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ORCID2nd ICBINB 2021: virtual
- 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 - Dyah Adila, Dongyeop Kang:
Understanding Out-of-distribution: A Perspective of Data Dynamics. 1-8 - Arno Blaas, Xavier Suau, Jason Ramapuram, Nicholas Apostoloff, Luca Zappella:
Challenges of Adversarial Image Augmentations. 9-14 - Ali Borji:
Shape Defense. 15-20 - Anthony L. Caterini, Gabriel Loaiza-Ganem:
Entropic Issues in Likelihood-Based OOD Detection. 21-26 - Amélie Chatelain, Amine Djeghri, Daniel Hesslow, Julien Launay:
Is the Number of Trainable Parameters All That Actually Matters? 27-32 - Cian Eastwood, Ian Mason, Christopher K. I. Williams:
Unit-level surprise in neural networks. 33-40 - Soufiane Hayou, Arnaud Doucet, Judith Rousseau:
The Curse of Depth in Kernel Regime. 41-47 - Nitya Kasturi, Igor L. Markov:
Text Ranking and Classification using Data Compression. 48-53 - Rainer Kelz, Gerhard Widmer:
Nonlinear Denoising, Linear Demixing. 54-58 - Chelsea Murray, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato:
Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not. 59-63 - Mike Van Ness, Madeleine Udell:
CDF Normalization for Controlling the Distribution of Hidden Nodes. 64-68 - Paulo Pirozelli, João F. N. Cortese:
The Beauty Everywhere: How Aesthetic Criteria Contribute to the Development of AI. 69-74 - David Rohde:
Causal Inference, is just Inference: A beautifully simple idea that not everyone accepts. 75-79 - Ke Alexander Wang, Danielle C. Maddix, Yuyang Wang:
GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics. 80-85
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