56 citations · 349 across the 25 of their papers we have counts for
7 papers · 1 filter
On Generalization Bounds of a Family of Recurrent Neural Networks
Minshuo Chen, Xingguo Li, Tuo Zhao
Recurrent Neural Networks (RNNs) have been widely applied to sequential data analysis. Due to their complicated modeling structures, however, the theory behind is still largely mis…
ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization
Xiangyi Chen, Sijia Liu, Kaidi Xu +4
The adaptive momentum method (AdaMM), which uses past gradients to update descent directions and learning rates simultaneously, has become one of the most popular first-order optim…
Target-based Hyperspectral Demixing via Generalized Robust PCA
Sirisha Rambhatla, Xingguo Li, Jarvis Haupt
Localizing targets of interest in a given hyperspectral (HS) image has applications ranging from remote sensing to surveillance. This task of target detection leverages the fact th…
A Dictionary-Based Generalization of Robust PCA Part II: Applications to Hyperspectral Demixing
Sirisha Rambhatla, Xingguo Li, Jineng Ren +1
We consider the task of localizing targets of interest in a hyperspectral (HS) image based on their spectral signature(s), by posing the problem as two distinct convex demixing tas…
A Dictionary Based Generalization of Robust PCA
Sirisha Rambhatla, Xingguo Li, Jarvis Haupt
We analyze the decomposition of a data matrix, assumed to be a superposition of a low-rank component and a component which is sparse in a known dictionary, using a convex demixing…
NOODL: Provable Online Dictionary Learning and Sparse Coding
Sirisha Rambhatla, Xingguo Li, Jarvis Haupt
We consider the dictionary learning problem, where the aim is to model the given data as a linear combination of a few columns of a matrix known as a dictionary, where the sparse w…
A Dictionary-Based Generalization of Robust PCA with Applications to Target Localization in Hyperspectral Imaging
Sirisha Rambhatla, Xingguo Li, Jineng Ren +1
We consider the decomposition of a data matrix assumed to be a superposition of a low-rank matrix and a component which is sparse in a known dictionary, using a convex demixing met…