Results 11 to 20 of about 735,278 (318)
Faster identification of optimal contraction sequences for tensor networks [PDF]
Physical Review E, 2014 The efficient evaluation of tensor expressions involving sums over multiple indices is of significant importance to many fields of research, including quantum many-body physics, loop quantum gravity, and quantum chemistry. The computational cost of evaluating an expression may depend strongly upon the order in which the index sums are evaluated, and ...
Robert N. C. Pfeifer +2 more
core +10 more sources
Tensor Contraction Layers for Parsimonious Deep Nets [PDF]
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 Tensors offer a natural representation for many kinds of data frequently encountered in machine learning. Images, for example, are naturally represented as third order tensors, where the modes correspond to height, width, and channels.
Jean Kossaifi +4 more
semanticscholar +9 more sources
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
doaj +3 more sources
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
doaj +3 more sources
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
openaire +8 more sources
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.
europepmc +5 more sources
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
semanticscholar +3 more sources
On the Optimal Linear Contraction Order of Tree Tensor Networks, and Beyond [PDF]
SIAM Journal on Scientific Computing, 2022 The contraction cost of a tensor network depends on the contraction order. However, the optimal contraction ordering problem is known to be NP-hard. We show that the linear contraction ordering problem for tree tensor networks admits a polynomial-time ...
Mihail Stoian +2 more
openalex +3 more sources
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
doaj +2 more sources
High-Performance Tensor Contraction without Transposition [PDF]
SIAM Journal on Scientific Computing, 2016 Tensor computations---in particular tensor contraction (TC)---are important kernels in many scientific computing applications. Due to the fundamental similarity of TC to matrix multiplication and to the availability of optimized implementations such as ...
D. Matthews
semanticscholar +5 more sources