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ORCID8th ICLR 2020: Addis Ababa, Ethiopia
- 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net 2020
Poster Presentations
- Yang You, Jing Li, Sashank J. Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, Cho-Jui Hsieh:
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes. - Duc Tam Nguyen, Chaithanya Kumar Mummadi, Thi-Phuong-Nhung Ngo, Thi Hoai Phuong Nguyen, Laura Beggel, Thomas Brox:
SELF: Learning to Filter Noisy Labels with Self-Ensembling. - Yu Chen, Lingfei Wu, Mohammed J. Zaki:
Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation. - Carlo D'Eramo, Davide Tateo
, Andrea Bonarini, Marcello Restelli, Jan Peters:
Sharing Knowledge in Multi-Task Deep Reinforcement Learning. - Leshem Choshen, Lior Fox, Zohar Aizenbud, Omri Abend:
On the Weaknesses of Reinforcement Learning for Neural Machine Translation. - Hao Yuan, Shuiwang Ji:
StructPool: Structured Graph Pooling via Conditional Random Fields. - Tianshu Yu, Runzhong Wang, Junchi Yan, Baoxin Li:
Learning deep graph matching with channel-independent embedding and Hungarian attention. - Chunyan Xu, Zhen Cui, Xiaobin Hong, Tong Zhang, Jian Yang, Wei Liu:
Graph inference learning for semi-supervised classification. - Siddharth Reddy, Anca D. Dragan, Sergey Levine:
SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards. - Sergei Popov, Stanislav Morozov, Artem Babenko:
Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data. - Yixiao Ge, Dapeng Chen, Hongsheng Li:
Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification. - Kai Han, Sylvestre-Alvise Rebuffi, Sébastien Ehrhardt, Andrea Vedaldi, Andrew Zisserman:
Automatically Discovering and Learning New Visual Categories with Ranking Statistics. - Qingfeng Lan, Yangchen Pan, Alona Fyshe, Martha White:
Maxmin Q-learning: Controlling the Estimation Bias of Q-learning. - Xingchao Peng, Zijun Huang, Yizhe Zhu, Kate Saenko:
Federated Adversarial Domain Adaptation. - Maha Elbayad, Jiatao Gu, Edouard Grave, Michael Auli:
Depth-Adaptive Transformer. - Huanrui Yang, Wei Wen, Hai Li:
DeepHoyer: Learning Sparser Neural Network with Differentiable Scale-Invariant Sparsity Measures. - Kaicheng Yu, Christian Sciuto, Martin Jaggi, Claudiu Musat, Mathieu Salzmann:
Evaluating The Search Phase of Neural Architecture Search. - Ye Yuan, Kris M. Kitani:
Diverse Trajectory Forecasting with Determinantal Point Processes. - Yang Yang, Yaxiong Yuan, Avraam Chatzimichailidis, Ruud J. G. van Sloun, Lei Lei, Symeon Chatzinotas:
ProxSGD: Training Structured Neural Networks under Regularization and Constraints. - Fan-Keng Sun, Cheng-Hao Ho, Hung-Yi Lee:
LAMOL: LAnguage MOdeling for Lifelong Language Learning. - Zhenyu Shi, Runsheng Yu, Xinrun Wang, Rundong Wang, Youzhi Zhang, Hanjiang Lai, Bo An:
Learning Expensive Coordination: An Event-Based Deep RL Approach. - Ze Ye, Kin Sum Liu, Tengfei Ma, Jie Gao, Chao Chen:
Curvature Graph Network. - Chen Xing, Sercan Ömer Arik, Zizhao Zhang, Tomas Pfister:
Distance-Based Learning from Errors for Confidence Calibration. - Tianshu Yu, Yikang Li, Baoxin Li:
Deep Learning of Determinantal Point Processes via Proper Spectral Sub-gradient. - Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, Yoshua Bengio:
N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. - Huaxiu Yao, Xian Wu, Zhiqiang Tao, Yaliang Li, Bolin Ding, Ruirui Li, Zhenhui Li:
Automated Relational Meta-learning. - Chongxuan Li, Chao Du, Kun Xu, Max Welling, Jun Zhu, Bo Zhang:
To Relieve Your Headache of Training an MRF, Take AdVIL. - Xiandong Zhao, Ying Wang, Xuyi Cai, Cheng Liu, Lei Zhang:
Linear Symmetric Quantization of Neural Networks for Low-precision Integer Hardware. - Mustafa Umit Oner, Hwee Kuan Lee, Wing-Kin Sung:
Weakly Supervised Clustering by Exploiting Unique Class Count. - Jaehong Yoon, Saehoon Kim, Eunho Yang, Sung Ju Hwang:
Scalable and Order-robust Continual Learning with Additive Parameter Decomposition. - Tameem Adel, Han Zhao, Richard E. Turner:
Continual Learning with Adaptive Weights (CLAW). - Nathan Inkawhich, Kevin J. Liang, Lawrence Carin, Yiran Chen:
