Amir Rosenfeld

Computer Vision and Pattern Recognition · 2016–2019

A researcher in the Science corpus: 5 papers; trusts 843 researchers and is trusted by 113 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 108 citations into it; #3,085 of 21,575 by trust here, #5,161 by citations
• Artificial Intelligence — 88 citations into it; #4,412 of 35,345 by trust here, #7,531 by citations
• Model Architectures — 30 citations into it
• Optimization & Training — 19 citations into it; #521 of 2,601 by trust here, #961 by citations
• Evaluation & Benchmarks — 17 citations into it
• Representation & Self-Supervised Learning — 15 citations into it; #626 of 3,145 by trust here, #1,358 by citations
• Natural Language Processing — 9 citations into it
• Media Technology — 8 citations into it

Whom Amir Rosenfeld trusts overall

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

1. Alex Krizhevsky
2. Karen Simonyan
3. Kaiming He
4. Geoffrey E. Hinton
5. Andrea Vedaldi
6. Diederik P. Kingma
7. Andrew Zisserman
8. Ilya Sutskever
9. Yoshua Bengio
10. Yann LeCun

Who trusts Amir Rosenfeld overall

1. Arun Mallya
2. Eden Belouadah
3. Andrea Gesmundo
4. Sen Lin
5. Li Yang
6. Pedro Morgado
7. Mustafa Burak Gurbuz
8. Jeffrey O. Zhang
9. Sylvestre-Alvise Rebuffi
10. Svetlana Lazebnik

Papers from Amir Rosenfeld's perspective

Ordered by how strongly the researchers Amir Rosenfeld 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. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Incremental Learning Through Deep Adaptation (2017) — 307 citations
• A Constructive Prediction of the Generalization Error Across Scales (2019) — 278 citations
• Visual Concept Recognition and Localization via Iterative Introspection (2016) — 23 citations
• Elephant in the room (2018)
• Totally Looks Like - How Humans Compare, Compared to Machines (2018) — 35 citations