no code implementations • 1 Mar 2024 • LiWei Wang, Xinru Liu, Aaron Smith, Yves Atchade
Cyclical MCMC is a novel MCMC framework recently proposed by Zhang et al. (2019) to address the challenge posed by high-dimensional multimodal posterior distributions like those arising in deep learning.
no code implementations • 6 Feb 2024 • Feifan Luo, Qingsong Li, Ling Hu, Xinru Liu, Haojun Xu, Haibo Wang, Ting Li, Shengjun Liu
We propose a novel constraint called Multiple Spectral filter Operators Preservation (MSFOR) to compute functional maps and based on it, develop an efficient deep functional map architecture called Deep MSFOP for shape matching.
no code implementations • 13 Dec 2023 • Aaron Cao, Vishwanatha M. Rao, Kejia Liu, Xinru Liu, Andrew F. Laine, Jia Guo
Subcortical segmentation remains challenging despite its important applications in quantitative structural analysis of brain MRI scans.
no code implementations • 10 Nov 2023 • Yves Atchade, Xinru Liu, Qiuyun Zhu
We show that the unrolling depth needed for the optimal statistical performance of GDNs is of order $\log(n)/\log(\varrho_n^{-1})$, where $n$ is the sample size, and $\varrho_n$ is the convergence rate of the corresponding gradient descent algorithm.
no code implementations • CVPR 2021 • Ling Hu, Qinsong Li, Shengjun Liu, Xinru Liu
The functional map framework has proven to be extremely effective for representing dense correspondences between deformable shapes.
1 code implementation • ECCV 2018 • Ruoxi Deng, Chunhua Shen, Shengjun Liu, Huibing Wang, Xinru Liu
Recent methods for boundary or edge detection built on Deep Convolutional Neural Networks (CNNs) typically suffer from the issue of predicted edges being thick and need post-processing to obtain crisp boundaries.