Transferable Perturbations of Deep Feature Distributions. - Hao Lu, Xingwen Zhang, Shuang Yang:
A Learning-based Iterative Method for Solving Vehicle Routing Problems. - Samuel Humeau, Kurt Shuster, Marie-Anne Lachaux, Jason Weston:
Poly-encoders: Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring. - Qian Lou, Feng Guo, Minje Kim, Lantao Liu, Lei Jiang:
AutoQ: Automated Kernel-Wise Neural Network Quantization. - Yao Shu, Wei Wang, Shaofeng Cai:
Understanding Architectures Learnt by Cell-based Neural Architecture Search. - Shiyu Huang, Hang Su, Jun Zhu, Ting Chen:
SVQN: Sequential Variational Soft Q-Learning Networks. - Kaixiang Lin, Jiayu Zhou:
Ranking Policy Gradient. - Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, Mario Lucic:
On Mutual Information Maximization for Representation Learning. - Xingyou Song, Yiding Jiang, Stephen Tu, Yilun Du, Behnam Neyshabur:
Observational Overfitting in Reinforcement Learning. - Connie Kou, Hwee Kuan Lee, Ee-Chien Chang, Teck Khim Ng:
Enhancing Transformation-Based Defenses Against Adversarial Attacks with a Distribution Classifier. - Yuhang Li, Xin Dong, Wei Wang:
Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks. - Yichi Zhou, Tongzheng Ren, Jialian Li, Dong Yan, Jun Zhu:
Lazy-CFR: fast and near-optimal regret minimization for extensive games with imperfect information. - Ruofan Liang, Tianlin Li, Longfei Li, Jing Wang, Quanshi Zhang:
Knowledge Consistency between Neural Networks and Beyond. - Justus Thies, Michael Zollhöfer, Christian Theobalt, Marc Stamminger, Matthias Nießner:
Image-guided Neural Object Rendering. - Yan Li, Ethan X. Fang, Huan Xu, Tuo Zhao:
Implicit Bias of Gradient Descent based Adversarial Training on Separable Data. - Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou, William Yang Wang:
TabFact: A Large-scale Dataset for Table-based Fact Verification. - Xingyou Song, Wenbo Gao, Yuxiang Yang, Krzysztof Choromanski, Aldo Pacchiano, Yunhao Tang:
ES-MAML: Simple Hessian-Free Meta Learning. - Hung Le, Truyen Tran, Svetha Venkatesh:
Neural Stored-program Memory. - Suraj Nair, Chelsea Finn:
Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation. - Tianshu Chu, Sandeep Chinchali, Sachin Katti:
Multi-agent Reinforcement Learning for Networked System Control. - Yan Zhang, Jonathon S. Hare, Adam Prügel-Bennett:
FSPool: Learning Set Representations with Featurewise Sort Pooling. - Taeuk Kim, Jihun Choi, Daniel Edmiston, Sang-goo Lee:
Are Pre-trained Language Models Aware of Phrases? Simple but Strong Baselines for Grammar Induction. - Xiaoran Xu, Wei Feng, Yunsheng Jiang, Xiaohui Xie, Zhiqing Sun, Zhi-Hong Deng:
Dynamically Pruned Message Passing Networks for Large-scale Knowledge Graph Reasoning. - Tianyu Pang, Kun Xu, Jun Zhu:
Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks. - Kien Do, Truyen Tran:
Theory and Evaluation Metrics for Learning Disentangled Representations. - 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:
Measuring Compositional Generalization: A Comprehensive Method on Realistic Data. - Tianyu Pang, Kun Xu, Yinpeng Dong, Chao Du, Ning Chen, Jun Zhu:
Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness. - Daniel Gissin, Shai Shalev-Shwartz, Amit Daniely:
The Implicit Bias of Depth: How Incremental Learning Drives Generalization. - Anirudh Goyal, Yoshua Bengio, Matthew M. Botvinick, Sergey Levine:
The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget. - Nasim Rahaman, Steffen Wolf, Anirudh Goyal, Roman Remme, Yoshua Bengio:
Learning the Arrow of Time for Problems in Reinforcement Learning. - Anirudh Goyal, Shagun Sodhani, Jonathan Binas, Xue Bin Peng, Sergey Levine, Yoshua Bengio:
Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives. - Chuanbiao Song, Kun He, Jiadong Lin, Liwei Wang, John E. Hopcroft:
Robust Local Features for Improving the Generalization of Adversarial Training. - Bennet Breier, Arno Onken:
Analysis of Video Feature Learning in Two-Stream CNNs on the Example of Zebrafish Swim Bout Classification. - Negar Hassanpour, Russell Greiner:
