablani, Rathinakumar Appuswamy, Dharmendra S. Modha |
| 2020 | Learning Compositional Koopman Operators for Model-Based Control. Yunzhu Li, Hao He, Jiajun Wu, Dina Katabi, Antonio Torralba |
| 2020 | Learning Disentangled Representations for CounterFactual Regression. Negar Hassanpour, Russell Greiner |
| 2020 | Learning Efficient Parameter Server Synchronization Policies for Distributed SGD. Rong Zhu, Sheng Yang, Andreas Pfadler, Zhengping Qian, Jingren Zhou |
| 2020 | Learning Execution through Neural Code fusion. Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi |
| 2020 | Learning Expensive Coordination: An Event-Based Deep RL Approach. Zhenyu Shi, Runsheng Yu, Xinrun Wang, Rundong Wang, Youzhi Zhang, Hanjiang Lai, Bo An |
| 2020 | Learning Heuristics for Quantified Boolean Formulas through Reinforcement Learning. Gil Lederman, Markus N. Rabe, Sanjit A. Seshia, Edward A. Lee |
| 2020 | Learning Hierarchical Discrete Linguistic Units from Visually-Grounded Speech. David Harwath, Wei-Ning Hsu, James R. Glass |
| 2020 | Learning Nearly Decomposable Value Functions Via Communication Minimization. Tonghan Wang, Jianhao Wang, Chongyi Zheng, Chongjie Zhang |
| 2020 | Learning Robust Representations via Multi-View Information Bottleneck. Marco Federici, Anjan Dutta, Patrick Forré, Nate Kushman, Zeynep Akata |
| 2020 | Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling. Yuping Luo, Huazhe Xu, Tengyu Ma |
| 2020 | Learning Space Partitions for Nearest Neighbor Search. Yihe Dong, Piotr Indyk, Ilya P. Razenshteyn, Tal Wagner |
| 2020 | Learning The Difference That Makes A Difference With Counterfactually-Augmented Data. Divyansh Kaushik, Eduard H. Hovy, Zachary Chase Lipton |
| 2020 | Learning To Explore Using Active Neural SLAM. Devendra Singh Chaplot, Dhiraj Gandhi, Saurabh Gupta, Abhinav Gupta, Ruslan Salakhutdinov |
| 2020 | Learning deep graph matching with channel-independent embedding and Hungarian attention. Tianshu Yu, Runzhong Wang, Junchi Yan, Baoxin Li |
| 2020 | Learning from Explanations with Neural Execution Tree. Ziqi Wang, Yujia Qin, Wenxuan Zhou, Jun Yan, Qinyuan Ye, Leonardo Neves, Zhiyuan Liu, Xiang Ren |
| 2020 | Learning from Rules Generalizing Labeled Exemplars. Abhijeet Awasthi, Sabyasachi Ghosh, Rasna Goyal, Sunita Sarawagi |
| 2020 | Learning from Unlabelled Videos Using Contrastive Predictive Neural 3D Mapping. Adam W. Harley, Shrinidhi Kowshika Lakshmikanth, Fangyu Li, Xian Zhou, Hsiao-Yu Fish Tung, Katerina Fragkiadaki |
| 2020 | Learning representations for binary-classification without backpropagation. Mathias Lechner |
| 2020 | Learning the Arrow of Time for Problems in Reinforcement Learning. Nasim Rahaman, Steffen Wolf, Anirudh Goyal, Roman Remme, Yoshua Bengio |
| 2020 | Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks. Haebeom Lee, Hayeon Lee, Donghyun Na, Saehoon Kim, Minseop Park, Eunho Yang, Sung Ju Hwang |
| 2020 | Learning to Control PDEs with Differentiable Physics. Philipp Holl, Nils Thuerey, Vladlen Koltun |
| 2020 | Learning to Coordinate Manipulation Skills via Skill Behavior Diversification. Youngwoon Lee, Jingyun Yang, Joseph J. Lim |
| 2020 | Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen Categories. Tiange Luo, Kaichun Mo, Zhiao Huang, Jiarui Xu, Siyu Hu, Liwei Wang, Hao Su |
| 2020 | Learning to Guide Random Search. Ozan Sener, Vladlen Koltun |
| 2020 | Learning to Learn by Zeroth-Order Oracle. Yangjun Ruan, Yuanhao Xiong, Sashank J. Reddi, Sanjiv Kumar, Cho-Jui Hsieh |
| 2020 | Learning to Link. Maria-Florina Balcan, Travis Dick, Manuel Lang |
| 2020 | Learning to Move with Affordance Maps. William Qi, Ravi Teja Mullapudi, Saurabh Gupta, Deva Ramanan |
| 2020 | Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees. Binghong Chen, Bo Dai, Qinjie Lin, Guo Ye, Han Liu, Le Song |
| 2020 | Learning to Represent Programs with Property Signatures. Augustus Odena, Charles Sutton |
| 2020 | Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering. Akari Asai, Kazuma Hashimoto, Hannaneh Hajishirzi, Richard Socher, Caiming Xiong |
| 2020 | Learning to solve the credit assignment problem. Benjamin James Lansdell, Prashanth Ravi Prakash, Konrad Paul Körding |
| 2020 | Learning transport cost from subset correspondence. Ruishan Liu, Akshay Balsubramani, James Zou |
| 2020 | Learning-Augmented Data Stream Algorithms. Tanqiu Jiang, Yi Li, Honghao Lin, Yisong Ruan, David P. Woodruff |
| 2020 | Linear Symmetric Quantization of Neural Networks for Low-precision Integer Hardware. Xiandong Zhao, Ying Wang, Xuyi Cai, Cheng Liu, Lei Zhang |
| 2020 | Lipschitz constant estimation of Neural Networks via sparse polynomial optimization. Fabian Latorre, Paul Rolland, Volkan Cevher |
| 2020 | Lite Transformer with Long-Short Range Attention. Zhanghao Wu, Zhijian Liu, Ji Lin, Yujun Lin, Song Han |
| 2020 | Locality and Compositionality in Zero-Shot Learning. Tristan Sylvain, Linda Petrini, R. Devon Hjelm |
| 2020 | Logic and the 2-Simplicial Transformer. James Clift, Dmitry Doryn, Daniel Murfet, James Wallbridge |
| 2020 | Lookahead: A Far-sighted Alternative of Magnitude-based Pruning. Sejun Park, Jaeho Lee, Sangwoo Mo, Jinwoo Shin |
| 2020 | Low-Resource Knowledge-Grounded Dialogue Generation. Xueliang Zhao, Wei Wu, Chongyang Tao, Can Xu, Dongyan Zhao, Rui Yan |
| 2020 | Low-dimensional statistical manifold embedding of directed graphs. Thorben Funke, Tian Guo, Alen Lancic, Nino Antulov-Fantulin |
| 2020 | MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius. Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, Liwei Wang |
| 2020 | MEMO: A Deep Network for Flexible Combination of Episodic Memories. Andrea Banino, Adrià Puigdomènech Badia, Raphael Köster, Martin J. Chadwick, Vinícius Flores Zambaldi, Demis Hassabis, Caswell Barry, Matthew M. Botvinick, Dharshan Kumaran, Charles Blundell |
| 2020 | MMA Training: Direct Input Space Margin Maximization through Adversarial Training. Gavin Weiguang Ding, Yash Sharma, Kry Yik Chau Lui, Ruitong Huang |
