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ORCIDRavid Shwartz-Ziv
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2020 – today
- 2024
[j2]Ravid Shwartz-Ziv, Yann LeCun:
To Compress or Not to Compress - Self-Supervised Learning and Information Theory: A Review. Entropy 26(3): 252 (2024)- [c9]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 - [c8]Ori Press, Ravid Shwartz-Ziv, Yann LeCun, Matthias Bethge:
The Entropy Enigma: Success and Failure of Entropy Minimization. ICML 2024 - [i26]Ori Press, Ravid Shwartz-Ziv, Yann LeCun, Matthias Bethge:
The Entropy Enigma: Success and Failure of Entropy Minimization. CoRR abs/2405.05012 (2024) - [i25]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 abs/2406.09366 (2024) - [i24]Ravid Shwartz-Ziv, Micah Goldblum, Arpit Bansal, C. Bayan Bruss, Yann LeCun, Andrew Gordon Wilson:
Just How Flexible are Neural Networks in Practice? CoRR abs/2406.11463 (2024) - [i23]Allen Roush, Yusuf Shabazz, Arvind Balaji, Peter Zhang, Stefano Mezza, Markus Zhang, Sanjay Basu, Sriram Vishwanath, Mehdi Fatemi, Ravid Shwartz-Ziv:
OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset. CoRR abs/2406.14657 (2024) - [i22]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 abs/2406.19314 (2024) - [i21]Niket Patel, Ravid Shwartz-Ziv:
Learning to Compress: Local Rank and Information Compression in Deep Neural Networks. CoRR abs/2410.07687 (2024) - [i20]Md Rifat Arefin, Gopeshh Subbaraj, Nicolas Gontier, Yann LeCun, Irina Rish, Ravid Shwartz-Ziv, Christopher Pal:
Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced Reasoning. CoRR abs/2411.02344 (2024) - [i19]Tal Zeevi, Ravid Shwartz-Ziv, Yann LeCun, Lawrence H. Staib, John A. Onofrey:
Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation. CoRR abs/2412.07169 (2024) - [i18]Oscar Skean, Md Rifat Arefin, Yann LeCun, Ravid Shwartz-Ziv:
Does Representation Matter? Exploring Intermediate Layers in Large Language Models. CoRR abs/2412.09563 (2024) - [i17]Katrina Drozdov, Ravid Shwartz-Ziv, Yann LeCun:
Video Representation Learning with Joint-Embedding Predictive Architectures. CoRR abs/2412.10925 (2024) - 2023
- [c7]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 - [c6]Ido Ben-Shaul, Ravid Shwartz-Ziv, Tomer Galanti, Shai Dekel, Yann LeCun:
Reverse Engineering Self-Supervised Learning. NeurIPS 2023 - [c5]Ravid Shwartz-Ziv, Randall Balestriero, Kenji Kawaguchi, Tim G. J. Rudner, Yann LeCun:
An Information Theory Perspective on Variance-Invariance-Covariance Regularization. NeurIPS 2023 - [c4]Ravid Shwartz-Ziv, Micah Goldblum, Yucen Lily Li, C. Bayan Bruss, Andrew Gordon Wilson:
Simplifying Neural Network Training Under Class Imbalance. NeurIPS 2023 - [i16]Ravid Shwartz-Ziv, Randall Balestriero, Kenji Kawaguchi, Tim G. J. Rudner, Yann LeCun:
An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization. CoRR abs/2303.00633 (2023) - [i15]Ravid Shwartz-Ziv, Yann LeCun:
To Compress or Not to Compress - Self-Supervised Learning and Information Theory: A Review. CoRR abs/2304.09355 (2023) - [i14]Ido Ben-Shaul, Ravid Shwartz-Ziv, Tomer Galanti, Shai Dekel, Yann LeCun:
Reverse Engineering Self-Supervised Learning. CoRR abs/2305.15614 (2023) - [i13]Jiachen Zhu, Ravid Shwartz-Ziv, Yubei Chen, Yann LeCun:
Variance-Covariance Regularization Improves Representation Learning. CoRR abs/2306.13292 (2023) - [i12]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. CoRR abs/2309.07311 (2023) - [i11]Ravid Shwartz-Ziv, Micah Goldblum, Yucen Lily Li, C. Bayan Bruss, Andrew Gordon Wilson:
Simplifying Neural Network Training Under Class Imbalance. CoRR abs/2312.02517 (2023) - 2022
- [j1]Ravid Shwartz-Ziv, Amitai Armon:
Tabular data: Deep learning is not all you need. Inf. Fusion 81: 84-90 (2022) - [c3]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. NeurIPS 2022 - [i10]Ravid Shwartz-Ziv:
Information Flow in Deep Neural Networks. CoRR abs/2202.06749 (2022) - [i9]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 abs/2205.10279 (2022) - [i8]Ravid Shwartz-Ziv, Randall Balestriero, Yann LeCun:
What Do We Maximize in Self-Supervised Learning? CoRR abs/2207.10081 (2022) - [i7]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 abs/2210.06441 (2022) - 2021
- [c2]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: 2811-2821 - [i6]Ravid Shwartz-Ziv, Itamar Ben-Ari, Amitai Armon:
Spatial-Temporal Convolutional Network for Spread Prediction of COVID-19. CoRR abs/2101.05304 (2021) - [i5]Ravid Shwartz-Ziv, Amitai Armon:
Tabular Data: Deep Learning is Not All You Need. CoRR abs/2106.03253 (2021) - 2020
- [i4]Zoe Piran, Ravid Shwartz-Ziv, Naftali Tishby:
The Dual Information Bottleneck. CoRR abs/2006.04641 (2020)
2010 – 2019
- 2019
- [c1]Ravid Shwartz-Ziv, Alexander A. Alemi:
Information in Infinite Ensembles of Infinitely-Wide Neural Networks. AABI 2019: 1-17 - [i3]Ravid Shwartz-Ziv, Alexander A. Alemi:
Information in Infinite Ensembles of Infinitely-Wide Neural Networks. CoRR abs/1911.09189 (2019) - 2018
- [i2]Itamar Ben-Ari, Ravid Shwartz-Ziv:
Attentioned Convolutional LSTM InpaintingNetwork for Anomaly Detection in Videos. CoRR abs/1811.10228 (2018) - 2017
- [i1]Ravid Shwartz-Ziv, Naftali Tishby:
Opening the Black Box of Deep Neural Networks via Information. CoRR abs/1703.00810 (2017)
Coauthor Index
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