We derive upper bounds on the Wasserstein distance (W 1), with respect to sup-norm,
between any continuous R d valued random field indexed by the n-sphere and the …

There is a recent and growing literature on large-width asymptotic and non-asymptotic
properties of deep Gaussian neural networks (NNs), namely NNs with weights initialized as …

We study the large-width asymptotics of random fully connected neural networks with
weights drawn from $\alpha $-stable distributions, a family of heavy-tailed distributions …

Neural network compression has been an increasingly important subject, not only due to its
practical relevance, but also due to its theoretical implications, as there is an explicit …

We consider deep neural networks in a Bayesian framework with a prior distribution
sampling the network weights at random. Following a recent idea of Agapiou and Castillo …

Neal (1996) proved that infinitely wide shallow Bayesian neural networks (BNN) converge to
Gaussian processes (GP), when the network weights have bounded prior variance. Cho & …

This article studies the infinite-width limit of deep feedforward neural networks whose
weights are dependent, and modelled via a mixture of Gaussian distributions. Each hidden …

There is a growing literature on the study of large-width properties of deep Gaussian neural
networks (NNs), ie deep NNs with Gaussian-distributed parameters or weights, and …

There is a growing interest on large-width asymptotic properties of Gaussian neural
networks (NNs), namely NNs whose weights are initialized according to Gaussian …

There is a recent and growing literature on large-width asymptotic properties of Gaussian
neural networks (NNs), namely NNs whose weights are initialized as Gaussian distributions …