Ravid Shwartz-Ziv

According to our database, Ravid Shwartz-Ziv authored at least 36 papers between 2017 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

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, August, 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, Ravid Shwartz-Ziv, Hong Yan, Vinjia Jain, Aman Chadha, Manoj Karkee, Jia Wu, Seyedali Mirjalili

CoRR, July, 2025

From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning.

Chen Shani, Dan Jurafsky, Yann LeCun, Ravid Shwartz-Ziv

CoRR, May, 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, February, 2025

LiveBench: A Challenging, Contamination-Limited LLM Benchmark.

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

Proceedings of the Thirteenth International Conference on Learning Representations, 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, Ravid Shwartz-Ziv

Proceedings of the Thirteenth International Conference on Learning Representations, 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

Proceedings of the Thirteenth International Conference on Learning Representations, 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

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025

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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

To Compress or Not to Compress - Self-Supervised Learning and Information Theory: A Review.

Ravid Shwartz-Ziv, Yann LeCun

Entropy, March, 2024

Video Representation Learning with Joint-Embedding Predictive Architectures.

Katrina Drozdov, Ravid Shwartz-Ziv, Yann LeCun

CoRR, 2024

Does Representation Matter? Exploring Intermediate Layers in Large Language Models.

Oscar Skean, Md Rifat Arefin, Yann LeCun, Ravid Shwartz-Ziv

CoRR, 2024

Learning to Compress: Local Rank and Information Compression in Deep Neural Networks.

Niket Patel, Ravid Shwartz-Ziv

CoRR, 2024

LiveBench: A Challenging, Contamination-Free LLM Benchmark.

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

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

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

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, Sanmi Koyejo

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, Ravid Shwartz-Ziv

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

The Entropy Enigma: Success and Failure of Entropy Minimization.

Ori Press, Ravid Shwartz-Ziv, Yann LeCun, Matthias Bethge

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Variance-Covariance Regularization Improves Representation Learning.

Jiachen Zhu, Ravid Shwartz-Ziv, Yubei Chen, Yann LeCun

CoRR, 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

Simplifying Neural Network Training Under Class Imbalance.

Ravid Shwartz-Ziv, Micah Goldblum, Yucen Lily Li, C. Bayan Bruss, Andrew Gordon Wilson

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

An Information Theory Perspective on Variance-Invariance-Covariance Regularization.

Ravid Shwartz-Ziv, Randall Balestriero, Kenji Kawaguchi, Tim G. J. Rudner, Yann LeCun

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Reverse Engineering Self-Supervised Learning.

Ido Ben-Shaul, Ravid Shwartz-Ziv, Tomer Galanti, Shai Dekel, Yann LeCun

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Tabular data: Deep learning is not all you need.

Ravid Shwartz-Ziv, Amitai Armon

Inf. Fusion, 2022

What Do We Maximize in Self-Supervised Learning?

Ravid Shwartz-Ziv, Randall Balestriero, Yann LeCun

CoRR, 2022

Information Flow in Deep Neural Networks.

Ravid Shwartz-Ziv

CoRR, 2022

Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors.

Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, Andrew Gordon Wilson

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Spatial-Temporal Convolutional Network for Spread Prediction of COVID-19.

Ravid Shwartz-Ziv, Itamar Ben-Ari, Amitai Armon

CoRR, 2021

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

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

2020

The Dual Information Bottleneck.

Zoe Piran, Ravid Shwartz-Ziv, Naftali Tishby

CoRR, 2020

2019

Information in Infinite Ensembles of Infinitely-Wide Neural Networks.

Ravid Shwartz-Ziv, Alexander A. Alemi

Proceedings of the Symposium on Advances in Approximate Bayesian Inference, 2019

2018

Attentioned Convolutional LSTM InpaintingNetwork for Anomaly Detection in Videos.

Itamar Ben-Ari, Ravid Shwartz-Ziv

CoRR, 2018

2017

Opening the Black Box of Deep Neural Networks via Information.

Ravid Shwartz-Ziv, Naftali Tishby

CoRR, 2017