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ORCID7th ICLR 2019: New Orleans, LA, USA
- 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net 2019
Oral Presentations
- Chengzhou Tang, Ping Tan:
BA-Net: Dense Bundle Adjustment Networks. - Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt:
Deterministic Variational Inference for Robust Bayesian Neural Networks. - Yikang Shen, Shawn Tan, Alessandro Sordoni, Aaron C. Courville:
Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks. - Andrew Brock, Jeff Donahue, Karen Simonyan:
Large Scale GAN Training for High Fidelity Natural Image Synthesis. - R. Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Philip Bachman, Adam Trischler, Yoshua Bengio:
Learning deep representations by mutual information estimation and maximization. - James Jordon, Jinsung Yoon, Mihaela van der Schaar:
KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks. - John Ingraham, Adam J. Riesselman, Chris Sander, Debora S. Marks:
Learning Protein Structure with a Differentiable Simulator. - Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, Wieland Brendel:
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. - Xiang Li, Luke Vilnis, Dongxu Zhang, Michael Boratko, Andrew McCallum:
Smoothing the Geometry of Probabilistic Box Embeddings. - Ping Li, Phan-Minh Nguyen:
On Random Deep Weight-Tied Autoencoders: Exact Asymptotic Analysis, Phase Transitions, and Implications to Training. - Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein:
Meta-Learning Update Rules for Unsupervised Representation Learning. - Sebastian Flennerhag, Pablo Garcia Moreno, Neil D. Lawrence, Andreas C. Damianou:
Transferring Knowledge across Learning Processes. - Jacob Menick, Nal Kalchbrenner:
Generating High fidelity Images with subscale pixel Networks and Multidimensional Upscaling. - Karol Gregor, George Papamakarios, Frederic Besse, Lars Buesing, Theophane Weber:
Temporal Difference Variational Auto-Encoder. - Jack Lindsey, Samuel A. Ocko, Surya Ganguli, Stéphane Deny:
A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs. - Felix Wu, Angela Fan, Alexei Baevski, Yann N. Dauphin, Michael Auli:
Pay Less Attention with Lightweight and Dynamic Convolutions. - Curtis Hawthorne, Andriy Stasyuk, Adam Roberts, Ian Simon, Cheng-Zhi Anna Huang, Sander Dieleman, Erich Elsen, Jesse H. Engel, Douglas Eck:
Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset. - Hung Le, Truyen Tran, Svetha Venkatesh
:
Learning to Remember More with Less Memorization. - Haohan Wang, Zexue He, Zachary C. Lipton, Eric P. Xing:
Learning Robust Representations by Projecting Superficial Statistics Out. - Florian Tramèr, Dan Boneh:
Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware. - Jiayuan Mao, Chuang Gan, Pushmeet Kohli, Joshua B. Tenenbaum, Jiajun Wu:
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision. - Jonathan Frankle, Michael Carbin:
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks. - Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud:
FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models. - Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka:
How Powerful are Graph Neural Networks?
Poster Presentations
- Chiyu Max Jiang, Dequan Wang, Jingwei Huang, Philip Marcus, Matthias Nießner:
Convolutional Neural Networks on Non-uniform Geometrical Signals Using Euclidean Spectral Transformation. - Ehsan Hosseini-Asl, Yingbo Zhou, Caiming Xiong, Richard Socher:
Augmented Cyclic Adversarial Learning for Low Resource Domain Adaptation. - Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov:
Variance Networks: When Expectation Does Not Meet Your Expectations. - Peter O'Connor, Efstratios Gavves, Max Welling:
Initialized Equilibrium Propagation for Backprop-Free Training. - Chun-Hao Chang, Elliot Creager, Anna Goldenberg, David Duvenaud:
Explaining Image Classifiers by Counterfactual Generation. - Namhoon Lee, Thalaiyasingam Ajanthan, Philip H. S. Torr:
Snip: single-Shot Network Pruning based on Connection sensitivity. - Bin Dai, David P. Wipf:
