Since optimization on Riemannian manifolds relies on the chosen metric, ...
Orthogonality constraints naturally appear in many machine learning prob...
We propose Riemannian preconditioned algorithms for the tensor completio...
Numerous problems in optics, quantum physics, stability analysis, and co...
Deep neural networks (DNNs) have witnessed great successes in semantic
s...
Ultrasound-guided nerve block anesthesia (UGNB) is a high-tech visual ne...
We study a type of Riemannian gradient descent (RGD) algorithm, designed...
Generative Adversarial Networks (GANs) have been proven hugely successfu...
Realizing today's cloud-level artificial intelligence functionalities
di...
We propose new Riemannian preconditioned algorithms for low-rank tensor
...
We propose Sentence Level Recurrent Topic Model (SLRTM), a new topic mod...