Eric Brochu

Artificial Intelligence · 2007–2010

A researcher in the Science corpus: 5 papers; trusts 58 researchers and is trusted by 126 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 42 citations into it; #1,916 of 35,345 by trust here, #3,609 by citations
• Reinforcement Learning — 23 citations into it; #250 of 2,242 by trust here, #411 by citations
• Computational Theory and Mathematics — 16 citations into it; #1,916 of 12,182 by trust here, #1,587 by citations
• Mathematical Optimization — 14 citations into it
• Management Science and Operations Research — 12 citations into it; #258 of 11,946 by trust here, #333 by citations
• Statistics and Probability — 11 citations into it
• Computer Networks and Communications — 10 citations into it
• Control and Systems Engineering — 8 citations into it

Whom Eric Brochu trusts overall

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

1. Donald R. Jones
2. M. Sasena
3. Daniel J. Lizotte
4. Richard S. Sutton
5. Radford M. Neal
6. Michael A. Osborne
7. Harold J. Kushner
8. Bruce E. Stuckman
9. Andrew Barto
10. Andrew Y. Ng

Who trusts Eric Brochu overall

1. Ziyun Wang
2. Nando de Freitas
3. Bobak Shahriari
4. Frank Hutter
5. Aaron Klein
6. Jasper Snoek
7. Matthias Feurer
8. Kirthevasan Kandasamy
9. Katharina Eggensperger
10. Stefan Falkner

Papers from Eric Brochu's perspective

Ordered by how strongly the researchers Eric Brochu 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. Learning Multiple Layers of Features from Tiny Images
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Reinforcement Learning: An Introduction
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning (2010) — 2,681 citations
• Portfolio Allocation for Bayesian Optimization (2010) — 317 citations
• A Bayesian exploration-exploitation approach for optimal online sensing and planning with a visually guided mobile robot (2009) — 256 citations
• A Bayesian interactive optimization approach to procedural animation design (2010) — 199 citations
• Active Preference Learning with Discrete Choice Data (2007) — 208 citations