Zobrazeno 1 - 10
of 75
pro vyhledávání: '"Chen, Ricky T. Q."'
Publikováno v:
NeurIPS 2024
Material discovery is a critical area of research with the potential to revolutionize various fields, including carbon capture, renewable energy, and electronics. However, the immense scale of the chemical space makes it challenging to explore all po
Externí odkaz:
http://arxiv.org/abs/2410.23405
Autor:
Holderrieth, Peter, Havasi, Marton, Yim, Jason, Shaul, Neta, Gat, Itai, Jaakkola, Tommi, Karrer, Brian, Chen, Ricky T. Q., Lipman, Yaron
We introduce generator matching, a modality-agnostic framework for generative modeling using arbitrary Markov processes. Generators characterize the infinitesimal evolution of a Markov process, which we leverage for generative modeling in a similar v
Externí odkaz:
http://arxiv.org/abs/2410.20587
Dynamical generative models that produce samples through an iterative process, such as Flow Matching and denoising diffusion models, have seen widespread use, but there have not been many theoretically-sound methods for improving these models with re
Externí odkaz:
http://arxiv.org/abs/2409.08861
Autor:
Gat, Itai, Remez, Tal, Shaul, Neta, Kreuk, Felix, Chen, Ricky T. Q., Synnaeve, Gabriel, Adi, Yossi, Lipman, Yaron
Despite Flow Matching and diffusion models having emerged as powerful generative paradigms for continuous variables such as images and videos, their application to high-dimensional discrete data, such as language, is still limited. In this work, we p
Externí odkaz:
http://arxiv.org/abs/2407.15595
Publikováno v:
ICML 2024
Crystalline materials are a fundamental component in next-generation technologies, yet modeling their distribution presents unique computational challenges. Of the plausible arrangements of atoms in a periodic lattice only a vanishingly small percent
Externí odkaz:
http://arxiv.org/abs/2406.04713
We investigate the optimal transport problem between probability measures when the underlying cost function is understood to satisfy a least action principle, also known as a Lagrangian cost. These generalizations are useful when connecting observati
Externí odkaz:
http://arxiv.org/abs/2406.00288
Autor:
Deng, Wei, Luo, Weijian, Tan, Yixin, Biloš, Marin, Chen, Yu, Nevmyvaka, Yuriy, Chen, Ricky T. Q.
Schr\"odinger bridge (SB) has emerged as the go-to method for optimizing transportation plans in diffusion models. However, SB requires estimating the intractable forward score functions, inevitably resulting in the costly implicit training loss base
Externí odkaz:
http://arxiv.org/abs/2405.04795
Publikováno v:
Mach. Learn.: Sci. Technol. 5 035061 (2024)
Orbital-free density functional theory (OF-DFT) for real-space systems has historically depended on Lagrange optimization techniques, primarily due to the inability of previously proposed electron density approaches to ensure the normalization constr
Externí odkaz:
http://arxiv.org/abs/2404.08764
Autor:
Shaul, Neta, Singer, Uriel, Chen, Ricky T. Q., Le, Matthew, Thabet, Ali, Pumarola, Albert, Lipman, Yaron
This paper introduces Bespoke Non-Stationary (BNS) Solvers, a solver distillation approach to improve sample efficiency of Diffusion and Flow models. BNS solvers are based on a family of non-stationary solvers that provably subsumes existing numerica
Externí odkaz:
http://arxiv.org/abs/2403.01329
Diffusion models have become the go-to method for large-scale generative models in real-world applications. These applications often involve data distributions confined within bounded domains, typically requiring ad-hoc thresholding techniques for bo
Externí odkaz:
http://arxiv.org/abs/2401.03228