Zobrazeno 1 - 10
of 136
pro vyhledávání: '"Bao, Chenglong"'
In this paper, we introduce a neural network-based method to address the high-dimensional dynamic unbalanced optimal transport (UOT) problem. Dynamic UOT focuses on the optimal transportation between two densities with unequal total mass, however, it
Externí odkaz:
http://arxiv.org/abs/2409.13188
In this paper, we introduce a Fast and Scalable Semi-supervised Multi-view Subspace Clustering (FSSMSC) method, a novel solution to the high computational complexity commonly found in existing approaches. FSSMSC features linear computational and spac
Externí odkaz:
http://arxiv.org/abs/2408.05707
We introduce a novel framework, called Interface Laplace learning, for graph-based semi-supervised learning. Motivated by the observation that an interface should exist between different classes where the function value is non-smooth, we introduce a
Externí odkaz:
http://arxiv.org/abs/2408.05419
Publikováno v:
J. Chem. Phys. 161, 124702 (2024)
Solid-water interfaces are crucial to many physical and chemical processes and are extensively studied using surface-specific sum-frequency generation (SFG) spectroscopy. To establish clear correlations between specific spectral signatures and distin
Externí odkaz:
http://arxiv.org/abs/2407.15338
The data bottleneck has emerged as a fundamental challenge in learning based image restoration methods. Researchers have attempted to generate synthesized training data using paired or unpaired samples to address this challenge. This study proposes S
Externí odkaz:
http://arxiv.org/abs/2403.17502
In this paper, we study the partial differential equation models of neural networks. Neural network can be viewed as a map from a simple base model to a complicate function. Based on solid analysis, we show that this map can be formulated by a convec
Externí odkaz:
http://arxiv.org/abs/2403.15726
It is essential to capture the true probability distribution of uncertain data in the distributionally robust optimization (DRO). The uncertain data presents multimodality in numerous application scenarios, in the sense that the probability density f
Externí odkaz:
http://arxiv.org/abs/2403.08169
In this paper, we study the method to reconstruct dynamical systems from data without time labels. Data without time labels appear in many applications, such as molecular dynamics, single-cell RNA sequencing etc. Reconstruction of dynamical system fr
Externí odkaz:
http://arxiv.org/abs/2312.04038
Addressing preferred orientation in single-particle cryo-EM through AI-generated auxiliary particles
The single-particle cryo-EM field faces the persistent challenge of preferred orientation, lacking general computational solutions. We introduce cryoPROS, an AI-based approach designed to address the above issue. By generating the auxiliary particles
Externí odkaz:
http://arxiv.org/abs/2309.14954
Autor:
Li, Zanyu, Bao, Chenglong
The Anderson Mixing (AM) method is a popular approach for accelerating fixed-point iterations by leveraging historical information from previous steps. In this paper, we introduce the Riemannian Anderson Mixing (RAM) method, an extension of AM to Rie
Externí odkaz:
http://arxiv.org/abs/2309.04091