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Split Bregman optimizer for online generalized CP tensor decomposition.
Abstract not provided.
Tensors for Statistical Analyses of Scientific Data: A Turbulent Combustion Perspective.
Abstract not provided.
Spectral Equivalence Properties of Higher-Order Tensor Product Finite Elements.
Abstract not provided.
Performance-Portable Sparse Tensor Decomposition Kernels on Emerging Parallel Architectures.
Abstract not provided.
Neural Network Layers for Prediction of Positive Definite Elastic Stiffness Tensors
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Majorize-Minimize Algorithms for Streaming Generalized Canonical Polyadic Tensor Decompositions.
Abstract not provided.
Stochastic Earth Model Effects on Full Waveform Seismic Moment Tensor Inversions.
Abstract not provided.
Systems and methods for tensor scheduling
A technique for efficient scheduling of operations in a program for parallelized execution thereof using a multi-processor runtime environment having two or more processors includes constraining the type or number of loop optimization transforms that may be explored such that memory and processing capacity available for the scheduling task are not exceeded, while facilitating a tradeoff between memory locality, parallelization, and/or data communication between memory modules of the multi-processor runtime environment.
Randomized Functional Sparse Tucker Tensor for Compression and Fast Visualization of Scientific Data.
Abstract not provided.
Moment tensor catalogue of California and Nevada using 3D Green's functions
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Tensor Sensitivity and long-range Coulomb interactions improve the accuracy and extensibility of Machine Learning Potential
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Computing Sparse Tensor Decompositions via Chapel and C++/MPI Interoperability without Intermediate I/O
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Streaming Generalized Canonical Polyadic Tensor Decompositions
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Incremental update techniques for online analysis of streaming tensor data
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Tensor Decomposition of Large-Scale Data with GenTen
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Sparse Symmetric Format for Tucker Decomposition
Tensor-based methods are receiving renewed attention in recent years due to their prevalence in diverse real-world applications. There is considerable literature on tensor representations and algorithms for tensor decompositions, both for dense and sparse tensors. Many applications in hypergraph analytics, machine learning, psychometry, and signal processing result in tensors that are both sparse and symmetric, making them an important class for further study. Similar to the critical Tensor Times Matrix chain operation (TTM c ) in general sparse tensors, the $\underline{S}$ parse $\underline{S}$ ymmetric $\underline{T}$ ensor $\underline{T}$ imes $\underline{S}$ ame $\underline{M}$ atrix $\underline{c}$ hain (S 3 TTM c ) operation is compute and memory intensive due to high tensor order and the associated factorial explosion in the number of non-zeros. We present the novel Compressed Sparse Symmetric (CSS) format for sparse symmetric tensors, along with an efficient parallel algorithm for the S 3 TTM c operation. We theoretically establish that S 3 TTM c on CSS achieves a better memory versus run-time trade-off compared to state-of-the-art implementations, and visualize the variation of the performance gap over the parameter space. We demonstrate experimental findings that confirm these results and achieve up to 2.72× speedup on synthetic and real datasets. The scaling of the algorithm on different test architectures is also showcased to highlight the effect of machine characteristics on algorithm performance.