Opening the Black Box of Deep Neural Networks via Information. - Ravid Shwartz-Ziv; Naftali Tishby (CoRR, 2017)

Attentioned Convolutional LSTM InpaintingNetwork for Anomaly Detection in Videos. - Itamar Ben-Ari; Ravid Shwartz-Ziv (CoRR, 2018)

Information in Infinite Ensembles of Infinitely-Wide Neural Networks. - Ravid Shwartz-Ziv; Alexander A. Alemi (AABI, 2019)

The Dual Information Bottleneck. - Zoe Piran; Ravid Shwartz-Ziv; Naftali Tishby (CoRR, 2020)

Automated Testing of Graphics Units by Deep-Learning Detection of Visual Anomalies. - Lev Faivishevsky; Adi Szeskin; Ashwin K. Muppalla; Ravid Shwartz-Ziv; Itamar Ben-Ari; Ronen Laperdon; Benjamin Melloul; Tahi Hollander; Tom Hope; Amitai Armon (KDD, 2021)

Spatial-Temporal Convolutional Network for Spread Prediction of COVID-19. - Ravid Shwartz-Ziv; Itamar Ben-Ari; Amitai Armon (CoRR, 2021)

Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors. - Ravid Shwartz-Ziv; Micah Goldblum; Hossein Souri; Sanyam Kapoor; Chen Zhu 001; Yann LeCun; Andrew Gordon Wilson (NeurIPS, 2022)

Information Flow in Deep Neural Networks. - Ravid Shwartz-Ziv (CoRR, 2022)

What Do We Maximize in Self-Supervised Learning? - Ravid Shwartz-Ziv; Randall Balestriero; Yann LeCun (CoRR, 2022)

Tabular data: Deep learning is not all you need. - Ravid Shwartz-Ziv; Amitai Armon (Inf. Fusion, 2022)

How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization. - Jonas Geiping; Micah Goldblum; Gowthami Somepalli; Ravid Shwartz-Ziv; Tom Goldstein; Andrew Gordon Wilson (ICLR, 2023)

Reverse Engineering Self-Supervised Learning. - Ido Ben-Shaul; Ravid Shwartz-Ziv; Tomer Galanti; Shai Dekel; Yann LeCun (NeurIPS, 2023)

An Information Theory Perspective on Variance-Invariance-Covariance Regularization. - Ravid Shwartz-Ziv; Randall Balestriero; Kenji Kawaguchi; Tim G. J. Rudner; Yann LeCun (NeurIPS, 2023)

Simplifying Neural Network Training Under Class Imbalance. - Ravid Shwartz-Ziv; Micah Goldblum; Yucen Lily Li; C. Bayan Bruss; Andrew Gordon Wilson (NeurIPS, 2023)

An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization. - Ravid Shwartz-Ziv; Randall Balestriero; Kenji Kawaguchi; Tim G. J. Rudner; Yann LeCun (CoRR, 2023)

Variance-Covariance Regularization Improves Representation Learning. - Jiachen Zhu 002; Ravid Shwartz-Ziv; Yubei Chen; Yann LeCun (CoRR, 2023)

Sudden Drops in the Loss: Syntax Acquisition, Phase Transitions, and Simplicity Bias in MLMs. - Angelica Chen; Ravid Shwartz-Ziv; Kyunghyun Cho; Matthew L. Leavitt; Naomi Saphra (ICLR, 2024)

The Entropy Enigma: Success and Failure of Entropy Minimization. - Ori Press; Ravid Shwartz-Ziv; Yann LeCun; Matthias Bethge (ICML, 2024)

OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset. - Allen Roush; Yusuf Shabazz; Arvind Balaji; Peter Zhang; Stefano Mezza; Markus Zhang; Sanjay Basu; Sriram Vishwanath; Ravid Shwartz-Ziv (NeurIPS, 2024)

Towards an Improved Understanding and Utilization of Maximum Manifold Capacity Representations. - 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; et al. (CoRR, 2024)

Just How Flexible are Neural Networks in Practice? - Ravid Shwartz-Ziv; Micah Goldblum; Arpit Bansal; C. Bayan Bruss; Yann LeCun; Andrew Gordon Wilson (CoRR, 2024)

OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset. - Allen Roush; Yusuf Shabazz; Arvind Balaji; Peter Zhang; Stefano Mezza; Markus Zhang; Sanjay Basu; Sriram Vishwanath; Mehdi Fatemi; Ravid Shwartz-Ziv (CoRR, 2024)

