Lauren Miller

Reinforcement Learning · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 11 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 35 citations into it
• Artificial Intelligence — 27 citations into it
• Control and Systems Engineering — 21 citations into it

Whom Lauren Miller trusts overall

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

1. Pieter Abbeel
2. Andrew Y. Ng
3. Sergey Levine
4. John Schulman
5. Geoffrey E. Hinton
6. Richard S. Sutton

Papers from Lauren Miller's perspective

Ordered by how strongly the researchers Lauren Miller 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. Human-level control through deep reinforcement learning
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. Trust Region Policy Optimization (International Conference on Machine Learning)
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• SWIRL: A sequential windowed inverse reinforcement learning algorithm for robot tasks with delayed rewards (2018) — 77 citations
• Multilateral surgical pattern cutting in 2D orthotropic gauze with deep reinforcement learning policies for tensioning (2017) — 147 citations