Ravid Shwartz-Ziv

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Yann LeCunChief AI Scientist at Facebook & Silver Professor at the Courant Institute, New York UniversityVerified email at cs.nyu.edu
Amitai ArmonIntelVerified email at cs.tau.ac.il
Micah GoldblumColumbia UniversityVerified email at columbia.edu
Andrew Gordon WilsonNew York UniversityVerified email at cims.nyu.edu
Randall BalestrieroAI ResearcherVerified email at brown.edu
Ido Maor, PhDNational Institutes of Health (NIH)Verified email at nih.gov
Mizrahi AdiThe Edmond and Lily Safra Center For Brain Sciences (ELSC)Verified email at mail.huji.ac.il
Tom GoldsteinVolpi-Cupal Professor of Computer Science, University of MarylandVerified email at cs.umd.edu
Kenji KawaguchiPresidential Young Professor, National University of SingaporeVerified email at nus.edu.sg
Haim SompolinskyProfessor of Molecular and Cellular Biology and of Physics, Harvard UniversityVerified email at mcb.harvard.edu
Jonas GeipingELLIS Institute Tübingen & Max Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Gowthami SomepalliUniversity of Maryland, College ParkVerified email at umd.edu
Tim G. J. RudnerFaculty Fellow, New York UniversityVerified email at nyu.edu
Hossein SouriSenior AI Researcher at SamsungVerified email at samsung.com
Chen ZhuMetaVerified email at meta.com
Alexander A AlemiGoogle ResearchVerified email at google.com
Zoe PiranHebrew University of JerusalemVerified email at mail.huji.ac.il
C. Bayan BrussCapital OneVerified email at capitalone.com
Amichai PainskyTel Aviv UniversityVerified email at tauex.tau.ac.il
Adi SzeskinIntel, Hebrew University of JerusalemVerified email at mail.huji.ac.il

Ravid Shwartz-Ziv

New York University

Verified email at nyu.edu - Homepage

machine learning deep learning representation learning theory neuroscience


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Opening the black box of deep neural networks via information R Shwartz-Ziv, N Tishby Entropy, 21(219), 3390, 2019	1682	2019
Tabular Data: Deep Learning is Not All You Need R Shwartz-Ziv, A Armon Information Fusion 81, 84-90, 2022	1208	2022
Information Flow in Deep Neural Networks R Shwartz Ziv arXiv preprint arXiv:2202.06749, 2022	177*	2022
To Compress or Not to Compress--Self-Supervised Learning and Information Theory: A Review R Shwartz-Ziv, Y LeCun arXiv preprint arXiv:2304.09355, 2023	92*	2023
Information in Infinite Ensembles of Infinitely-Wide Neural Networks R Shwartz-Ziv, AA Alemi Proceedings of the Symposium on Advances in Approximate Bayesian Inference, 2019	59*	2019
The dual information bottleneck Z Piran, R Shwartz-Ziv, N Tishby https://arxiv.org/abs/2006.04641, 2020	45*	2020
Representation compression and generalization in deep neural networks R Shwartz-Ziv, A Painsky, N Tishby https://openreview.net/pdf?id=SkeL6sCqK7, 2019	44*	2019
What Do We Maximize in Self-Supervised Learning? R Shwartz-Ziv, R Balestriero, Y LeCun ICML 2022: Pre-training: Perspectives, Pitfalls, and Paths Forward workshop, 2022	39*	2022
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors R Shwartz-Ziv, M Goldblum, H Souri, S Kapoor, C Zhu, Y LeCun, ... NeurIPS 2022, 2022	38	2022
Neural correlates of learning pure tones or natural sounds in the auditory cortex I Maor, R Shwartz-Ziv, L Feigin, Y Elyada, H Sompolinsky, A Mizrahi Frontiers in neural circuits 13, 82, 2020	37*	2020
How much data are augmentations worth? An investigation into scaling laws, invariance, and implicit regularization J Geiping, M Goldblum, G Somepalli, R Shwartz-Ziv, T Goldstein, ... arXiv preprint arXiv:2210.06441, 2022	29	2022
Sudden Drops in the Loss: Syntax Acquisition, Phase Transitions, and Simplicity Bias in MLMs A Chen, R Shwartz-Ziv, K Cho, ML Leavitt, N Saphra arXiv preprint arXiv:2309.07311, 2023	27*	2023
Reverse engineering self-supervised learning I Ben-Shaul, R Shwartz-Ziv, T Galanti, S Dekel, Y LeCun Advances in Neural Information Processing Systems 36, 58324-58345, 2023	25	2023
Attentioned convolutional LSTM inpainting network for anomaly detection in videos R Shwartz-Ziv, I Ben-Ari NIPS 2018 Workshop on Systems for ML, 2018	19*	2018
LiveBench: A Challenging, Contamination-Free LLM Benchmark C White, S Dooley, M Roberts, A Pal, B Feuer, S Jain, R Shwartz-Ziv, ...	9*	2024
An information theory perspective on variance-invariance-covariance regularization R Shwartz-Ziv, R Balestriero, K Kawaguchi, TGJ Rudner, Y LeCun Advances in Neural Information Processing Systems 36, 33965-33998, 2023	7*	2023
The entropy enigma: Success and failure of entropy minimization O Press, R Shwartz-Ziv, Y LeCun, M Bethge Forty-first International Conference on Machine Learning (ICML 2024), 2024	6	2024
Simplifying neural network training under class imbalance R Shwartz-Ziv, M Goldblum, Y Li, CB Bruss, AG Wilson Advances in Neural Information Processing Systems 36, 2024	4*	2024
Variance-covariance regularization improves representation learning J Zhu, K Evtimova, Y Chen, R Shwartz-Ziv, Y LeCun arXiv preprint arXiv:2306.13292, 2023	4	2023
Sequence modeling using a memory controller extension for LSTM R Shwartz-Ziv, I Ben-Ari NIPS 2017 Time Series Workshop, 2017	3	2017

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