Thomas S. A. Wallis

Computer Vision and Pattern Recognition · 2016–2020

A researcher in the Science corpus: 4 papers; trusts 80 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 65 citations into it; #11,922 of 21,575 by trust here, #12,992 by citations
• Cognitive Neuroscience — 45 citations into it; #10,617 of 10,852 by trust here, #8,619 by citations
• Artificial Intelligence — 39 citations into it
• Statistics and Probability — 17 citations into it
• Evaluation & Benchmarks — 12 citations into it
• Model Architectures — 9 citations into it
• Computer Graphics and Computer-Aided Design — 6 citations into it
• Generative Models — 5 citations into it

Whom Thomas S. A. Wallis trusts overall

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

1. Andrew Gelman
2. William N. Venables
3. Karen Simonyan
4. Alex Krizhevsky
5. Daniel L. K. Yamins
6. John K. Kruschke
7. Yihui Xie
8. Geoffrey E. Hinton
9. Leon A. Gatys
10. Joseph Hilbe

Who trusts Thomas S. A. Wallis overall

1. Robert Geirhos
2. Wenguan Wang
3. Marcella Cornia
4. Jia Li
5. Lucas Theis
6. Matthias Bethge
7. Drew Linsley
8. Junting Pan

Papers from Thomas S. A. Wallis's perspective

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

Their papers

• DeepGaze II: Reading fixations from deep features trained on object recognition (2016) — 317 citations
• Testing models of peripheral encoding using metamerism in an oddity paradigm. (2016) — 46 citations
• The Notorious Difficulty of Comparing Human and Machine Perception (2020) — 35 citations
• A parametric texture model based on deep convolutional features closely matches texture appearance for humans (2017) — 63 citations