E. C. Alley

Molecular Biology · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 10 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 128 citations into it; #4,091 of 17,773 by trust here, #4,409 by citations
• Computational Biology & Bioinformatics — 53 citations into it; #409 of 660 by trust here, #295 by citations
• Genetics — 36 citations into it
• Artificial Intelligence — 35 citations into it
• Materials Chemistry — 26 citations into it
• Computational Theory and Mathematics — 17 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it
• Evaluation & Benchmarks — 7 citations into it

Whom E. C. Alley trusts overall

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

1. Mohammed Alquraishi
2. Nobuhiko Tokuriki
3. Philip A. Romero
4. Tim Salimans
5. M. Packer
6. Surojit Biswas
7. Diederik P. Kingma
8. Julie Dawn Thompson
9. Geoffrey E. Hinton
10. Frances H. Arnold

Who trusts E. C. Alley overall

1. Jin Li
2. Jesse Vig
3. Joshua Meier
4. Roshan Rao
5. Ali Madani
6. Kevin Kaichuang Yang
7. Brian L. Hie
8. Michael Jendrusch
9. Xingjie Pan
10. Shaun M. Kandathil

Papers from E. C. Alley's perspective

Ordered by how strongly the researchers E. C. Alley trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Unified rational protein engineering with sequence-based deep representation learning (2019) — 1,084 citations
• Low-N protein engineering with data-efficient deep learning (2020) — 433 citations
• Unified rational protein engineering with sequence-only deep representation learning (2019) — 47 citations