Publications

Ravid Shwartz-Ziv, Randall, Balestriero, Yann, LeCun (2022). What Do We Maximize in Self-Supervised Learning?. In ICML 2022: Pre-training: Perspectives, Pitfalls, and Paths Forward workshop.

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

Ravid Shwartz-Ziv, Armon Amitai (2022). Tabular Data: Deep Learning is Not All You Need. In Information Fusion.

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

Ido Maor, Ravid Shwartz-Ziv, Libi Feigin, Yishai Elyada, Haim Sompolinsky, Adi Mizrahi (2020). Neural Correlates of Learning Pure Tones or Natural Sounds in the Auditory Cortex. Frontiers in Neural Circuits.

Ravid Shwartz-Ziv, Alexander A Alemi (2020). Information in Infinite Ensembles of Infinitely-Wide Neural Networks. In The Symposium on Advances in Approximate Bayesian Inference.

Ben-Ari Itamar(?), Ravid Shwartz-Ziv(?) (2018). Attentioned Convolutional LSTM Inpaintingv Network for Anomaly Detection in Videos. NIPS 2018 Workshop on Systems for ML.

Ravid Shwartz-Ziv(?), Ben-Ari Itamar(?) (2017). Sequence Modeling Using a Memory Controller Extension for LSTM. NIPS 2017 Time Series Workshop.

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

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Ravid Shwartz-Ziv, Armon Amitai (2017). Tabular Data: Deep Learning is Not All You Need. In TICML 2021 Workshop AutoML.

Faivishevsky Lev, Muppalla Ashwin, Ravid Shwartz-Ziv, Laperdon Ronen, Melloul Benjamin, Hollander Tahi, Amitai Armon (0001). Automated Testing of Graphics Units by Deep-Learning Detection of Visual Anomalies. NIPS 2018 Machine Learning for Systems Workshop.