Learning Disentangled Representations for CounterFactual Regression. - Yuu Jinnai, Jee Won Park, Marlos C. Machado, George Dimitri Konidaris:
Exploration in Reinforcement Learning with Deep Covering Options. - Dongsheng An, Yang Guo, Na Lei, Zhongxuan Luo, Shing-Tung Yau, Xianfeng Gu:
Ae-OT: a New Generative Model based on Extended Semi-discrete Optimal transport. - James Clift, Dmitry Doryn, Daniel Murfet, James Wallbridge:
Logic and the 2-Simplicial Transformer. - Allan Zhou, Eric Jang, Daniel Kappler, Alexander Herzog, Mohi Khansari, Paul Wohlhart, Yunfei Bai, Mrinal Kalakrishnan, Sergey Levine, Chelsea Finn:
Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards. - Yunhan Jia, Yantao Lu, Junjie Shen, Qi Alfred Chen, Hao Chan, Zhenyu Zhong, Tao Wei:
Fooling Detection Alone is Not Enough: Adversarial Attack against Multiple Object Tracking. - Junnan Li, Richard Socher, Steven C. H. Hoi:
DivideMix: Learning with Noisy Labels as Semi-supervised Learning. - Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, Quanquan Gu:
Improving Adversarial Robustness Requires Revisiting Misclassified Examples. - 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:
V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control. - Liyao Xiang, Hao Zhang, Haotian Ma, Yifan Zhang, Jie Ren, Quanshi Zhang:
Interpretable Complex-Valued Neural Networks for Privacy Protection. - Chaoyue Liu, Mikhail Belkin:
Accelerating SGD with momentum for over-parameterized learning. - Yuki Markus Asano, Christian Rupprecht, Andrea Vedaldi:
A critical analysis of self-supervision, or what we can learn from a single image. - Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem:
Disentangling Factors of Variations Using Few Labels. - Mary Phuong, Christoph H. Lampert:
Functional vs. parametric equivalence of ReLU networks. - Joan Serrà, David Álvarez, Vicenç Gómez, Olga Slizovskaia, José F. Núñez, Jordi Luque:
Input Complexity and Out-of-distribution Detection with Likelihood-based Generative Models. - Victor Zhong, Tim Rocktäschel, Edward Grefenstette:
RTFM: Generalising to New Environment Dynamics via Reading. - Andreas Loukas:
What graph neural networks cannot learn: depth vs width. - Nabiha Asghar, Lili Mou, Kira A. Selby, Kevin D. Pantasdo, Pascal Poupart, Xin Jiang:
Progressive Memory Banks for Incremental Domain Adaptation. - Sébastien Racanière, Andrew K. Lampinen, Adam Santoro, David P. Reichert, Vlad Firoiu, Timothy P. Lillicrap:
Automated curriculum generation through setter-solver interactions. - Gino Brunner, Yang Liu, Damian Pascual, Oliver Richter, Massimiliano Ciaramita, Roger Wattenhofer:
On Identifiability in Transformers. - Tingwu Wang, Jimmy Ba:
Exploring Model-based Planning with Policy Networks. - Yuping Luo, Huazhe Xu, Tengyu Ma:
Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling. - David Brandfonbrener, Joan Bruna:
Geometric Insights into the Convergence of Nonlinear TD Learning. - Yujia Bao, Menghua Wu, Shiyu Chang, Regina Barzilay:
Few-shot Text Classification with Distributional Signatures. - Jun-Kun Wang, Chi-Heng Lin, Jacob D. Abernethy:
Escaping Saddle Points Faster with Stochastic Momentum. - Adam Gleave, Michael Dennis, Cody Wild, Neel Kant, Sergey Levine, Stuart Russell:
Adversarial Policies: Attacking Deep Reinforcement Learning. - Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, Durk Kingma:
VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation. - Harsh Shrivastava, Xinshi Chen, Binghong Chen, Guanghui Lan, Srinivas Aluru, Han Liu, Le Song:
GLAD: Learning Sparse Graph Recovery. - Vassilis N. Ioannidis, Siheng Chen, Georgios B. Giannakis:
Pruned Graph Scattering Transforms. - Wenhan Xiong, Jingfei Du, William Yang Wang, Veselin Stoyanov:
Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model. - Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
Can gradient clipping mitigate label noise? - Anton Sinitsin, Vsevolod Plokhotnyuk, Dmitry V. Pyrkin, Sergei Popov, Artem Babenko:
Editable Neural Networks. - Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi:
