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pro vyhledávání: '"Yim, Jason"'
Combining discrete and continuous data is an important capability for generative models. We present Discrete Flow Models (DFMs), a new flow-based model of discrete data that provides the missing link in enabling flow-based generative models to be app
Externí odkaz:
http://arxiv.org/abs/2402.04997
Autor:
Yim, Jason, Campbell, Andrew, Mathieu, Emile, Foong, Andrew Y. K., Gastegger, Michael, Jiménez-Luna, José, Lewis, Sarah, Satorras, Victor Garcia, Veeling, Bastiaan S., Noé, Frank, Barzilay, Regina, Jaakkola, Tommi S.
Publikováno v:
Transactions on Machine Learning Research 2024
Protein design often begins with the knowledge of a desired function from a motif which motif-scaffolding aims to construct a functional protein around. Recently, generative models have achieved breakthrough success in designing scaffolds for a range
Externí odkaz:
http://arxiv.org/abs/2401.04082
Generating protein sequences that fold into a intended 3D structure is a fundamental step in de novo protein design. De facto methods utilize autoregressive generation, but this eschews higher order interactions that could be exploited to improve inf
Externí odkaz:
http://arxiv.org/abs/2312.02447
Autor:
Yim, Jason, Campbell, Andrew, Foong, Andrew Y. K., Gastegger, Michael, Jiménez-Luna, José, Lewis, Sarah, Satorras, Victor Garcia, Veeling, Bastiaan S., Barzilay, Regina, Jaakkola, Tommi, Noé, Frank
We present FrameFlow, a method for fast protein backbone generation using SE(3) flow matching. Specifically, we adapt FrameDiff, a state-of-the-art diffusion model, to the flow-matching generative modeling paradigm. We show how flow matching can be a
Externí odkaz:
http://arxiv.org/abs/2310.05297
Autor:
Yim, Jason
Construction of a scaffold structure that supports a desired motif, conferring protein function, shows promise for the design of vaccines and enzymes. But a general solution to this motif-scaffolding problem remains open. Current machine-learning tec
Externí odkaz:
https://hdl.handle.net/1721.1/150230
Autor:
Kirjner, Andrew, Yim, Jason, Samusevich, Raman, Bracha, Shahar, Jaakkola, Tommi, Barzilay, Regina, Fiete, Ila
The ability to engineer novel proteins with higher fitness for a desired property would be revolutionary for biotechnology and medicine. Modeling the combinatorially large space of sequences is infeasible; prior methods often constrain optimization t
Externí odkaz:
http://arxiv.org/abs/2307.00494
Autor:
Yim, Jason, Trippe, Brian L., De Bortoli, Valentin, Mathieu, Emile, Doucet, Arnaud, Barzilay, Regina, Jaakkola, Tommi
Publikováno v:
International Conference of Machine Learning (ICML) 2023
The design of novel protein structures remains a challenge in protein engineering for applications across biomedicine and chemistry. In this line of work, a diffusion model over rigid bodies in 3D (referred to as frames) has shown success in generati
Externí odkaz:
http://arxiv.org/abs/2302.02277
Autor:
Trippe, Brian L., Yim, Jason, Tischer, Doug, Baker, David, Broderick, Tamara, Barzilay, Regina, Jaakkola, Tommi
Construction of a scaffold structure that supports a desired motif, conferring protein function, shows promise for the design of vaccines and enzymes. But a general solution to this motif-scaffolding problem remains open. Current machine-learning tec
Externí odkaz:
http://arxiv.org/abs/2206.04119
Akademický článek
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Autor:
Tomita, Tyler M., Browne, James, Shen, Cencheng, Chung, Jaewon, Patsolic, Jesse L., Falk, Benjamin, Yim, Jason, Priebe, Carey E., Burns, Randal, Maggioni, Mauro, Vogelstein, Joshua T.
Publikováno v:
Journal of Machine Learning Research 21(104), 1-39, 2020
Decision forests, including Random Forests and Gradient Boosting Trees, have recently demonstrated state-of-the-art performance in a variety of machine learning settings. Decision forests are typically ensembles of axis-aligned decision trees; that i
Externí odkaz:
http://arxiv.org/abs/1506.03410