Ravid Shwartz-Ziv

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 29

Top Topics

Class Imbalance

Jensen Shannon Divergence

Information Theory

Top Venues

Publications

Colin White, Samuel Dooley, Manley Roberts, Arka Pal, Benjamin Feuer, Siddhartha Jain, Ravid Shwartz-Ziv, Neel Jain, Khalid Saifullah, Siddartha Naidu, Chinmay Hegde, Yann LeCun, Tom Goldstein, Willie Neiswanger, Micah Goldblum
LiveBench: A Challenging, Contamination-Free LLM Benchmark. CoRR (2024)
Ravid Shwartz-Ziv, Yann LeCun
To Compress or Not to Compress - Self-Supervised Learning and Information Theory: A Review. Entropy 26 (3) (2024)
Angelica Chen, Ravid Shwartz-Ziv, Kyunghyun Cho, Matthew L. Leavitt, Naomi Saphra
Sudden Drops in the Loss: Syntax Acquisition, Phase Transitions, and Simplicity Bias in MLMs. ICLR (2024)
Ravid Shwartz-Ziv, Micah Goldblum, Arpit Bansal, C. Bayan Bruss, Yann LeCun, Andrew Gordon Wilson
Just How Flexible are Neural Networks in Practice? CoRR (2024)
Ori Press, Ravid Shwartz-Ziv, Yann LeCun, Matthias Bethge
The Entropy Enigma: Success and Failure of Entropy Minimization. CoRR (2024)
Rylan Schaeffer, Victor Lecomte, Dhruv Bhandarkar Pai, Andres Carranza, Berivan Isik, Alyssa Unell, Mikail Khona, Thomas E. Yerxa, Yann LeCun, SueYeon Chung, Andrey Gromov, Ravid Shwartz-Ziv, Sanmi Koyejo
Towards an Improved Understanding and Utilization of Maximum Manifold Capacity Representations. CoRR (2024)
Ido Ben-Shaul, Ravid Shwartz-Ziv, Tomer Galanti, Shai Dekel, Yann LeCun
Reverse Engineering Self-Supervised Learning. NeurIPS (2023)
Ravid Shwartz-Ziv, Yann LeCun
To Compress or Not to Compress - Self-Supervised Learning and Information Theory: A Review. CoRR (2023)
Jonas Geiping, Micah Goldblum, Gowthami Somepalli, Ravid Shwartz-Ziv, Tom Goldstein, Andrew Gordon Wilson
How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization. ICLR (2023)
Ravid Shwartz-Ziv, Randall Balestriero, Kenji Kawaguchi, Tim G. J. Rudner, Yann LeCun
An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization. CoRR (2023)
Jiachen Zhu, Ravid Shwartz-Ziv, Yubei Chen, Yann LeCun
Variance-Covariance Regularization Improves Representation Learning. CoRR (2023)
Ido Ben-Shaul, Ravid Shwartz-Ziv, Tomer Galanti, Shai Dekel, Yann LeCun
Reverse Engineering Self-Supervised Learning. CoRR (2023)
Ravid Shwartz-Ziv, Micah Goldblum, Yucen Lily Li, C. Bayan Bruss, Andrew Gordon Wilson
Simplifying Neural Network Training Under Class Imbalance. NeurIPS (2023)
Ravid Shwartz-Ziv, Randall Balestriero, Kenji Kawaguchi, Tim G. J. Rudner, Yann LeCun
An Information Theory Perspective on Variance-Invariance-Covariance Regularization. NeurIPS (2023)
Ravid Shwartz-Ziv, Micah Goldblum, Yucen Lily Li, C. Bayan Bruss, Andrew Gordon Wilson
Simplifying Neural Network Training Under Class Imbalance. CoRR (2023)
Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, Andrew Gordon Wilson
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors. CoRR (2022)
Ravid Shwartz-Ziv, Randall Balestriero, Yann LeCun
What Do We Maximize in Self-Supervised Learning? CoRR (2022)
Ravid Shwartz-Ziv, Amitai Armon
Tabular data: Deep learning is not all you need. Inf. Fusion 81 (2022)
Ravid Shwartz-Ziv
Information Flow in Deep Neural Networks. CoRR (2022)
Jonas Geiping, Micah Goldblum, Gowthami Somepalli, Ravid Shwartz-Ziv, Tom Goldstein, Andrew Gordon Wilson
How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization. CoRR (2022)
Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, Andrew G. Wilson
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors. NeurIPS (2022)
Ravid Shwartz-Ziv, Amitai Armon
Tabular Data: Deep Learning is Not All You Need. CoRR (2021)
Lev Faivishevsky, Adi Szeskin, Ashwin K. Muppalla, Ravid Shwartz-Ziv, Itamar Ben-Ari, Ronen Laperdon, Benjamin Melloul, Tahi Hollander, Tom Hope, Amitai Armon
Automated Testing of Graphics Units by Deep-Learning Detection of Visual Anomalies. KDD (2021)
Ravid Shwartz-Ziv, Itamar Ben-Ari, Amitai Armon
Spatial-Temporal Convolutional Network for Spread Prediction of COVID-19. CoRR (2021)
Zoe Piran, Ravid Shwartz-Ziv, Naftali Tishby
The Dual Information Bottleneck. CoRR (2020)
Ravid Shwartz-Ziv, Alexander A. Alemi
Information in Infinite Ensembles of Infinitely-Wide Neural Networks. CoRR (2019)
Ravid Shwartz-Ziv, Alexander A. Alemi
Information in Infinite Ensembles of Infinitely-Wide Neural Networks. AABI (2019)
Itamar Ben-Ari, Ravid Shwartz-Ziv
Attentioned Convolutional LSTM InpaintingNetwork for Anomaly Detection in Videos. CoRR (2018)
Ravid Shwartz-Ziv, Naftali Tishby
Opening the Black Box of Deep Neural Networks via Information. CoRR (2017)