Ravid Shwartz-Ziv

62 papers A* 15A 1Journal 45Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Beyond the Loss Curve: Scaling Laws, Active Learning, and the Limits of Learning from Exact Posteriors. CoRR Arian Khorasani, Nathaniel Chen, Yug D. Oswal, Akshat Santhana Gopalan, Egemen Kolemen, Ravid Shwartz-Ziv
2026	J	`jnl`	Do Multi-Agents Dream of Electric Screens? Achieving Perfect Accuracy on AndroidWorld Through Task Decomposition. CoRR Pierre-Louis Favreau, Jean-Pierre Lo, Clement Guiguet, Charles Simon-Meunier, Nicolas Dehandschoewercker, Allen G. Roush, Judah Goldfeder, Ravid Shwartz-Ziv
2026	J	`jnl`	Latent Transfer Attack: Adversarial Examples via Generative Latent Spaces. CoRR Eitan Shaar, Ariel Shaulov, Yalcin Tur, Gal Chechik, Ravid Shwartz-Ziv
2026	J	`jnl`	Soft Clustering Anchors for Self-Supervised Speech Representation Learning in Joint Embedding Prediction Architectures. CoRR Georgios Ioannides, Adrian Kieback, Judah Goldfeder, Linsey Pang, Aman Chadha, Aaron Elkins, Yann LeCun, Ravid Shwartz-Ziv
2026	J	`jnl`	UAT-LITE: Inference-Time Uncertainty-Aware Attention for Pretrained Transformers. CoRR Elias Hossain, Shubhashis Roy Dipta, Subash Neupane, Rajib Rana, Ravid Shwartz-Ziv, Ivan Garibay, Niloofar Yousefi
2026	J	`jnl`	When Attention Collapses: How Degenerate Layers in LLMs Enable Smaller, Stronger Models. Trans. Mach. Learn. Res. Sunny Sanyal, Ravid Shwartz-Ziv, Alex Dimakis, Sujay Sanghavi
2025	J	`jnl`	A superpersuasive autonomous policy debating system. CoRR Allen Roush, Devin Gonier, John Hines, Judah Goldfeder, Philippe Martin Wyder, Sanjay Basu, Ravid Shwartz-Ziv
2025	J	`jnl`	Antislop: A Comprehensive Framework for Identifying and Eliminating Repetitive Patterns in Language Models. CoRR Samuel Paech, Allen Roush, Judah Goldfeder, Ravid Shwartz-Ziv
2025	J	`jnl`	Attention Sinks and Compression Valleys in LLMs are Two Sides of the Same Coin. CoRR Enrique Queipo-de-Llano, Alvaro Arroyo, Federico Barbero, Xiaowen Dong, Michael M. Bronstein, Yann LeCun, Ravid Shwartz-Ziv
2025	J	`jnl`	Exploring Human-AI Conceptual Alignment through the Prism of Chess. CoRR Semyon Lomaso, Judah Goldfeder, Mehmet Hamza Erol, Matthew So, Yao Yan, Addison Howard, J. Nathan Kutz, Ravid Shwartz-Ziv
2025	A	`conf`	Fine-Tuning with Uncertainty-Aware Priors Makes Vision and Language Foundation Models More Reliable. AISTATS Tim G. J. Rudner, Xiang Pan, Yucen Lily Li, Ravid Shwartz-Ziv, Andrew Gordon Wilson
2025	J	`jnl`	From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning. CoRR Chen Shani, Dan Jurafsky, Yann LeCun, Ravid Shwartz-Ziv
2025	J	`jnl`	JEPA as a Neural Tokenizer: Learning Robust Speech Representations with Density Adaptive Attention. CoRR Georgios Ioannides, Christos Constantinou, Aman Chadha, Aaron Elkins, Linsey Pang, Ravid Shwartz-Ziv, Yann LeCun
2025	A*	`conf`	Layer by Layer: Uncovering Hidden Representations in Language Models. ICML Oscar Skean, Md Rifat Arefin, Dan Zhao, Niket Patel, Jalal Naghiyev, Yann LeCun, Ravid Shwartz-Ziv
2025	J	`jnl`	Layer by Layer: Uncovering Hidden Representations in Language Models. CoRR Oscar Skean, Md Rifat Arefin, Dan Zhao, Niket Patel, Jalal Naghiyev, Yann LeCun, Ravid Shwartz-Ziv
2025	A*	`conf`	LiveBench: A Challenging, Contamination-Limited LLM Benchmark. ICLR Colin White, Samuel Dooley, Manley Roberts, Arka Pal, Benjamin Feuer, Siddhartha Jain, Ravid Shwartz-Ziv, Neel Jain, Khalid Saifullah, Sreemanti Dey, Shubh-Agrawal, Sandeep Singh Sandha, Siddartha V. Naidu, Chinmay Hegde, Yann LeCun, Tom Goldstein, Willie Neiswanger, Micah Goldblum
2025	J	`jnl`	Measure what Matters: Psychometric Evaluation of AI with Situational Judgment Tests. CoRR Alexandra Yost, Shreyans Jain, Shivam Raval, Grant Corser, Allen Roush, Nina Xu, Jacqueline Hammack, Ravid Shwartz-Ziv, Amirali Abdullah
