ICML

Triplet Distillation for Deep Face Recognition

Yushu Feng, Huan Wang, Daniel T. Yi, Roland Hu

Comments 5 pages, 2 tables, accpeted by ICML 2019 ODML-CDNNR Workshop

SWALP : Stochastic Weight Averaging in Low-Precision Training

Guandao Yang, Tianyi Zhang, Polina Kirichenko, Junwen Bai, Andrew Gordon Wilson, Christopher De Sa

Comments Published at ICML 2019

Invariant-equivariant representation learning for multi-class data

Ilya Feige

Comments 8 pages, 5 figures, 2 tables, 2 appendices

Journal ref ICML 2019

Emerging Convolutions for Generative Normalizing Flows

Emiel Hoogeboom, Rianne van den Berg, Max Welling

Comments Accepted at International Conference on Machine Learning (ICML) 2019

The Evolved Transformer

David R. So, Chen Liang, Quoc V. Le

Comments ICML version with SOTA results

Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning

Casey Chu, Jose Blanchet, Peter Glynn

Comments ICML 2019

Revisiting the Softmax Bellman Operator: New Benefits and New Perspective

Zhao Song, Ronald E. Parr, Lawrence Carin

Comments To appear in ICML 2019

FloWaveNet : A Generative Flow for Raw Audio

Sungwon Kim, Sang-gil Lee, Jongyoon Song, Jaehyeon Kim, Sungroh Yoon

Comments 9 pages, ICML'2019

Invertible Residual Networks

Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Jörn-Henrik Jacobsen

Journal ref Proceedings of the International Conference on Machine Learning (ICML), 2019

Open Vocabulary Learning on Source Code with a Graph-Structured Cache

Milan Cvitkovic, Badal Singh, Anima Anandkumar

Comments Published in the International Conference on Machine Learning (ICML 2019), 13 pages

Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance

Cong Xie, Oluwasanmi Koyejo, Indranil Gupta

Comments ICML 2019

Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory

Ron Amit, Ron Meir

Comments Accepted to ICML 2018

POPQORN: Quantifying Robustness of Recurrent Neural Networks

Ching-Yun Ko, Zhaoyang Lyu, Tsui-Wei Weng, Luca Daniel, Ngai Wong, Dahua Lin

Comments 10 pages, Ching-Yun Ko and Zhaoyang Lyu contributed equally, accepted to ICML 2019. Please see arXiv source codes for the appendix by clicking [Other formats]

Stratospheric Aerosol Injection as a Deep Reinforcement Learning Problem

Christian Schroeder de Witt, Thomas Hornigold

Comments Awarded Poster and Spotlight Oral at Climate Change: How Can AI Help? (Workshop) at International Conference on Machine Learning, Long Beach, California, 2019

Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces

Philipp Becker, Harit Pandya, Gregor Gebhardt, Cheng Zhao, James Taylor, Gerhard Neumann

Comments accepted at ICML 2019

Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models

Mor Shpigel Nacson, Suriya Gunasekar, Jason D. Lee, Nathan Srebro, Daniel Soudry

Comments ICML Camera ready version

Weakly-Supervised Temporal Localization via Occurrence Count Learning

Julien Schroeter, Kirill Sidorov, David Marshall

Comments Accepted at ICML 2019

LiDAR Sensor modeling and Data augmentation with GANs for Autonomous driving

Ahmad El Sallab, Ibrahim Sobh, Mohamed Zahran, Nader Essam

Comments Accepted at ICML Workshop on AI for Autonomous Driving

Robust Learning from Untrusted Sources

Nikola Konstantinov, Christoph Lampert

Comments Accepted to International Conference on Machine Learning (ICML), 2019; Camera-ready version

Dream Distillation: A Data-Independent Model Compression Framework

Kartikeya Bhardwaj, Naveen Suda, Radu Marculescu

Comments Presented at the ICML 2019 Joint Workshop on On-Device Machine Learning & Compact Deep Neural Network Representations (ODML-CDNNR)

Privacy Preserving Adjacency Spectral Embedding on Stochastic Blockmodels

Li Chen

Comments Accepted at Learning and Reasoning with Graph-Structured Representations at ICML 2019

Non-Parametric Priors For Generative Adversarial Networks

Rajhans Singh, Pavan Turaga, Suren Jayasuriya, Ravi Garg, Martin W. Braun

Journal ref International Conference on Machine Learning (2019)

Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning

Frederik Benzing, Marcelo Matheus Gauy, Asier Mujika, Anders Martinsson, Angelika Steger

Comments ICML 2019 camera ready version; new version includes additional plots in the appendix

Tensor Variable Elimination for Plated Factor Graphs

Fritz Obermeyer, Eli Bingham, Martin Jankowiak, Justin Chiu, Neeraj Pradhan, Alexander Rush, Noah Goodman

Comments To appear at ICML; 17 pages

On Variational Bounds of Mutual Information

Ben Poole, Sherjil Ozair, Aaron van den Oord, Alexander A. Alemi, George Tucker

Comments ICML 2019

Adaptive Sensor Placement for Continuous Spaces

James A Grant, Alexis Boukouvalas, Ryan-Rhys Griffiths, David S Leslie, Sattar Vakili, Enrique Munoz de Cote

Comments 13 pages, accepted to ICML 2019

Data Poisoning Attacks on Stochastic Bandits

Fang Liu, Ness Shroff

Comments Accepted by ICML 2019

On Norm-Agnostic Robustness of Adversarial Training

Bai Li, Changyou Chen, Wenlin Wang, Lawrence Carin

Comments 4 pages, 2 figures, presented at the ICML 2019 Workshop on Uncertainty and Robustness in Deep Learning. arXiv admin note: text overlap with arXiv:1809.03113

Tight Kernel Query Complexity of Kernel Ridge Regression and Kernel $k$-means Clustering

Manuel Fernandez, David P. Woodruff, Taisuke Yasuda

Comments 27 pages, to appear in ICML 2019

IPC: A Benchmark Data Set for Learning with Graph-Structured Data

Patrick Ferber, Tengfei Ma, Siyu Huo, Jie Chen, Michael Katz

Comments ICML 2019 Workshop on Learning and Reasoning with Graph-Structured Data. The data set is accessible from https://github.com/IBM/IPC-graph-data