Nadav Cohen
Artificial Intelligence · with Arora · 2015–2022
A researcher in the Science corpus: 8 papers; trusts 426 researchers and is trusted by 90 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 155 citations into it; #4,272 of 35,345 by trust here, #4,627 by citations
- Computer Vision and Pattern Recognition — 119 citations into it; #4,038 of 21,575 by trust here, #5,711 by citations
- Optimization & Training — 72 citations into it; #810 of 2,601 by trust here, #717 by citations
- Computational Mechanics — 52 citations into it; #1,985 of 10,001 by trust here, #1,304 by citations
- Model Architectures — 41 citations into it
- Scaling & Learning Theory — 40 citations into it; #136 of 428 by trust here, #206 by citations
- Representation & Self-Supervised Learning — 33 citations into it
- Computational Theory and Mathematics — 24 citations into it
Whom Nadav Cohen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Kaiming He
- Yoshua Bengio
- Diederik P. Kingma
- Alex Krizhevsky
- Yann LeCun
- Ilya Sutskever
- Christian Szegedy
- Karen Simonyan
- Ross B. Girshick
Who trusts Nadav Cohen overall
- Or Sharir
- Amnon Shashua
- Noam Razin
- Kai Zhang
- Andrzej S. Cichocki
- Simon Shaolei Du
- Eugene Lee
- Wei Hu
- Jean Kossaifi
- Ohad Shamir
Papers from Nadav Cohen's perspective
Ordered by how strongly the researchers Nadav Cohen trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Zero-Shot Super-Resolution Using Deep Internal Learning (2017) — 980 citations
- On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization (2018) — 563 citations
- On the Expressive Power of Deep Learning: A Tensor Analysis (2015) — 493 citations
- Implicit Regularization in Deep Matrix Factorization (2019) — 634 citations
- Semantic Segmentation in Art Paintings (2022) — 37 citations
- Inductive Bias of Deep Convolutional Networks through Pooling Geometry (2016) — 142 citations
- A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks (2018) — 350 citations
- Implicit Regularization in Deep Learning May Not Be Explainable by Norms (2020) — 174 citations