Daniel S. Brown

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 6 papers; trusts 168 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 139 citations into it; #10,251 of 35,345 by trust here, #12,761 by citations
• Reinforcement Learning — 128 citations into it; #1,399 of 2,242 by trust here, #1,319 by citations
• Control and Systems Engineering — 41 citations into it; #7,132 of 8,257 by trust here, #2,890 by citations
• Management Science and Operations Research — 33 citations into it
• Computer Vision and Pattern Recognition — 22 citations into it; #19,760 of 21,575 by trust here, #17,757 by citations
• Optimization & Training — 15 citations into it
• Statistics and Probability — 13 citations into it
• Cognitive Neuroscience — 12 citations into it

Whom Daniel S. Brown trusts overall

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

1. Andrew Y. Ng
2. Richard S. Sutton
3. Diederik P. Kingma
4. Pieter Abbeel
5. Deepak Ramachandran
6. Geoffrey E. Hinton
7. Martin L. Puterman
8. John Schulman
9. Sergey Levine
10. Jonathan Ho

Who trusts Daniel S. Brown overall

1. Scott Niekum
2. Oliver Kroemer
3. Zaynah Javed
4. Saurabh Arora
5. Marek Petrik
6. Jacob Buckman
7. Banghua Zhu
8. Maryam Zare
9. Benjamin Eysenbach
10. Russell T. Coleman

Papers from Daniel S. Brown's perspective

Ordered by how strongly the researchers Daniel S. Brown 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. GENERATIVE ADVERSARIAL NETS
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations (2019) — 425 citations
• Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences (2020) — 116 citations
• Risk-Aware Active Inverse Reinforcement Learning (2018) — 63 citations
• Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications (2018) — 85 citations
• Bayesian Robust Optimization for Imitation Learning (2020) — 43 citations
• Policy Gradient Bayesian Robust Optimization for Imitation Learning (2021) — 20 citations