Nicholas R. Waytowich

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; #23,993 of 35,345 by trust here, #15,236 by citations
• Cognitive Neuroscience — 44 citations into it
• Reinforcement Learning — 39 citations into it; #1,963 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 Nicholas R. Waytowich trusts overall

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

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Diederik P. Kingma
5. W. B. Knox
6. J. Urgen Schmidhuber
7. Yann LeCun
8. Ilya Sutskever
9. Yoshua Bengio
10. Andrew Barto

Who trusts Nicholas R. Waytowich overall

1. Kimin Lee
2. Dzmitry Bahdanau
3. Tom Everitt
4. Jan Leike
5. Lin Guan
6. Ruohan Zhang
7. Banghua Zhu
8. Siddharth Reddy
9. Riku Arakawa

Papers from Nicholas R. Waytowich's perspective

Ordered by how strongly the researchers Nicholas R. Waytowich 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. ImageNet: A large-scale hierarchical image database
9. Going deeper with convolutions
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

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