LiveBench: A Challenging, Contamination-Free LLM Benchmark. - Colin White; Samuel Dooley; Manley Roberts; Arka Pal; Benjamin Feuer; Siddhartha Jain 001; Ravid Shwartz-Ziv; Neel Jain; Khalid Saifullah; Siddartha Naidu; Chinmay Hegde; Yann LeCun; et al. (CoRR, 2024)

Learning to Compress: Local Rank and Information Compression in Deep Neural Networks. - Niket Patel; Ravid Shwartz-Ziv (CoRR, 2024)

Does Representation Matter? Exploring Intermediate Layers in Large Language Models. - Oscar Skean; Md Rifat Arefin; Yann LeCun; Ravid Shwartz-Ziv (CoRR, 2024)

Video Representation Learning with Joint-Embedding Predictive Architectures. - Katrina Drozdov; Ravid Shwartz-Ziv; Yann LeCun (CoRR, 2024)

To Compress or Not to Compress - Self-Supervised Learning and Information Theory: A Review. - Ravid Shwartz-Ziv; Yann LeCun (Entropy, 2024)

Fine-Tuning with Uncertainty-Aware Priors Makes Vision and Language Foundation Models More Reliable. - Tim G. J. Rudner; Xiang Pan; Yucen Lily Li; Ravid Shwartz-Ziv; Andrew Gordon Wilson (AISTATS, 2025)

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation. - Tal Zeevi; Ravid Shwartz-Ziv; Yann LeCun; Lawrence H. Staib; John A. Onofrey (CVPR, 2025)

Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced Reasoning. - Md Rifat Arefin; Gopeshh Subbaraj; Nicolas Gontier; Yann LeCun; Irina Rish; Ravid Shwartz-Ziv; Christopher Pal (ICLR, 2025)

Turning Up the Heat: Min-p Sampling for Creative and Coherent LLM Outputs. - Nguyen Nhat Minh; Andrew Baker; Clement Neo; Allen G. Roush; Andreas Kirsch 004; Ravid Shwartz-Ziv (ICLR, 2025)

LiveBench: A Challenging, Contamination-Limited LLM Benchmark. - Colin White; Samuel Dooley; Manley Roberts; Arka Pal; Benjamin Feuer; Siddhartha Jain 001; Ravid Shwartz-Ziv; Neel Jain; Khalid Saifullah; Sreemanti Dey; Shubh-Agrawal; Sandeep Singh Sandha; et al. (ICLR, 2025)

Layer by Layer: Uncovering Hidden Representations in Language Models. - Oscar Skean; Md Rifat Arefin; Dan Zhao; Niket Patel; Jalal Naghiyev; Yann LeCun; Ravid Shwartz-Ziv (CoRR, 2025)

From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning. - Chen Shani; Dan Jurafsky; Yann LeCun; Ravid Shwartz-Ziv (CoRR, 2025)

Thinking Beyond Tokens: From Brain-Inspired Intelligence to Cognitive Foundations for Artificial General Intelligence and its Societal Impact. - 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; et al. (CoRR, 2025)

The Illusion of Progress: Re-evaluating Hallucination Detection in LLMs. - Denis Janiak; Jakub Binkowski; Albert Sawczyn; Bogdan Gabrys; Ravid Shwartz-Ziv; Tomasz Kajdanowicz (CoRR, 2025)

Attention Sinks and Compression Valleys in LLMs are Two Sides of the Same Coin. - Enrique Queipo-de-Llano; Alvaro Arroyo; Federico Barbero; Xiaowen Dong 001; Michael M. Bronstein; Yann LeCun; Ravid Shwartz-Ziv (CoRR, 2025)

Antislop: A Comprehensive Framework for Identifying and Eliminating Repetitive Patterns in Language Models. - Samuel Paech; Allen Roush; Judah Goldfeder; Ravid Shwartz-Ziv (CoRR, 2025)

Measure what Matters: Psychometric Evaluation of AI with Situational Judgment Tests. - Alexandra Yost; Shreyans Jain; Shivam Raval; Grant Corser; Allen Roush; Nina Xu; Jacqueline Hammack; Ravid Shwartz-Ziv; Amirali Abdullah (CoRR, 2025)

Exploring Human-AI Conceptual Alignment through the Prism of Chess. - Semyon Lomaso; Judah Goldfeder; Mehmet Hamza Erol; Matthew So; Yao Yan; Addison Howard; J. Nathan Kutz; Ravid Shwartz-Ziv (CoRR, 2025)