Diagnosing and Enhancing VAE Models. - Yandong Wen, Mahmoud Al Ismail, Weiyang Liu, Bhiksha Raj, Rita Singh:
Disjoint Mapping Network for Cross-modal Matching of Voices and Faces. - Michael Chang, Abhishek Gupta, Sergey Levine, Thomas L. Griffiths:
Automatically Composing Representation Transformations as a Means for Generalization. - Seung Wook Kim, Makarand Tapaswi, Sanja Fidler:
Visual Reasoning by Progressive Module Networks. - Roman Novak, Lechao Xiao, Yasaman Bahri, Jaehoon Lee, Greg Yang, Jiri Hron, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein:
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes. - Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, Gerald Tesauro:
Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference. - Tsung-Han Lin, Ping Tak Peter Tang:
Sparse Dictionary Learning by Dynamical Neural Networks. - Yunbo Wang, Lu Jiang, Ming-Hsuan Yang, Li-Jia Li, Mingsheng Long, Li Fei-Fei:
Eidetic 3D LSTM: A Model for Video Prediction and Beyond. - Jialin Liu, Xiaohan Chen, Zhangyang Wang, Wotao Yin:
ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA. - Guodong Zhang, Chaoqi Wang, Bowen Xu, Roger B. Grosse:
Three Mechanisms of Weight Decay Regularization. - Wengong Jin, Kevin Yang, Regina Barzilay, Tommi S. Jaakkola:
Learning Multimodal Graph-to-Graph Translation for Molecule Optimization. - Ali Mousavi, Gautam Dasarathy, Richard G. Baraniuk:
A Data-Driven and Distributed Approach to Sparse Signal Representation and Recovery. - Nan Lu, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama:
On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data. - Honghua Dong, Jiayuan Mao, Tian Lin, Chong Wang, Lihong Li, Denny Zhou:
Neural Logic Machines. - Christian Hansen, Casper Hansen, Stephen Alstrup, Jakob Grue Simonsen, Christina Lioma:
Neural Speed Reading with Structural-Jump-LSTM. - Jonathan Uesato, Ananya Kumar, Csaba Szepesvári, Tom Erez, Avraham Ruderman, Keith Anderson, Krishnamurthy (Dj) Dvijotham, Nicolas Heess, Pushmeet Kohli:
Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures. - Lars Buesing, Theophane Weber, Yori Zwols, Nicolas Heess, Sébastien Racanière, Arthur Guez, Jean-Baptiste Lespiau:
Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search. - Sijia Liu, Pin-Yu Chen, Xiangyi Chen, Mingyi Hong:
signSGD via Zeroth-Order Oracle. - Ali Razavi, Aäron van den Oord, Ben Poole, Oriol Vinyals:
Preventing Posterior Collapse with delta-VAEs. - Yuping Luo, Huazhe Xu, Yuanzhi Li, Yuandong Tian, Trevor Darrell, Tengyu Ma:
Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees. - Iou-Jen Liu, Jian Peng, Alexander G. Schwing:
Knowledge Flow: Improve Upon Your Teachers. - Mohit Sharma, Arjun Sharma, Nicholas Rhinehart, Kris M. Kitani:
Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information. - Zichao Wang, Randall Balestriero, Richard G. Baraniuk:
A Max-Affine Spline Perspective of Recurrent Neural Networks. - Izzeddin Gur, Ulrich Rückert, Aleksandra Faust, Dilek Hakkani-Tür:
Learning to Navigate the Web. - Kai Yuanqing Xiao, Vincent Tjeng, Nur Muhammad (Mahi) Shafiullah, Aleksander Madry:
Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability. - Xiang Jiang, Mohammad Havaei, Farshid Varno, Gabriel Chartrand, Nicolas Chapados, Stan Matwin:
Learning to Learn with Conditional Class Dependencies. - Josh Merel, Arun Ahuja, Vu Pham, Saran Tunyasuvunakool, Siqi Liu, Dhruva Tirumala, Nicolas Heess, Greg Wayne:
Hierarchical Visuomotor Control of Humanoids. - Arthur Pajot, Emmanuel de Bézenac, Patrick Gallinari:
Unsupervised Adversarial Image Reconstruction. - Peng Cao, Yilun Xu, Yuqing Kong, Yizhou Wang:
Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds. - Haowen Xu, Hao Zhang, Zhiting Hu, Xiaodan Liang, Ruslan Salakhutdinov, Eric P. Xing:
AutoLoss: Learning Discrete Schedule for Alternate Optimization. - Drew Linsley, Dan Shiebler, Sven Eberhardt, Thomas Serre:
Learning what and where to attend. - Chao Gao, Jiyi Liu, Yuan Yao, Weizhi Zhu:
