ICLR

Continual and Multi-task Reinforcement Learning With Shared Episodic Memory

Artyom Y. Sorokin, Mikhail S. Burtsev

Comments Presented at the Task-Agnostic Reinforcement Learning Workshop at ICLR 2019

Deep Learning to Predict Student Outcomes

Byung-Hak Kim

Comments Accepted as oral presentation to ICLR 2019, AI for Social Good Workshop. arXiv admin note: substantial text overlap with arXiv:1809.06686, arXiv:1804.07405

Dynamic Sparse Graph for Efficient Deep Learning

Liu Liu, Lei Deng, Xing Hu, Maohua Zhu, Guoqi Li, Yufei Ding, Yuan Xie

Comments ICLR 2019

Learning Graph Neural Networks with Noisy Labels

Hoang NT, Choong Jun Jin, Tsuyoshi Murata

Comments 5 pages, 4 figures, 3 tables; Appeared as a poster presentation at Limited Labeled Data (LLD) Workshop, ICLR 2019

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

Jooyoung Lee, Seunghyun Cho, Seung-Kwon Beack

Comments Published as a conference paper at ICLR 2019. The test code, evaluation results and reconstructed images are publicly available at https://github.com/JooyoungLeeETRI/CA_Entropy_Model

Information asymmetry in KL-regularized RL

Alexandre Galashov, Siddhant M. Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz, Guillaume Desjardins, Wojciech M. Czarnecki, Yee Whye Teh, Razvan Pascanu, Nicolas Heess

Comments Accepted as a conference paper at ICLR 2019

Learning Programmatically Structured Representations with Perceptor Gradients

Svetlin Penkov, Subramanian Ramamoorthy

Comments Published as a conference paper at ICLR 2019

Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference

Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, Gerald Tesauro

Comments ICLR 2019

Local SGD Converges Fast and Communicates Little

Sebastian U. Stich

Comments to appear at ICLR 2019, 19 pages

Quality Evaluation of GANs Using Cross Local Intrinsic Dimensionality

Sukarna Barua, Xingjun Ma, Sarah Monazam Erfani, Michael E. Houle, James Bailey

Comments The first and original version of this paper was submitted to ICLR 2019 conference. Submission link: https://openreview.net/pdf?id=BJgYl205tQ

Quasi-hyperbolic momentum and Adam for deep learning

Jerry Ma, Denis Yarats

Comments Published as a conference paper at ICLR 2019. This version corrects one typological error in the published text

Efficient GAN-Based Anomaly Detection

Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, Vijay Ramaseshan Chandrasekhar

Comments Updated version of this work is published at ICDM 2018, see arXiv:1812.02288 . Submitted to the ICLR Workshop 2018

Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning

Werner Zellinger, Thomas Grubinger, Edwin Lughofer, Thomas Natschläger, Susanne Saminger-Platz

Comments Extended journal version published: https://doi.org/10.1016/j.ins.2019.01.025

Journal ref International Conference on Learning Representations (ICLR), 2017

Aggregated Momentum: Stability Through Passive Damping

James Lucas, Shengyang Sun, Richard Zemel, Roger Grosse

Comments 11 primary pages, 11 supplementary pages, 12 figures total

Journal ref International Conference on Learning Representations, 2019

On the Use of ArXiv as a Dataset

Colin B. Clement, Matthew Bierbaum, Kevin P. O'Keeffe, Alexander A. Alemi

Comments 7 pages, 3 tables, 2 figures, ICLR 2019 workshop RLGM submission

Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors

Vitalii Zhelezniak, Aleksandar Savkov, April Shen, Francesco Moramarco, Jack Flann, Nils Y. Hammerla

Comments Published as a conference paper at ICLR 2019

Advancing GraphSAGE with A Data-Driven Node Sampling

Jihun Oh, Kyunghyun Cho, Joan Bruna

Comments 6 pages, 2 tables, ICLR 2019 workshop on Representation Learning on Graphs and Manifolds

Multilingual Neural Machine Translation with Knowledge Distillation

Xu Tan, Yi Ren, Di He, Tao Qin, Zhou Zhao, Tie-Yan Liu

Comments Accepted by ICLR 2019

Invariant and Equivariant Graph Networks

Haggai Maron, Heli Ben-Hamu, Nadav Shamir, Yaron Lipman

Comments ICLR 2019

The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision

Jiayuan Mao, Chuang Gan, Pushmeet Kohli, Joshua B. Tenenbaum, Jiajun Wu

Comments ICLR 2019 (Oral). Project page: http://nscl.csail.mit.edu/

Analysis of Confident-Classifiers for Out-of-distribution Detection

Sachin Vernekar, Ashish Gaurav, Taylor Denouden, Buu Phan, Vahdat Abdelzad, Rick Salay, Krzysztof Czarnecki

Comments SafeML 2019 ICLR workshop paper

Stochastic Optimization of Sorting Networks via Continuous Relaxations

Aditya Grover, Eric Wang, Aaron Zweig, Stefano Ermon

Comments ICLR 2019

Neural Logic Machines

Honghua Dong, Jiayuan Mao, Tian Lin, Chong Wang, Lihong Li, Denny Zhou

Comments ICLR 2019. Project page: https://sites.google.com/view/neural-logic-machines

Bayesian policy selection using active inference

Ozan Çatal, Johannes Nauta, Tim Verbelen, Pieter Simoens, Bart Dhoedt

Comments ICLR 2019 Workshop on Structure & priors in reinforcement learning

Fast Graph Representation Learning with PyTorch Geometric

Matthias Fey, Jan Eric Lenssen

Comments ICLR 2019 (RLGM Workshop)

Learnable Embedding Space for Efficient Neural Architecture Compression

Shengcao Cao, Xiaofang Wang, Kris M. Kitani

Comments ICLR 2019 - Code available here: https://github.com/Friedrich1006/ESNAC

The Scientific Method in the Science of Machine Learning

Jessica Zosa Forde, Michela Paganini

Comments 4 pages + 1 appendix. Presented at the ICLR 2019 Debugging Machine Learning Models workshop

Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers

Yonatan Geifman, Guy Uziel, Ran El-Yaniv

Comments Accepted to ICLR 2019

Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability

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

Journal ref International Conference on Learning Representations (ICLR) 2019

Differentiable Learning-to-Normalize via Switchable Normalization

Ping Luo, Jiamin Ren, Zhanglin Peng, Ruimao Zhang, Jingyu Li

Comments International Conference on Learning Representations (ICLR)