5 papers
Accelerated Learning of High Dimensional Functions with a Tensor-Featured Training Network
Karl Pierce, Yuehaw Khoo, Haizhao Yang
In this work we present a method to accelerate the optimization of learning high dimensional functions using deep neural network (DNN). This optimization procedure introduces conte…
Accelerating the Canonical Polyadic Alternating Least Squares Optimization via a Randomized Interpolative Decomposition
Israa Fakih, Laura Grigori, Karl Pierce
We present a novel leverage score-based sampling strategy for the randomized alternating least squares optimization (ALS) of the canonical polyadic decomposition (CPD-ALS). Unlike…
A CPD-enabled low-scaling environment solver in a coupled cluster based static quantum embedding theory
Karl Pierce, Muhammad Talha Aziz, Avijit Shee +1
We incorporate a canonical polyadic decomposition (CPD) based low-level solver as a means to accelerate the environment-level solver for the recently developed MPCC embedding frame…
Towards Using Matrix-Free Tensor Decompositions to Systematically Improve Approximate Tensor-Networks
Karl Pierce
We investigate a novel approach to approximate tensor-network contraction via the exact, matrix-free decomposition of full tensor-networks. We study this method as a means to elimi…
Using Matrix-Free Tensor-Network Optimizations to Construct a Reduced-Scaling and Robust Second-Order Møller-Plesset Theory
Karl Pierce, Miguel Morales
We investigate the efficient combination of the canonical polyadic decomposition (CPD) and tensor hyper-contraction (THC) approaches. We first present a novel low-cost CPD solver w…