Tensors, or multidimensional arrays, are data structures that can naturally represent visual
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …

Tensors are higher-order extensions of matrices. While matrix methods form the cornerstone
of traditional machine learning and data analysis, tensor methods have been gaining …

Convolutional neural networks typically consist of many convolutional layers followed by
one or more fully connected layers. While convolutional layers map between high-order …

We propose two randomized algorithms for low-rank Tucker decomposition of tensors. The
algorithms, which incorporate sketching, only require a single pass of the input tensor and …

The computation of convolution layers in deep neural networks typically rely on high
performance routines that trade space for time by using additional memory (either for …

This article is devoted to graphics processing unit (GPU) kernel optimization and
performance analysis of three tensor-product operations arising in finite element methods …

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 …

Tensor network methods are a class of numerical tools and algorithms to study many-body
quantum systems in and out of equilibrium, based on tailored variational wave functions …

Sparse tensor contractions appear commonly in many applications. Efficiently computing a
two sparse tensor product is challenging: It not only inherits the challenges from common …

We show that numerical computations based on tensor renormalization group (TRG)
methods can be significantly accelerated with PyTorch on graphics processing units (GPUs) …