Ravid Shwartz-Ziv

According to our database, Ravid Shwartz-Ziv authored at least 17 papers between 2017 and 2023.

Collaborative distances:

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

2023

Variance-Covariance Regularization Improves Representation Learning.

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

CoRR, 2023

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

Ravid Shwartz-Ziv, 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