Mariya Popova

Computational Theory and Mathematics · 2017–2019

A researcher in the Science corpus: 2 papers; trusts 10 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 29 citations into it; #7,806 of 12,182 by trust here, #1,463 by citations
• Artificial Intelligence — 17 citations into it; #35,316 of 35,345 by trust here, #23,731 by citations
• Materials Chemistry — 17 citations into it; #5,655 of 11,513 by trust here, #5,658 by citations
• Computational Chemistry & Drug Discovery — 16 citations into it
• Molecular Biology — 16 citations into it; #9,353 of 17,773 by trust here, #6,685 by citations

Whom Mariya Popova trusts overall

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

1. Ronald J. Williams
2. Alexander Tropsha
3. Noel M. O'Boyle
4. Peter Ertl
5. Elizabeth Kelly
6. Ilya Sutskever
7. Denis Fourches
8. Geoffrey E. Hinton
9. Yoshua Bengio
10. Junyoung Chung

Who trusts Mariya Popova overall

1. Frank No'e
2. Austin Tripp
3. Miha Škalič
4. Zhuangdi Zhu
5. Łukasz Maziarka
6. Rohan Gupta
7. Erik A. Daxberger
8. Alexandre Tkatchenko
9. Kaixiang Lin
10. J. Jiménez

Papers from Mariya Popova's perspective

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

1. Long Short-Term Memory
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Attention is All you Need (Neural Information Processing Systems)
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. Going deeper with convolutions

Their papers

• NeMo: a toolkit for building AI applications using Neural Modules (2019) — 411 citations
• Deep reinforcement learning for de novo drug design (2017) — 1,235 citations