Maximilian Hüttenrauch

Reinforcement Learning · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 63 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 38 citations into it; #1,458 of 2,242 by trust here, #1,895 by citations
• Artificial Intelligence — 34 citations into it; #18,909 of 35,345 by trust here, #27,551 by citations
• Computer Networks and Communications — 21 citations into it; #4,694 of 9,886 by trust here, #7,140 by citations
• Computational Theory and Mathematics — 11 citations into it; #7,141 of 12,182 by trust here, #8,294 by citations
• Mechanical Engineering — 11 citations into it; #7,036 of 8,300 by trust here, #8,205 by citations
• Control and Systems Engineering — 9 citations into it
• Management Science and Operations Research — 8 citations into it
• Aerospace Engineering — 7 citations into it

Whom Maximilian Hüttenrauch trusts overall

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

1. Diederik P. Kingma
2. Matthew J. Hausknecht
3. John Schulman
4. Arthur Gretton
5. Richard S. Sutton
6. Adrian Šošić
7. Geoffrey E. Hinton
8. Timothy P. Lillicrap
9. Longxin Lin
10. Volodymyr Mnih

Who trusts Maximilian Hüttenrauch overall

1. Tabish Rashid
2. Thanh Thi Nguyen
3. Anuj Mahajan
4. Mikayel Samvelyan
5. Jianhao Wang
6. Christian Schröder de Witt
7. Ngoc Duy Nguyen
8. Adrian Šošić
9. Shimon Whiteson

Papers from Maximilian Hüttenrauch's perspective

Ordered by how strongly the researchers Maximilian Hüttenrauch 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Local Communication Protocols for Learning Complex Swarm Behaviors with Deep Reinforcement Learning (2017) — 32 citations
• Guided Deep Reinforcement Learning for Swarm Systems (2017) — 149 citations
• Deep Reinforcement Learning for Swarm Systems (2018) — 250 citations