Global optimization of expensive, potentially gradient-free functions has long been a critical component of many complex problems in science and engineering.

PaI methods manage to find trainable subnetworks that outperform random pruning, their performance in terms of both accuracy and computational reduction is far from satisfactory compared to post-training pruning and the understanding of PaI is missing.

PaI methods manage to find trainable subnetworks that outperform random pruning, their performance in terms of both accuracy and computational reduction is far from satisfactory compared to post-training pruning and the understanding of PaI is missing.

The fundamental problem of non-parametric two-sample testing consists in detecting the difference between any two distributions having access only to samples from these.

Low-complexity single-image super-resolution based on nonnegative neighbor embedding.

Probabilistic modelling lies at the heart of machine learning.