Michael Figurnov
Molecular Biology · 2016–2024
A researcher in the Science corpus: 8 papers; trusts 319 researchers and is trusted by 12 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 165 citations into it
- Artificial Intelligence — 87 citations into it; #28,496 of 35,345 by trust here, #7,606 by citations
- Computer Vision and Pattern Recognition — 74 citations into it; #9,544 of 21,575 by trust here, #5,754 by citations
- Generative Models — 35 citations into it
- Computational Theory and Mathematics — 32 citations into it
- Probabilistic & Bayesian Methods — 27 citations into it
- Model Architectures — 24 citations into it; #2,229 of 2,278 by trust here, #827 by citations
- Optimization & Training — 18 citations into it
Whom Michael Figurnov trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Diederik P. Kingma
- Geoffrey E. Hinton
- Alex Graves
- Alex Krizhevsky
- Yann LeCun
- Yoshua Bengio
- Shaoqing Ren
- Ilya Sutskever
- Ross B. Girshick
Who trusts Michael Figurnov overall
- Zuxuan Wu
- Gao Huang
- Yulin Wang
- Thomas Verelst
- Rameswar Panda
- Xuanyi Dong
- Shu Kong
- Yi Yang
- Charles Herrmann
- Linfeng Zhang
Papers from Michael Figurnov's perspective
Ordered by how strongly the researchers Michael Figurnov trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Spatially Adaptive Computation Time for Residual Networks (2016) — 382 citations
- Monte Carlo Gradient Estimation in Machine Learning (2019) — 518 citations
- Variational Autoencoder with Arbitrary Conditioning (2018) — 171 citations
- Highly accurate protein structure prediction with AlphaFold (2021) — 37,531 citations
- Highly accurate protein structure prediction for the human proteome (2021) — 2,524 citations
- AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models (2021) — 3,102 citations
- Accurate structure prediction of biomolecular interactions with AlphaFold 3 (2024) — 12,322 citations
- Applying and improving AlphaFold at CASP14 (2021) — 354 citations