| 2020 | Making Efficient Use of Demonstrations to Solve Hard Exploration Problems. Çaglar Gülçehre, Tom Le Paine, Bobak Shahriari, Misha Denil, Matt Hoffman, Hubert Soyer, Richard Tanburn, Steven Kapturowski, Neil C. Rabinowitz, Duncan Williams, Gabriel Barth-Maron, Ziyu Wang, Nando de Freitas, Worlds Team |
| 2020 | Making Sense of Reinforcement Learning and Probabilistic Inference. Brendan O'Donoghue, Ian Osband, Catalin Ionescu |
| 2020 | Masked Based Unsupervised Content Transfer. Ron Mokady, Sagie Benaim, Lior Wolf, Amit Bermano |
| 2020 | Massively Multilingual Sparse Word Representations. Gábor Berend |
| 2020 | Mathematical Reasoning in Latent Space. Dennis Lee, Christian Szegedy, Markus N. Rabe, Sarah M. Loos, Kshitij Bansal |
| 2020 | Maximum Likelihood Constraint Inference for Inverse Reinforcement Learning. Dexter R. R. Scobee, S. Shankar Sastry |
| 2020 | Maxmin Q-learning: Controlling the Estimation Bias of Q-learning. Qingfeng Lan, Yangchen Pan, Alona Fyshe, Martha White |
| 2020 | Measuring Compositional Generalization: A Comprehensive Method on Realistic Data. Daniel Keysers, Nathanael Schärli, Nathan Scales, Hylke Buisman, Daniel Furrer, Sergii Kashubin, Nikola Momchev, Danila Sinopalnikov, Lukasz Stafiniak, Tibor Tihon, Dmitry Tsarkov, Xiao Wang, Marc van Zee, Olivier Bousquet |
| 2020 | Measuring and Improving the Use of Graph Information in Graph Neural Networks. Yifan Hou, Jie Zhang, James Cheng, Kaili Ma, Richard T. B. Ma, Hongzhi Chen, Ming-Chang Yang |
| 2020 | Measuring the Reliability of Reinforcement Learning Algorithms. Stephanie C. Y. Chan, Samuel Fishman, Anoop Korattikara, John F. Canny, Sergio Guadarrama |
| 2020 | Memory-Based Graph Networks. Amir Hosein Khas Ahmadi, Kaveh Hassani, Parsa Moradi, Leo Lee, Quaid Morris |
| 2020 | Meta Dropout: Learning to Perturb Latent Features for Generalization. Haebeom Lee, Taewook Nam, Eunho Yang, Sung Ju Hwang |
| 2020 | Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies. Sungryull Sohn, Hyunjae Woo, Jongwook Choi, Honglak Lee |
| 2020 | Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples. Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle |
| 2020 | Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization. Michael Volpp, Lukas P. Fröhlich, Kirsten Fischer, Andreas Doerr, Stefan Falkner, Frank Hutter, Christian Daniel |
| 2020 | Meta-Learning Deep Energy-Based Memory Models. Sergey Bartunov, Jack W. Rae, Simon Osindero, Timothy P. Lillicrap |
| 2020 | Meta-Learning with Warped Gradient Descent. Sebastian Flennerhag, Andrei A. Rusu, Razvan Pascanu, Francesco Visin, Hujun Yin, Raia Hadsell |
| 2020 | Meta-Learning without Memorization. Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine, Chelsea Finn |
| 2020 | Meta-Q-Learning. Rasool Fakoor, Pratik Chaudhari, Stefano Soatto, Alexander J. Smola |
| 2020 | Meta-learning curiosity algorithms. Ferran Alet, Martin F. Schneider, Tomás Lozano-Pérez, Leslie Pack Kaelbling |
| 2020 | MetaPix: Few-Shot Video Retargeting. Jessica Lee, Deva Ramanan, Rohit Girdhar |
| 2020 | Minimizing FLOPs to Learn Efficient Sparse Representations. Biswajit Paria, Chih-Kuan Yeh, Ian En-Hsu Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos |
| 2020 | Mirror-Generative Neural Machine Translation. Zaixiang Zheng, Hao Zhou, Shujian Huang, Lei Li, Xin-Yu Dai, Jiajun Chen |
| 2020 | Mixed Precision DNNs: All you need is a good parametrization. Stefan Uhlich, Lukas Mauch, Fabien Cardinaux, Kazuki Yoshiyama, Javier Alonso García, Stephen Tiedemann, Thomas Kemp, Akira Nakamura |
| 2020 | Mixed-curvature Variational Autoencoders. Ondrej Skopek, Octavian-Eugen Ganea, Gary Bécigneul |
| 2020 | Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models. Cheolhyoung Lee, Kyunghyun Cho, Wanmo Kang |
| 2020 | Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks. Tianyu Pang, Kun Xu, Jun Zhu |
| 2020 | Model Based Reinforcement Learning for Atari. Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H. Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, Henryk Michalewski |
| 2020 | Model-Augmented Actor-Critic: Backpropagating through Paths. Ignasi Clavera, Yao Fu, Pieter Abbeel |
| 2020 | Model-based reinforcement learning for biological sequence design. Christof Angermüller, David Dohan, David Belanger, Ramya Deshpande, Kevin Murphy, Lucy J. Colwell |
| 2020 | Mogrifier LSTM. Gábor Melis, Tomás Kociský, Phil Blunsom |
| 2020 | Monotonic Multihead Attention. Xutai Ma, Juan Miguel Pino, James Cross, Liezl Puzon, Jiatao Gu |
| 2020 | Multi-Agent Interactions Modeling with Correlated Policies. Minghuan Liu, Ming Zhou, Weinan Zhang, Yuzheng Zhuang, Jun Wang, Wulong Liu, Yong Yu |
| 2020 | Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells. Gengchen Mai, Krzysztof Janowicz, Bo Yan, Rui Zhu, Ling Cai, Ni Lao |
| 2020 | Multi-agent Reinforcement Learning for Networked System Control. Tianshu Chu, Sandeep Chinchali, Sachin Katti |
| 2020 | Multilingual Alignment of Contextual Word Representations. Steven Cao, Nikita Kitaev, Dan Klein |
| 2020 | Multiplicative Interactions and Where to Find Them. Siddhant M. Jayakumar, Wojciech M. Czarnecki, Jacob Menick, Jonathan Schwarz, Jack W. Rae, Simon Osindero, Yee Whye Teh, Tim Harley, Razvan Pascanu |
| 2020 | Mutual Information Gradient Estimation for Representation Learning. Liangjian Wen, Yiji Zhou, Lirong He, Mingyuan Zhou, Zenglin Xu |
| 2020 | Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification. Yixiao Ge, Dapeng Chen, Hongsheng Li |
| 2020 | N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, Yoshua Bengio |
| 2020 | NAS evaluation is frustratingly hard. Antoine Yang, Pedro M. Esperança, Fabio Maria Carlucci |
| 2020 | NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search. Arber Zela, Julien Siems, Frank Hutter |
| 2020 | NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search. Xuanyi Dong, Yi Yang |
| 2020 | Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks. Jiadong Lin, Chuanbiao Song, Kun He, Liwei Wang, John E. Hopcroft |
