Rachel Fong

Artificial Intelligence · 2017

A researcher in the Science corpus: 2 papers; trusts 35 researchers and is trusted by 110 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 58 citations into it; #11,700 of 35,345 by trust here, #3,783 by citations
• Reinforcement Learning — 49 citations into it; #434 of 2,242 by trust here, #191 by citations
• Computer Vision and Pattern Recognition — 38 citations into it; #3,464 of 21,575 by trust here, #2,872 by citations
• Control and Systems Engineering — 25 citations into it
• Computational Theory and Mathematics — 11 citations into it
• Optimization & Training — 7 citations into it
• Management Science and Operations Research — 6 citations into it

Whom Rachel Fong trusts overall

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

1. David G. Lowe
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Karen Simonyan
5. Yoshua Bengio
6. Alex Krizhevsky
7. Jimmy Ba
8. Ilya Sutskever
9. Sergey Levine
10. E. Todorov

Who trusts Rachel Fong overall

1. Sergey Levine
2. Stephen James
3. Pieter Abbeel
4. Shansi Zhang
5. Marcin Andrychowicz
6. Chelsea Finn
7. Ofir Nachum
8. Lerrel Pinto
9. Trevor Darrell
10. Kristen Grauman

Papers from Rachel Fong's perspective

Ordered by how strongly the researchers Rachel Fong 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Domain randomization for transferring deep neural networks from simulation to the real world (2017) — 3,883 citations
• Hindsight Experience Replay (2017) — 2,808 citations