- Paulo Pirozelli, João F. N. Cortese:
The Beauty Everywhere: How Aesthetic Criteria Contribute to the Development of AI. ICBINB@NeurIPS 2021: 69-74 - David Rohde:
Causal Inference, is just Inference: A beautifully simple idea that not everyone accepts. ICBINB@NeurIPS 2021: 75-79 - Ke Alexander Wang, Danielle C. Maddix, Yuyang Wang:
GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics. ICBINB@NeurIPS 2021: 80-85 - Melanie F. Pradier, Aaron Schein, Stephanie L. Hyland, Francisco J. R. Ruiz, Jessica Zosa Forde:
I (Still) Can't Believe It's Not Better! Workshop at NeurIPS 2021, Virtual Workshop, December 13, 2021. Proceedings of Machine Learning Research 163, PMLR 2021 [contents] - 2020
- Ziyu Wang, Bin Dai
, David Wipf, Jun Zhu:
Further Analysis of Outlier Detection with Deep Generative Models. ICBINB@NeurIPS 2020: 11-20 - Stella Biderman, Walter J. Scheirer:
Pitfalls in Machine Learning Research: Reexamining the Development Cycle. ICBINB@NeurIPS 2020: 106-117 - Ramiro Daniel Camino, Radu State, Christian A. Hammerschmidt:
Oversampling Tabular Data with Deep Generative Models: Is it worth the effort? ICBINB@NeurIPS 2020: 148-157 - Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig:
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. ICBINB@NeurIPS 2020: 60-69 - Maurice Frank, Maximilian Ilse:
Problems using deep generative models for probabilistic audio source separation. ICBINB@NeurIPS 2020: 53-59 - 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. ICBINB@NeurIPS 2020: 1-10 - W. Ronny Huang, Zeyad Emam, Micah Goldblum, Liam Fowl, Justin K. Terry, Furong Huang, Tom Goldstein:
Understanding Generalization Through Visualizations. ICBINB@NeurIPS 2020: 87-97 - Emilio Jorge, Hannes Eriksson, Christos Dimitrakakis, Debabrota Basu, Divya Grover:
Inferential Induction: A Novel Framework for Bayesian Reinforcement Learning. ICBINB@NeurIPS 2020: 43-52 - Seungjae Jung, Kyung-Min Kim, Hanock Kwak, Young-Jin Park:
A Worrying Analysis of Probabilistic Time-series Models for Sales Forecasting. ICBINB@NeurIPS 2020: 98-105 - Ilya Kavalerov, Wojciech Czaja, Rama Chellappa:
A study of quality and diversity in K+1 GANs. ICBINB@NeurIPS 2020: 129-135 - Sachin Kumar, Yulia Tsvetkov:
End-to-End Differentiable GANs for Text Generation. ICBINB@NeurIPS 2020: 118-128 - 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. ICBINB@NeurIPS 2020: 76-86 - Jovana Mitrovic, Brian McWilliams, Mélanie Rey:
Less can be more in contrastive learning. ICBINB@NeurIPS 2020: 70-75 - Mihaela Rosca, Theophane Weber, Arthur Gretton, Shakir Mohamed:
A case for new neural network smoothness constraints. ICBINB@NeurIPS 2020: 21-32 - Yannick Rudolph, Ulf Brefeld, Uwe Dick:
Graph Conditional Variational Models: Too Complex for Multiagent Trajectories? ICBINB@NeurIPS 2020: 136-147 - Siwen Yan, Devendra Singh Dhami, Sriraam Natarajan:
The Curious Case of Stacking Boosted Relational Dependency Networks. ICBINB@NeurIPS 2020: 33-42 - 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 [contents]
