Low-rank matrices play a fundamental role in modeling and computational methods for
signal processing and machine learning. In many applications where low-rank matrices …

We consider the problem of recovering a complete (ie, square and invertible) matrix A 0,
from Y∈ R n× p with Y= A 0 X 0, provided X 0 is sufficiently sparse. This recovery problem is …

This survey describes probabilistic algorithms for linear algebraic computations, such as
factorizing matrices and solving linear systems. It focuses on techniques that have a proven …

Coprime arrays can achieve an increased number of degrees of freedom by deriving the
equivalent signals of a virtual array. However, most existing methods fail to utilize all …

High-dimensional probability offers insight into the behavior of random vectors, random
matrices, random subspaces, and objects used to quantify uncertainty in high dimensions …

Abstract Quantized Neural Networks (QNNs) are often used to improve network efficiency
during the inference phase, ie after the network has been trained. Extensive research in the …

Distributed model training suffers from communication overheads due to frequent gradient
updates transmitted between compute nodes. To mitigate these overheads, several studies …

This text provides a framework in which the main objectives of the field of uncertainty
quantification (UQ) are defined and an overview of the range of mathematical methods by …

Random matrices now play a role in many areas of theoretical, applied, and computational
mathematics. Therefore, it is desirable to have tools for studying random matrices that are …

Abstract Machine learning pipelines often rely on optimizers procedures to make discrete
decisions (eg, sorting, picking closest neighbors, or shortest paths). Although these discrete …