Wen Torng

Molecular Biology · 2017–2018

A researcher in the Science corpus: 5 papers; trusts 56 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 62 citations into it; #1,450 of 17,773 by trust here, #6,685 by citations
• Computational Theory and Mathematics — 49 citations into it; #6,936 of 12,182 by trust here, #2,931 by citations
• Materials Chemistry — 36 citations into it; #869 of 11,513 by trust here, #6,817 by citations
• Computer Vision and Pattern Recognition — 25 citations into it
• Artificial Intelligence — 19 citations into it
• Computational Biology & Bioinformatics — 16 citations into it
• Pharmacology — 15 citations into it
• Evaluation & Benchmarks — 11 citations into it

Whom Wen Torng trusts overall

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

1. The Uniprot Consortium
2. Geoffrey E. Hinton
3. L. Eon Bottou
4. John Scott Bridle
5. Alex Krizhevsky
6. Tianyun Liu
7. Yoshua Bengio
8. Karen Simonyan
9. Russ B. Altman
10. Steven E. Brenner

Who trusts Wen Torng overall

1. Artur Meller
2. Hannes Stärk
3. Raphael J. L. Townshend
4. Russ B. Altman
5. Michael D. Ward
6. Xiao Wang
7. Georgy Derevyanko
8. Octavian-Eugen Ganea
9. Namrata Anand-Achim
10. Uzair Aslam Bhatti

Papers from Wen Torng's perspective

Ordered by how strongly the researchers Wen Torng 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Going deeper with convolutions
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Biological and Functional Relevance of CASP Predictions (2017) — 7 citations
• Graph Convolutional Neural Networks for Predicting Drug-Target Interactions (2018) — 317 citations
• 3D deep convolutional neural networks for amino acid environment similarity analysis (2017) — 147 citations
• High precision protein functional site detection using 3D convolutional neural networks (2018) — 72 citations
• Machine learning in chemoinformatics and drug discovery (2018) — 772 citations