Alun David Preece

Artificial Intelligence · 2012–2020

A researcher in the Science corpus: 13 papers; trusts 332 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 198 citations into it; #12,677 of 35,345 by trust here, #8,525 by citations
• Safety, Robustness & Interpretability — 122 citations into it
• Computer Vision and Pattern Recognition — 71 citations into it
• Natural Language Processing — 26 citations into it
• Probabilistic & Bayesian Methods — 20 citations into it
• Safety Research — 15 citations into it
• Model Architectures — 13 citations into it
• AI Ethics, Fairness & Society — 12 citations into it

Whom Alun David Preece trusts overall

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

1. William R. Swartout
2. Thomas G. Dietterich
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yoshua Bengio
6. Zachary Chase Lipton
7. Nicholas A. Diakopoulos
8. L. Eon Bottou
9. Karen Simonyan
10. Yann LeCun

Who trusts Alun David Preece overall

1. Giulia Vilone
2. Longo Luca
3. Richard J. Tomsett
4. Dave Braines
5. Waddah Saeed
6. Amina Adadi
7. Ivy Munoko
8. Federico Cerutti
9. Christian Walter Peter Omlin
10. Kacper Sokol

Papers from Alun David Preece's perspective

Ordered by how strongly the researchers Alun David Preece 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. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
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
• Explainable AI for Intelligence Augmentation in Multi-Domain Operations (2019) — 17 citations
• Uncertainty-aware situational understanding (2019) — 3 citations
• Asking 'Why' in AI: Explainability of intelligent systems - perspectives and challenges (2018) — 145 citations
• Uncertainty Aware AI ML: Why and How (2018) — 22 citations
• Stakeholders in Explainable AI (2018) — 183 citations
• Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems (2018) — 191 citations
• Cognitive computing for coalition situational understanding (2017) — 20 citations
• Deep learning for situational understanding (2017) — 15 citations
• Integrating hard and soft information sources for D2D using controlled natural language (2012) — 47 citations