Patrick F. Riley

Computational Theory and Mathematics · 2015–2021

A researcher in the Science corpus: 8 papers; trusts 26 researchers and is trusted by 152 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 73 citations into it; #177 of 12,182 by trust here, #18 by citations
• Molecular Biology — 60 citations into it
• Materials Chemistry — 51 citations into it; #82 of 11,513 by trust here, #544 by citations
• Artificial Intelligence — 49 citations into it
• Computational Chemistry & Drug Discovery — 48 citations into it; #97 of 425 by trust here, #78 by citations
• Computer Vision and Pattern Recognition — 27 citations into it; #9,385 of 21,575 by trust here, #6,281 by citations
• Graph Learning — 21 citations into it; #32 of 693 by trust here, #16 by citations
• Evaluation & Benchmarks — 12 citations into it

Whom Patrick F. Riley trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Alex Krizhevsky
5. Yann LeCun
6. Ruslan Salakhutdinov
7. Oriol Vinyals
8. Ilya Sutskever
9. Jimmy Ba
10. Christian Szegedy

Who trusts Patrick F. Riley overall

1. Garrett B. Goh
2. Yoshua Bengio
3. Wengong Jin
4. Johannes Klicpera
5. Matthias Fey
6. William L. Hamilton
7. Petar Velickovic
8. Weihua Hu
9. Kyle Swanson
10. Sam Schoenholz

Papers from Patrick F. Riley's perspective

Ordered by how strongly the researchers Patrick F. Riley 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. Going deeper with convolutions
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Tensor Field Networks: Rotation- and Translation-Equivariant Neural Networks for 3D Point Clouds (2018) — 1,225 citations
• Neural Message Passing for Quantum Chemistry (2017) — 9,235 citations
• Molecular graph convolutions: moving beyond fingerprints (2016) — 1,608 citations
• Massively Multitask Networks for Drug Discovery (2015) — 499 citations
• Optimization of Molecules via Deep Reinforcement Learning (2018) — 665 citations
• CACHE (Critical Assessment of Computational Hit-finding Experiments): A public-private partnership benchmarking initiative to enable the development of computational methods for hit-finding (2021) — 83 citations
• Fast machine learning models of electronic and energetic properties consistently reach approximation errors better than DFT accuracy (2017) — 19 citations
• Machine learning prediction errors better than DFT accuracy (2017) — 36 citations