David Chan

Computer Vision and Pattern Recognition · with Canny · 2015–2019

A researcher in the Science corpus: 3 papers; trusts 359 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 65 citations into it; #9,514 of 21,575 by trust here, #11,721 by citations
• Artificial Intelligence — 30 citations into it
• Representation & Self-Supervised Learning — 23 citations into it; #2,771 of 3,145 by trust here, #3,009 by citations
• Signal Processing — 11 citations into it
• Evaluation & Benchmarks — 9 citations into it
• Experimental and Cognitive Psychology — 8 citations into it; #3,368 of 9,731 by trust here, #5,545 by citations
• Model Architectures — 8 citations into it
• Information Retrieval & Search — 7 citations into it

Whom David Chan trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. L. V. D. van der Maaten
4. Kaiming He
5. Yann LeCun
6. Yoshua Bengio
7. Karen Simonyan
8. David G. Lowe
9. Sam T. Roweis
10. Jia Deng

Who trusts David Chan overall

1. Dmitry Kobak
2. Anna C. Belkina
3. Shan Li
4. Mekhail Mustak
5. Cong Fu
6. Philipp Berens
7. Chris Ciccolella
8. Shervin Minaee
9. Ali Mollahosseini
10. Weihong Deng

Papers from David Chan's perspective

Ordered by how strongly the researchers David Chan 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. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Going deeper in facial expression recognition using deep neural networks (2015) — 963 citations
• GPU accelerated t-distributed stochastic neighbor embedding (2019) — 79 citations
• T-SNE-CUDA: GPU-Accelerated T-SNE and its Applications to Modern Data (2018) — 106 citations