We show that standard ResNet architectures can be made invertible, allowing the same
model to be used for classification, density estimation, and generation. Typically, enforcing …

We study the problem of estimating the trace of a matrix A that can only be accessed through
matrix-vector multiplication. We introduce a new randomized algorithm, Hutch++, which …

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 …

We present a new method for evaluating and training unnormalized density models. Our
approach only requires access to the gradient of the unnormalized model's log-density. We …

Unions of graph multiplier operators are an important class of linear operators for processing
signals defined on graphs. We present a novel method to efficiently distribute the application …

The digital ecosystem continues to expand around the world and is revolutionising the way
markets are researched. Indeed, consumer experiences are advertised and disseminated …

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 …

Randomized trace estimation is a popular and well-studied technique that approximates the
trace of a large-scale matrix B by computing the average of x^ T Bx x TB x for many samples …

We introduce a novel algorithm for approximating the logarithm of the determinant of a
symmetric positive definite (SPD) matrix. The algorithm is randomized and approximates the …

Quantum computing offers the potential of exponential speedups for certain classical
computations. Over the last decade, many quantum machine learning (QML) algorithms …