Opening the black box of deep neural networks via information R Shwartz-Ziv, N Tishby Entropy, 21(219), 3390, 2019 | 1655 | 2019 |
Tabular Data: Deep Learning is Not All You Need R Shwartz-Ziv, A Armon Information Fusion 81, 84-90, 2022 | 1122 | 2022 |
Information Flow in Deep Neural Networks R Shwartz Ziv arXiv preprint arXiv:2202.06749, 2022 | 174* | 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 | 82* | 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 | 45* | 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 | 38* | 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 | 37 | 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 | 28 | 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 | 26* | 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 | 19* | 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 |
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 | 4 | 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 |
An Information-Theoretic Understanding of Maximum Manifold Capacity Representations R Schaeffer, B Isik, V Lecomte, M Khona, Y LeCun, A Gromov, ... NeurIPS 2023 workshop: Information-Theoretic Principles in Cognitive Systems, 2023 | 3* | 2023 |
Sequence modeling using a memory controller extension for LSTM R Shwartz-Ziv, I Ben-Ari NIPS 2017 Time Series Workshop, 2017 | 3 | 2017 |
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 | 2 | 2023 |