Nathan H. Jay

Reinforcement Learning · 2019

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 12 citations into it
• Artificial Intelligence — 11 citations into it
• Computer Networks and Communications — 8 citations into it

Whom Nathan H. Jay trusts overall

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

1. John Schulman
2. Richard S. Sutton
3. Keith Winstein
4. Andrew Barto
5. Bernhard Schölkopf
6. Sergey Levine
7. David Silver
8. Horia Mania
9. Sangtae Ha
10. Pieter Abbeel

Papers from Nathan H. Jay's perspective

Ordered by how strongly the researchers Nathan H. Jay 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. Reinforcement Learning: An Introduction
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Deep Reinforcement Learning Perspective on Internet Congestion Control (2019) — 407 citations