Robust estimation via Generative Adversarial Networks. - Jinsung Yoon, James Jordon, Mihaela van der Schaar:
INVASE: Instance-wise Variable Selection using Neural Networks. - Andrei A. Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, Raia Hadsell:
Meta-Learning with Latent Embedding Optimization. - Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz:
Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach. - Pengcheng Yin, Graham Neubig, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt:
Learning to Represent Edits. - Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, Nicolas Heess:
Neural Probabilistic Motor Primitives for Humanoid Control. - Caio Corro, Ivan Titov:
Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder. - Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak A. Rao, Bruno Ribeiro:
Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs. - Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, Geoffrey J. Gordon:
An Empirical Study of Example Forgetting during Deep Neural Network Learning. - R. Thomas McCoy, Tal Linzen, Ewan Dunbar, Paul Smolensky:
RNNs implicitly implement tensor-product representations. - Saeed Amizadeh, Sergiy Matusevych, Markus Weimer:
Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach. - Xitong Gao, Yiren Zhao, Lukasz Dudziak, Robert D. Mullins, Cheng-Zhong Xu:
Dynamic Channel Pruning: Feature Boosting and Suppression. - Jeremy Bernstein, Jiawei Zhao, Kamyar Azizzadenesheli, Anima Anandkumar:
signSGD with Majority Vote is Communication Efficient and Fault Tolerant. - Senthil Purushwalkam, Abhinav Gupta, Danny M. Kaufman, Bryan C. Russell:
Bounce and Learn: Modeling Scene Dynamics with Real-World Bounces. - Pramod Kaushik Mudrakarta, Mark Sandler, Andrey Zhmoginov, Andrew G. Howard:
K for the Price of 1: Parameter-efficient Multi-task and Transfer Learning. - Arturo Deza, Aditya Jonnalagadda, Miguel P. Eckstein:
Towards Metamerism via Foveated Style Transfer. - Makoto Yamada, Denny Wu, Yao-Hung Hubert Tsai, Hirofumi Ohta, Ruslan Salakhutdinov, Ichiro Takeuchi, Kenji Fukumizu:
Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator. - Siqi Liu, Guy Lever, Josh Merel, Saran Tunyasuvunakool, Nicolas Heess, Thore Graepel:
Emergent Coordination Through Competition. - Andrew Ilyas, Logan Engstrom, Aleksander Madry:
Prior Convictions: Black-box Adversarial Attacks with Bandits and Priors. - Fumihiro Sasaki, Tetsuya Yohira, Atsuo Kawaguchi:
Sample Efficient Imitation Learning for Continuous Control. - Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov:
Generative Code Modeling with Graphs. - Alessandro Achille, Matteo Rovere, Stefano Soatto:
Critical Learning Periods in Deep Networks. - Aloïs Pourchot, Olivier Sigaud:
CEM-RL: Combining evolutionary and gradient-based methods for policy search. - Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel:
LanczosNet: Multi-Scale Deep Graph Convolutional Networks. - Jörn-Henrik Jacobsen, Jens Behrmann, Richard S. Zemel, Matthias Bethge:
Excessive Invariance Causes Adversarial Vulnerability. - Paulo E. Rauber, Avinash Ummadisingu, Filipe Mutz, Jürgen Schmidhuber:
Hindsight policy gradients. - Liangchen Luo, Yuanhao Xiong, Yan Liu, Xu Sun:
Adaptive Gradient Methods with Dynamic Bound of Learning Rate. - Ilya Loshchilov, Frank Hutter:
Decoupled Weight Decay Regularization. - Panayotis Mertikopoulos, Bruno Lecouat, Houssam Zenati, Chuan-Sheng Foo, Vijay Chandrasekhar, Georgios Piliouras:
Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile. - Xiaodong Gu, Kyunghyun Cho, Jung-Woo Ha, Sunghun Kim:
DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder. - John Wieting, Douwe Kiela:
No Training Required: Exploring Random Encoders for Sentence Classification. - Tingwu Wang, Yuhao Zhou, Sanja Fidler, Jimmy Ba:
Neural Graph Evolution: Towards Efficient Automatic Robot Design. - Ziyu Wang, Tongzheng Ren, Jun Zhu, Bo Zhang:
