Results 11 to 20 of about 737,908 (337)
On the Decomposition of Tensors by Contraction [PDF]
Reviews of Modern Physics, 1949 The decomposition of tensors into irreducible representations of the orthogonal groups is calculated for three and four dimensions. The connection is shown with the problem of the allowed values of ordinary and isotopic spin for a given symmetry of the spacial eigenfunction of a nuclear system.
Giulio Racah
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Efficient Parallel Sparse Tensor Contraction
IEEE Transactions on Parallel and Distributed SystemsWe investigate the performance of algorithms for sparse tensor-sparse tensor multiplication (SpGETT). This operation, also called sparse tensor contraction, is a higher order analogue of the sparse matrix-sparse matrix multiplication (SpGEMM) operation ...
Somesh Singh, Bora Uçar
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Fast counting with tensor networks [PDF]
SciPost Physics, 2019 We introduce tensor network contraction algorithms for counting satisfying assignments of constraint satisfaction problems (#CSPs). We represent each arbitrary #CSP formula as a tensor network, whose full contraction yields the number of satisfying ...
Stefanos Kourtis, Claudio Chamon, Eduardo R. Mucciolo, Andrei E. Ruckenstein
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Tensor networks contraction and the belief propagation algorithm [PDF]
Physical Review Research, 2021 Belief propagation is a well-studied message-passing algorithm that runs over graphical models and can be used for approximate inference and approximation of local marginals.
R. Alkabetz, I. Arad
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Jet: Fast quantum circuit simulations with parallel task-based tensor-network contraction [PDF]
Quantum, 2022 We introduce a new open-source software library $Jet$, which uses task-based parallelism to obtain speed-ups in classical tensor-network simulations of quantum circuits.
Trevor Vincent +6 more
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Parameterization of Tensor Network Contraction [PDF]
arXiv: Data Structures and Algorithms, 2019 We present a conceptually clear and algorithmically useful framework for parameterizing the costs of tensor network contraction. Our framework is completely general, applying to tensor networks with arbitrary bond dimensions, open legs, and hyperedges ...
O\u27Gorman, Bryan
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GENERALIZED HARDI INVARIANTS BY METHOD OF TENSOR CONTRACTION. [PDF]
Proc IEEE Int Symp Biomed Imaging, 2014 Invariants play an important role in diffusion MRI (dMRI). They represent tissue properties, such as diffusion anisotropy, and are used for registration, tissue segmentation and classification, as well as white matter integrity measures in clinical studies of debilitating brain diseases.
Gur Y, Johnson CR.
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Minimum Cost Loop Nests for Contraction of a Sparse Tensor with a Tensor Network [PDF]
Proceedings of the 36th ACM Symposium on Parallelism in Algorithms and Architectures, 2023 Sparse tensor decomposition and completion are common in numerous applications, ranging from machine learning to computational quantum chemistry. Typically, the main bottleneck in optimization of these models are contractions of a single large sparse ...
Raghavendra Kanakagiri, Edgar Solomonik
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Diffusion Tensor Imaging of Skeletal Muscle Contraction Using Oscillating Gradient Spin Echo [PDF]
Frontiers in Neurology, 2021 Diffusion tensor imaging (DTI) measures water diffusion in skeletal muscle tissue and allows for muscle assessment in a broad range of neuromuscular diseases.
Valentina Mazzoli +4 more
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FLAASH: Flexible Accelerator Architecture for Sparse High-Order Tensor Contraction [PDF]
arXiv.orgTensors play a vital role in machine learning (ML) and often exhibit properties best explored while maintaining high-order. Efficiently performing ML computations requires taking advantage of sparsity, but generalized hardware support is challenging ...
Gabriel Kulp +2 more
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