Nadav Cohen
21 papers · 2016–2025 · 7 conferences · across top CS/AI conferences
Achievements
Jump to papers ↓+10 more ↓ Show less ↑
π Academic Marathon (9) π Interdisciplinary Bridge π§ Keyword Pioneer π Conference Polyglot (7) π Cross-Pollinator (12)
π
Conference Polyglot
(7)
π
Academic Marathon
(9)
π¬
Deep Specialist
(10)
π
Keyword Champion
π§¬
Topic Evolution
π₯
Unstoppable
(8)
π
Conference Pioneer
π
Century Club
(21)
β
The Questioner
ποΈ
Keyword Collector
(59)
Conferences
NIPS (6)
ICLR (5)
ICML (5)
CVPR (2)
AISTATS (1)
COLT (1)
EMNLP (1)
Top co-authors
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
βZero-Shotβ 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