Bradly C. Stadie

Artificial Intelligence · 2015–2018

A researcher in the Science corpus: 5 papers; trusts 198 researchers and is trusted by 178 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 134 citations into it; #1,656 of 35,345 by trust here, #2,514 by citations
• Reinforcement Learning — 129 citations into it; #256 of 2,242 by trust here, #238 by citations
• Computer Vision and Pattern Recognition — 59 citations into it; #6,361 of 21,575 by trust here, #7,750 by citations
• Representation & Self-Supervised Learning — 35 citations into it
• Optimization & Training — 24 citations into it
• Control and Systems Engineering — 22 citations into it; #739 of 8,257 by trust here, #172 by citations
• Management Science and Operations Research — 12 citations into it; #1,586 of 11,946 by trust here, #1,105 by citations
• Computer Networks and Communications — 8 citations into it

Whom Bradly C. Stadie trusts overall

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

1. J. Urgen Schmidhuber
2. Richard S. Sutton
3. Sergey Levine
4. Geoffrey E. Hinton
5. John Schulman
6. Diederik P. Kingma
7. Pieter Abbeel
8. Andrew Y. Ng
9. Yoshua Bengio
10. Michael Kearns

Who trusts Bradly C. Stadie overall

1. Sergey Levine
2. Abhishek Gupta
3. Pieter Abbeel
4. Chelsea Finn
5. Faraz Torabi
6. Jason Aaron Edward Weston
7. Yuxi Li
8. Ashley D. Edwards
9. Deepak Pathak
10. Brendan O'Donoghue

Papers from Bradly C. Stadie's perspective

Ordered by how strongly the researchers Bradly C. Stadie 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• One-Shot Imitation Learning (2017) — 745 citations
• Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models (2015) — 542 citations
• Third-Person Imitation Learning (2017) — 246 citations
• Evolved Policy Gradients (2018) — 238 citations
• Some Considerations on Learning to Explore via Meta-Reinforcement Learning (2018) — 130 citations