Self-supervision as an emerging technique has been employed to train convolutional neural
networks (CNNs) for more transferrable, generalizable, and robust representation learning …

The problem of estimating the trace of matrix functions appears in applications ranging from
machine learning and scientific computing, to computational biology. This paper presents an …

This paper is concerned with two improved variants of the Hutch++ algorithm for estimating
the trace of a square matrix, implicitly given through matrix-vector products. Hutch++ …

We introduce an algorithm for estimating the trace of a matrix function using implicit products
with a symmetric matrix. Existing methods for implicit trace estimation of a matrix function …

Computation of the trace of a matrix function plays an important role in many scientific
computing applications, including applications in machine learning, computational physics …

Many fields require computing the trace of the inverse of a large, sparse matrix. Since dense
matrix methods are not practical, the typical method used for such computations is the …

The increasing number of applications requiring the solution of large-scale singular value
problems has rekindled an interest in iterative methods for the SVD. Some promising recent …

Distributed estimation of the inverse Hessian by determinantal averaging Page 1
Distributed estimation of the inverse Hessian by determinantal averaging Michał Derezinski …

Understanding the singular value spectrum of a matrix $ A\in\mathbb {R}^{n\times n} $ is a
fundamental task in countless applications. In matrix multiplication time, it is possible to …

We discuss various applications of trace estimation techniques for evaluating functions of
the form tr (f (A)) where f is certain function. The first problem we consider that can be cast in …