Guillaume Lamoureux

Molecular Biology · 2003–2018

A researcher in the Science corpus: 3 papers; trusts 23 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 32 citations into it; #11,747 of 17,773 by trust here, #13,009 by citations
• Materials Chemistry — 24 citations into it; #4,423 of 11,513 by trust here, #8,063 by citations
• Computational Biology & Bioinformatics — 17 citations into it
• Computational Theory and Mathematics — 12 citations into it
• Spectroscopy — 9 citations into it
• Artificial Intelligence — 8 citations into it
• Computational Chemistry & Drug Discovery — 8 citations into it
• Computer Vision and Pattern Recognition — 8 citations into it

Whom Guillaume Lamoureux trusts overall

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

1. Thorsten Joachims
2. W. L. Jorgensen
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Andriy Kryshtafovych
6. Michael W. Mahoney
7. John Moult
8. Karolis Uziela
9. T. Darden
10. Sergey Ioffe

Who trusts Guillaume Lamoureux overall

1. Semen O. Yesylevskyy
2. Sandeep Patel
3. Lars V. Schäfer
4. I. Tsimpanogiannis
5. Alexander D. MacKerell
6. Charles L. Brooks III
7. Durba Sengupta
8. Siewert-Jan Marrink
9. Hans Senn
10. Othonas A. Moultos

Papers from Guillaume Lamoureux's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
3. A Fast Learning Algorithm for Deep Belief Nets
4. This PDF file includes: Materials and Methods
5. Backpropagation Applied to Handwritten Zip Code Recognition
6. ImageNet classification with deep convolutional neural networks
7. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
8. Attention is All you Need (Neural Information Processing Systems)
9. Gradient-based learning applied to document recognition
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Deep convolutional networks for quality assessment of protein folds (2018) — 119 citations
• Modeling induced polarization with classical Drude oscillators: Theory and molecular dynamics simulation algorithm (2003) — 612 citations
• A simple polarizable model of water based on classical Drude oscillators (2003)