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Volume 137: "I Can't Believe It's Not Better!" at NeurIPS Workshops, 12 December 2020, NeurIPS Workshop, Virtual

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Editors:

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Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning

Elliott Gordon-Rodriguez, Gabriel Loaiza-Ganem, Geoff Pleiss, John Patrick Cunningham; Proceedings on "I Can't Believe It's Not Better!" at NeurIPS Workshops, PMLR 137:1-10

Further Analysis of Outlier Detection with Deep Generative Models

Ziyu Wang, Bin Dai, David Wipf, Jun Zhu; Proceedings on "I Can't Believe It's Not Better!" at NeurIPS Workshops, PMLR 137:11-20

A case for new neural network smoothness constraints

Mihaela Rosca, Theophane Weber, Arthur Gretton, Shakir Mohamed; Proceedings on "I Can't Believe It's Not Better!" at NeurIPS Workshops, PMLR 137:21-32

The Curious Case of Stacking Boosted Relational Dependency Networks

Siwen Yan, Devendra Singh Dhami, Sriraam Natarajan; Proceedings on "I Can't Believe It's Not Better!" at NeurIPS Workshops, PMLR 137:33-42

Inferential Induction: A Novel Framework for Bayesian Reinforcement Learning

Emilio Jorge, Hannes Eriksson, Christos Dimitrakakis, Debabrota Basu, Divya Grover; Proceedings on "I Can't Believe It's Not Better!" at NeurIPS Workshops, PMLR 137:43-52

Problems using deep generative models for probabilistic audio source separation

Maurice Frank, Maximilian Ilse; Proceedings on "I Can't Believe It's Not Better!" at NeurIPS Workshops, PMLR 137:53-59

Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering

Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig; Proceedings on "I Can't Believe It's Not Better!" at NeurIPS Workshops, PMLR 137:60-69

Less can be more in contrastive learning

Jovana Mitrovic, Brian McWilliams, Melanie Rey; Proceedings on "I Can't Believe It's Not Better!" at NeurIPS Workshops, PMLR 137:70-75

Decision-Aware Model Learning for Actor-Critic Methods: When Theory Does Not Meet Practice

Ângelo G. Lovatto, Thiago P. Bueno, Denis D. Mauá, Leliane N. Barros; Proceedings on "I Can't Believe It's Not Better!" at NeurIPS Workshops, PMLR 137:76-86

Understanding Generalization Through Visualizations

W. Ronny Huang, Zeyad Emam, Micah Goldblum, Liam Fowl, Justin K. Terry, Furong Huang, Tom Goldstein; Proceedings on "I Can't Believe It's Not Better!" at NeurIPS Workshops, PMLR 137:87-97

A Worrying Analysis of Probabilistic Time-series Models for Sales Forecasting

Seungjae Jung, Kyung-Min Kim, Hanock Kwak, Young-Jin Park; Proceedings on "I Can't Believe It's Not Better!" at NeurIPS Workshops, PMLR 137:98-105

Pitfalls in Machine Learning Research: Reexamining the Development Cycle

Stella Biderman, Walter J. Scheirer; Proceedings on "I Can't Believe It's Not Better!" at NeurIPS Workshops, PMLR 137:106-117

End-to-End Differentiable GANs for Text Generation

Sachin Kumar, Yulia Tsvetkov; Proceedings on "I Can't Believe It's Not Better!" at NeurIPS Workshops, PMLR 137:118-128

A study of quality and diversity in K+1 GANs

Ilya Kavalerov, Wojciech Czaja, Rama Chellappa; Proceedings on "I Can't Believe It's Not Better!" at NeurIPS Workshops, PMLR 137:129-135

Graph Conditional Variational Models: Too Complex for Multiagent Trajectories?

Yannick Rudolph, Ulf Brefeld, Uwe Dick; Proceedings on "I Can't Believe It's Not Better!" at NeurIPS Workshops, PMLR 137:136-147

Oversampling Tabular Data with Deep Generative Models: Is it worth the effort?

Ramiro D. Camino, Radu State, Christian A. Hammerschmidt; Proceedings on "I Can't Believe It's Not Better!" at NeurIPS Workshops, PMLR 137:148-157

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