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Found 12 papers, 9 papers with code
Application Performance Modeling via Tensor Completion
We consider alternative piecewise/grid-based models and supervised learning models for six applications and demonstrate that CP decomposition optimized using tensor completion offers higher prediction accuracy and memory-efficiency for high-dimensional performance modeling.
Cost-efficient Gaussian Tensor Network Embeddings for Tensor-structured Inputs
We provide a systematic way to design tensor network embeddings consisting of Gaussian random tensors, such that for inputs with more general tensor network structures, both the sketch size (row size of $S$) and the sketching computational cost are low.
Alternating Mahalanobis Distance Minimization for Stable and Accurate CP Decomposition
Computing these critical points in an alternating manner motivates an alternating optimization algorithm which corresponds to alternating least squares algorithm in the matrix case.
ATD: Augmenting CP Tensor Decomposition by Self Supervision
This paper addresses the above challenges by proposing augmented tensor decomposition (ATD), which effectively incorporates data augmentations and self-supervised learning (SSL) to boost downstream classification.
MTC: Multiresolution Tensor Completion from Partial and Coarse Observations
Our MTC model explores tensor mode properties and leverages the hierarchy of resolutions to recursively initialize an optimization setup, and optimizes on the coupled system using alternating least squares.
Fast and Accurate Randomized Algorithms for Low-rank Tensor Decompositions
Experimental results show that this new ALS algorithm, combined with a new initialization scheme based on randomized range finder, yields up to $22. 0\%$ relative decomposition residual improvement compared to the state-of-the-art sketched randomized algorithm for Tucker decomposition of various synthetic and real datasets.
Distributed-Memory DMRG via Sparse and Dense Parallel Tensor Contractions
The Density Matrix Renormalization Group (DMRG) algorithm is a powerful tool for solving eigenvalue problems to model quantum systems.
Distributed, Parallel, and Cluster Computing Strongly Correlated Electrons Computational Physics
AutoHOOT: Automatic High-Order Optimization for Tensors
High-order optimization methods, including Newton's method and its variants as well as alternating minimization methods, dominate the optimization algorithms for tensor decompositions and tensor networks.
Mathematical Software Numerical Analysis Numerical Analysis
Accelerating Alternating Least Squares for Tensor Decomposition by Pairwise Perturbation
The alternating least squares algorithm for CP and Tucker decomposition is dominated in cost by the tensor contractions necessary to set up the quadratic optimization subproblems.
Numerical Analysis Numerical Analysis
Breaking the 49-Qubit Barrier in the Simulation of Quantum Circuits
With the current rate of progress in quantum computing technologies, 50-qubit systems will soon become a reality.
Quantum Physics
Scaling betweenness centrality using communication-efficient sparse matrix multiplication
Betweenness centrality (BC) is a crucial graph problem that measures the significance of a vertex by the number of shortest paths leading through it.
Distributed, Parallel, and Cluster Computing Discrete Mathematics Mathematical Software G.1.0; G.2.2
Sparse Tensor Algebra as a Parallel Programming Model
Dense and sparse tensors allow the representation of most bulk data structures in computational science applications.
Mathematical Software
