Zobrazeno 1 - 10
of 166
pro vyhledávání: '"Liu, Cheng‐Hao"'
Generative Flow Networks (GFlowNets), a new family of probabilistic samplers, have recently emerged as a promising framework for learning stochastic policies that generate high-quality and diverse objects proportionally to their rewards. However, exi
Externí odkaz:
http://arxiv.org/abs/2406.01901
Autor:
Koziarski, Michał, Rekesh, Andrei, Shevchuk, Dmytro, van der Sloot, Almer, Gaiński, Piotr, Bengio, Yoshua, Liu, Cheng-Hao, Tyers, Mike, Batey, Robert A.
Generative models hold great promise for small molecule discovery, significantly increasing the size of search space compared to traditional in silico screening libraries. However, most existing machine learning methods for small molecule generation
Externí odkaz:
http://arxiv.org/abs/2406.08506
Autor:
Huguet, Guillaume, Vuckovic, James, Fatras, Kilian, Thibodeau-Laufer, Eric, Lemos, Pablo, Islam, Riashat, Liu, Cheng-Hao, Rector-Brooks, Jarrid, Akhound-Sadegh, Tara, Bronstein, Michael, Tong, Alexander, Bose, Avishek Joey
Proteins are essential for almost all biological processes and derive their diverse functions from complex 3D structures, which are in turn determined by their amino acid sequences. In this paper, we exploit the rich biological inductive bias of amin
Externí odkaz:
http://arxiv.org/abs/2405.20313
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:
Akhound-Sadegh, Tara, Rector-Brooks, Jarrid, Bose, Avishek Joey, Mittal, Sarthak, Lemos, Pablo, Liu, Cheng-Hao, Sendera, Marcin, Ravanbakhsh, Siamak, Gidel, Gauthier, Bengio, Yoshua, Malkin, Nikolay, Tong, Alexander
Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-body systems, is a foundational problem in science. In this paper, we propose Iterated Denoising Energy Matchi
Externí odkaz:
http://arxiv.org/abs/2402.06121
Autor:
Volokhova, Alexandra, Koziarski, Michał, Hernández-García, Alex, Liu, Cheng-Hao, Miret, Santiago, Lemos, Pablo, Thiede, Luca, Yan, Zichao, Aspuru-Guzik, Alán, Bengio, Yoshua
Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule. In this paper we propose to use GFlowNet for sampling conformations of small molecules from the Boltzmann distribution,
Externí odkaz:
http://arxiv.org/abs/2310.14782
We tackle the problem of sampling from intractable high-dimensional density functions, a fundamental task that often appears in machine learning and statistics. We extend recent sampling-based approaches that leverage controlled stochastic processes
Externí odkaz:
http://arxiv.org/abs/2310.02679
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
Publikováno v:
Transactions on Machine Learning Research (TMLR) 07/2024 https://openreview.net/forum?id=dLaazW9zuF
In the last decades, the capacity to generate large amounts of data in science and engineering applications has been growing steadily. Meanwhile, machine learning has progressed to become a suitable tool to process and utilise the available data. Non
Externí odkaz:
http://arxiv.org/abs/2306.11715