Michael Crawshaw

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 1,317 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 119 citations into it; #12,883 of 35,345 by trust here, #23,731 by citations
• Computer Vision and Pattern Recognition — 74 citations into it; #8,475 of 21,575 by trust here, #15,529 by citations
• Natural Language Processing — 28 citations into it
• Reinforcement Learning — 24 citations into it
• Model Architectures — 20 citations into it
• Representation & Self-Supervised Learning — 19 citations into it; #1,446 of 3,145 by trust here, #2,558 by citations
• Optimization & Training — 18 citations into it
• Evaluation & Benchmarks — 15 citations into it

Whom Michael Crawshaw trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Ilya Sutskever
5. Diederik P. Kingma
6. Kaiming He
7. Yann LeCun
8. Ronald J. Williams
9. Ross B. Girshick
10. Alex Graves

Who trusts Michael Crawshaw overall

1. Ronghang Hu
2. Zhiqi Li
3. Ahmed El Ouadrhiri
4. Wenhai Wang
5. Prashant Bhat
6. Eleni Triantafillou
7. Tal Ridnik
8. Chen Zhang
9. Hongyang Li
10. Yahui Liu

Papers from Michael Crawshaw's perspective

Ordered by how strongly the researchers Michael Crawshaw trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Multi-Task Learning with Deep Neural Networks: A Survey (2020) — 804 citations