ICLR 2019

502 papers

Smoothing the Geometry of Probabilistic Box Embeddings

Xiang Li, Luke Vilnis, Dongxu Zhang, Michael Boratko, Andrew McCallum

Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability

Kai Y. Xiao, Vincent Tjeng, Nur Muhammad (Mahi) Shafiullah, Aleksander Madry

On Random Deep Weight-Tied Autoencoders: Exact Asymptotic Analysis, Phase Transitions, and Implications to Training

Ping Li, Phan-Minh Nguyen

Convolutional Neural Networks on Non-uniform Geometrical Signals Using Euclidean Spectral Transformation

Chiyu Max Jiang, Dequan Wang, Jingwei Huang, Philip Marcus, Matthias Niessner

Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets

Penghang Yin, Jiancheng Lyu, SHUAI ZHANG, Stanley Osher, Yingyong Qi

Per-Tensor Fixed-Point Quantization of the Back-Propagation Algorithm

Charbel Sakr, Naresh Shanbhag

RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks

Xiuyuan Cheng, Qiang Qiu, Robert Calderbank, Guillermo Sapiro

Large Scale Graph Learning From Smooth Signals

Vassilis Kalofolias, Nathanael Perraudin

Deep, Skinny Neural Networks are not Universal Approximators

Jesse Johnson

L2-Nonexpansive Neural Networks

Haifeng Qian, Mark N. Wegman

Generating Liquid Simulations with Deformation-aware Neural Networks

Lukas Prantl, Boris Bonev, Nils Thuerey

Adversarial Imitation via Variational Inverse Reinforcement Learning

Ahmed H. Qureshi, Byron Boots, Michael C. Yip

Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer

David Berthelot*, Colin Raffel*, Aurko Roy, Ian Goodfellow

Learning Localized Generative Models for 3D Point Clouds via Graph Convolution

Diego Valsesia, Giulia Fracastoro, Enrico Magli

Caveats for information bottleneck in deterministic scenarios

Artemy Kolchinsky, Brendan D. Tracey, Steven Van Kuyk

A Kernel Random Matrix-Based Approach for Sparse PCA

Mohamed El Amine Seddik, Mohamed Tamaazousti, Romain Couillet

The relativistic discriminator: a key element missing from standard GAN

Alexia Jolicoeur-Martineau

INVASE: Instance-wise Variable Selection using Neural Networks

Jinsung Yoon, James Jordon, Mihaela van der Schaar

Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams

Mohammad Kachuee, Orpaz Goldstein, Kimmo Kärkkäinen, Sajad Darabi, Majid Sarrafzadeh

Adversarial Reprogramming of Neural Networks

Gamaleldin F. Elsayed, Ian Goodfellow, Jascha Sohl-dickstein

DARTS: Differentiable Architecture Search

Hanxiao Liu, Karen Simonyan, Yiming Yang

DPSNet: End-to-end Deep Plane Sweep Stereo

Sunghoon Im, Hae-Gon Jeon, Stephen Lin, In So Kweon

Preconditioner on Matrix Lie Group for SGD

Xi-Lin Li

Diffusion Scattering Transforms on Graphs

Fernando Gama, Alejandro Ribeiro, Joan Bruna

Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator

Makoto Yamada, Denny Wu, Yao-Hung Hubert Tsai, Hirofumi Ohta, Ruslan Salakhutdinov

Hierarchical interpretations for neural network predictions

Chandan Singh, W. James Murdoch, Bin Yu

Dynamic Sparse Graph for Efficient Deep Learning

Liu Liu, Lei Deng, Xing Hu, Maohua Zhu, Guoqi Li

Learning to Understand Goal Specifications by Modelling Reward

Dzmitry Bahdanau, Felix Hill, Jan Leike, Edward Hughes, Arian Hosseini

Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware

Florian Tramer, Dan Boneh

LEARNING FACTORIZED REPRESENTATIONS FOR OPEN-SET DOMAIN ADAPTATION

Mahsa Baktashmotlagh, Masoud Faraki, Tom Drummond, Mathieu Salzmann

Adversarial Domain Adaptation for Stable Brain-Machine Interfaces

Ali Farshchian, Juan A. Gallego, Joseph P. Cohen, Yoshua Bengio, Lee E. Miller

Supervised Policy Update for Deep Reinforcement Learning

Quan Vuong, Yiming Zhang, Keith W. Ross

Differentiable Learning-to-Normalize via Switchable Normalization

Ping Luo, Jiamin Ren, zhanglin peng, Ruimao Zhang, Jingyu Li

M^3RL: Mind-aware Multi-agent Management Reinforcement Learning

Tianmin Shu, Yuandong Tian

Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information

Mohit Sharma, Arjun Sharma, Nicholas Rhinehart, Kris M. Kitani

Are adversarial examples inevitable?

