Alpha Albert Lee

Computational Theory and Mathematics · 2016–2020

A researcher in the Science corpus: 7 papers; trusts 17 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 77 citations into it; #10,661 of 12,182 by trust here, #2,161 by citations
• Materials Chemistry — 67 citations into it; #11,431 of 11,513 by trust here, #7,310 by citations
• Molecular Biology — 52 citations into it; #16,216 of 17,773 by trust here, #15,531 by citations
• Artificial Intelligence — 37 citations into it
• Computational Chemistry & Drug Discovery — 30 citations into it; #385 of 425 by trust here, #407 by citations
• Computer Vision and Pattern Recognition — 18 citations into it
• Statistics and Probability — 16 citations into it
• Spectroscopy — 12 citations into it

Whom Alpha Albert Lee trusts overall

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

1. Radford M. Neal
2. W. L. Jorgensen
3. David Kristjanson Duvenaud
4. Matthew A. Turk
5. George C. Schatz
6. Thomas Unterthiner
7. Dávid Bajusz
8. Remigijus Didziapetris
9. P. Gedeck
10. Geoffrey E. Hinton

Who trusts Alpha Albert Lee overall

1. Kyle Swanson
2. O. V. Lilienfeld
3. Amil Merchant
4. Klaus‐Robert Müller 
5. Kamal Choudhary

Papers from Alpha Albert Lee's perspective

Ordered by how strongly the researchers Alpha Albert Lee 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. Learning Multiple Layers of Features from Tiny Images
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. ImageNet: A large-scale hierarchical image database
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction (2018) — 981 citations
• Predicting materials properties without crystal structure: deep representation learning from stoichiometry (2019) — 346 citations
• Ligand biological activity predicted by cleaning positive and negative chemical correlations (2019) — 29 citations
• Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning (2020) — 764 citations
• Optimal Design of Experiments by Combining Coarse and Fine Measurements. (2017) — 4 citations
• Predicting protein–ligand affinity with a random matrix framework (2016) — 32 citations
• Geometry of energy landscapes and the optimizability of deep neural networks (2018) — 33 citations