Publications

Minh Nguyen Nhat, Baker Andrew, Neo Clement, Roush Allen, Kirsch Andreas, Ravid Shwartz-Ziv (2025). Turning Up the Heat: Min-p Sampling for Creative and Coherent LLM Outputs. In ICLR.

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Arefin Md Rifat, Subbaraj Gopeshh, Gontier Nicolas, LeCun Yann, Rish Irina, Ravid Shwartz-Ziv, Pal, Christopher (2024). Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced Reasoning. In ICLR.

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White Colin, Dooley Samuel, Roberts Manley, Pal Arka, Feuer Ben, Jain Siddhartha, Ravid Shwartz-Ziv, Jain Neel, Saifullah Khalid, Naidu Siddartha, Hegde Chinmay, LeCun Yann, Goldstein Tom, Neiswanger Willie, Goldblum Micah (2024). LiveBench: A Challenging, Contamination-Free LLM Benchmark. In ICLR.

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Roush Allen, Shabazz Yusuf, Balaji Arvind, Zhang Peter, Mezza Stefano, Zhang Markus, Basu Sanjay, Vishwanath Sriram, Fatemi Mehdi, Ravid Shwartz-Ziv (2024). OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset. In NeurIPS.

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Sanyal Sunny, Ravid Shwartz-Ziv, Dimakis Alexandros G., Sanghavi Sujay (2024). Inheritune: Training Smaller Yet More Attentive Language Models. arXiv.

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Ravid Shwartz-Ziv, Balestriero Randall, Kawaguchi Kenji, Rudner Tim GJ, LeCun Yann (2023). An Information Theory Perspective on Variance-Invariance-Covariance Regularization. In NeurIPS.

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Ravid Shwartz-Ziv, Goldblum Micah, Li Yucen, Bruss C. Bayan, Wilson Andrew G. (2023). Back to Basics: Revisiting Standard Deep Learning Components for Class Imbalance. In NeurIPS.

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Ravid Shwartz-Ziv, Ravid, Shwartz-Ziv, Yann, LeCun (2023). To Compress or Not to Compress--Self-Supervised Learning and Information Theory: A Review. In Entropy.

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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.

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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.

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

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

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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.

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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.

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

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

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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.

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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.

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