Learning Execution through Neural Code fusion. - Wuyang Chen, Xinyu Gong, Xianming Liu, Qian Zhang, Yuan Li, Zhangyang Wang:
FasterSeg: Searching for Faster Real-time Semantic Segmentation. - Yi Lin Sung, Sung-Hsien Hsieh, Soo-Chang Pei, Chun-Shien Lu:
Difference-Seeking Generative Adversarial Network-Unseen Sample Generation. - Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang:
Stochastic AUC Maximization with Deep Neural Networks. - Vitor Guizilini, Rui Hou, Jie Li, Rares Ambrus, Adrien Gaidon:
Semantically-Guided Representation Learning for Self-Supervised Monocular Depth. - Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, Liwei Wang:
MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius. - Yao Qin, Nicholas Frosst, Sara Sabour, Colin Raffel, Garrison W. Cottrell, Geoffrey E. Hinton:
Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions. - Xuwang Yin, Soheil Kolouri, Gustavo K. Rohde:
GAT: Generative Adversarial Training for Adversarial Example Detection and Robust Classification. - Dongqi Han, Kenji Doya, Jun Tani:
Variational Recurrent Models for Solving Partially Observable Control Tasks. - Whiyoung Jung, Giseung Park, Youngchul Sung:
Population-Guided Parallel Policy Search for Reinforcement Learning. - Yi Ren, Shangmin Guo, Matthieu Labeau, Shay B. Cohen, Simon Kirby:
Compositional languages emerge in a neural iterated learning model. - Zhichao Huang, Tong Zhang:
Black-Box Adversarial Attack with Transferable Model-based Embedding. - Haotao Wang, Tianlong Chen, Zhangyang Wang, Kede Ma:
I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively. - Cheolhyoung Lee, Kyunghyun Cho, Wanmo Kang:
Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models. - Yuanhao Wang, Kefan Dong, Xiaoyu Chen, Liwei Wang:
Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP. - John Zarka, Louis Thiry, Tomás Angles, Stéphane Mallat:
Deep Network Classification by Scattering and Homotopy Dictionary Learning. - Ben Mussay, Margarita Osadchy, Vladimir Braverman, Samson Zhou, Dan Feldman:
Data-Independent Neural Pruning via Coresets. - Arsalan Sharif-Nassab, Saber Salehkaleybar, S. Jamaloddin Golestani:
Bounds on Over-Parameterization for Guaranteed Existence of Descent Paths in Shallow ReLU Networks. - Sung-Ik Choi, Sae-Young Chung:
Novelty Detection Via Blurring. - Fengxiang He, Bohan Wang, Dacheng Tao:
Piecewise linear activations substantially shape the loss surfaces of neural networks. - Fan Yang, Ling Chen, Fan Zhou, Yusong Gao, Wei Cao:
Relational State-Space Model for Stochastic Multi-Object Systems. - Rong Zhu, Sheng Yang, Andreas Pfadler, Zhengping Qian, Jingren Zhou:
Learning Efficient Parameter Server Synchronization Policies for Distributed SGD. - Weixun Wang, Tianpei Yang, Yong Liu, Jianye Hao, Xiaotian Hao, Yujing Hu, Yingfeng Chen, Changjie Fan, Yang Gao:
Action Semantics Network: Considering the Effects of Actions in Multiagent Systems. - Oran Gafni, Lior Wolf, Yaniv Taigman:
Vid2Game: Controllable Characters Extracted from Real-World Videos. - Wangchunshu Zhou, Tao Ge, Ke Xu, Furu Wei, Ming Zhou:
Self-Adversarial Learning with Comparative Discrimination for Text Generation. - Jisoo Lee, Sae-Young Chung:
Robust training with ensemble consensus. - Shen Li, Bryan Hooi, Gim Hee Lee:
Identifying through Flows for Recovering Latent Representations. - Jinyuan Jia, Xiaoyu Cao, Binghui Wang, Neil Zhenqiang Gong:
Certified Robustness for Top-k Predictions against Adversarial Perturbations via Randomized Smoothing. - Tabish Rashid, Bei Peng, Wendelin Boehmer, Shimon Whiteson:
Optimistic Exploration even with a Pessimistic Initialisation. - Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, Jifeng Dai:
VL-BERT: Pre-training of Generic Visual-Linguistic Representations. - Hang Gao, Xizhou Zhu, Stephen Lin, Jifeng Dai:
Deformable Kernels: Adapting Effective Receptive Fields for Object Deformation. - Andrey Malinin, Bruno Mlodozeniec, Mark J. F. Gales:
Ensemble Distribution Distillation. - Saar Barkai, Ido Hakimi, Assaf Schuster:
Gap-Aware Mitigation of Gradient Staleness.