Brian Cheung

Artificial Intelligence · with Olshausen · 2014–2019

A researcher in the Science corpus: 3 papers; trusts 159 researchers and is trusted by 60 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it; #23,390 of 35,345 by trust here, #13,760 by citations
• Computer Vision and Pattern Recognition — 52 citations into it; #3,987 of 21,575 by trust here, #5,464 by citations
• Representation & Self-Supervised Learning — 35 citations into it; #2,251 of 3,145 by trust here, #1,527 by citations
• Optimization & Training — 19 citations into it
• Model Architectures — 15 citations into it
• Reinforcement Learning — 7 citations into it
• Cognitive Neuroscience — 6 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Brian Cheung trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Yann LeCun
5. Diederik P. Kingma
6. J. Urgen Schmidhuber
7. Ilya Sutskever
8. Ian J. Goodfellow
9. Kaiming He
10. Ruslan Salakhutdinov

Who trusts Brian Cheung overall

1. Hsin-Ying Lee
2. Francesco Locatello
3. Divya Saxena
4. Scott E. Reed
5. Evani Radiya-Dixit
6. Theofanis Karaletsos
7. Irina Higgins
8. Yao-Hung Hubert Tsai
9. Bharath Hariharan
10. Hung-Yu Tseng

Papers from Brian Cheung's perspective

Ordered by how strongly the researchers Brian Cheung 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Discovering Hidden Factors of Variation in Deep Networks (2014) — 199 citations
• Meta-Learning Update Rules for Unsupervised Representation Learning (2018) — 132 citations
• Superposition of many models into one (2019) — 127 citations