Niv Cohen

Artificial Intelligence · 2020

A researcher in the Science corpus: 2 papers; trusts 199 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 39 citations into it; #7,639 of 35,345 by trust here, #19,439 by citations
• Computer Vision and Pattern Recognition — 28 citations into it; #7,113 of 21,575 by trust here, #15,162 by citations
• Computer Networks and Communications — 12 citations into it; #2,088 of 9,886 by trust here, #5,518 by citations
• Representation & Self-Supervised Learning — 10 citations into it
• Safety, Robustness & Interpretability — 5 citations into it

Whom Niv Cohen trusts overall

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

1. Ian T. Jolliffe
2. Paul Bergmann
3. Alex Krizhevsky
4. Maria-Elena Nilsback
5. Andrew Zisserman
6. Individualized Treat
7. Geoffrey E. Hinton
8. Kaiming He
9. Liron Bergman
10. Ross B. Girshick

Who trusts Niv Cohen overall

1. Hanqiu Deng
2. Yajun Chen
3. Kilian Batzner
4. Jiawei Yu1
5. Guodong Wang
6. Chun-Liang Li
7. Thomas Defard
8. Xuan Zhang
9. Zhaopeng Gu
10. Karsten Roth

Papers from Niv Cohen's perspective

Ordered by how strongly the researchers Niv 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. ImageNet: A large-scale hierarchical image database
6. Learning Multiple Layers of Features from Tiny Images
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. GENERATIVE ADVERSARIAL NETS
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Sub-Image Anomaly Detection with Deep Pyramid Correspondences (2020) — 675 citations
• PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation (2020) — 333 citations