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Anna Potapenko
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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
295712021
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
23992021
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
23462021
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
15602024
Compressive transformers for long-range sequence modelling
JW Rae, A Potapenko, SM Jayakumar, TP Lillicrap
arXiv preprint arXiv:1911.05507, 2019
5732019
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
3372021
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
232*2020
Additive regularization of topic models
K Vorontsov, A Potapenko
Machine Learning 101, 303-323, 2015
2092015
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
1192014
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
1032020
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
732021
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
582015
Robust PLSA performs better than LDA
A Potapenko, K Vorontsov
Advances in Information Retrieval: 35th European Conference on IR Research …, 2013
422013
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
371911
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
332018
EM-like algorithms for probabilistic topic modeling
KV Vorontsov, AA Potapenko
Mashin. Obuchenie Analiz Dannykh 1 (6), 657-686, 2013
252013
Regularization, robustness and sparsity of probabilistic topic models
KV Vorontsov, AA Potapenko
Computer research and modeling 4 (4), 693-706, 2012
222012
Learning and evaluating sparse interpretable sentence embeddings
V Trifonov, OE Ganea, A Potapenko, T Hofmann
arXiv preprint arXiv:1809.08621, 2018
212018
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
52014
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