5 citations · 10 across the 5 of their papers we have counts for
8 papers
High-Performance Star-M SVD for Big Data Compression
Md Taufique Hussain, Grey Ballard, Aditya Devarakonda +3
In the era of big data, effectively compressing large datasets while performing complex mathematical operations is crucial. Tensor-based decomposition methods have shown superior c…
Matrix-free Neural Preconditioner for the Dirac Operator in Lattice Gauge Theory
Yixuan Sun, Srinivas Eswar, Yin Lin +5
Linear systems arise in generating samples and in calculating observables in lattice quantum chromodynamics~(QCD). Solving the Hermitian positive definite systems, which are sparse…
Structured Column Subset Selection for Bayesian Optimal Experimental Design
Hugo Díaz, Arvind K. Saibaba, Srinivas Eswar +2
We consider optimal experimental design (OED) for Bayesian inverse problems, where the experimental design variables have a certain multiway structure. Given different experime…
Bayesian D-Optimal Experimental Designs via Column Subset Selection
Srinivas Eswar, Vishwas Rao, Arvind K. Saibaba
This paper tackles optimal sensor placement for Bayesian linear inverse problems, a popular version of the more general Optimal Experimental Design (OED) problem, using the D-optim…
An Alternating Rank-K Nonnegative Least Squares Framework (ARkNLS) for Nonnegative Matrix Factorization
Delin Chu, Wenya Shi, Srinivas Eswar +1
Nonnegative matrix factorization (NMF) is a prominent technique for data dimensionality reduction that has been widely used for text mining, computer vision, pattern discovery, and…
PLANC: Parallel Low Rank Approximation with Non-negativity Constraints
Srinivas Eswar, Koby Hayashi, Grey Ballard +3
We consider the problem of low-rank approximation of massive dense non-negative tensor data, for example to discover latent patterns in video and imaging applications. As the size…