Lewis Smith

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 170 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 82 citations into it; #12,906 of 35,345 by trust here, #15,542 by citations
• Computer Vision and Pattern Recognition — 44 citations into it
• Probabilistic & Bayesian Methods — 34 citations into it
• Safety, Robustness & Interpretability — 28 citations into it
• Optimization & Training — 17 citations into it
• Evaluation & Benchmarks — 14 citations into it
• Generative Models — 14 citations into it
• Signal Processing — 8 citations into it; #2,438 of 7,572 by trust here, #3,309 by citations

Whom Lewis Smith trusts overall

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

1. Yann LeCun
2. Diederik P. Kingma
3. Yarin Gal
4. Kaiming He
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Alex Krizhevsky
8. Ian J. Goodfellow
9. Nicholas Carlini
10. Christian Szegedy

Who trusts Lewis Smith overall

1. Andrey Malinin
2. Biraja Ghoshal
3. Jian Liang
4. Mark John Francis Gales
5. Christian Henning
6. Joost R. van Amersfoort
7. Allan Tucker
8. S. Murray
9. Jishnu Mukhoti
10. Ran He

Papers from Lewis Smith's perspective

Ordered by how strongly the researchers Lewis Smith 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Understanding Measures of Uncertainty for Adversarial Example Detection (2018) — 421 citations
• Simple and Scalable Epistemic Uncertainty Estimation Using a Single Deep Deterministic Neural Network (2020) — 55 citations
• Uncertainty Estimation Using a Single Deep Deterministic Neural Network (2020) — 560 citations
• A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks (2019) — 125 citations