ICLR

Non-Vacuous Generalization Bounds at the ImageNet Scale: A PAC-Bayesian Compression Approach

Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz

Comments 16 pages, 1 figure. Accepted at ICLR 2019

Pay Less Attention with Lightweight and Dynamic Convolutions

Felix Wu, Angela Fan, Alexei Baevski, Yann N. Dauphin, Michael Auli

Comments 14 pages, ICLR oral

ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

Han Cai, Ligeng Zhu, Song Han

Comments ICLR 2019

K for the Price of 1: Parameter-efficient Multi-task and Transfer Learning

Pramod Kaushik Mudrakarta, Mark Sandler, Andrey Zhmoginov, Andrew Howard

Comments published at ICLR 2019

Do Deep Generative Models Know What They Don't Know?

Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan

Comments ICLR 2019

textTOvec: Deep Contextualized Neural Autoregressive Topic Models of Language with Distributed Compositional Prior

Pankaj Gupta, Yatin Chaudhary, Florian Buettner, Hinrich Schütze

Comments Published in #ICLR2019 International Conference on Learning Representations

SNIP: Single-shot Network Pruning based on Connection Sensitivity

Namhoon Lee, Thalaiyasingam Ajanthan, Philip H. S. Torr

Comments ICLR 2019

Adversarial Imitation via Variational Inverse Reinforcement Learning

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

Comments Paper published at ICLR 2019

Learning Self-Imitating Diverse Policies

Tanmay Gangwani, Qiang Liu, Jian Peng

Comments ICLR 2019

GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, Samuel R. Bowman

Comments ICLR 2019; https://gluebenchmark.com/

Large-Scale Answerer in Questioner's Mind for Visual Dialog Question Generation

Sang-Woo Lee, Tong Gao, Sohee Yang, Jaejun Yoo, Jung-Woo Ha

Comments Accepted for ICLR 2019. Camera ready version. Our code is publically available: https://github.com/naver/aqm-plus

On the Sensitivity of Adversarial Robustness to Input Data Distributions

Gavin Weiguang Ding, Kry Yik Chau Lui, Xiaomeng Jin, Luyu Wang, Ruitong Huang

Comments ICLR 2019, Seventh International Conference on Learning Representations

A Statistical Approach to Assessing Neural Network Robustness

Stefan Webb, Tom Rainforth, Yee Whye Teh, M. Pawan Kumar

Comments To appear at the 7th International Conference on Learning Representations (ICLR 2019), New Orleans

Learning to Represent Edits

Pengcheng Yin, Graham Neubig, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt

Comments ICLR 2019

Learning deep representations by mutual information estimation and maximization

R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, Yoshua Bengio

Comments Accepted as an oral presentation at the International Conference for Learning Representations (ICLR), 2019

ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech

Wei Ping, Kainan Peng, Jitong Chen

Comments Published at ICLR 2019. (v3: add important details & discussion in Appendix A)

Learning Latent Superstructures in Variational Autoencoders for Deep Multidimensional Clustering

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

Comments Published in ICLR 2019

code2seq: Generating Sequences from Structured Representations of Code

Uri Alon, Shaked Brody, Omer Levy, Eran Yahav

Comments Accepted to ICLR'2019

Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder

Caio Corro, Ivan Titov

Comments Accepted at ICLR 2019

There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average

Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson

Comments Appears at ICLR 2019

DOM-Q-NET: Grounded RL on Structured Language

Sheng Jia, Jamie Kiros, Jimmy Ba

Comments International Conference on Learning Representations (ICLR), 2019

Structured Adversarial Attack: Towards General Implementation and Better Interpretability

Kaidi Xu, Sijia Liu, Pu Zhao, Pin-Yu Chen, Huan Zhang, Quanfu Fan, Deniz Erdogmus, Yanzhi Wang, Xue Lin

Comments Published as a conference paper at ICLR 2019

Hindsight policy gradients

Paulo Rauber, Avinash Ummadisingu, Filipe Mutz, Juergen Schmidhuber

Comments Accepted to ICLR 2019

Generating Liquid Simulations with Deformation-aware Neural Networks

Lukas Prantl, Boris Bonev, Nils Thuerey

Comments ICLR 2019, further information and videos at https://ge.in.tum.de/publications/2017-prantl-defonn/

CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model

Florian Mai, Lukas Galke, Ansgar Scherp

Comments Conference paper at ICLR 2019

Journal ref In International Conference on Learning Representations 2019

Competitive Experience Replay

Hao Liu, Alexander Trott, Richard Socher, Caiming Xiong

Comments Published as a conference paper at Seventh International Conference on Learning Representations(ICLR 2019)

Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams

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

Comments https://openreview.net/forum?id=S1eOHo09KX

Journal ref International Conference on Learning Representations (ICLR), 2019

SPIGAN: Privileged Adversarial Learning from Simulation

Kuan-Hui Lee, German Ros, Jie Li, Adrien Gaidon

Comments Accepted by ICLR 2019

Riemannian Adaptive Optimization Methods

Gary Bécigneul, Octavian-Eugen Ganea

Comments Accepted at International Conference on Learning Representations (ICLR), 2019

Evaluating Robustness of Neural Networks with Mixed Integer Programming

Vincent Tjeng, Kai Xiao, Russ Tedrake

Comments Accepted as a conference paper at ICLR 2019