James G. Lyons

Molecular Biology · 2014–2018

A researcher in the Science corpus: 5 papers; trusts 5 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 57 citations into it; #8,047 of 17,773 by trust here, #5,405 by citations
• Materials Chemistry — 45 citations into it; #9,265 of 11,513 by trust here, #5,923 by citations
• Artificial Intelligence — 11 citations into it

Whom James G. Lyons trusts overall

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

1. Rasmus Berg Palm
2. Yoshua Bengio
3. Eshel Faraggi
4. Guoli Wang
5. Geoffrey E. Hinton
6. Jens Meiler
7. Stephen F. Altschul
8. J. Urgen Schmidhuber
9. Burkhard Rost
10. Yang Zhang

Who trusts James G. Lyons overall

1. Chaopeng Shen
2. Seonwoo Min
3. Yujuan Gao
4. Debswapna Bhattacharya
5. Rhys Heffernan
6. Yuedong Yang
7. Jack Hanson
8. Sheng Wang
9. Christof Angermueller
10. Kuldip K. Paliwal

Papers from James G. Lyons's perspective

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

Their papers

• Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto‐encoder deep neural network (2014) — 152 citations
• Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning (2015) — 346 citations
• SPIDER2: A Package to Predict Secondary Structure, Accessible Surface Area, and Main-Chain Torsional Angles by Deep Neural Networks. (2017) — 149 citations
• Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteins (2016) — 99 citations
• SPIN2: Predicting sequence profiles from protein structures using deep neural networks (2018) — 101 citations