2025	A*	`conf`	Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation. CVPR Tal Zeevi, Ravid Shwartz-Ziv, Yann LeCun, Lawrence H. Staib, John A. Onofrey
2025	A*	`conf`	Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced Reasoning. ICLR Md Rifat Arefin, Gopeshh Subbaraj, Nicolas Gontier, Yann LeCun, Irina Rish, Ravid Shwartz-Ziv, Christopher Pal
2025	A*	`conf`	The Illusion of Progress: Re-evaluating Hallucination Detection in LLMs. EMNLP Denis Janiak, Jakub Binkowski, Albert Sawczyn, Bogdan Gabrys, Ravid Shwartz-Ziv, Tomasz Kajdanowicz
2025	J	`jnl`	The Illusion of Progress: Re-evaluating Hallucination Detection in LLMs. CoRR Denis Janiak, Jakub Binkowski, Albert Sawczyn, Bogdan Gabrys, Ravid Shwartz-Ziv, Tomasz Kajdanowicz
2025	J	`jnl`	Thinking Beyond Tokens: From Brain-Inspired Intelligence to Cognitive Foundations for Artificial General Intelligence and its Societal Impact. CoRR Rizwan Qureshi, Ranjan Sapkota, Abbas Shah, Amgad Muneer, Anas Zafar, Ashmal Vayani, Maged Shoman, Abdelrahman B. M. Eldaly, Kai Zhang, Ferhat Sadak, Shaina Raza, Xinqi Fan, Ravid Shwartz-Ziv, Hong Yan, Vinjia Jain, Aman Chadha, Manoj Karkee, Jia Wu, Seyedali Mirjalili
2025	A*	`conf`	Turning Up the Heat: Min-p Sampling for Creative and Coherent LLM Outputs. ICLR Nguyen Nhat Minh, Andrew Baker, Clement Neo, Allen G. Roush, Andreas Kirsch, Ravid Shwartz-Ziv
2025	J	`jnl`	You Had One Job: Per-Task Quantization Using LLMs' Hidden Representations. CoRR Amit Levi, Raz Lapid, Rom Himelstein, Yaniv Nemcovsky, Ravid Shwartz-Ziv, Avi Mendelson
2024	J	`jnl`	Does Representation Matter? Exploring Intermediate Layers in Large Language Models. CoRR Oscar Skean, Md Rifat Arefin, Yann LeCun, Ravid Shwartz-Ziv
2024	J	`jnl`	Just How Flexible are Neural Networks in Practice? CoRR Ravid Shwartz-Ziv, Micah Goldblum, Arpit Bansal, C. Bayan Bruss, Yann LeCun, Andrew Gordon Wilson
2024	J	`jnl`	Learning to Compress: Local Rank and Information Compression in Deep Neural Networks. CoRR Niket Patel, Ravid Shwartz-Ziv
2024	J	`jnl`	LiveBench: A Challenging, Contamination-Free LLM Benchmark. CoRR 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
2024	A*	`conf`	OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset. NeurIPS Allen Roush, Yusuf Shabazz, Arvind Balaji, Peter Zhang, Stefano Mezza, Markus Zhang, Sanjay Basu, Sriram Vishwanath, Ravid Shwartz-Ziv
2024	J	`jnl`	OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset. CoRR Allen Roush, Yusuf Shabazz, Arvind Balaji, Peter Zhang, Stefano Mezza, Markus Zhang, Sanjay Basu, Sriram Vishwanath, Mehdi Fatemi, Ravid Shwartz-Ziv
2024	J	`jnl`	Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation. CoRR Tal Zeevi, Ravid Shwartz-Ziv, Yann LeCun, Lawrence H. Staib, John A. Onofrey
2024	J	`jnl`	Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced Reasoning. CoRR Md Rifat Arefin, Gopeshh Subbaraj, Nicolas Gontier, Yann LeCun, Irina Rish, Ravid Shwartz-Ziv, Christopher Pal
2024	A*	`conf`	Sudden Drops in the Loss: Syntax Acquisition, Phase Transitions, and Simplicity Bias in MLMs. ICLR Angelica Chen, Ravid Shwartz-Ziv, Kyunghyun Cho, Matthew L. Leavitt, Naomi Saphra
2024	A*	`conf`	The Entropy Enigma: Success and Failure of Entropy Minimization. ICML Ori Press, Ravid Shwartz-Ziv, Yann LeCun, Matthias Bethge
2024	J	`jnl`	The Entropy Enigma: Success and Failure of Entropy Minimization. CoRR Ori Press, Ravid Shwartz-Ziv, Yann LeCun, Matthias Bethge
2024	J	`jnl`	To Compress or Not to Compress - Self-Supervised Learning and Information Theory: A Review. Entropy Ravid Shwartz-Ziv, Yann LeCun
2024	J	`jnl`	Towards an Improved Understanding and Utilization of Maximum Manifold Capacity Representations. CoRR 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
