Tim de Bruin

Reinforcement Learning · 2015–2018

A researcher in the Science corpus: 4 papers; trusts 92 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 121 citations into it; #1,490 of 2,242 by trust here, #1,864 by citations
• Artificial Intelligence — 101 citations into it; #30,336 of 35,345 by trust here, #25,809 by citations
• Control and Systems Engineering — 29 citations into it
• Management Science and Operations Research — 27 citations into it; #10,309 of 11,946 by trust here, #9,383 by citations
• Computational Theory and Mathematics — 25 citations into it
• Computer Vision and Pattern Recognition — 19 citations into it
• Optimization & Training — 19 citations into it
• Cognitive Neuroscience — 11 citations into it; #8,598 of 10,852 by trust here, #9,453 by citations

Whom Tim de Bruin trusts overall

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

1. Diederik P. Kingma
2. Richard S. Sutton
3. Yoshua Bengio
4. Jimmy Ba
5. Geoffrey E. Hinton
6. Volodymyr Mnih
7. Ronald J. Williams
8. Sergey Levine
9. David Silver
10. Sergey Ioffe

Who trusts Tim de Bruin overall

1. Che Wang
2. Fan Zhou
3. Jens Kober
4. Jelle Munk
5. Christos Kaplanis
6. Zubair Muhammad Fadlullah
7. Keith W. Ross
8. Rishabh Agarwal
9. Robert Babuška
10. Scott Fujimoto

Papers from Tim de Bruin's perspective

Ordered by how strongly the researchers Tim de Bruin 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Improved deep reinforcement learning for robotics through distribution-based experience retention (2016) — 58 citations
• Reinforcement learning for control: Performance, stability, and deep approximators (2018) — 446 citations
• Off-policy experience retention for deep actor-critic learning (2016) — 8 citations
• The importance of experience replay database composition in deep reinforcement learning (2015) — 88 citations