| Highly accurate protein structure prediction with AlphaFold J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, ... nature 596 (7873), 583-589, 2021 | 29167 | 2021 |
| Highly accurate protein structure prediction for the human proteome K Tunyasuvunakool, J Adler, Z Wu, T Green, M Zielinski, A Žídek, ... Nature 596 (7873), 590-596, 2021 | 2380 | 2021 |
| Protein complex prediction with AlphaFold-Multimer R Evans, M O’Neill, A Pritzel, N Antropova, A Senior, T Green, A Žídek, ... biorxiv, 2021.10. 04.463034, 2021 | 2313 | 2021 |
| Accurate structure prediction of biomolecular interactions with AlphaFold 3 J Abramson, J Adler, J Dunger, R Evans, T Green, A Pritzel, ... Nature, 1-3, 2024 | 1401 | 2024 |
| Compressive transformers for long-range sequence modelling JW Rae, A Potapenko, SM Jayakumar, TP Lillicrap arXiv preprint arXiv:1911.05507, 2019 | 569 | 2019 |
| Applying and improving AlphaFold at CASP14 J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, ... Proteins: Structure, Function, and Bioinformatics 89 (12), 1711-1721, 2021 | 331 | 2021 |
| High accuracy protein structure prediction using deep learning J Jumper, R Evans, A Pritzel, T Green, M Figurnov, K Tunyasuvunakool, ... Fourteenth critical assessment of techniques for protein structure …, 2020 | 231* | 2020 |
| Additive regularization of topic models K Vorontsov, A Potapenko Machine Learning 101, 303-323, 2015 | 208 | 2015 |
| Highly accurate protein structure prediction with AlphaFold., 2021, 596 J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, ... DOI: https://doi. org/10.1038/s41586-021-03819-2, 583-589, 0 | 163 | |
| Tutorial on probabilistic topic modeling: Additive regularization for stochastic matrix factorization K Vorontsov, A Potapenko Analysis of Images, Social Networks and Texts: Third International …, 2014 | 120 | 2014 |
| Multi-agent communication meets natural language: Synergies between functional and structural language learning A Lazaridou, A Potapenko, O Tieleman arXiv preprint arXiv:2005.07064, 2020 | 103 | 2020 |
| This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. A. Cowie, B. Romera-Paredes, S. Nikolov, R. Jain, J J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, ... Adler, T. Back, S. Petersen, D. Reiman, E. Clancy, M. Zielinski, M …, 2021 | 73 | 2021 |
| Additive regularization of topic models for topic selection and sparse factorization K Vorontsov, A Potapenko, A Plavin Statistical Learning and Data Sciences: Third International Symposium, SLDS …, 2015 | 57 | 2015 |
| Robust PLSA performs better than LDA A Potapenko, K Vorontsov Advances in Information Retrieval: 35th European Conference on IR Research …, 2013 | 42 | 2013 |
| Compressive transformers for long-range sequence modelling. arXiv preprint, 2019 JW Rae, A Potapenko, SM Jayakumar, C Hillier, TP Lillicrap URL https://arxiv. org/abs, 1911 | 36 | 1911 |
| Interpretable probabilistic embeddings: bridging the gap between topic models and neural networks A Potapenko, A Popov, K Vorontsov Artificial Intelligence and Natural Language: 6th Conference, AINL 2017, St …, 2018 | 33 | 2018 |
| EM-like algorithms for probabilistic topic modeling KV Vorontsov, AA Potapenko Mashin. Obuchenie Analiz Dannykh 1 (6), 657-686, 2013 | 24 | 2013 |
| Regularization, robustness and sparsity of probabilistic topic models KV Vorontsov, AA Potapenko Computer research and modeling 4 (4), 693-706, 2012 | 22 | 2012 |
| Learning and evaluating sparse interpretable sentence embeddings V Trifonov, OE Ganea, A Potapenko, T Hofmann arXiv preprint arXiv:1809.08621, 2018 | 20 | 2018 |
| Regularization of probabilistic topic models to improve interpretability and determine the number of topics KV Vorontsov, AA Potapenko Компьютерная лингвистика и интеллектуальные технологии, 707-719, 2014 | 5 | 2014 |
Konstantin VorontsovDorodnicyn Computing Centre of RASVerified email at phystech.edu