Ryan-Rhys Griffiths

Computational Theory and Mathematics · 2017–2023

A researcher in the Science corpus: 3 papers; trusts 67 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 83 citations into it; #7,614 of 12,182 by trust here, #6,165 by citations
• Artificial Intelligence — 78 citations into it
• Materials Chemistry — 67 citations into it; #5,447 of 11,513 by trust here, #9,657 by citations
• Generative Models — 32 citations into it
• Molecular Biology — 30 citations into it
• Computational Chemistry & Drug Discovery — 28 citations into it
• Computer Vision and Pattern Recognition — 22 citations into it
• Management Science and Operations Research — 14 citations into it

Whom Ryan-Rhys Griffiths trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Jasper Snoek
4. Yoshua Bengio
5. Ilya Sutskever
6. Wengong Jin
7. David Kristjanson Duvenaud
8. Alex Krizhevsky
9. Rafael Gómez‐Bombarelli
10. Radford M. Neal

Who trusts Ryan-Rhys Griffiths overall

1. Austin Tripp
2. Erik A. Daxberger
3. José Miguel Hernández-Lobato

Papers from Ryan-Rhys Griffiths's perspective

Ordered by how strongly the researchers Ryan-Rhys Griffiths 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. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Constrained Bayesian Optimization for Automatic Chemical Design (2017) — 78 citations
• Constrained Bayesian optimization for automatic chemical design using variational autoencoders (2019) — 317 citations
• Mathematical Capabilities of ChatGPT (2023) — 589 citations