Download chapterPDF Similar Tensor Arrays – A Framework for Storage of Tensor Array Data Anders Brun, Marcos Martin-Fernandez, Burak Acar, Emma Munoz-Moreno, Leila Cammoun, Andreas Sigfridsson et al. Pages 407-428 User Interfaces to Interact with Tensor Fields ...
We present an algorithm based on a compressed data structure for sparse tensors and show that many computational redundancies during TTMc can be identifi ed and pruned without the memory overheads of memoization. In addition, our algorithm can further reduce the number of operations by exploiting ...
In the era of big data, the high-order high-dimensional tensor structure has attracted more and more attention, which further leads to the extensive study on tensor theory, computation and applications. Analogous to the matrix case, the positive semidefinite tensors, which form an important class...
Here, we restrict for definiteness to two phases. Two distinct cases are considered, each comes along with the described variety of topology, with arbitrary phase fractions, and with the same number of data: • Universal CNN: E1,E2∈[1,1000]GPa, ν1,ν2∈[0,0.4], • Specialized ...
However, no explicit formula for the underlying constraints is known (this is a fundamental difference with the case of totally decomposable antisymmetric tensors). Working out these constraints in ...Lim, M. H. (1992). Conditions on decomposable symmetric tensors as an algebraic variety. Linear...
2017, IEEE Transactions on Signal Processing Tensor robust principal component analysis: Exact recovery of corrupted low-rank tensors via convex optimization 2016, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Novel methods for multilinear data completion and...
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Only AutoTen is able to successfully estimate the rank for the enron dataset, but FRAPPE is also very close. Unfortunately, we are limited in the amount of data we can use for this evaluation because there are very few real-world tensors with known ranks. While it is difficult to draw ...
For example, CNN is a class of machine learning techniques that primarily uses convolution between input data and kernel data, which can be decomposed into multiplication and accumulation operations. Using a central processing unit (CPU) and its main memory to instantiate and execute machine learning...
Open Math. 2019; 17:1136–1146 Open Mathematics Research Article Yuan Zhang, Ying Zhang, and Gang Wang* Exclusion sets in the S-type eigenvalue localization sets for tensors https://doi.org/10.1515/math-2019-0090 Received October 19, 2018; accepted August 26, 2019 Abstract: In this paper...