Paul Drews

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; #11,523 of 35,345 by trust here, #12,761 by citations
• Reinforcement Learning — 27 citations into it; #2,013 of 2,242 by trust here, #1,545 by citations
• Control and Systems Engineering — 21 citations into it; #2,293 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 Paul Drews trusts overall

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

1. Grady Williams
2. Diederik P. Kingma
3. Dean A. Pomerleau
4. Geoffrey E. Hinton
5. Fisher Yu
6. Alex Kendall
7. Alex Krizhevsky
8. Jimmy Ba
9. Jan Peters
10. Liang-Chieh Chen

Who trusts Paul Drews overall

1. Yunpeng Pan
2. Grady Williams
3. Ching-An Cheng
4. Sorin Mihai Grigorescu
5. Keuntaek Lee
6. Evangelos A. Theodorou
7. Kamil Saigol
8. Nolan Wagener
9. Somil Bansal
10. Brian Goldfain

Papers from Paul Drews's perspective

Ordered by how strongly the researchers Paul Drews 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. Going deeper with convolutions

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