Richard Evans

Artificial Intelligence · with Kohli · 2015–2021

A researcher in the Science corpus: 8 papers; trusts 50 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 76 citations into it; #9,347 of 35,345 by trust here, #8,999 by citations
• Computer Vision and Pattern Recognition — 36 citations into it
• Natural Language Processing — 34 citations into it; #1,724 of 5,688 by trust here, #2,407 by citations
• Model Architectures — 31 citations into it
• Electrical and Electronic Engineering — 5 citations into it
• Signal Processing — 5 citations into it

Whom Richard Evans trusts overall

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

1. Ilya Sutskever
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Yoshua Bengio
6. Alex Graves
7. Yann LeCun
8. Kaiming He
9. Tomas Mikolov
10. Jimmy Ba

Who trusts Richard Evans overall

1. Shuai Tang
2. A.S. d'Avila Garcez
3. Zhengyao Jiang
4. A. Sadeghian
5. Kathryn Tunyasuvunakool
6. Luc De Raedt
7. Yufei Feng
8. Forough Arabshahi
9. Leon Weber
10. Gabriel Ryan

Papers from Richard Evans's perspective

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

Their papers

• Learning Explanatory Rules from Noisy Data (2017) — 557 citations
• Deep Reinforcement Learning in Large Discrete Action Spaces (2015) — 640 citations
• Highly accurate protein structure prediction with AlphaFold (2021) — 37,531 citations
• Improved protein structure prediction using potentials from deep learning (2020) — 2,936 citations
• Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13) (2019) — 268 citations
• Protein complex prediction with AlphaFold-Multimer (2021) — 3,230 citations
• Applying and improving AlphaFold at CASP14 (2021) — 354 citations
• Can Neural Networks Understand Logical Entailment? (2018) — 139 citations