Substantial progress has been made recently on develo** provably accurate and efficient
algorithms for low-rank matrix factorization via nonconvex optimization. While conventional …

Inverse optimal control (IOC) is a powerful theory that addresses the inverse problems in
control systems, robotics, Machine Learning (ML) and optimization taking into account the …

Machine learning develops rapidly, which has made many theoretical breakthroughs and is
widely applied in various fields. Optimization, as an important part of machine learning, has …

Hyperspectral (HS) imaging, also known as image spectrometry, is a landmark technique in
geoscience and remote sensing (RS). In the past decade, enormous efforts have been made …

In this work, we present a Parametric Deep Energy Method (P-DEM) for elasticity problems
accounting for strain gradient effects. The approach is based on physics-informed neural …

In this paper, we propose a new technique named\textit {Stochastic Path-Integrated
Differential EstimatoR}(SPIDER), which can be used to track many deterministic quantities of …

Adam and RMSProp are two of the most influential adaptive stochastic algorithms for
training deep neural networks, which have been pointed out to be divergent even in the …

Error feedback (EF), also known as error compensation, is an immensely popular
convergence stabilization mechanism in the context of distributed training of supervised …

Traditional analyses in non-convex optimization typically rely on the smoothness
assumption, namely requiring the gradients to be Lipschitz. However, recent evidence …

We introduce VideoPrism, a general-purpose video encoder that tackles diverse video
understanding tasks with a single frozen model. We pretrain VideoPrism on a …