Tensors are data structures used by machine-learning models. Both of the new methods seek to efficiently exploit what's known as sparsity -- zero values -- in the tensors.

Tomographic measurement of dielectric tensors at optical frequency. Nature Materials, 2022; 21 (3): 317 DOI: 10.1038/s41563-022-01202-8 ...

Tensors provide a consistent map of real world elements that the neural network can understand. For instance, in image recognition, tensors are used to represent pixel data as matrices, ...

Dan Fleisch briefly explains some vector and tensor concepts from A Student’s Guide to Vectors and Tensors. In the field of machine learning, tensors are used as representations for many applications, ...

Tensors, or multiindexed arrays, generalize matrices (two dimensional) to three dimensions and beyond. Due to the great ability of tensors to model higher-order data and nonlinear functions, tensor ...

A symmetric tensor is completely positive (CP) if it is a sum of tensor powers of nonnegative vectors. This paper characterizes completely positive binary tensors. We show that a binary tensor is ...

Because isomorphic tensors present differently, discovering that they're secretly the same can actually be quite difficult. Grochow's project will allow him to move back and forth between creating new ...

Such tensors are very small in size, so when handling actual data sets, the cores would crunch through little blocks of larger matrices, building up the final answer.