| 2020 | Network Deconvolution. Chengxi Ye, Matthew Evanusa, Hua He, Anton Mitrokhin, Tom Goldstein, James A. Yorke, Cornelia Fermüller, Yiannis Aloimonos |
| 2020 | Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning. Kimin Lee, Kibok Lee, Jinwoo Shin, Honglak Lee |
| 2020 | NeurQuRI: Neural Question Requirement Inspector for Answerability Prediction in Machine Reading Comprehension. Seohyun Back, Sai Chetan Chinthakindi, Akhil Kedia, Haejun Lee, Jaegul Choo |
| 2020 | Neural Arithmetic Units. Andreas Madsen, Alexander Rosenberg Johansen |
| 2020 | Neural Epitome Search for Architecture-Agnostic Network Compression. Daquan Zhou, Xiaojie Jin, Qibin Hou, Kaixin Wang, Jianchao Yang, Jiashi Feng |
| 2020 | Neural Execution of Graph Algorithms. Petar Velickovic, Rex Ying, Matilde Padovano, Raia Hadsell, Charles Blundell |
| 2020 | Neural Machine Translation with Universal Visual Representation. Zhuosheng Zhang, Kehai Chen, Rui Wang, Masao Utiyama, Eiichiro Sumita, Zuchao Li, Hai Zhao |
| 2020 | Neural Module Networks for Reasoning over Text. Nitish Gupta, Kevin Lin, Dan Roth, Sameer Singh, Matt Gardner |
| 2020 | Neural Network Branching for Neural Network Verification. Jingyue Lu, M. Pawan Kumar |
| 2020 | Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data. Sergei Popov, Stanislav Morozov, Artem Babenko |
| 2020 | Neural Outlier Rejection for Self-Supervised Keypoint Learning. Jiexiong Tang, Hanme Kim, Vitor Guizilini, Sudeep Pillai, Rares Ambrus |
| 2020 | Neural Policy Gradient Methods: Global Optimality and Rates of Convergence. Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang |
| 2020 | Neural Stored-program Memory. Hung Le, Truyen Tran, Svetha Venkatesh |
| 2020 | Neural Symbolic Reader: Scalable Integration of Distributed and Symbolic Representations for Reading Comprehension. Xinyun Chen, Chen Liang, Adams Wei Yu, Denny Zhou, Dawn Song, Quoc V. Le |
| 2020 | Neural Tangents: Fast and Easy Infinite Neural Networks in Python. Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, Samuel S. Schoenholz |
| 2020 | Neural Text Generation With Unlikelihood Training. Sean Welleck, Ilia Kulikov, Stephen Roller, Emily Dinan, Kyunghyun Cho, Jason Weston |
| 2020 | Neural tangent kernels, transportation mappings, and universal approximation. Ziwei Ji, Matus Telgarsky, Ruicheng Xian |
| 2020 | Never Give Up: Learning Directed Exploration Strategies. Adrià Puigdomènech Badia, Pablo Sprechmann, Alex Vitvitskyi, Zhaohan Daniel Guo, Bilal Piot, Steven Kapturowski, Olivier Tieleman, Martín Arjovsky, Alexander Pritzel, Andrew Bolt, Charles Blundell |
| 2020 | Non-Autoregressive Dialog State Tracking. Hung Le, Richard Socher, Steven C. H. Hoi |
| 2020 | Novelty Detection Via Blurring. Sung-Ik Choi, Sae-Young Chung |
| 2020 | Oblique Decision Trees from Derivatives of ReLU Networks. Guang-He Lee, Tommi S. Jaakkola |
| 2020 | Observational Overfitting in Reinforcement Learning. Xingyou Song, Yiding Jiang, Stephen Tu, Yilun Du, Behnam Neyshabur |
| 2020 | On Bonus Based Exploration Methods In The Arcade Learning Environment. Adrien Ali Taïga, William Fedus, Marlos C. Machado, Aaron C. Courville, Marc G. Bellemare |
| 2020 | On Computation and Generalization of Generative Adversarial Imitation Learning. Minshuo Chen, Yizhou Wang, Tianyi Liu, Zhuoran Yang, Xingguo Li, Zhaoran Wang, Tuo Zhao |
| 2020 | On Generalization Error Bounds of Noisy Gradient Methods for Non-Convex Learning. Jian Li, Xuanyuan Luo, Mingda Qiao |
| 2020 | On Identifiability in Transformers. Gino Brunner, Yang Liu, Damian Pascual, Oliver Richter, Massimiliano Ciaramita, Roger Wattenhofer |
| 2020 | On Mutual Information Maximization for Representation Learning. Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, Mario Lucic |
| 2020 | On Robustness of Neural Ordinary Differential Equations. Hanshu Yan, Jiawei Du, Vincent Y. F. Tan, Jiashi Feng |
| 2020 | On Solving Minimax Optimization Locally: A Follow-the-Ridge Approach. Yuanhao Wang, Guodong Zhang, Jimmy Ba |
| 2020 | On Universal Equivariant Set Networks. Nimrod Segol, Yaron Lipman |
| 2020 | On the "steerability" of generative adversarial networks. Ali Jahanian, Lucy Chai, Phillip Isola |
| 2020 | On the Convergence of FedAvg on Non-IID Data. Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, Zhihua Zhang |
| 2020 | On the Equivalence between Positional Node Embeddings and Structural Graph Representations. Balasubramaniam Srinivasan, Bruno Ribeiro |
| 2020 | On the Global Convergence of Training Deep Linear ResNets. Difan Zou, Philip M. Long, Quanquan Gu |
| 2020 | On the Need for Topology-Aware Generative Models for Manifold-Based Defenses. Uyeong Jang, Susmit Jha, Somesh Jha |
| 2020 | On the Relationship between Self-Attention and Convolutional Layers. Jean-Baptiste Cordonnier, Andreas Loukas, Martin Jaggi |
| 2020 | On the Variance of the Adaptive Learning Rate and Beyond. Liyuan Liu, Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, Jiawei Han |
| 2020 | On the Weaknesses of Reinforcement Learning for Neural Machine Translation. Leshem Choshen, Lior Fox, Zohar Aizenbud, Omri Abend |
| 2020 | On the interaction between supervision and self-play in emergent communication. Ryan Lowe, Abhinav Gupta, Jakob N. Foerster, Douwe Kiela, Joelle Pineau |
| 2020 | Once-for-All: Train One Network and Specialize it for Efficient Deployment. Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han |
| 2020 | One-Shot Pruning of Recurrent Neural Networks by Jacobian Spectrum Evaluation. Matthew Shunshi Zhang, Bradly C. Stadie |
| 2020 | Online and stochastic optimization beyond Lipschitz continuity: A Riemannian approach. Kimon Antonakopoulos, Elena Veronica Belmega, Panayotis Mertikopoulos |
| 2020 | Optimal Strategies Against Generative Attacks. Roy Mor, Erez Peterfreund, Matan Gavish, Amir Globerson |
| 2020 | Optimistic Exploration even with a Pessimistic Initialisation. Tabish Rashid, Bei Peng, Wendelin Boehmer, Shimon Whiteson |
| 2020 | Option Discovery using Deep Skill Chaining. Akhil Bagaria, George Konidaris |
