Zobrazeno 1 - 10
of 108
pro vyhledávání: '"KHOO, YUEHAW"'
Autor:
Wang, Yi, Khoo, Yuehaw
Convex relaxation methods are powerful tools for studying the lowest energy of many-body problems. By relaxing the representability conditions for marginals to a set of local constraints, along with a global semidefinite constraint, a polynomial-time
Externí odkaz:
http://arxiv.org/abs/2408.00235
Analyzing dynamical data often requires information of the temporal labels, but such information is unavailable in many applications. Recovery of these temporal labels, closely related to the seriation or sequencing problem, becomes crucial in the st
Externí odkaz:
http://arxiv.org/abs/2406.13635
Global polynomial optimization is an important tool across applied mathematics, with many applications in operations research, engineering, and physical sciences. In various settings, the polynomials depend on external parameters that may be random.
Externí odkaz:
http://arxiv.org/abs/2406.08954
Autor:
Melia, Owen, Tsang, Olivia, Charisopoulos, Vasileios, Khoo, Yuehaw, Hoskins, Jeremy, Willett, Rebecca
Interpreting scattered acoustic and electromagnetic wave patterns is a computational task that enables remote imaging in a number of important applications, including medical imaging, geophysical exploration, sonar and radar detection, and nondestruc
Externí odkaz:
http://arxiv.org/abs/2405.13214
We propose to improve the convergence properties of the single-reference coupled cluster (CC) method through an augmented Lagrangian formalism. The conventional CC method changes a linear high-dimensional eigenvalue problem with exponential size into
Externí odkaz:
http://arxiv.org/abs/2403.16381
Nonparametric density models are of great interest in various scientific and engineering disciplines. Classical density kernel methods, while numerically robust and statistically sound in low-dimensional settings, become inadequate even in moderate h
Externí odkaz:
http://arxiv.org/abs/2401.11646
Autor:
Hur, YoonHaeng, Khoo, Yuehaw
While matching procedures based on pairwise distances are conceptually appealing and thus favored in practice, theoretical guarantees for such procedures are rarely found in the literature. We propose and analyze matching procedures based on distance
Externí odkaz:
http://arxiv.org/abs/2312.12641
We propose an approach to directly estimate the moments or marginals for a high-dimensional equilibrium distribution in statistical mechanics, via solving the high-dimensional Fokker-Planck equation in terms of low-order cluster moments or marginals.
Externí odkaz:
http://arxiv.org/abs/2306.03292
Autor:
Chen, Yian, Khoo, Yuehaw
In this paper, we propose a general framework for solving high-dimensional partial differential equations with tensor networks. Our approach uses Monte-Carlo simulations to update the solution and re-estimates the new solution from samples as a tenso
Externí odkaz:
http://arxiv.org/abs/2305.17884
Fueled by the expressive power of deep neural networks, normalizing flows have achieved spectacular success in generative modeling, or learning to draw new samples from a distribution given a finite dataset of training samples. Normalizing flows have
Externí odkaz:
http://arxiv.org/abs/2305.02460