Function Space Particle Optimization for Bayesian Neural Networks. - Kaidi Xu, Sijia Liu, Pu Zhao, Pin-Yu Chen, Huan Zhang, Quanfu Fan, Deniz Erdogmus, Yanzhi Wang, Xue Lin:
Structured Adversarial Attack: Towards General Implementation and Better Interpretability. - Chiyu Max Jiang, Jingwei Huang, Karthik Kashinath, Prabhat, Philip Marcus, Matthias Nießner:
Spherical CNNs on Unstructured Grids. - Eirikur Agustsson, Alexander Sage, Radu Timofte, Luc Van Gool:
Optimal Transport Maps For Distribution Preserving Operations on Latent Spaces of Generative Models. - Michael Lutter, Christian Ritter, Jan Peters:
Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning. - Charbel Sakr, Naigang Wang, Chia-Yu Chen, Jungwook Choi, Ankur Agrawal, Naresh R. Shanbhag, Kailash Gopalakrishnan:
Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks. - Adrià Garriga-Alonso, Carl Edward Rasmussen, Laurence Aitchison:
Deep Convolutional Networks as shallow Gaussian Processes. - Kihyuk Sohn, Wenling Shang, Xiang Yu, Manmohan Chandraker:
Unsupervised Domain Adaptation for Distance Metric Learning. - Benedikt Pfülb, Alexander Gepperth:
A comprehensive, application-oriented study of catastrophic forgetting in DNNs. - Shiv Shankar, Sunita Sarawagi:
Posterior Attention Models for Sequence to Sequence Learning. - Mike Lewis, Angela Fan:
Generative Question Answering: Learning to Answer the Whole Question. - Prajit Ramachandran, Quoc V. Le:
Diversity and Depth in Per-Example Routing Models. - Rahaf Aljundi, Marcus Rohrbach, Tinne Tuytelaars:
Selfless Sequential Learning. - Tianmin Shu, Yuandong Tian:
M^3RL: Mind-aware Multi-agent Management Reinforcement Learning. - Andrei Atanov, Arsenii Ashukha, Kirill Struminsky, Dmitry P. Vetrov, Max Welling:
The Deep Weight Prior. - Thomas Elsken, Jan Hendrik Metzen, Frank Hutter:
Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution. - Titouan Parcollet, Mirco Ravanelli, Mohamed Morchid, Georges Linarès, Chiheb Trabelsi, Renato De Mori, Yoshua Bengio:
Quaternion Recurrent Neural Networks. - Chris Donahue, Julian J. McAuley, Miller S. Puckette:
Adversarial Audio Synthesis. - Xi-Lin Li:
Preconditioner on Matrix Lie Group for SGD. - Patrick H. Chen, Si Si, Sanjiv Kumar, Yang Li, Cho-Jui Hsieh:
Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks. - Tiago Ramalho, Marta Garnelo:
Adaptive Posterior Learning: few-shot learning with a surprise-based memory module. - Alexandre Piché, Valentin Thomas, Cyril Ibrahim, Yoshua Bengio, Chris Pal:
Probabilistic Planning with Sequential Monte Carlo methods. - Kendall Lowrey, Aravind Rajeswaran, Sham M. Kakade, Emanuel Todorov, Igor Mordatch:
Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control. - Meng Fang, Cheng Zhou, Bei Shi, Boqing Gong, Jia Xu, Tong Zhang:
DHER: Hindsight Experience Replay for Dynamic Goals. - Hsin-Yuan Huang, Eunsol Choi, Wen-tau Yih:
FlowQA: Grasping Flow in History for Conversational Machine Comprehension. - Frederic Runge, Danny Stoll, Stefan Falkner, Frank Hutter:
Learning to Design RNA. - Grigorios G. Chrysos, Jean Kossaifi, Stefanos Zafeiriou:
Robust Conditional Generative Adversarial Networks. - Karel Chvalovský:
Top-Down Neural Model For Formulae. - Xiao Zhang, David Evans:
Cost-Sensitive Robustness against Adversarial Examples. - Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, Nathan Srebro:
The role of over-parametrization in generalization of neural networks. - Fernando Gama, Alejandro Ribeiro, Joan Bruna:
Diffusion Scattering Transforms on Graphs. - Zhang Xinyi, Lihui Chen:
Capsule Graph Neural Network. - Haichuan Yang, Yuhao Zhu, Ji Liu:
Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking. - Ori Press, Tomer Galanti, Sagie Benaim, Lior Wolf:
Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer. - Yi Zhou, Junjie Yang, Huishuai Zhang, Yingbin Liang, Vahid Tarokh:
SGD Converges to Global Minimum in Deep Learning via Star-convex Path. - Wei Dai, Yi Zhou, Nanqing Dong, Hao Zhang, Eric P. Xing:
Toward Understanding the Impact of Staleness in Distributed Machine Learning. - Wanyun Cui, Guangyu Zheng,