| 2020 | Order Learning and Its Application to Age Estimation. Kyungsun Lim, Nyeong-Ho Shin, Young-Yoon Lee, Chang-Su Kim |
| 2020 | Overlearning Reveals Sensitive Attributes. Congzheng Song, Vitaly Shmatikov |
| 2020 | PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction. Sangdon Park, Osbert Bastani, Nikolai Matni, Insup Lee |
| 2020 | PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search. Yuhui Xu, Lingxi Xie, Xiaopeng Zhang, Xin Chen, Guo-Jun Qi, Qi Tian, Hongkai Xiong |
| 2020 | PCMC-Net: Feature-based Pairwise Choice Markov Chains. Alix Lhéritier |
| 2020 | Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks. Alejandro Molina, Patrick Schramowski, Kristian Kersting |
| 2020 | PairNorm: Tackling Oversmoothing in GNNs. Lingxiao Zhao, Leman Akoglu |
| 2020 | Pay Attention to Features, Transfer Learn Faster CNNs. Kafeng Wang, Xitong Gao, Yiren Zhao, Xingjian Li, Dejing Dou, Cheng-Zhong Xu |
| 2020 | Permutation Equivariant Models for Compositional Generalization in Language. Jonathan Gordon, David Lopez-Paz, Marco Baroni, Diane Bouchacourt |
| 2020 | Phase Transitions for the Information Bottleneck in Representation Learning. Tailin Wu, Ian S. Fischer |
| 2020 | Physics-as-Inverse-Graphics: Unsupervised Physical Parameter Estimation from Video. Miguel Jaques, Michael Burke, Timothy M. Hospedales |
| 2020 | Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics. Sungyong Seo, Chuizheng Meng, Yan Liu |
| 2020 | Picking Winning Tickets Before Training by Preserving Gradient Flow. Chaoqi Wang, Guodong Zhang, Roger B. Grosse |
| 2020 | Piecewise linear activations substantially shape the loss surfaces of neural networks. Fengxiang He, Bohan Wang, Dacheng Tao |
| 2020 | Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning. Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, Dmitry P. Vetrov |
| 2020 | Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP. Haonan Yu, Sergey Edunov, Yuandong Tian, Ari S. Morcos |
| 2020 | Plug and Play Language Models: A Simple Approach to Controlled Text Generation. Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, Rosanne Liu |
| 2020 | Poly-encoders: Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring. Samuel Humeau, Kurt Shuster, Marie-Anne Lachaux, Jason Weston |
| 2020 | Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks. Ziwei Ji, Matus Telgarsky |
| 2020 | Population-Guided Parallel Policy Search for Reinforcement Learning. Whiyoung Jung, Giseung Park, Youngchul Sung |
| 2020 | Posterior sampling for multi-agent reinforcement learning: solving extensive games with imperfect information. Yichi Zhou, Jialian Li, Jun Zhu |
| 2020 | Pre-training Tasks for Embedding-based Large-scale Retrieval. Wei-Cheng Chang, Felix X. Yu, Yin-Wen Chang, Yiming Yang, Sanjiv Kumar |
| 2020 | Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations. Yichi Zhang, Ritchie Zhao, Weizhe Hua, Nayun Xu, G. Edward Suh, Zhiru Zhang |
| 2020 | Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks. Tribhuvanesh Orekondy, Bernt Schiele, Mario Fritz |
| 2020 | Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control. Nir Levine, Yinlam Chow, Rui Shu, Ang Li, Mohammad Ghavamzadeh, Hung Bui |
| 2020 | Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model. Wenhan Xiong, Jingfei Du, William Yang Wang, Veselin Stoyanov |
| 2020 | Principled Weight Initialization for Hypernetworks. Oscar Chang, Lampros Flokas, Hod Lipson |
| 2020 | Probabilistic Connection Importance Inference and Lossless Compression of Deep Neural Networks. Xin Xing, Long Sha, Pengyu Hong, Zuofeng Shang, Jun S. Liu |
| 2020 | Probability Calibration for Knowledge Graph Embedding Models. Pedro Tabacof, Luca Costabello |
| 2020 | Program Guided Agent. Shao-Hua Sun, Te-Lin Wu, Joseph J. Lim |
| 2020 | Progressive Learning and Disentanglement of Hierarchical Representations. Zhiyuan Li, Jaideep Vitthal Murkute, Prashnna Kumar Gyawali, Linwei Wang |
| 2020 | Progressive Memory Banks for Incremental Domain Adaptation. Nabiha Asghar, Lili Mou, Kira A. Selby, Kevin D. Pantasdo, Pascal Poupart, Xin Jiang |
| 2020 | Projection-Based Constrained Policy Optimization. Tsung-Yen Yang, Justinian Rosca, Karthik Narasimhan, Peter J. Ramadge |
| 2020 | Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks. Wei Hu, Lechao Xiao, Jeffrey Pennington |
| 2020 | Provable Filter Pruning for Efficient Neural Networks. Lucas Liebenwein, Cenk Baykal, Harry Lang, Dan Feldman, Daniela Rus |
| 2020 | Provable robustness against all adversarial $l_p$-perturbations for $p\geq 1$. Francesco Croce, Matthias Hein |
| 2020 | ProxSGD: Training Structured Neural Networks under Regularization and Constraints. Yang Yang, Yaxiong Yuan, Avraam Chatzimichailidis, Ruud J. G. van Sloun, Lei Lei, Symeon Chatzinotas |
| 2020 | Pruned Graph Scattering Transforms. Vassilis N. Ioannidis, Siheng Chen, Georgios B. Giannakis |
| 2020 | Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. Yurong You, Yan Wang, Wei-Lun Chao, Divyansh Garg, Geoff Pleiss, Bharath Hariharan, Mark E. Campbell, Kilian Q. Weinberger |
| 2020 | Pure and Spurious Critical Points: a Geometric Study of Linear Networks. Matthew Trager, Kathlén Kohn, Joan Bruna |
| 2020 | Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP. Yuanhao Wang, Kefan Dong, Xiaoyu Chen, Liwei Wang |
| 2020 | Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel. Xin Qiu, Elliot Meyerson, Risto Miikkulainen |
| 2020 | Quantifying the Cost of Reliable Photo Authentication via High-Performance Learned Lossy Representations. Pawel Korus, Nasir D. Memon |
| 2020 | Quantum Algorithms for Deep Convolutional Neural Networks. Iordanis Kerenidis, Jonas Landman, Anupam Prakash |
| 2020 | Query-efficient Meta Attack to Deep Neural Networks. Jiawei Du, Hu Zhang, Joey Tianyi Zhou, Yi Yang, Jiashi Feng |
| 2020 | Query2box: Reasoning over Knowledge Graphs in Vector Space Using Box Embeddings. Hongyu Ren, Weihua Hu, Jure Leskovec |
| 2020 | RGBD-GAN: Unsupervised 3D Representation Learning From Natural Image Datasets via RGBD Image Synthesis. Atsuhiro Noguchi, Tatsuya Harada |
