Oscar Li

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 510 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 95 citations into it; #8,834 of 21,575 by trust here, #7,499 by citations
• Artificial Intelligence — 72 citations into it; #5,111 of 35,345 by trust here, #6,229 by citations
• Safety, Robustness & Interpretability — 22 citations into it; #856 of 2,251 by trust here, #939 by citations
• Representation & Self-Supervised Learning — 15 citations into it
• Model Architectures — 14 citations into it
• Biophysics — 9 citations into it
• Probabilistic & Bayesian Methods — 8 citations into it
• Signal Processing — 6 citations into it

Whom Oscar Li trusts overall

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

1. Geoffrey E. Hinton
2. Karen Simonyan
3. Yoshua Bengio
4. Alex Krizhevsky
5. Kaiming He
6. David G. Lowe
7. Ruslan Salakhutdinov
8. Diederik P. Kingma
9. Yann LeCun
10. Matthew D. Zeiler

Who trusts Oscar Li overall

1. Cynthia Rudin
2. Mark T. Keane
3. Peter Hase
4. Wieland Brendel
5. Riccardo Guidotti
6. Eoin M. Kenny
7. Zhi Chen
8. Eunji Kim
9. Yitao Peng
10. Sercan Ö. Arik

Papers from Oscar Li's perspective

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

Their papers

• This looks like that: deep learning for interpretable image recognition (2018) — 1,568 citations
• Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains its Predictions (2017) — 677 citations
• Interpretable Image Recognition with Hierarchical Prototypes (2019) — 138 citations