Thomas F. Miller

Molecular Biology · 2012–2022

A researcher in the Science corpus: 8 papers; trusts 36 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 148 citations into it
• Materials Chemistry — 98 citations into it
• Computational Theory and Mathematics — 79 citations into it
• Atomic and Molecular Physics, and Optics — 55 citations into it
• Artificial Intelligence — 36 citations into it
• Computational Biology & Bioinformatics — 32 citations into it
• Spectroscopy — 28 citations into it
• Generative Models — 21 citations into it

Whom Thomas F. Miller trusts overall

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

1. Risi Kondor
2. Yang Zhang
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Yang Song
6. Jimmy Ba
7. Jonathan Ho
8. Yoshua Bengio
9. Alex Krizhevsky
10. Max Welling

Papers from Thomas F. Miller's perspective

Ordered by how strongly the researchers Thomas F. Miller 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features (2020) — 263 citations
• Dynamic-Backbone Protein-Ligand Structure Prediction with Multiscale Generative Diffusion Models (2022) — 30 citations
• OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy. (2021) — 87 citations
• Suppression of Dendrite Formation via Pulse Charging in Rechargeable Lithium Metal Batteries (2012) — 186 citations
• State-specific protein–ligand complex structure prediction with a multiscale deep generative model (2022) — 173 citations
• The Molpro quantum chemistry package. (2020) — 976 citations
• Ring-polymer molecular dynamics: quantum effects in chemical dynamics from classical trajectories in an extended phase space. (2013) — 625 citations
• A Simple, Exact Density-Functional-Theory Embedding Scheme (2012) — 366 citations