| 2020 | RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments. Roberta Raileanu, Tim Rocktäschel |
| 2020 | RNA Secondary Structure Prediction By Learning Unrolled Algorithms. Xinshi Chen, Yu Li, Ramzan Umarov, Xin Gao, Le Song |
| 2020 | RNNs Incrementally Evolving on an Equilibrium Manifold: A Panacea for Vanishing and Exploding Gradients? Anil Kag, Ziming Zhang, Venkatesh Saligrama |
| 2020 | RTFM: Generalising to New Environment Dynamics via Reading. Victor Zhong, Tim Rocktäschel, Edward Grefenstette |
| 2020 | RaCT: Toward Amortized Ranking-Critical Training For Collaborative Filtering. Sam Lobel, Chunyuan Li, Jianfeng Gao, Lawrence Carin |
| 2020 | RaPP: Novelty Detection with Reconstruction along Projection Pathway. Ki Hyun Kim, Sangwoo Shim, Yongsub Lim, Jongseob Jeon, Jeongwoo Choi, Byungchan Kim, Andre S. Yoon |
| 2020 | Ranking Policy Gradient. Kaixiang Lin, Jiayu Zhou |
| 2020 | Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML. Aniruddh Raghu, Maithra Raghu, Samy Bengio, Oriol Vinyals |
| 2020 | ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning. Weihao Yu, Zihang Jiang, Yanfei Dong, Jiashi Feng |
| 2020 | ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring. David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, Colin Raffel |
| 2020 | Real or Not Real, that is the Question. Yuanbo Xiangli, Yubin Deng, Bo Dai, Chen Change Loy, Dahua Lin |
| 2020 | Reanalysis of Variance Reduced Temporal Difference Learning. Tengyu Xu, Zhe Wang, Yi Zhou, Yingbin Liang |
| 2020 | Reconstructing continuous distributions of 3D protein structure from cryo-EM images. Ellen D. Zhong, Tristan Bepler, Joseph H. Davis, Bonnie Berger |
| 2020 | Recurrent neural circuits for contour detection. Drew Linsley, Junkyung Kim, Alekh Ashok, Thomas Serre |
| 2020 | Reducing Transformer Depth on Demand with Structured Dropout. Angela Fan, Edouard Grave, Armand Joulin |
| 2020 | Reformer: The Efficient Transformer. Nikita Kitaev, Lukasz Kaiser, Anselm Levskaya |
| 2020 | Regularizing activations in neural networks via distribution matching with the Wasserstein metric. Taejong Joo, Donggu Kang, Byunghoon Kim |
| 2020 | Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs. Aditya Paliwal, Felix Gimeno, Vinod Nair, Yujia Li, Miles Lubin, Pushmeet Kohli, Oriol Vinyals |
| 2020 | Reinforced active learning for image segmentation. Arantxa Casanova, Pedro O. Pinheiro, Negar Rostamzadeh, Christopher J. Pal |
| 2020 | Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation. Yu Chen, Lingfei Wu, Mohammed J. Zaki |
| 2020 | Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives. Anirudh Goyal, Shagun Sodhani, Jonathan Binas, Xue Bin Peng, Sergey Levine, Yoshua Bengio |
| 2020 | Relational State-Space Model for Stochastic Multi-Object Systems. Fan Yang, Ling Chen, Fan Zhou, Yusong Gao, Wei Cao |
| 2020 | Residual Energy-Based Models for Text Generation. Yuntian Deng, Anton Bakhtin, Myle Ott, Arthur Szlam, Marc'Aurelio Ranzato |
| 2020 | Restricting the Flow: Information Bottlenecks for Attribution. Karl Schulz, Leon Sixt, Federico Tombari, Tim Landgraf |
| 2020 | Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness. Tianyu Pang, Kun Xu, Yinpeng Dong, Chao Du, Ning Chen, Jun Zhu |
| 2020 | Rethinking the Hyperparameters for Fine-tuning. Hao Li, Pratik Chaudhari, Hao Yang, Michael Lam, Avinash Ravichandran, Rahul Bhotika, Stefano Soatto |
| 2020 | Revisiting Self-Training for Neural Sequence Generation. Junxian He, Jiatao Gu, Jiajun Shen, Marc'Aurelio Ranzato |
| 2020 | Ridge Regression: Structure, Cross-Validation, and Sketching. Sifan Liu, Edgar Dobriban |
| 2020 | Robust And Interpretable Blind Image Denoising Via Bias-Free Convolutional Neural Networks. Sreyas Mohan, Zahra Kadkhodaie, Eero P. Simoncelli, Carlos Fernandez-Granda |
| 2020 | Robust Local Features for Improving the Generalization of Adversarial Training. Chuanbiao Song, Kun He, Jiadong Lin, Liwei Wang, John E. Hopcroft |
| 2020 | Robust Reinforcement Learning for Continuous Control with Model Misspecification. Daniel J. Mankowitz, Nir Levine, Rae Jeong, Abbas Abdolmaleki, Jost Tobias Springenberg, Yuanyuan Shi, Jackie Kay, Todd Hester, Timothy A. Mann, Martin A. Riedmiller |
| 2020 | Robust Subspace Recovery Layer for Unsupervised Anomaly Detection. Chieh-Hsin Lai, Dongmian Zou, Gilad Lerman |
| 2020 | Robust anomaly detection and backdoor attack detection via differential privacy. Min Du, Ruoxi Jia, Dawn Song |
| 2020 | Robust training with ensemble consensus. Jisoo Lee, Sae-Young Chung |
| 2020 | Robustness Verification for Transformers. Zhouxing Shi, Huan Zhang, Kai-Wei Chang, Minlie Huang, Cho-Jui Hsieh |
| 2020 | Rotation-invariant clustering of neuronal responses in primary visual cortex. Ivan Ustyuzhaninov, Santiago A. Cadena, Emmanouil Froudarakis, Paul G. Fahey, Edgar Y. Walker, Erick Cobos, Jacob Reimer, Fabian H. Sinz, Andreas S. Tolias, Matthias Bethge, Alexander S. Ecker |
| 2020 | Rényi Fair Inference. Sina Baharlouei, Maher Nouiehed, Ahmad Beirami, Meisam Razaviyayn |
| 2020 | SAdam: A Variant of Adam for Strongly Convex Functions. Guanghui Wang, Shiyin Lu, Quan Cheng, Weiwei Tu, Lijun Zhang |
| 2020 | SCALOR: Generative World Models with Scalable Object Representations. Jindong Jiang, Sepehr Janghorbani, Gerard de Melo, Sungjin Ahn |
| 2020 | SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Lasse Espeholt, Raphaël Marinier, Piotr Stanczyk, Ke Wang, Marcin Michalski |
| 2020 | SELF: Learning to Filter Noisy Labels with Self-Ensembling. Duc Tam Nguyen, Chaithanya Kumar Mummadi, Thi-Phuong-Nhung Ngo, Thi Hoai Phuong Nguyen, Laura Beggel, Thomas Brox |
| 2020 | SNODE: Spectral Discretization of Neural ODEs for System Identification. Alessio Quaglino, Marco Gallieri, Jonathan Masci, Jan Koutník |
| 2020 | SNOW: Subscribing to Knowledge via Channel Pooling for Transfer & Lifelong Learning of Convolutional Neural Networks. Chungkuk Yoo, Bumsoo Kang, Minsik Cho |
