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ORCID1st ICBINB 2020: virtual
- Jessica Zosa Forde, Francisco J. R. Ruiz, Melanie F. Pradier, Aaron Schein:
"I Can't Believe It's Not Better!" at NeurIPS Workshops, Virtual, December 12, 2020. Proceedings of Machine Learning Research 137, PMLR 2020 - Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham:
Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning. 1-10 - Ziyu Wang, Bin Dai
, David Wipf, Jun Zhu:
Further Analysis of Outlier Detection with Deep Generative Models. 11-20 - Mihaela Rosca, Theophane Weber, Arthur Gretton, Shakir Mohamed:
A case for new neural network smoothness constraints. 21-32 - Siwen Yan, Devendra Singh Dhami, Sriraam Natarajan:
The Curious Case of Stacking Boosted Relational Dependency Networks. 33-42 - Emilio Jorge, Hannes Eriksson, Christos Dimitrakakis, Debabrota Basu, Divya Grover:
Inferential Induction: A Novel Framework for Bayesian Reinforcement Learning. 43-52 - Maurice Frank, Maximilian Ilse:
Problems using deep generative models for probabilistic audio source separation. 53-59 - Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig:
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. 60-69 - Jovana Mitrovic, Brian McWilliams, Mélanie Rey:
Less can be more in contrastive learning. 70-75 - Ângelo Gregório Lovatto, Thiago Pereira Bueno, Denis Deratani Mauá, Leliane N. de Barros:
Decision-Aware Model Learning for Actor-Critic Methods: When Theory Does Not Meet Practice. 76-86 - W. Ronny Huang, Zeyad Emam, Micah Goldblum, Liam Fowl, Justin K. Terry, Furong Huang, Tom Goldstein:
Understanding Generalization Through Visualizations. 87-97 - Seungjae Jung, Kyung-Min Kim, Hanock Kwak, Young-Jin Park:
A Worrying Analysis of Probabilistic Time-series Models for Sales Forecasting. 98-105 - Stella Biderman, Walter J. Scheirer:
Pitfalls in Machine Learning Research: Reexamining the Development Cycle. 106-117 - Sachin Kumar, Yulia Tsvetkov:
End-to-End Differentiable GANs for Text Generation. 118-128 - Ilya Kavalerov, Wojciech Czaja, Rama Chellappa:
A study of quality and diversity in K+1 GANs. 129-135 - Yannick Rudolph, Ulf Brefeld, Uwe Dick:
Graph Conditional Variational Models: Too Complex for Multiagent Trajectories? 136-147 - Ramiro Daniel Camino, Radu State, Christian A. Hammerschmidt:
Oversampling Tabular Data with Deep Generative Models: Is it worth the effort? 148-157
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