John B. O. Mitchell

Computational Theory and Mathematics · 2007–2014

A researcher in the Science corpus: 3 papers; trusts 25 researchers and is trusted by 51 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 37 citations into it; #1,233 of 12,182 by trust here, #3,681 by citations
• Molecular Biology — 23 citations into it; #1,276 of 17,773 by trust here, #12,465 by citations
• Computational Chemistry & Drug Discovery — 19 citations into it; #54 of 425 by trust here, #223 by citations
• Materials Chemistry — 13 citations into it
• Spectroscopy — 11 citations into it; #95 of 1,674 by trust here, #861 by citations
• Artificial Intelligence — 8 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it
• Analytical Chemistry — 5 citations into it

Whom John B. O. Mitchell trusts overall

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

1. Laura Elena Raileanu
2. Alexander Tropsha
3. Alessandro Lusci
4. Renxiao Wang
5. Gerta Rücker
6. S. Parasuraman
7. Kilian Stoffel
8. Gerhard Klebe
9. Andy Liaw
10. Gianluca Pollastri

Who trusts John B. O. Mitchell overall

1. Tânia F Cova
2. Antoine Daina
3. An Chen
4. Marwin H. S. Segler
5. Alexandru Korotcov
6. Andrew F. Zahrt
7. A. Pais
8. Olivier Michielin
9. Xu Zhang
10. Valery Tkachenko

Papers from John B. O. Mitchell's perspective

Ordered by how strongly the researchers John B. O. Mitchell trusts endorse each paper. Same corpus, a different order for every researcher.

1. Improving neural networks by preventing co-adaptation of feature detectors
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Learning Multiple Layers of Features from Tiny Images
8. Attention is All you Need (Neural Information Processing Systems)
9. Protein data bank
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Machine learning methods in chemoinformatics (2014) — 412 citations
• Random Forest Models To Predict Aqueous Solubility (2007) — 365 citations
• A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking (2010)