Guillaume Pagès

Molecular Biology · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 22 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 79 citations into it; #7,648 of 17,773 by trust here, #9,851 by citations
• Materials Chemistry — 50 citations into it; #4,662 of 11,513 by trust here, #8,063 by citations
• Computational Biology & Bioinformatics — 34 citations into it
• Computational Theory and Mathematics — 20 citations into it; #6,709 of 12,182 by trust here, #3,796 by citations
• Spectroscopy — 18 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Artificial Intelligence — 6 citations into it
• Computational Chemistry & Drug Discovery — 5 citations into it

Whom Guillaume Pagès trusts overall

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

1. Kliment Olechnovic
2. Adam T. Zemla
3. Djork-Arné Clevert
4. Č. Venclovas
5. Izhar Wallach
6. Valerio Mariani
7. Krzysztof Ginalski
8. Georgy Derevyanko
9. Arjun Ray
10. Renzhi Cao

Who trusts Guillaume Pagès overall

1. Bowen Jing
2. Raphael J. L. Townshend
3. Stephan Eismann
4. Patricia Suriana

Papers from Guillaume Pagès's perspective

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

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

Their papers

• Smooth orientation-dependent scoring function for coarse-grained protein quality assessment (2018) — 52 citations
• Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment (2019) — 104 citations
• Protein model quality assessment using 3D oriented convolutional neural networks (2018) — 82 citations
• Assessment of chemical‐crosslink‐assisted protein structure modeling in CASP13 (2020) — 20 citations
• Assessment of chemical-crosslink-assisted protein structure modeling in CASP13 (2019) — 11 citations