Mark J. Debord

Reinforcement Learning · 2019

A researcher in the Science corpus: 1 paper; trusts 17 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 38 citations into it
• Artificial Intelligence — 30 citations into it

Whom Mark J. Debord trusts overall

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

1. Richard S. Sutton
2. Ronald J. Williams
3. Jakob N. Foerster
4. G. Tesauro
5. Volodymyr Mnih
6. David Silver
7. Geoffrey E. Hinton
8. Andrew Barto
9. Diederik P. Kingma
10. Yoshua Bengio

Papers from Mark J. Debord's perspective

Ordered by how strongly the researchers Mark J. Debord 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. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
8. Reinforcement Learning: An Introduction
9. Human-level control through deep reinforcement learning
10. Asynchronous Methods for Deep Reinforcement Learning

Their papers

• CESMA: Centralized Expert Supervises Multi-Agents (2019) — 6 citations