Vernon J. Lawhern

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 3 papers; trusts 38 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 54 citations into it; #23,992 of 35,345 by trust here, #15,236 by citations
• Cognitive Neuroscience — 44 citations into it
• Reinforcement Learning — 39 citations into it; #1,962 of 2,242 by trust here, #1,415 by citations
• Computer Vision and Pattern Recognition — 20 citations into it
• Control and Systems Engineering — 17 citations into it
• Optimization & Training — 10 citations into it
• Model Architectures — 9 citations into it
• Management Science and Operations Research — 6 citations into it

Whom Vernon J. Lawhern trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Krizhevsky
4. J. Urgen Schmidhuber
5. Yann LeCun
6. Richard S. Sutton
7. Yoshua Bengio
8. Karen Simonyan
9. Christian Szegedy
10. Ilya Sutskever

Who trusts Vernon J. Lawhern overall

1. Kevin Trebing
2. Hamdi Altaheri
3. Dongrui Wu
4. Wei-Bang Jiang
5. Yonghao Song
6. Yi Ding
7. Guodong Chen
8. Ce Ju
9. Yannick Roy
10. Xiang Li

Papers from Vernon J. Lawhern's perspective

Ordered by how strongly the researchers Vernon J. Lawhern 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spaces (2017) — 303 citations
• Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Sparse Reward Environments (2019) — 34 citations
• EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces (2016) — 4,485 citations