Kobi Cohen

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 4 papers; trusts 52 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it
• Computer Networks and Communications — 39 citations into it; #2,066 of 9,886 by trust here, #4,950 by citations
• Electrical and Electronic Engineering — 32 citations into it; #2,341 of 16,539 by trust here, #9,402 by citations
• Optimization & Training — 16 citations into it
• Reinforcement Learning — 15 citations into it
• Computational Mechanics — 9 citations into it; #6,769 of 10,001 by trust here, #8,947 by citations
• Computer Vision and Pattern Recognition — 8 citations into it
• Management Science and Operations Research — 7 citations into it

Whom Kobi Cohen trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Qing Zhao
2. Hado van Hasselt
3. Volodymyr Mnih
4. David Silver
5. Richard S. Sutton
6. Ziyun Wang
7. Keqin Liu
8. Yuxi Li
9. Koray Kavukcuoglu
10. Geoffrey E. Hinton

Who trusts Kobi Cohen overall

1. Yasar Sinan Nasir
2. Oshri Naparstek
3. Yiding Yu
4. Qian Mao
5. Khaled Ben Letaief
6. Dongning Guo
7. Nan Zhao
8. Jihong Park

Papers from Kobi Cohen's perspective

Ordered by how strongly the researchers Kobi Cohen trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Federated Learning: A signal processing perspective (2021) — 169 citations
• Deep Multi-User Reinforcement Learning for Dynamic Spectrum Access in Multichannel Wireless Networks (2017) — 104 citations
• On Analog Gradient Descent Learning Over Multiple Access Fading Channels (2019) — 182 citations
• Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access (2017) — 431 citations