Phillip Swazinna

Reinforcement Learning · 2020

A researcher in the Science corpus: 1 paper; trusts 67 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 41 citations into it
• Artificial Intelligence — 33 citations into it; #25,069 of 35,345 by trust here, #31,361 by citations
• Computational Theory and Mathematics — 10 citations into it
• Management Science and Operations Research — 9 citations into it
• Electrical and Electronic Engineering — 8 citations into it
• Control and Systems Engineering — 7 citations into it; #5,574 of 8,257 by trust here, #6,764 by citations

Whom Phillip Swazinna trusts overall

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

1. Martin A. Riedmiller
2. Diederik P. Kingma
3. Ronald J. Williams
4. J. Urgen Schmidhuber
5. Richard S. Sutton
6. Geoffrey E. Hinton
7. Sergey Levine
8. Scott Fujimoto
9. Yoshua Bengio
10. John Schulman

Who trusts Phillip Swazinna overall

1. Byung-Jun Lee
2. Tianhe Yu
3. Scott Emmons
4. Rafael Rafailov
5. Jongmin Lee
6. Benjamin Eysenbach
7. Sergey Levine
8. Aviral Kumar

Papers from Phillip Swazinna's perspective

Ordered by how strongly the researchers Phillip Swazinna 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Overcoming Model Bias for Robust Offline Deep Reinforcement Learning (2020) — 95 citations