Sophie Saskin

Reinforcement Learning · 2019

A researcher in the Science corpus: 1 paper; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 26 citations into it
• Artificial Intelligence — 18 citations into it
• Control and Systems Engineering — 6 citations into it

Whom Sophie Saskin trusts overall

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

1. Geoffrey E. Hinton
2. Pieter Abbeel
3. David E. Rumelhart
4. Richard S. Sutton
5. Chelsea Finn
6. Andrew Y. Ng
7. Ronald J. Williams
8. Diederik P. Kingma
9. Sergey Levine
10. Stéphane Ross

Papers from Sophie Saskin's perspective

Ordered by how strongly the researchers Sophie Saskin 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Deep Reinforcement Learning from Policy-Dependent Human Feedback (2019) — 111 citations