Emma Jordan

Reinforcement Learning · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 16 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 37 citations into it
• Artificial Intelligence — 33 citations into it
• Management Science and Operations Research — 10 citations into it
• Statistics and Probability — 8 citations into it
• Computational Theory and Mathematics — 6 citations into it

Whom Emma Jordan trusts overall

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

1. Philip S. Thomas
2. Richard S. Sutton
3. Geoffrey E. Hinton
4. Stefan Schaal
5. Andrew Barto
6. Zoubin Ghahramani
7. Javier García
8. Yoshua Bengio
9. Ronald J. Williams
10. Ilya Sutskever

Papers from Emma Jordan's perspective

Ordered by how strongly the researchers Emma Jordan trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Reinforcement Learning: An Introduction
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Long Short-Term Memory
9. ImageNet: A large-scale hierarchical image database
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Learning Action Representations for Reinforcement Learning (2019) — 193 citations
• Towards Safe Policy Improvement for Non-Stationary MDPs (2020) — 46 citations