Michael Schukat

Reinforcement Learning · 2017

A researcher in the Science corpus: 1 paper; trusts 10 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 15 citations into it
• Artificial Intelligence — 9 citations into it

Whom Michael Schukat trusts overall

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

1. Volodymyr Mnih
2. Longxin Lin
3. Diederik P. Kingma
4. Mohit Sewak
5. van der D David Pol
6. Andrew Barto
7. Yann LeCun
8. Richard S. Sutton
9. Geoffrey E. Hinton
10. Sascha Lange

Who trusts Michael Schukat overall

1. Ammar Haydari
2. Hua Wei

Papers from Michael Schukat's perspective

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

1. Gradient-based learning applied to document recognition
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Long Short-Term Memory
6. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
7. Attention is All you Need (Neural Information Processing Systems)
8. Human-level control through deep reinforcement learning
9. Learning Multiple Layers of Features from Tiny Images
10. Asynchronous Methods for Deep Reinforcement Learning

Their papers

• Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learning (2017) — 348 citations