| 2020 | SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition. Zhixuan Lin, Yi-Fu Wu, Skand Vishwanath Peri, Weihao Sun, Gautam Singh, Fei Deng, Jindong Jiang, Sungjin Ahn |
| 2020 | SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards. Siddharth Reddy, Anca D. Dragan, Sergey Levine |
| 2020 | SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models. Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen |
| 2020 | SVQN: Sequential Variational Soft Q-Learning Networks. Shiyu Huang, Hang Su, Jun Zhu, Ting Chen |
| 2020 | Sample Efficient Policy Gradient Methods with Recursive Variance Reduction. Pan Xu, Felicia Gao, Quanquan Gu |
| 2020 | Sampling-Free Learning of Bayesian Quantized Neural Networks. Jiahao Su, Milan Cvitkovic, Furong Huang |
| 2020 | Scalable Model Compression by Entropy Penalized Reparameterization. Deniz Oktay, Johannes Ballé, Saurabh Singh, Abhinav Shrivastava |
| 2020 | Scalable Neural Methods for Reasoning With a Symbolic Knowledge Base. William W. Cohen, Haitian Sun, R. Alex Hofer, Matthew Siegler |
| 2020 | Scalable and Order-robust Continual Learning with Additive Parameter Decomposition. Jaehong Yoon, Saehoon Kim, Eunho Yang, Sung Ju Hwang |
| 2020 | Scale-Equivariant Steerable Networks. Ivan Sosnovik, Michal Szmaja, Arnold W. M. Smeulders |
| 2020 | Scaling Autoregressive Video Models. Dirk Weissenborn, Oscar Täckström, Jakob Uszkoreit |
| 2020 | Selection via Proxy: Efficient Data Selection for Deep Learning. Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, Matei Zaharia |
| 2020 | Self-Adversarial Learning with Comparative Discrimination for Text Generation. Wangchunshu Zhou, Tao Ge, Ke Xu, Furu Wei, Ming Zhou |
| 2020 | Self-Supervised Learning of Appliance Usage. Chen-Yu Hsu, Abbas Zeitoun, Guang-He Lee, Dina Katabi, Tommi S. Jaakkola |
| 2020 | Self-labelling via simultaneous clustering and representation learning. Yuki Markus Asano, Christian Rupprecht, Andrea Vedaldi |
| 2020 | Semantically-Guided Representation Learning for Self-Supervised Monocular Depth. Vitor Guizilini, Rui Hou, Jie Li, Rares Ambrus, Adrien Gaidon |
| 2020 | Semi-Supervised Generative Modeling for Controllable Speech Synthesis. Raza Habib, Soroosh Mariooryad, Matt Shannon, Eric Battenberg, R. J. Skerry-Ryan, Daisy Stanton, David Kao, Tom Bagby |
| 2020 | Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue. Byeongchang Kim, Jaewoo Ahn, Gunhee Kim |
| 2020 | Sharing Knowledge in Multi-Task Deep Reinforcement Learning. Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters |
| 2020 | Shifted and Squeezed 8-bit Floating Point format for Low-Precision Training of Deep Neural Networks. Léopold Cambier, Anahita Bhiwandiwalla, Ting Gong, Oguz H. Elibol, Mehran Nekuii, Hanlin Tang |
| 2020 | Short and Sparse Deconvolution - A Geometric Approach. Yenson Lau, Qing Qu, Han-Wen Kuo, Pengcheng Zhou, Yuqian Zhang, John Wright |
| 2020 | Sign Bits Are All You Need for Black-Box Attacks. Abdullah Al-Dujaili, Una-May O'Reilly |
| 2020 | Sign-OPT: A Query-Efficient Hard-label Adversarial Attack. Minhao Cheng, Simranjit Singh, Patrick H. Chen, Pin-Yu Chen, Sijia Liu, Cho-Jui Hsieh |
| 2020 | Simple and Effective Regularization Methods for Training on Noisily Labeled Data with Generalization Guarantee. Wei Hu, Zhiyuan Li, Dingli Yu |
| 2020 | Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning. Hengyuan Hu, Jakob N. Foerster |
| 2020 | Single Episode Policy Transfer in Reinforcement Learning. Jiachen Yang, Brenden K. Petersen, Hongyuan Zha, Daniel M. Faissol |
| 2020 | Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets. Dongxian Wu, Yisen Wang, Shu-Tao Xia, James Bailey, Xingjun Ma |
| 2020 | Sliced Cramer Synaptic Consolidation for Preserving Deeply Learned Representations. Soheil Kolouri, Nicholas A. Ketz, Andrea Soltoggio, Praveen K. Pilly |
| 2020 | SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum. Jianyu Wang, Vinayak Tantia, Nicolas Ballas, Michael G. Rabbat |
| 2020 | Smooth markets: A basic mechanism for organizing gradient-based learners. David Balduzzi, Wojciech M. Czarnecki, Tom Anthony, Ian Gemp, Edward Hughes, Joel Z. Leibo, Georgios Piliouras, Thore Graepel |
| 2020 | Smoothness and Stability in GANs. Casey Chu, Kentaro Minami, Kenji Fukumizu |
| 2020 | Span Recovery for Deep Neural Networks with Applications to Input Obfuscation. Rajesh Jayaram, David P. Woodruff, Qiuyi Zhang |
| 2020 | Sparse Coding with Gated Learned ISTA. Kailun Wu, Yiwen Guo, Ziang Li, Changshui Zhang |
| 2020 | Spectral Embedding of Regularized Block Models. Nathan de Lara, Thomas Bonald |
| 2020 | Spike-based causal inference for weight alignment. Jordan Guerguiev, Konrad P. Körding, Blake A. Richards |
| 2020 | SpikeGrad: An ANN-equivalent Computation Model for Implementing Backpropagation with Spikes. Johannes C. Thiele, Olivier Bichler, Antoine Dupret |
| 2020 | Stable Rank Normalization for Improved Generalization in Neural Networks and GANs. Amartya Sanyal, Philip H. S. Torr, Puneet K. Dokania |
| 2020 | State Alignment-based Imitation Learning. Fangchen Liu, Zhan Ling, Tongzhou Mu, Hao Su |
| 2020 | State-only Imitation with Transition Dynamics Mismatch. Tanmay Gangwani, Jian Peng |
| 2020 | Stochastic AUC Maximization with Deep Neural Networks. Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang |
| 2020 | Stochastic Conditional Generative Networks with Basis Decomposition. Ze Wang, Xiuyuan Cheng, Guillermo Sapiro, Qiang Qiu |
| 2020 | Stochastic Weight Averaging in Parallel: Large-Batch Training That Generalizes Well. Vipul Gupta, Santiago Akle Serrano, Dennis DeCoste |
| 2020 | Strategies for Pre-training Graph Neural Networks. Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay S. Pande, Jure Leskovec |
| 2020 | StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding. Wei Wang, Bin Bi, Ming Yan, Chen Wu, Jiangnan Xia, Zuyi Bao, Liwei Peng, Luo Si |
| 2020 | StructPool: Structured Graph Pooling via Conditional Random Fields. Hao Yuan, Shuiwang Ji |
| 2020 | Structured Object-Aware Physics Prediction for Video Modeling and Planning. Jannik Kossen, Karl Stelzner, Marcel Hussing, Claas Voelcker, Kristian Kersting |
