Nadav Cohen  —  Publications (see also Google Scholar)

+ Supervised student paper
* Primary authorship

Conference Proceedings

+ Continuous vs. Discrete Optimization of Deep Neural Networks (extended arXiv version)
Omer Elkabetz and Nadav Cohen. Jul’21 (v1), Dec’21 (v2).
Conference on Neural Information Processing Systems (NeurIPS) 2021, Spotlight Track (top 3%).

+ Implicit Regularization in Tensor Factorization
Noam Razin, Asaf Maman and Nadav Cohen. Feb’21.
International Conference on Machine Learning (ICML) 2021.

+ Implicit Regularization in Deep Learning May Not Be Explainable by Norms (extended arXiv version)
Noam Razin and Nadav Cohen. May’20 (v1), Oct’20 (v2).
Conference on Neural Information Processing Systems (NeurIPS) 2020.

* Implicit Regularization in Deep Matrix Factorization
Sanjeev Arora, Nadav Cohen, Wei Hu and Yuping Luo (alphabetical order). Jun’19 (v1), Oct’19 (v2).
Conference on Neural Information Processing Systems (NeurIPS) 2019, Spotlight Track (top 3%).

* A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks
Sanjeev Arora, Nadav Cohen, Noah Golowich and Wei Hu (alphabetical order). Oct’18 (v1), Nov’18 (v2).
International Conference on Learning Representations (ICLR) 2019.

* On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization
Sanjeev Arora, Nadav Cohen and Elad Hazan (alphabetical order). Feb’18.
International Conference on Machine Learning (ICML) 2018.

* Boosting Dilated Convolutional Networks with Mixed Tensor Decompositions (extended arXiv version)
Nadav Cohen, Ronen Tamari and Amnon Shashua. Apr’17.
International Conference on Learning Representations (ICLR) 2018, Oral Track (top 1%).

Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design (extended arXiv version)
Yoav Levine, David Yakira, Nadav Cohen and Amnon Shashua. Apr’17.
International Conference on Learning Representations (ICLR) 2018.

* Inductive Bias of Deep Convolutional Networks through Pooling Geometry
Nadav Cohen and Amnon Shashua. May’16 (v1), Nov’16 (v2).
International Conference on Learning Representations (ICLR) 2017.

* Convolutional Rectifier Networks as Generalized Tensor Decompositions (extended arXiv version)
Nadav Cohen and Amnon Shashua. Mar’16.
International Conference on Machine Learning (ICML) 2016.

* On the Expressive Power of Deep Learning: A Tensor Analysis
Nadav Cohen, Or Sharir and Amnon Shashua. Sep’15 (v1), Feb’16 (v2).
Conference on Learning Theory (COLT) 2016.

* Deep SimNets
Nadav Cohen, Or Sharir and Amnon Shashua. Jun’15 (v1), Nov’15 (v2).
Conference on Computer Vision and Pattern Recognition (CVPR) 2016.

Journals

Quantum Entanglement in Deep Learning Architectures (arXiv version)
Yoav Levine, Or Sharir, Nadav Cohen, Amnon Shashua. Mar’18 (v1), Feb’19 (v2).
Physical Review Letters (PRL).

Invited Papers, Workshops and Preprints

* Analysis and Design of Convolutional Networks via Hierarchical Tensor Decompositions
Nadav Cohen, Or Sharir, Yoav Levine, Ronen Tamari, David Yakira and Amnon Shashua. Jun’17.
Intel Collaborative Research Institute Special Issue on Deep Learning Theory.

Tensorial Mixture Models
Or Sharir, Ronen Tamari, Nadav Cohen and Amnon Shashua. Oct’16.
arXiv preprint.

* SimNets: A Generalization of Convolutional Networks
Nadav Cohen and Amnon Shashua. Oct’14 (v1), Dec’14 (v2).
Conference on Neural Information Processing Systems (NeurIPS) 2014, Deep Learning Workshop.