Brian Goldfain

Computer Vision and Pattern Recognition · 2016–2019

A researcher in the Science corpus: 6 papers; trusts 78 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 50 citations into it
• Aerospace Engineering — 32 citations into it
• Artificial Intelligence — 32 citations into it; #11,524 of 35,345 by trust here, #12,761 by citations
• Reinforcement Learning — 27 citations into it; #2,014 of 2,242 by trust here, #1,545 by citations
• Control and Systems Engineering — 21 citations into it; #2,294 of 8,257 by trust here, #1,409 by citations
• Robotics & Control — 14 citations into it
• Model Architectures — 6 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Brian Goldfain trusts overall

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

1. Frank Dellaert
2. Grady Williams
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Diederik P. Kingma
6. Jan Peters
7. Morgan Quigley
8. Michael Kaess
9. Evangelos A. Theodorou
10. Stefan Schaal

Who trusts Brian Goldfain overall

1. Grady Williams
2. Paul Drews
3. Keuntaek Lee
4. Nolan Wagener
5. Yunpeng Pan
6. Ching-An Cheng
7. Evangelos A. Theodorou
8. Kamil Saigol
9. Aravind Rajeswaran
10. Edouard Leurent

Papers from Brian Goldfain's perspective

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

Their papers

• Information theoretic MPC for model-based reinforcement learning (2017) — 671 citations
• Aggressive driving with model predictive path integral control (2016) — 612 citations
• AutoRally: An Open Platform for Aggressive Autonomous Driving (2019) — 122 citations
• Information-Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving (2017) — 416 citations
• Aggressive Deep Driving: Combining Convolutional Neural Networks and Model Predictive Control (2017) — 64 citations
• Aggressive Deep Driving: Model Predictive Control with a CNN Cost Model (2017) — 44 citations