David Ménendez Hurtado

Molecular Biology · 2016–2020

A researcher in the Science corpus: 8 papers; trusts 65 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 158 citations into it; #3,363 of 17,773 by trust here, #7,205 by citations
• Computational Biology & Bioinformatics — 100 citations into it
• Materials Chemistry — 72 citations into it; #5,085 of 11,513 by trust here, #9,513 by citations
• Computational Theory and Mathematics — 27 citations into it; #10,179 of 12,182 by trust here, #9,804 by citations
• Artificial Intelligence — 16 citations into it
• Spectroscopy — 16 citations into it
• Genetics — 13 citations into it
• Computer Vision and Pattern Recognition — 12 citations into it

Whom David Ménendez Hurtado trusts overall

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

1. Björn Wallner
2. Arne Elofsson
3. David C. Jones
4. Mirco Michel
5. Marcin J. Skwark
6. Karolis Uziela
7. Yang Zhang
8. Sergey Ovchinnikov
9. Jinbo Xu
10. Geoffrey E. Hinton

Who trusts David Ménendez Hurtado overall

1. Mirco Michel
2. Badri Adhikari
3. Bowen Jing
4. Arne Elofsson
5. Federico Baldassarre
6. Stephan Eismann
7. Raphael J. L. Townshend
8. Jianlin Cheng
9. Patricia Suriana
10. Vignesh Ram Somnath

Papers from David Ménendez Hurtado's perspective

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

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

Their papers

• Methods for estimation of model accuracy in CASP12 (2017) — 32 citations
• Predicting accurate contacts in thousands of Pfam domain families using PconsC3 (2017) — 42 citations
• Large-scale structure prediction by improved contact predictions and model quality assessment (2017) — 34 citations
• ProQ3D: improved model quality assessments using deep learning (2016) — 159 citations
• Deep transfer learning in the assessment of the quality of protein models (2018) — 36 citations
• GraphQA: protein model quality assessment using graph convolutional networks (2020) — 127 citations
• PconsC4: fast, free, easy, and accurate contact predictions (2018) — 4 citations
• Accurate contact predictions for thousands of protein families using PconsC3 (2016) — 4 citations