| 2020 | Sub-policy Adaptation for Hierarchical Reinforcement Learning. Alexander C. Li, Carlos Florensa, Ignasi Clavera, Pieter Abbeel |
| 2020 | Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control. Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty |
| 2020 | Symplectic Recurrent Neural Networks. Zhengdao Chen, Jianyu Zhang, Martín Arjovsky, Léon Bottou |
| 2020 | Synthesizing Programmatic Policies that Inductively Generalize. Jeevana Priya Inala, Osbert Bastani, Zenna Tavares, Armando Solar-Lezama |
| 2020 | TabFact: A Large-scale Dataset for Table-based Fact Verification. Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou, William Yang Wang |
| 2020 | Target-Embedding Autoencoders for Supervised Representation Learning. Daniel Jarrett, Mihaela van der Schaar |
| 2020 | Tensor Decompositions for Temporal Knowledge Base Completion. Timothée Lacroix, Guillaume Obozinski, Nicolas Usunier |
| 2020 | The Break-Even Point on Optimization Trajectories of Deep Neural Networks. Stanislaw Jastrzebski, Maciej Szymczak, Stanislav Fort, Devansh Arpit, Jacek Tabor, Kyunghyun Cho, Krzysztof J. Geras |
| 2020 | The Curious Case of Neural Text Degeneration. Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, Yejin Choi |
| 2020 | The Early Phase of Neural Network Training. Jonathan Frankle, David J. Schwab, Ari S. Morcos |
| 2020 | The Gambler's Problem and Beyond. Baoxiang Wang, Shuai Li, Jiajin Li, Siu On Chan |
| 2020 | The Implicit Bias of Depth: How Incremental Learning Drives Generalization. Daniel Gissin, Shai Shalev-Shwartz, Amit Daniely |
| 2020 | The Ingredients of Real World Robotic Reinforcement Learning. Henry Zhu, Justin Yu, Abhishek Gupta, Dhruv Shah, Kristian Hartikainen, Avi Singh, Vikash Kumar, Sergey Levine |
| 2020 | The Local Elasticity of Neural Networks. Hangfeng He, Weijie J. Su |
| 2020 | The Logical Expressiveness of Graph Neural Networks. Pablo Barceló, Egor V. Kostylev, Mikaël Monet, Jorge Pérez, Juan L. Reutter, Juan Pablo Silva |
| 2020 | The Shape of Data: Intrinsic Distance for Data Distributions. Anton Tsitsulin, Marina Munkhoeva, Davide Mottin, Panagiotis Karras, Alexander M. Bronstein, Ivan V. Oseledets, Emmanuel Müller |
| 2020 | The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget. Anirudh Goyal, Yoshua Bengio, Matthew M. Botvinick, Sergey Levine |
| 2020 | The asymptotic spectrum of the Hessian of DNN throughout training. Arthur Jacot, Franck Gabriel, Clément Hongler |
| 2020 | The intriguing role of module criticality in the generalization of deep networks. Niladri S. Chatterji, Behnam Neyshabur, Hanie Sedghi |
| 2020 | Theory and Evaluation Metrics for Learning Disentangled Representations. Kien Do, Truyen Tran |
| 2020 | Thieves on Sesame Street! Model Extraction of BERT-based APIs. Kalpesh Krishna, Gaurav Singh Tomar, Ankur P. Parikh, Nicolas Papernot, Mohit Iyyer |
| 2020 | Thinking While Moving: Deep Reinforcement Learning with Concurrent Control. Ted Xiao, Eric Jang, Dmitry Kalashnikov, Sergey Levine, Julian Ibarz, Karol Hausman, Alexander Herzog |
| 2020 | To Relieve Your Headache of Training an MRF, Take AdVIL. Chongxuan Li, Chao Du, Kun Xu, Max Welling, Jun Zhu, Bo Zhang |
| 2020 | Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control. Tsui-Wei Weng, Krishnamurthy (Dj) Dvijotham, Jonathan Uesato, Kai Xiao, Sven Gowal, Robert Stanforth, Pushmeet Kohli |
| 2020 | Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets. Mingrui Liu, Youssef Mroueh, Jerret Ross, Wei Zhang, Xiaodong Cui, Payel Das, Tianbao Yang |
| 2020 | Towards Fast Adaptation of Neural Architectures with Meta Learning. Dongze Lian, Yin Zheng, Yintao Xu, Yanxiong Lu, Leyu Lin, Peilin Zhao, Junzhou Huang, Shenghua Gao |
| 2020 | Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Models. Xisen Jin, Zhongyu Wei, Junyi Du, Xiangyang Xue, Xiang Ren |
| 2020 | Towards Stabilizing Batch Statistics in Backward Propagation of Batch Normalization. Junjie Yan, Ruosi Wan, Xiangyu Zhang, Wei Zhang, Yichen Wei, Jian Sun |
| 2020 | Towards Stable and Efficient Training of Verifiably Robust Neural Networks. Huan Zhang, Hongge Chen, Chaowei Xiao, Sven Gowal, Robert Stanforth, Bo Li, Duane S. Boning, Cho-Jui Hsieh |
| 2020 | Towards Verified Robustness under Text Deletion Interventions. Johannes Welbl, Po-Sen Huang, Robert Stanforth, Sven Gowal, Krishnamurthy (Dj) Dvijotham, Martin Szummer, Pushmeet Kohli |
| 2020 | Towards a Deep Network Architecture for Structured Smoothness. Haroun Habeeb, Oluwasanmi Koyejo |
| 2020 | Towards neural networks that provably know when they don't know. Alexander Meinke, Matthias Hein |
| 2020 | Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators. Daniel Stoller, Sebastian Ewert, Simon Dixon |
| 2020 | Training Recurrent Neural Networks Online by Learning Explicit State Variables. Somjit Nath, Vincent Liu, Alan Chan, Xin Li, Adam White, Martha White |
| 2020 | Training binary neural networks with real-to-binary convolutions. Brais Martínez, Jing Yang, Adrian Bulat, Georgios Tzimiropoulos |
| 2020 | Training individually fair ML models with sensitive subspace robustness. Mikhail Yurochkin, Amanda Bower, Yuekai Sun |
| 2020 | Tranquil Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds. Lukas Prantl, Nuttapong Chentanez, Stefan Jeschke, Nils Thuerey |
| 2020 | Transferable Perturbations of Deep Feature Distributions. Nathan Inkawhich, Kevin J. Liang, Lawrence Carin, Yiran Chen |
| 2020 | Transferring Optimality Across Data Distributions via Homotopy Methods. Matilde Gargiani, Andrea Zanelli, Quoc Tran-Dinh, Moritz Diehl, Frank Hutter |
| 2020 | Transformer-XH: Multi-Evidence Reasoning with eXtra Hop Attention. Chen Zhao, Chenyan Xiong, Corby Rosset, Xia Song, Paul N. Bennett, Saurabh Tiwary |
| 2020 | Tree-Structured Attention with Hierarchical Accumulation. Xuan-Phi Nguyen, Shafiq R. Joty, Steven C. H. Hoi, Richard Socher |
| 2020 | Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference. Ting-Kuei Hu, Tianlong Chen, Haotao Wang, Zhangyang Wang |
