Search Results for author:
Found 6 papers, 1 papers with code
On Cyclical MCMC Sampling
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.
Deep MSFOP: Multiple Spectral filter Operators Preservation in Deep Functional Maps for Unsupervised Shape Matching
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.
TABSurfer: a Hybrid Deep Learning Architecture for Subcortical Segmentation
Subcortical segmentation remains challenging despite its important applications in quantitative structural analysis of brain MRI scans.
A statistical perspective on algorithm unrolling models for inverse problems
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.
Efficient Deformable Shape Correspondence via Multiscale Spectral Manifold Wavelets Preservation
The functional map framework has proven to be extremely effective for representing dense correspondences between deformable shapes.
Learning to predict crisp boundaries
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.
