James T. Wilson

Artificial Intelligence · 2015–2018

A researcher in the Science corpus: 3 papers; trusts 240 researchers and is trusted by 54 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 77 citations into it; #5,288 of 35,345 by trust here, #4,772 by citations
• Computer Vision and Pattern Recognition — 70 citations into it; #4,318 of 21,575 by trust here, #3,489 by citations
• Optimization & Training — 23 citations into it
• Efficiency & Compression — 22 citations into it; #183 of 1,264 by trust here, #229 by citations
• Signal Processing — 15 citations into it
• Computational Theory and Mathematics — 14 citations into it; #4,550 of 12,182 by trust here, #8,756 by citations
• Model Architectures — 13 citations into it
• Management Science and Operations Research — 11 citations into it; #4,180 of 11,946 by trust here, #8,906 by citations

Whom James T. Wilson trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Yann LeCun
4. Alex Krizhevsky
5. Ilya Sutskever
6. Diederik P. Kingma
7. Hugo Larochelle
8. Jimmy Ba
9. Kaiming He
10. L. Eon Bottou

Who trusts James T. Wilson overall

1. Jia-Nan Wu
2. Yunhe Wang
3. Song Han
4. Willie Neiswanger
5. Yu Cheng
6. Xundong Wu
7. Yuanming Shi
8. Dacheng Tao
9. Pratik Chaudhari
10. Mohammad Rastegari

Papers from James T. Wilson's perspective

Ordered by how strongly the researchers James T. Wilson 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. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Compressing Neural Networks with the Hashing Trick (2015) — 1,240 citations
• Compressing Convolutional Neural Networks in the Frequency Domain (2015) — 155 citations
• Maximizing acquisition functions for Bayesian optimization (2018) — 316 citations