← Authors

Nadav Cohen

21 papers · 2016–2025 · 7 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🐝 Cross-Pollinator (12) πŸ—ΊοΈ Taxonomy Completionist (17) πŸŒ‰ Interdisciplinary Bridge 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (7) πŸƒ Academic Marathon (9) πŸ”¬ Deep Specialist (10) πŸ† Keyword Champion 🧬 Topic Evolution πŸ”₯ Unstoppable (8) πŸš€ Conference Pioneer πŸ’Ž Century Club (21) ❓ The Questioner πŸ—ƒοΈ Keyword Collector (59)

Conferences

NIPS (6) ICLR (5) ICML (5) CVPR (2) AISTATS (1) COLT (1) EMNLP (1)

Top co-authors

Noam Razin (6) Amnon Shashua (5) Amir Globerson (5) Raja Giryes (4) Edo Cohen-Karlik (4) Sanjeev Arora (3) Asaf Maman (2) Wei Hu (2) Or Sharir (2) Itamar Zimerman (1)

Keywords

gradient descent (6) convolutional neural network (5) implicit regularization (4) tensor decomposition (2) neural network optimization (2) tensor factorization (2) deep neural network (2) dynamical system (2) matrix factorization (2) recurrent neural network (2) model architecture (2) feature space (1) deep learning theory (1) global convergence (1) zero-shot learning (1) graph structure (1) knowledge graph (1) coreference resolution (1) expressive power (1) image reconstruction (1)

Papers

DeciMamba: Exploring the Length Extrapolation Potential of Mamba ICLR 2025
Implicit Bias of Policy Gradient in Linear Quadratic Control: Extrapolation to Unseen Initial States ICML 2024
Provable Benefits of Complex Parameterizations for Structured State Space Models NIPS 2024
Data-driven Coreference-based Ontology Building EMNLP 2024
What Makes Data Suitable for a Locally Connected Neural Network? A Necessary and Sufficient Condition Based on Quantum Entanglement. NIPS 2023
Learning Low Dimensional State Spaces with Overparameterized Recurrent Neural Nets ICLR 2023
On the Ability of Graph Neural Networks to Model Interactions Between Vertices NIPS 2023
Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks ICML 2022
On the Implicit Bias of Gradient Descent for Temporal Extrapolation AISTATS 2022
Continuous vs. Discrete Optimization of Deep Neural Networks NIPS 2021
Implicit Regularization in Tensor Factorization ICML 2021
Implicit Regularization in Deep Learning May Not Be Explainable by Norms NIPS 2020
Implicit Regularization in Deep Matrix Factorization NIPS 2019
A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks ICLR 2019
Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design ICLR 2018
Boosting Dilated Convolutional Networks with Mixed Tensor Decompositions ICLR 2018
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization ICML 2018
β€œZero-Shot” Super-Resolution Using Deep Internal Learning CVPR 2018
Deep SimNets CVPR 2016
Convolutional Rectifier Networks as Generalized Tensor Decompositions ICML 2016
On the Expressive Power of Deep Learning: A Tensor Analysis COLT 2016