Anna Potapenko

DeepMind (United Kingdom) — United Kingdom

Computer Science · Physical Sciences

13h-index61.0kcitations23works5606.02yr avg
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Research Topics

Topic Modeling(13), Natural Language Processing Techniques(12), Protein Structure and Dynamics(6), Advanced Text Analysis Techniques(5), Computational and Text Analysis Methods(5)

Publications23 total

Addendum: Accurate structure prediction of biomolecular interactions with AlphaFold 3
Nature·2024· 248 citedOpen Access
Accurate structure prediction of biomolecular interactions with AlphaFold 3
Nature·2024· 11.0k citedOpen Access
Applying and improving <scp>AlphaFold</scp> at <scp>CASP14</scp>
Proteins Structure Function and Bioinformatics·2021· 404 citedOpen Access
Protein complex prediction with AlphaFold-Multimer
2021· 3.9k cited
Author response for "Applying and improving AlphaFold at CASP14"
2021· 1 citedOpen Access
Highly accurate protein structure prediction for the human proteome
Nature·2021· 3.1k citedOpen Access
Highly accurate protein structure prediction with AlphaFold
Nature·2021· 42.1k citedOpen Access
Multi-agent Communication meets Natural Language: Synergies between\n Functional and Structural Language Learning
arXiv (Cornell University)·2020Open Access
Multi-agent Communication meets Natural Language: Synergies between Functional and Structural Language Learning
2020· 11 citedOpen Access
Compressive Transformers for Long-Range Sequence Modelling
arXiv (Cornell University)·2019· 49 citedOpen Access
Structural and functional learning for learning language use.
2019
Learning and Evaluating Sparse Interpretable Sentence Embeddings
arXiv (Cornell University)·2018Open Access
Probabilistic Approach for Embedding Arbitrary Features of Text
Lecture notes in computer science·2018
Learning and Evaluating Sparse Interpretable Sentence Embeddings
2018· 14 citedOpen Access
Interpretable Probabilistic Embeddings: Bridging the Gap Between Topic Models and Neural Networks
Communications in computer and information science·2017· 2 citedOpen Access
Interpretable probabilistic embeddings: bridging the gap between topic\n models and neural networks
arXiv (Cornell University)·2017Open Access
Additive Regularization of Topic Mo d- els for Topic Selection and Sparse Factorization
Lecture notes in computer science·2015· 1 cited
Additive Regularization of Topic Models for Topic Selection and Sparse Factorization
Lecture notes in computer science·2015· 32 cited
Additive regularization of topic models
Machine Learning·2014· 109 citedOpen Access
Tutorial on Probabilistic Topic Modeling: Additive Regularization for Stochastic Matrix Factorization
Communications in computer and information science·2014· 58 cited
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Frequent Co-authors

Konstantin Vorontsov(9), John Jumper(7), Alexander Pritzel(7), Tim Green(7), Olaf Ronneberger(7), Kathryn Tunyasuvunakool(7), Augustin Žídek(7), Alex Bridgland(7), Andrew Cowie(7), Rishub Jain(7), Pushmeet Kohli(7), Demis Hassabis(7), K Taki(6), Michael Figurnov(6), Andrew J. Ballard(6), Jonas Adler(6), David Reiman(6), Russ Bates(5), Ellen Clancy(5), Andrew Senior(5)