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Noam Razin
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2020 – today
- 2024
- [c10]Noam Razin, Hattie Zhou, Omid Saremi, Vimal Thilak, Arwen Bradley, Preetum Nakkiran, Joshua M. Susskind, Etai Littwin:
Vanishing Gradients in Reinforcement Finetuning of Language Models. ICLR 2024 - [c9]Hattie Zhou, Arwen Bradley, Etai Littwin, Noam Razin, Omid Saremi, Joshua M. Susskind, Samy Bengio, Preetum Nakkiran:
What Algorithms can Transformers Learn? A Study in Length Generalization. ICLR 2024 - [c8]Noam Razin, Yotam Alexander, Edo Cohen-Karlik, Raja Giryes, Amir Globerson, Nadav Cohen:
Implicit Bias of Policy Gradient in Linear Quadratic Control: Extrapolation to Unseen Initial States. ICML 2024 - [i10]Noam Razin, Yotam Alexander, Edo Cohen-Karlik, Raja Giryes, Amir Globerson, Nadav Cohen:
Implicit Bias of Policy Gradient in Linear Quadratic Control: Extrapolation to Unseen Initial States. CoRR abs/2402.07875 (2024) - 2023
- [c7]Yotam Alexander, Nimrod De La Vega, Noam Razin, Nadav Cohen:
What Makes Data Suitable for a Locally Connected Neural Network? A Necessary and Sufficient Condition Based on Quantum Entanglement. NeurIPS 2023 - [c6]Noam Razin, Tom Verbin, Nadav Cohen:
On the Ability of Graph Neural Networks to Model Interactions Between Vertices. NeurIPS 2023 - [i9]Yotam Alexander, Nimrod De La Vega, Noam Razin, Nadav Cohen:
What Makes Data Suitable for a Locally Connected Neural Network? A Necessary and Sufficient Condition Based on Quantum Entanglement. CoRR abs/2303.11249 (2023) - [i8]Hattie Zhou, Arwen Bradley, Etai Littwin, Noam Razin, Omid Saremi, Josh M. Susskind, Samy Bengio, Preetum Nakkiran:
What Algorithms can Transformers Learn? A Study in Length Generalization. CoRR abs/2310.16028 (2023) - [i7]Noam Razin, Hattie Zhou, Omid Saremi, Vimal Thilak, Arwen Bradley, Preetum Nakkiran, Joshua M. Susskind, Etai Littwin:
Vanishing Gradients in Reinforcement Finetuning of Language Models. CoRR abs/2310.20703 (2023) - 2022
- [c5]Noam Razin, Asaf Maman, Nadav Cohen:
Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks. ICML 2022: 18422-18462 - [i6]Noam Razin, Asaf Maman, Nadav Cohen:
Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks. CoRR abs/2201.11729 (2022) - [i5]Noam Razin, Tom Verbin, Nadav Cohen:
On the Ability of Graph Neural Networks to Model Interactions Between Vertices. CoRR abs/2211.16494 (2022) - 2021
- [c4]Noam Razin, Asaf Maman, Nadav Cohen:
Implicit Regularization in Tensor Factorization. ICML 2021: 8913-8924 - [i4]Noam Razin, Asaf Maman, Nadav Cohen:
Implicit Regularization in Tensor Factorization. CoRR abs/2102.09972 (2021) - 2020
- [c3]Oren Barkan, Noam Razin, Itzik Malkiel, Ori Katz, Avi Caciularu, Noam Koenigstein:
Scalable Attentive Sentence Pair Modeling via Distilled Sentence Embedding. AAAI 2020: 3235-3242 - [c2]Itzik Malkiel, Oren Barkan, Avi Caciularu, Noam Razin, Ori Katz, Noam Koenigstein:
Optimizing BERT for Unlabeled Text-Based Items Similarity. EMNLP (Findings) 2020: 1704-1714 - [c1]Noam Razin, Nadav Cohen:
Implicit Regularization in Deep Learning May Not Be Explainable by Norms. NeurIPS 2020 - [i3]Noam Razin, Nadav Cohen:
Implicit Regularization in Deep Learning May Not Be Explainable by Norms. CoRR abs/2005.06398 (2020) - [i2]Itzik Malkiel, Oren Barkan, Avi Caciularu, Noam Razin, Ori Katz, Noam Koenigstein:
RecoBERT: A Catalog Language Model for Text-Based Recommendations. CoRR abs/2009.13292 (2020)
2010 – 2019
- 2019
- [i1]Oren Barkan, Noam Razin, Itzik Malkiel, Ori Katz, Avi Caciularu, Noam Koenigstein:
Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding. CoRR abs/1908.05161 (2019)
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