Georgy Derevyanko

Molecular Biology · 2018

A researcher in the Science corpus: 1 paper; trusts 23 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 29 citations into it; #11,746 of 17,773 by trust here, #13,009 by citations
• Materials Chemistry — 20 citations into it; #7,312 of 11,513 by trust here, #9,387 by citations
• Computational Biology & Bioinformatics — 17 citations into it
• Computational Theory and Mathematics — 12 citations into it
• Artificial Intelligence — 8 citations into it
• Computer Vision and Pattern Recognition — 8 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Spectroscopy — 7 citations into it

Whom Georgy Derevyanko trusts overall

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

1. Thorsten Joachims
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Andriy Kryshtafovych
5. Karolis Uziela
6. John Moult
7. Sergey Ioffe
8. Yang Zhang
9. Yann LeCun
10. Kliment Olechnovic

Who trusts Georgy Derevyanko overall

1. Raphael J. L. Townshend
2. Vignesh Ram Somnath
3. Guillaume Pagès
4. Bowen Jing
5. Soumya Sanyal
6. Zuobai Zhang
7. Pedro Hermosilla
8. Patricia Suriana
9. Stephan Eismann
10. Charlotte Bunne

Papers from Georgy Derevyanko's perspective

Ordered by how strongly the researchers Georgy Derevyanko 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. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Deep convolutional networks for quality assessment of protein folds (2018) — 119 citations