Grigory Khimulya

Molecular Biology · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 10 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 128 citations into it; #4,090 of 17,773 by trust here, #4,409 by citations
• Computational Biology & Bioinformatics — 53 citations into it; #408 of 660 by trust here, #295 by citations
• Genetics — 36 citations into it
• Artificial Intelligence — 35 citations into it
• Materials Chemistry — 26 citations into it
• Computational Theory and Mathematics — 17 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it
• Evaluation & Benchmarks — 7 citations into it

Whom Grigory Khimulya trusts overall

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

1. Mohammed Alquraishi
2. Nobuhiko Tokuriki
3. Philip A. Romero
4. Surojit Biswas
5. Diederik P. Kingma
6. M. Packer
7. Tim Salimans
8. Frances H. Arnold
9. Geoffrey E. Hinton
10. Yoshua Bengio

Who trusts Grigory Khimulya overall

1. Jesse Vig
2. Joshua Meier
3. Michael Jendrusch
4. Jin Li
5. Ali Madani
6. Roshan Rao
7. Brian L. Hie
8. Kevin Kaichuang Yang
9. Alexey Strokach
10. Xingjie Pan

Papers from Grigory Khimulya's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Attention is All you Need (Neural Information Processing Systems)
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Unified rational protein engineering with sequence-based deep representation learning (2019) — 1,084 citations
• Low-N protein engineering with data-efficient deep learning (2020) — 433 citations
• Unified rational protein engineering with sequence-only deep representation learning (2019) — 47 citations