Ramzan Umarov

Molecular Biology · 2016–2020

A researcher in the Science corpus: 4 papers; trusts 18 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 54 citations into it; #14,656 of 17,773 by trust here, #10,695 by citations
• Artificial Intelligence — 19 citations into it
• Computational Biology & Bioinformatics — 15 citations into it
• Optimization & Training — 6 citations into it
• Materials Chemistry — 5 citations into it
• Natural Language Processing — 5 citations into it

Whom Ramzan Umarov trusts overall

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

1. Kuo-Chen Chou
2. John R. Nickolls
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. J. Urgen Schmidhuber
7. Martin G. Reese
8. Fabian Pedregosa
9. Alex Graves
10. Ilya Sutskever

Who trusts Ramzan Umarov overall

1. Yu Li

Papers from Ramzan Umarov's perspective

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

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

Their papers

• DEEPre: sequence-based enzyme EC number prediction by deep learning (2017) — 268 citations
• RNA Secondary Structure Prediction By Learning Unrolled Algorithms (2020) — 137 citations
• Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks (2016) — 217 citations
• Analysis of transcript-deleterious variants in Mendelian disorders: implications for RNA-based diagnostics (2020) — 75 citations