Ali Shafahi, W. Ronny Huang, Christoph Studer, Soheil Feizi, Tom Goldstein

An analytic theory of generalization dynamics and transfer learning in deep linear networks

Andrew K. Lampinen, Surya Ganguli

A rotation-equivariant convolutional neural network model of primary visual cortex

Alexander S. Ecker, Fabian H. Sinz, Emmanouil Froudarakis, Paul G. Fahey, Santiago A. Cadena

ANYTIME MINIBATCH: EXPLOITING STRAGGLERS IN ONLINE DISTRIBUTED OPTIMIZATION

Nuwan Ferdinand, Haider Al-Lawati, Stark Draper, Matthew Nokleby

Generating Multiple Objects at Spatially Distinct Locations

Tobias Hinz, Stefan Heinrich, Stefan Wermter

Analysis of Quantized Models

Lu Hou, Ruiliang Zhang, James T. Kwok

Context-adaptive Entropy Model for End-to-end Optimized Image Compression

Jooyoung Lee, Seunghyun Cho, Seung-Kwon Beack

Practical lossless compression with latent variables using bits back coding

James Townsend, Thomas Bird, David Barber

Sample Efficient Imitation Learning for Continuous Control

Fumihiro Sasaki, Tetsuya Yohira, Atsuo Kawaguchi

Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection

Tue Le, Tuan Nguyen, Trung Le, Dinh Phung, Paul Montague

Sample Efficient Adaptive Text-to-Speech

Yutian Chen, Yannis Assael, Brendan Shillingford, David Budden, Scott Reed

Distribution-Interpolation Trade off in Generative Models

Damian Leśniak, Igor Sieradzki, Igor Podolak

Conditional Network Embeddings

Bo Kang, Jefrey Lijffijt, Tijl De Bie

Deterministic Variational Inference for Robust Bayesian Neural Networks

Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato

Variance Networks: When Expectation Does Not Meet Your Expectations

Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov

Learning to Remember More with Less Memorization

Hung Le, Truyen Tran, Svetha Venkatesh

DELTA: DEEP LEARNING TRANSFER USING FEATURE MAP WITH ATTENTION FOR CONVOLUTIONAL NETWORKS

Xingjian Li, Haoyi Xiong, Hanchao Wang, Yuxuan Rao, Liping Liu

signSGD via Zeroth-Order Oracle

Sijia Liu, Pin-Yu Chen, Xiangyi Chen, Mingyi Hong

Identifying and Controlling Important Neurons in Neural Machine Translation

Anthony Bau, Yonatan Belinkov, Hassan Sajjad, Nadir Durrani, Fahim Dalvi

Initialized Equilibrium Propagation for Backprop-Free Training

Peter O'Connor, Efstratios Gavves, Max Welling

Variational Smoothing in Recurrent Neural Network Language Models

Lingpeng Kong, Gábor Melis, Wang Ling, Lei Yu, Dani Yogatama

Learning Latent Superstructures in Variational Autoencoders for Deep Multidimensional Clustering

Xiaopeng Li, Zhourong Chen, Leonard K. M. Poon, Nevin L. Zhang

Reward Constrained Policy Optimization

Chen Tessler, Daniel J. Mankowitz, Shie Mannor

Quaternion Recurrent Neural Networks

Titouan Parcollet, Mirco Ravanelli, Mohamed Morchid, Georges Linarès, Chiheb Trabelsi

DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder

Xiaodong Gu, Kyunghyun Cho, Jung-Woo Ha, Sunghun Kim

StrokeNet: A Neural Painting Environment

Ningyuan Zheng, Yifan Jiang, Dingjiang Huang

Large-Scale Study of Curiosity-Driven Learning

Yuri Burda, Harri Edwards, Deepak Pathak, Amos Storkey, Trevor Darrell

Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization

Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama

Feed-forward Propagation in Probabilistic Neural Networks with Categorical and Max Layers

