Jeremy Morton

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 30 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 24 citations into it
• Reinforcement Learning — 19 citations into it
• Statistical and Nonlinear Physics — 15 citations into it; #7,311 of 10,127 by trust here, #7,024 by citations
• Computer Vision and Pattern Recognition — 12 citations into it
• Computational Mechanics — 11 citations into it; #7,511 of 10,001 by trust here, #7,173 by citations
• Control and Systems Engineering — 6 citations into it; #3,473 of 8,257 by trust here, #3,145 by citations
• Optimization & Training — 5 citations into it

Whom Jeremy Morton trusts overall

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

1. Clarence W. Rowley
2. Kaiming He
3. Pieter Abbeel
4. Milan Korda
5. Steven Diamond
6. Geoffrey E. Hinton
7. Joshua L. Proctor
8. Eric R. Ziegel
9. John Schulman
10. Alex Krizhevsky

Who trusts Jeremy Morton overall

1. Nachiket Deo
2. Sangseung Lee
3. Philipp Holl
4. Zijie Li
5. Edouard Leurent
6. Yunzhu Li
7. Mohan Manubhai Trivedi
8. Donghyun You
9. Nils Thürey
10. Vladlen Koltun

Papers from Jeremy Morton's perspective

Ordered by how strongly the researchers Jeremy Morton 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. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Deep Dynamical Modeling and Control of Unsteady Fluid Flows (2018) — 189 citations
• Imitating driver behavior with generative adversarial networks (2017) — 448 citations