Richard J. Tomsett

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 7 papers; trusts 197 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 142 citations into it; #27,315 of 35,345 by trust here, #10,207 by citations
• Safety, Robustness & Interpretability — 97 citations into it
• Computer Vision and Pattern Recognition — 59 citations into it
• Natural Language Processing — 17 citations into it

Whom Richard J. Tomsett trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Zachary Chase Lipton
4. Yoshua Bengio
5. Yann LeCun
6. Cynthia Rudin
7. Diederik P. Kingma
8. Scott M. Lundberg
9. Karen Simonyan
10. Kaiming He

Who trusts Richard J. Tomsett overall

1. Alun David Preece
2. Michael Chromik
3. Vadim Borisov
4. Dave Braines
5. Carlos A. Zednik
6. Kacper Sokol

Papers from Richard J. Tomsett's perspective

Ordered by how strongly the researchers Richard J. Tomsett 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Interpretability of deep learning models: A survey of results (2017) — 469 citations
• Sanity Checks for Saliency Metrics (2019) — 210 citations
• Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI (2020) — 175 citations
• Explaining Motion Relevance for Activity Recognition in Video Deep Learning Models (2020) — 16 citations
• Uncertainty-aware situational understanding (2019) — 3 citations
• Stakeholders in Explainable AI (2018) — 183 citations
• Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems (2018) — 191 citations