2024	J	`jnl`	Video Representation Learning with Joint-Embedding Predictive Architectures. CoRR Katrina Drozdov, Ravid Shwartz-Ziv, Yann LeCun
2023	A*	`conf`	An Information Theory Perspective on Variance-Invariance-Covariance Regularization. NeurIPS Ravid Shwartz-Ziv, Randall Balestriero, Kenji Kawaguchi, Tim G. J. Rudner, Yann LeCun
2023	J	`jnl`	An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization. CoRR Ravid Shwartz-Ziv, Randall Balestriero, Kenji Kawaguchi, Tim G. J. Rudner, Yann LeCun
2023	A*	`conf`	How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization. ICLR Jonas Geiping, Micah Goldblum, Gowthami Somepalli, Ravid Shwartz-Ziv, Tom Goldstein, Andrew Gordon Wilson
2023	A*	`conf`	Reverse Engineering Self-Supervised Learning. NeurIPS Ido Ben-Shaul, Ravid Shwartz-Ziv, Tomer Galanti, Shai Dekel, Yann LeCun
2023	J	`jnl`	Reverse Engineering Self-Supervised Learning. CoRR Ido Ben-Shaul, Ravid Shwartz-Ziv, Tomer Galanti, Shai Dekel, Yann LeCun
2023	A*	`conf`	Simplifying Neural Network Training Under Class Imbalance. NeurIPS Ravid Shwartz-Ziv, Micah Goldblum, Yucen Lily Li, C. Bayan Bruss, Andrew Gordon Wilson
2023	J	`jnl`	Simplifying Neural Network Training Under Class Imbalance. CoRR Ravid Shwartz-Ziv, Micah Goldblum, Yucen Lily Li, C. Bayan Bruss, Andrew Gordon Wilson
2023	J	`jnl`	Sudden Drops in the Loss: Syntax Acquisition, Phase Transitions, and Simplicity Bias in MLMs. CoRR Angelica Chen, Ravid Shwartz-Ziv, Kyunghyun Cho, Matthew L. Leavitt, Naomi Saphra
2023	J	`jnl`	To Compress or Not to Compress - Self-Supervised Learning and Information Theory: A Review. CoRR Ravid Shwartz-Ziv, Yann LeCun
2023	J	`jnl`	Variance-Covariance Regularization Improves Representation Learning. CoRR Jiachen Zhu, Ravid Shwartz-Ziv, Yubei Chen, Yann LeCun
2022	J	`jnl`	How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization. CoRR Jonas Geiping, Micah Goldblum, Gowthami Somepalli, Ravid Shwartz-Ziv, Tom Goldstein, Andrew Gordon Wilson
2022	J	`jnl`	Information Flow in Deep Neural Networks. CoRR Ravid Shwartz-Ziv
2022	A*	`conf`	Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors. NeurIPS Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, Andrew Gordon Wilson
2022	J	`jnl`	Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors. CoRR Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, Andrew Gordon Wilson
2022	J	`jnl`	Tabular data: Deep learning is not all you need. Inf. Fusion Ravid Shwartz-Ziv, Amitai Armon
2022	J	`jnl`	What Do We Maximize in Self-Supervised Learning? CoRR Ravid Shwartz-Ziv, Randall Balestriero, Yann LeCun
2021	A*	`conf`	Automated Testing of Graphics Units by Deep-Learning Detection of Visual Anomalies. KDD Lev Faivishevsky, Adi Szeskin, Ashwin K. Muppalla, Ravid Shwartz-Ziv, Itamar Ben-Ari, Ronen Laperdon, Benjamin Melloul, Tahi Hollander, Tom Hope, Amitai Armon
2021	J	`jnl`	Spatial-Temporal Convolutional Network for Spread Prediction of COVID-19. CoRR Ravid Shwartz-Ziv, Itamar Ben-Ari, Amitai Armon
2021	J	`jnl`	Tabular Data: Deep Learning is Not All You Need. CoRR Ravid Shwartz-Ziv, Amitai Armon
2020	J	`jnl`	The Dual Information Bottleneck. CoRR Zoe Piran, Ravid Shwartz-Ziv, Naftali Tishby
2019	—	`conf`	Information in Infinite Ensembles of Infinitely-Wide Neural Networks. AABI Ravid Shwartz-Ziv, Alexander A. Alemi
2019	J	`jnl`	Information in Infinite Ensembles of Infinitely-Wide Neural Networks. CoRR Ravid Shwartz-Ziv, Alexander A. Alemi
2018	J	`jnl`	Attentioned Convolutional LSTM InpaintingNetwork for Anomaly Detection in Videos. CoRR Itamar Ben-Ari, Ravid Shwartz-Ziv
2017	J	`jnl`	Opening the Black Box of Deep Neural Networks via Information. CoRR Ravid Shwartz-Ziv, Naftali Tishby