Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Korablyov, Maksym"'
Autor:
Korablyov, Maksym, Liu, Cheng-Hao, Jain, Moksh, van der Sloot, Almer M., Jolicoeur, Eric, Ruediger, Edward, Nica, Andrei Cristian, Bengio, Emmanuel, Lapchevskyi, Kostiantyn, St-Cyr, Daniel, Schuetz, Doris Alexandra, Butoi, Victor Ion, Rector-Brooks, Jarrid, Blackburn, Simon, Feng, Leo, Nekoei, Hadi, Gottipati, SaiKrishna, Vijayan, Priyesh, Gupta, Prateek, Rampášek, Ladislav, Avancha, Sasikanth, Bacon, Pierre-Luc, Hamilton, William L., Paige, Brooks, Misra, Sanchit, Jastrzebski, Stanislaw Kamil, Kaul, Bharat, Precup, Doina, Hernández-Lobato, José Miguel, Segler, Marwin, Bronstein, Michael, Marinier, Anne, Tyers, Mike, Bengio, Yoshua
Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exhibit a property of interest remains a significant challenge. We introduce LambdaZero, a generative active lear
Externí odkaz:
http://arxiv.org/abs/2405.01616
Autor:
Bose, Avishek Joey, Akhound-Sadegh, Tara, Huguet, Guillaume, Fatras, Kilian, Rector-Brooks, Jarrid, Liu, Cheng-Hao, Nica, Andrei Cristian, Korablyov, Maksym, Bronstein, Michael, Tong, Alexander
The computational design of novel protein structures has the potential to impact numerous scientific disciplines greatly. Toward this goal, we introduce FoldFlow, a series of novel generative models of increasing modeling power based on the flow-matc
Externí odkaz:
http://arxiv.org/abs/2310.02391
Autor:
Rector-Brooks, Jarrid, Madan, Kanika, Jain, Moksh, Korablyov, Maksym, Liu, Cheng-Hao, Chandar, Sarath, Malkin, Nikolay, Bengio, Yoshua
Generative flow networks (GFlowNets) are amortized variational inference algorithms that treat sampling from a distribution over compositional objects as a sequential decision-making problem with a learnable action policy. Unlike other algorithms for
Externí odkaz:
http://arxiv.org/abs/2306.17693
Autor:
Madan, Kanika, Rector-Brooks, Jarrid, Korablyov, Maksym, Bengio, Emmanuel, Jain, Moksh, Nica, Andrei, Bosc, Tom, Bengio, Yoshua, Malkin, Nikolay
Generative flow networks (GFlowNets) are a family of algorithms for training a sequential sampler of discrete objects under an unnormalized target density and have been successfully used for various probabilistic modeling tasks. Existing training obj
Externí odkaz:
http://arxiv.org/abs/2209.12782
Autor:
Bertin, Paul, Rector-Brooks, Jarrid, Sharma, Deepak, Gaudelet, Thomas, Anighoro, Andrew, Gross, Torsten, Martinez-Pena, Francisco, Tang, Eileen L., S, Suraj M, Regep, Cristian, Hayter, Jeremy, Korablyov, Maksym, Valiante, Nicholas, van der Sloot, Almer, Tyers, Mike, Roberts, Charles, Bronstein, Michael M., Lairson, Luke L., Taylor-King, Jake P., Bengio, Yoshua
For large libraries of small molecules, exhaustive combinatorial chemical screens become infeasible to perform when considering a range of disease models, assay conditions, and dose ranges. Deep learning models have achieved state of the art results
Externí odkaz:
http://arxiv.org/abs/2202.04202
Compact data representations are one approach for improving generalization of learned functions. We explicitly illustrate the relationship between entropy and cardinality, both measures of compactness, including how gradient descent on the former red
Externí odkaz:
http://arxiv.org/abs/2112.03143
This paper is about the problem of learning a stochastic policy for generating an object (like a molecular graph) from a sequence of actions, such that the probability of generating an object is proportional to a given positive reward for that object
Externí odkaz:
http://arxiv.org/abs/2106.04399
Autor:
Lahlou, Salem, Jain, Moksh, Nekoei, Hadi, Butoi, Victor Ion, Bertin, Paul, Rector-Brooks, Jarrid, Korablyov, Maksym, Bengio, Yoshua
Epistemic Uncertainty is a measure of the lack of knowledge of a learner which diminishes with more evidence. While existing work focuses on using the variance of the Bayesian posterior due to parameter uncertainty as a measure of epistemic uncertain
Externí odkaz:
http://arxiv.org/abs/2102.08501
Autor:
Liu, Cheng-Hao, Korablyov, Maksym, Jastrzębski, Stanisław, Włodarczyk-Pruszyński, Paweł, Bengio, Yoshua, Segler, Marwin H. S.
De novo molecule generation often results in chemically unfeasible molecules. A natural idea to mitigate this problem is to bias the search process towards more easily synthesizable molecules using a proxy for synthetic accessibility. However, using
Externí odkaz:
http://arxiv.org/abs/2011.13042
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