Nadav Cohen

According to our database, Nadav Cohen authored at least 26 papers between 2012 and 2025.

Collaborative distances:

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

2025

Do Neural Networks Need Gradient Descent to Generalize? A Theoretical Study.

Yotam Alexander, Yonatan Slutzky, Yuval Ran-Milo, Nadav Cohen

CoRR, June, 2025

DeciMamba: Exploring the Length Extrapolation Potential of Mamba.

Assaf Ben-Kish, Itamar Zimerman, Shady Abu-Hussein, Nadav Cohen, Amir Globerson, Lior Wolf, Raja Giryes

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

The Implicit Bias of Structured State Space Models Can Be Poisoned With Clean Labels.

Yonatan Slutzky, Yotam Alexander, Noam Razin, Nadav Cohen

CoRR, 2024

Lecture Notes on Linear Neural Networks: A Tale of Optimization and Generalization in Deep Learning.

Nadav Cohen, Noam Razin

CoRR, 2024

Provable Benefits of Complex Parameterizations for Structured State Space Models.

Yuval Ran-Milo, Eden Lumbroso, Edo Cohen-Karlik, Raja Giryes, Amir Globerson, Nadav Cohen

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Implicit Bias of Policy Gradient in Linear Quadratic Control: Extrapolation to Unseen Initial States.

Noam Razin, Yotam Alexander, Edo Cohen-Karlik, Raja Giryes, Amir Globerson, Nadav Cohen

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

On the Ability of Graph Neural Networks to Model Interactions Between Vertices.

Noam Razin, Tom Verbin, Nadav Cohen

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

What Makes Data Suitable for a Locally Connected Neural Network? A Necessary and Sufficient Condition Based on Quantum Entanglement.

Yotam Alexander, Nimrod De La Vega, Noam Razin, Nadav Cohen

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning Low Dimensional State Spaces with Overparameterized Recurrent Neural Nets.

Edo Cohen-Karlik, Itamar Menuhin-Gruman, Raja Giryes, Nadav Cohen, Amir Globerson

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Learning Low Dimensional State Spaces with Overparameterized Recurrent Neural Network.

Edo Cohen-Karlik, Itamar Menuhin-Gruman, Nadav Cohen, Raja Giryes, Amir Globerson

CoRR, 2022

Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks.

Noam Razin, Asaf Maman, Nadav Cohen

Proceedings of the International Conference on Machine Learning, 2022

On the Implicit Bias of Gradient Descent for Temporal Extrapolation.

Edo Cohen-Karlik, Avichai Ben David, Nadav Cohen, Amir Globerson

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Continuous vs. Discrete Optimization of Deep Neural Networks.

Omer Elkabetz, Nadav Cohen

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Implicit Regularization in Tensor Factorization.

Noam Razin, Asaf Maman, Nadav Cohen

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Implicit Regularization in Deep Learning May Not Be Explainable by Norms.

Noam Razin, Nadav Cohen

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2018

Bridging Many-Body Quantum Physics and Deep Learning via Tensor Networks.

Yoav Levine, Or Sharir, Nadav Cohen, Amnon Shashua

CoRR, 2018

Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design.

Yoav Levine, David Yakira, Nadav Cohen, Amnon Shashua

Proceedings of the 6th International Conference on Learning Representations, 2018

Boosting Dilated Convolutional Networks with Mixed Tensor Decompositions.

Nadav Cohen, Ronen Tamari, Amnon Shashua

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Analysis and Design of Convolutional Networks via Hierarchical Tensor Decompositions.

Nadav Cohen, Or Sharir, Yoav Levine, Ronen Tamari, David Yakira, Amnon Shashua

CoRR, 2017

Inductive Bias of Deep Convolutional Networks through Pooling Geometry.

Nadav Cohen, Amnon Shashua

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Tensorial Mixture Models.

Or Sharir, Ronen Tamari, Nadav Cohen, Amnon Shashua

CoRR, 2016

Convolutional Rectifier Networks as Generalized Tensor Decompositions.

Nadav Cohen, Amnon Shashua

Proceedings of the 33nd International Conference on Machine Learning, 2016

Deep SimNets.

Nadav Cohen, Or Sharir, Amnon Shashua

Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

On the Expressive Power of Deep Learning: A Tensor Analysis.

Nadav Cohen, Or Sharir, Amnon Shashua

Proceedings of the 29th Conference on Learning Theory, 2016

2014

SimNets: A Generalization of Convolutional Networks.

Nadav Cohen, Amnon Shashua

CoRR, 2014

2012

Complex Floating Point - A Novel Data Word Representation for DSP Processors.

Nadav Cohen, Shlomo Weiss

IEEE Trans. Circuits Syst. I Regul. Pap., 2012