Reliability analysis of high-dimensional models using low-rank tensor approximations

At the heart of a good understanding of any quantum control problem, in- cluding the efficiency, accuracy, and stability of quantum control experiments as well as simulations,

In this paper, we show that in the context of kernel ridge regression, for approx- imations based on a random subset of columns of the original kernel matrix, the rank p may be

This model that we call Kruskal Convolutional Sparse Coding ( K-CSC ) (or Low-Rank Multivariate Convolutional Sparse Cod- ing) offers the advantages of 1) taking into account

We compare HOLLR with the following methods: regularized least squares RLS, low-rank regression LRR described in Section 2.2, a multilinear approach based on tensor trace

The similarity of delay states during clustering are evaluated on the basis of not only the in- and out-degrees of the nodes (the total inbound and outbound delays), but

Keywords: optimization potential evaluation method, exergy analysis, vapor compression refrig- eration plants, optimization potential index, OPI, free

the reconstruction of the activation tensors using a known dictionary – for different values of the noise ratio, and rank constraint R (for TC-FISTA ).. Each method was run with

Although this seems to mean that (convex) nuclear norm regularized approximation (3) is not easier to solve than (non- convex) tensor low-rank approximation, in this contribution