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Publication search results
found 40 matches
- 2022
- Simran Kaur, Jeremy Cohen, Zachary Chase Lipton:
On the Maximum Hessian Eigenvalue and Generalization. ICBINB 2022: 51-65 - Tatiana Likhomanenko, Ronan Collobert, Navdeep Jaitly, Samy Bengio:
Continuous Soft Pseudo-Labeling in ASR. ICBINB 2022: 66-84 - Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John P. Cunningham, Jesse C. Cresswell, Anthony L. Caterini:
Denoising Deep Generative Models. ICBINB 2022: 41-50 - Abhishek Moturu, Sayali Joshi, Andrea S. Doria, Anna Goldenberg:
Volume-based Performance not Guaranteed by Promising Patch-based Results in Medical Imaging. ICBINB 2022: 85-93 - Elre T. Oldewage, John Bronskill, Richard E. Turner:
Adversarial Attacks are a Surprisingly Strong Baseline for Poisoning Few-Shot Meta-Learners. ICBINB 2022: 27-40 - Rebecca Saul, Mohammad Mahmudul Alam, John Hurwitz, Edward Raff, Tim Oates, James Holt:
Lempel-Ziv Networks. ICBINB 2022: 1-11 - Sheheryar Zaidi, Tudor Berariu, Hyunjik Kim, Jörg Bornschein, Claudia Clopath, Yee Whye Teh, Razvan Pascanu:
When Does Re-initialization Work? ICBINB 2022: 12-26 - Javier Antorán, Arno Blaas, Fan Feng, Sahra Ghalebikesabi, Ian Mason, Melanie F. Pradier, David Rohde, Francisco J. R. Ruiz, Aaron Schein:
Proceedings on "I Can't Believe It's Not Better! - Understanding Deep Learning Through Empirical Falsification" at NeurIPS 2022 Workshops, 03 December 2022, New Orleans, Louisiana, USA. Proceedings of Machine Learning Research 187, PMLR 2022 [contents] - 2021
- Dyah Adila, Dongyeop Kang:
Understanding Out-of-distribution: A Perspective of Data Dynamics. ICBINB@NeurIPS 2021: 1-8 - Arno Blaas, Xavier Suau, Jason Ramapuram, Nicholas Apostoloff, Luca Zappella:
Challenges of Adversarial Image Augmentations. ICBINB@NeurIPS 2021: 9-14 - Ali Borji:
Shape Defense. ICBINB@NeurIPS 2021: 15-20 - Anthony L. Caterini, Gabriel Loaiza-Ganem:
Entropic Issues in Likelihood-Based OOD Detection. ICBINB@NeurIPS 2021: 21-26 - Amélie Chatelain, Amine Djeghri, Daniel Hesslow, Julien Launay:
Is the Number of Trainable Parameters All That Actually Matters? ICBINB@NeurIPS 2021: 27-32 - Cian Eastwood, Ian Mason, Christopher K. I. Williams:
Unit-level surprise in neural networks. ICBINB@NeurIPS 2021: 33-40 - Soufiane Hayou, Arnaud Doucet, Judith Rousseau:
The Curse of Depth in Kernel Regime. ICBINB@NeurIPS 2021: 41-47 - Nitya Kasturi, Igor L. Markov:
Text Ranking and Classification using Data Compression. ICBINB@NeurIPS 2021: 48-53 - Rainer Kelz, Gerhard Widmer:
Nonlinear Denoising, Linear Demixing. ICBINB@NeurIPS 2021: 54-58 - Chelsea Murray, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato:
Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not. ICBINB@NeurIPS 2021: 59-63 - Mike Van Ness, Madeleine Udell:
CDF Normalization for Controlling the Distribution of Hidden Nodes. ICBINB@NeurIPS 2021: 64-68 - 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
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