research
∙
06/15/2023
Optimization on product manifolds under a preconditioned metric
Since optimization on Riemannian manifolds relies on the chosen metric, ...
share
research
∙
03/29/2023
Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality Constraints
Orthogonality constraints naturally appear in many machine learning prob...
research
∙
02/28/2023
Riemannian preconditioned algorithms for tensor completion via tensor ring decomposition
We propose Riemannian preconditioned algorithms for the tensor completio...
research
∙
11/17/2022
Optimization on the symplectic Stiefel manifold: SR decomposition-based retraction and applications
Numerous problems in optics, quantum physics, stability analysis, and co...
research
∙
05/20/2022
UCC: Uncertainty guided Cross-head Co-training for Semi-Supervised Semantic Segmentation
Deep neural networks (DNNs) have witnessed great successes in semantic s...
research
∙
05/17/2022
Brachial Plexus Nerve Trunk Segmentation Using Deep Learning: A Comparative Study with Doctors' Manual Segmentation
Ultrasound-guided nerve block anesthesia (UGNB) is a high-tech visual ne...
research
∙
03/13/2022
On the analysis of optimization with fixed-rank matrices: a quotient geometric view
We study a type of Riemannian gradient descent (RGD) algorithm, designed...
research
∙
11/30/2021
EAGAN: Efficient Two-stage Evolutionary Architecture Search for GANs
Generative Adversarial Networks (GANs) have been proven hugely successfu...
research
∙
08/17/2021
Edge AI without Compromise: Efficient, Versatile and Accurate Neurocomputing in Resistive Random-Access Memory
Realizing today's cloud-level artificial intelligence functionalities di...
research
∙
01/26/2021
New Riemannian preconditioned algorithms for tensor completion via polyadic decomposition
We propose new Riemannian preconditioned algorithms for low-rank tensor ...
research
∙
04/07/2016
