Samuel Stanton

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 3 papers; trusts 127 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 107 citations into it; #18,936 of 35,345 by trust here, #19,018 by citations
• Computer Vision and Pattern Recognition — 52 citations into it
• Reinforcement Learning — 49 citations into it; #1,676 of 2,242 by trust here, #2,125 by citations
• Model Architectures — 27 citations into it; #1,705 of 2,278 by trust here, #1,774 by citations
• Control and Systems Engineering — 20 citations into it
• Computational Theory and Mathematics — 19 citations into it
• Optimization & Training — 14 citations into it; #2,010 of 2,601 by trust here, #2,428 by citations
• Management Science and Operations Research — 12 citations into it

Whom Samuel Stanton trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yann LeCun
5. Kaiming He
6. Yoshua Bengio
7. Jimmy Ba
8. Richard S. Sutton
9. Ilya Sutskever
10. Sergey Ioffe

Who trusts Samuel Stanton overall

1. Michael Janner
2. David W. Romero
3. Fartash Faghri
4. Victor Garcia Satorras
5. Qiyang Li
6. Denis Yarats
7. Johannes Brandstetter
8. Jiaxiang Wu

Papers from Samuel Stanton's perspective

Ordered by how strongly the researchers Samuel Stanton 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data (2020) — 396 citations
• On the model-based stochastic value gradient for continuous reinforcement learning (2020) — 81 citations
• Does Knowledge Distillation Really Work? (2021) — 292 citations