Grady Williams

Computer Vision and Pattern Recognition · 2015–2017

A researcher in the Science corpus: 7 papers; trusts 77 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 46 citations into it
• Reinforcement Learning — 34 citations into it; #2,015 of 2,242 by trust here, #1,545 by citations
• Aerospace Engineering — 31 citations into it
• Artificial Intelligence — 26 citations into it; #8,648 of 35,345 by trust here, #12,083 by citations
• Control and Systems Engineering — 22 citations into it; #616 of 8,257 by trust here, #759 by citations
• Model Architectures — 6 citations into it
• Game & Decision Theory — 5 citations into it
• Ocean Engineering — 5 citations into it

Whom Grady Williams trusts overall

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

1. Jan Peters
2. Evangelos A. Theodorou
3. Stefan Schaal
4. Diederik P. Kingma
5. Geoffrey E. Hinton
6. E. Todorov
7. Christopher G. Atkeson
8. Pieter Abbeel
9. Hilbert J. Kappen
10. Dean A. Pomerleau

Who trusts Grady Williams overall

1. Paul Drews
2. Yilun Du
3. Arthur Argenson
4. Anusha Nagabandi
5. Igor Mordatch
6. Sergey Levine
7. Brian Goldfain
8. Yunpeng Pan
9. Gabriel Dulac-Arnold
10. Evangelos A. Theodorou

Papers from Grady Williams's perspective

Ordered by how strongly the researchers Grady Williams 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. ImageNet: A large-scale hierarchical image database
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
• Model Predictive Path Integral Control: From Theory to Parallel Computation (2017) — 510 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
• Model Predictive Path Integral Control using Covariance Variable Importance Sampling (2015) — 222 citations