| 2020 | Truth or backpropaganda? An empirical investigation of deep learning theory. Micah Goldblum, Jonas Geiping, Avi Schwarzschild, Michael Moeller, Tom Goldstein |
| 2020 | U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation. Junho Kim, Minjae Kim, Hyeonwoo Kang, Kwanghee Lee |
| 2020 | Unbiased Contrastive Divergence Algorithm for Training Energy-Based Latent Variable Models. Yixuan Qiu, Lingsong Zhang, Xiao Wang |
| 2020 | Uncertainty-guided Continual Learning with Bayesian Neural Networks. Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach |
| 2020 | Understanding Architectures Learnt by Cell-based Neural Architecture Search. Yao Shu, Wei Wang, Shaofeng Cai |
| 2020 | Understanding Generalization in Recurrent Neural Networks. Zhuozhuo Tu, Fengxiang He, Dacheng Tao |
| 2020 | Understanding Knowledge Distillation in Non-autoregressive Machine Translation. Chunting Zhou, Jiatao Gu, Graham Neubig |
| 2020 | Understanding Why Neural Networks Generalize Well Through GSNR of Parameters. Jinlong Liu, Yunzhi Bai, Guoqing Jiang, Ting Chen, Huayan Wang |
| 2020 | Understanding and Improving Information Transfer in Multi-Task Learning. Sen Wu, Hongyang R. Zhang, Christopher Ré |
| 2020 | Understanding and Robustifying Differentiable Architecture Search. Arber Zela, Thomas Elsken, Tonmoy Saikia, Yassine Marrakchi, Thomas Brox, Frank Hutter |
| 2020 | Understanding l4-based Dictionary Learning: Interpretation, Stability, and Robustness. Yuexiang Zhai, Hermish Mehta, Zhengyuan Zhou, Yi Ma |
| 2020 | Understanding the Limitations of Conditional Generative Models. Ethan Fetaya, Jörn-Henrik Jacobsen, Will Grathwohl, Richard S. Zemel |
| 2020 | Understanding the Limitations of Variational Mutual Information Estimators. Jiaming Song, Stefano Ermon |
| 2020 | Universal Approximation with Certified Networks. Maximilian Baader, Matthew Mirman, Martin T. Vechev |
| 2020 | Unpaired Point Cloud Completion on Real Scans using Adversarial Training. Xuelin Chen, Baoquan Chen, Niloy J. Mitra |
| 2020 | Unrestricted Adversarial Examples via Semantic Manipulation. Anand Bhattad, Min Jin Chong, Kaizhao Liang, Bo Li, David A. Forsyth |
| 2020 | Unsupervised Clustering using Pseudo-semi-supervised Learning. Divam Gupta, Ramachandran Ramjee, Nipun Kwatra, Muthian Sivathanu |
| 2020 | Unsupervised Model Selection for Variational Disentangled Representation Learning. Sunny Duan, Loic Matthey, Andre Saraiva, Nick Watters, Chris Burgess, Alexander Lerchner, Irina Higgins |
| 2020 | V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control. H. Francis Song, Abbas Abdolmaleki, Jost Tobias Springenberg, Aidan Clark, Hubert Soyer, Jack W. Rae, Seb Noury, Arun Ahuja, Siqi Liu, Dhruva Tirumala, Nicolas Heess, Dan Belov, Martin A. Riedmiller, Matthew M. Botvinick |
| 2020 | V4D: 4D Convolutional Neural Networks for Video-level Representation Learning. Shiwen Zhang, Sheng Guo, Weilin Huang, Matthew R. Scott, Limin Wang |
| 2020 | VL-BERT: Pre-training of Generic Visual-Linguistic Representations. Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, Jifeng Dai |
| 2020 | VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning. Luisa M. Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, Shimon Whiteson |
| 2020 | Variance Reduction With Sparse Gradients. Melih Elibol, Lihua Lei, Michael I. Jordan |
| 2020 | Variational Autoencoders for Highly Multivariate Spatial Point Processes Intensities. Baichuan Yuan, Xiaowei Wang, Jianxin Ma, Chang Zhou, Andrea L. Bertozzi, Hongxia Yang |
| 2020 | Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling. Hao Zhang, Bo Chen, Long Tian, Zhengjue Wang, Mingyuan Zhou |
| 2020 | Variational Recurrent Models for Solving Partially Observable Control Tasks. Dongqi Han, Kenji Doya, Jun Tani |
| 2020 | Variational Template Machine for Data-to-Text Generation. Rong Ye, Wenxian Shi, Hao Zhou, Zhongyu Wei, Lei Li |
| 2020 | Vid2Game: Controllable Characters Extracted from Real-World Videos. Oran Gafni, Lior Wolf, Yaniv Taigman |
| 2020 | VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation. Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, Durk Kingma |
| 2020 | Watch the Unobserved: A Simple Approach to Parallelizing Monte Carlo Tree Search. Anji Liu, Jianshu Chen, Mingze Yu, Yu Zhai, Xuewen Zhou, Ji Liu |
| 2020 | Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards. Allan Zhou, Eric Jang, Daniel Kappler, Alexander Herzog, Mohi Khansari, Paul Wohlhart, Yunfei Bai, Mrinal Kalakrishnan, Sergey Levine, Chelsea Finn |
| 2020 | Weakly Supervised Clustering by Exploiting Unique Class Count. Mustafa Umit Oner, Hwee Kuan Lee, Wing-Kin Sung |
| 2020 | Weakly Supervised Disentanglement with Guarantees. Rui Shu, Yining Chen, Abhishek Kumar, Stefano Ermon, Ben Poole |
| 2020 | What Can Neural Networks Reason About? Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S. Du, Ken-ichi Kawarabayashi, Stefanie Jegelka |
| 2020 | What graph neural networks cannot learn: depth vs width. Andreas Loukas |
| 2020 | White Noise Analysis of Neural Networks. Ali Borji, Sikun Lin |
| 2020 | Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity. Jingzhao Zhang, Tianxing He, Suvrit Sra, Ali Jadbabaie |
| 2020 | Why Not to Use Zero Imputation? Correcting Sparsity Bias in Training Neural Networks. Joonyoung Yi, Juhyuk Lee, Kwang Joon Kim, Sung Ju Hwang, Eunho Yang |
| 2020 | You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings. Daniel Ruffinelli, Samuel Broscheit, Rainer Gemulla |
| 2020 | You Only Train Once: Loss-Conditional Training of Deep Networks. Alexey Dosovitskiy, Josip Djolonga |
| 2020 | Your classifier is secretly an energy based model and you should treat it like one. Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky |
| 2020 | vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations. Alexei Baevski, Steffen Schneider, Michael Auli |
| 2020 | word2ket: Space-efficient Word Embeddings inspired by Quantum Entanglement. Aliakbar Panahi, Seyran Saeedi, Tomasz Arodz |