Alexander Shekhovtsov, Boris Flach

Function Space Particle Optimization for Bayesian Neural Networks

Ziyu Wang, Tongzheng Ren, Jun Zhu, Bo Zhang

Hierarchical Visuomotor Control of Humanoids

Josh Merel, Arun Ahuja, Vu Pham, Saran Tunyasuvunakool, Siqi Liu

Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds

Peng Cao*, Yilun Xu*, Yuqing Kong, Yizhou Wang

Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural Networks

Jose Oramas, Kaili Wang, Tinne Tuytelaars

LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos

Elke Kirschbaum, Manuel Haußmann, Steffen Wolf, Hannah Sonntag, Justus Schneider

Hindsight policy gradients

Paulo Rauber, Avinash Ummadisingu, Filipe Mutz, Jürgen Schmidhuber

Visual Reasoning by Progressive Module Networks

Seung Wook Kim, Makarand Tapaswi, Sanja Fidler

Attentive Neural Processes

Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, Ali Eslami

Robust Conditional Generative Adversarial Networks

Grigorios G. Chrysos, Jean Kossaifi, Stefanos Zafeiriou

Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search

Lars Buesing, Theophane Weber, Yori Zwols, Nicolas Heess, Sébastien Racanière

ROBUST ESTIMATION VIA GENERATIVE ADVERSARIAL NETWORKS

Chao Gao, jiyi LIU, Yuan Yao, Weizhi ZHU

Imposing Category Trees Onto Word-Embeddings Using A Geometric Construction

Tiansi Dong, Chrisitan Bauckhage, Hailong Jin, Juanzi Li, Olaf Cremers

Execution-Guided Neural Program Synthesis

Xinyun Chen, Chang Liu, Dawn Song

Near-Optimal Representation Learning for Hierarchical Reinforcement Learning

Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine

Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering

Victor Zhong, Caiming Xiong, Nitish Shirish Keskar, Richard Socher

A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation

Akhilesh Gotmare, Nitish Shirish Keskar, Caiming Xiong, Richard Socher

Adaptive Posterior Learning: few-shot learning with a surprise-based memory module

Tiago Ramalho, Marta Garnelo

On the Turing Completeness of Modern Neural Network Architectures

Jorge Pérez, Javier Marinković, Pablo Barceló

Towards the first adversarially robust neural network model on MNIST

Lukas Schott, Jonas Rauber, Matthias Bethge, Wieland Brendel

Evaluating Robustness of Neural Networks with Mixed Integer Programming

Vincent Tjeng, Kai Y. Xiao, Russ Tedrake

The Unusual Effectiveness of Averaging in GAN Training

Yasin Yaz{\i}c{\i}, Chuan-Sheng Foo, Stefan Winkler, Kim-Hui Yap, Georgios Piliouras

Graph Wavelet Neural Network

Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng

Double Viterbi: Weight Encoding for High Compression Ratio and Fast On-Chip Reconstruction for Deep Neural Network

Daehyun Ahn, Dongsoo Lee, Taesu Kim, Jae-Joon Kim

Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy

Yuan Xie, Boyi Liu, Qiang Liu, Zhaoran Wang, Yuan Zhou

Augmented Cyclic Adversarial Learning for Low Resource Domain Adaptation

Ehsan Hosseini-Asl, Yingbo Zhou, Caiming Xiong, Richard Socher

Meta-Learning For Stochastic Gradient MCMC

Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato

Efficient Lifelong Learning with A-GEM

Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, Mohamed Elhoseiny

Optimal Transport Maps For Distribution Preserving Operations on Latent Spaces of Generative Models

Eirikur Agustsson, Alexander Sage, Radu Timofte, Luc Van Gool

Learning a SAT Solver from Single-Bit Supervision

Daniel Selsam, Matthew Lamm, Benedikt B\"{u}nz, Percy Liang, Leonardo de Moura

Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration

Xiaoshuai Zhang, Yiping Lu, Jiaying Liu, Bin Dong

Learning Recurrent Binary/Ternary Weights

Arash Ardakani, Zhengyun Ji, Sean C. Smithson, Brett H. Meyer, Warren J. Gross

Generative Code Modeling with Graphs

Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov

Trellis Networks for Sequence Modeling

Shaojie Bai, J. Zico Kolter, Vladlen Koltun

Meta-Learning Probabilistic Inference for Prediction

Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, Richard Turner

Critical Learning Periods in Deep Networks

Alessandro Achille, Matteo Rovere, Stefano Soatto

Attention, Learn to Solve Routing Problems!

Wouter Kool, Herke van Hoof, Max Welling