David Watson

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 247 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 85 citations into it; #23,854 of 35,345 by trust here, #17,776 by citations
• Safety, Robustness & Interpretability — 40 citations into it
• Computer Vision and Pattern Recognition — 32 citations into it
• Statistics and Probability — 22 citations into it
• AI Ethics, Fairness & Society — 15 citations into it
• Cognitive Neuroscience — 15 citations into it; #10,112 of 10,852 by trust here, #8,619 by citations
• Safety Research — 15 citations into it
• Sociology and Political Science — 14 citations into it

Whom David Watson trusts overall

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

1. Jerome H. Friedman
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Leo Breiman
5. Yoshua Bengio
6. Ian J. Goodfellow
7. Yann LeCun
8. Scott M. Lundberg
9. Ilya Sutskever
10. Robert Tibshirani

Who trusts David Watson overall

1. Luciano Floridi
2. Giulia Vilone
3. Jie Zhang
4. S. Lo Piano
5. Sebastian Gehrmann
6. Arleen Salles
7. Hyesun Choung
8. Longo Luca
9. Momin M. Malik
10. Zong-Ming Zhang

Papers from David Watson's perspective

Ordered by how strongly the researchers David Watson 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. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence (2019) — 120 citations
• The explanation game: a formal framework for interpretable machine learning (2020) — 68 citations
• Clinical applications of machine learning algorithms: beyond the black box (2019) — 413 citations