Jonathan S. Rosenfeld

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 191 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 48 citations into it; #23,633 of 35,345 by trust here, #14,438 by citations
• Computer Vision and Pattern Recognition — 37 citations into it; #15,377 of 21,575 by trust here, #10,677 by citations
• Model Architectures — 15 citations into it
• Efficiency & Compression — 14 citations into it
• Optimization & Training — 13 citations into it
• Natural Language Processing — 8 citations into it

Whom Jonathan S. Rosenfeld trusts overall

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

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

Who trusts Jonathan S. Rosenfeld overall

1. Andrew Jones
2. Jared Kaplan
3. Ibrahim M. Alabdulmohsin
4. Jared Kaplan
5. Danny Hernandez
6. Andrei Ivanov
7. Jonathan Frankle
8. Utkarsh Sharma
9. Marcus Hutter
10. T. Henighan

Papers from Jonathan S. Rosenfeld's perspective

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

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

Their papers

• A Constructive Prediction of the Generalization Error Across Scales (2019) — 278 citations
• On the Predictability of Pruning Across Scales (2020) — 48 citations