The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …

Works on lottery ticket hypothesis (LTH) and single-shot network pruning (SNIP) have raised
a lot of attention currently on post-training pruning (iterative magnitude pruning), and before …

Transfer-learning methods aim to improve performance in a data-scarce target domain using
a model pretrained on a data-rich source domain. A cost-efficient strategy, linear probing …

In this paper, we introduce a new perspective on training deep neural networks capable of
state-of-the-art performance without the need for the expensive over-parameterization by …

The application of cloud computing has increased tremendously in both public and private
organizations. However, attacks on cloud computing pose a serious threat to confidentiality …

The success of deep ensembles on improving predictive performance, uncertainty
estimation, and out-of-distribution robustness has been extensively studied in the machine …

This article does not propose any novel algorithm or new hardware for sparsity. Instead, it
aims to serve the" common good" for the increasingly prosperous Sparse Neural Network …

Deep reinforcement learning (DRL) agents are trained through trial-and-error interactions
with the environment. This leads to a long training time for dense neural networks to achieve …

A new line of research for feature selection based on neural networks has recently emerged.
Despite its superiority to classical methods, it requires many training iterations to converge …

Tomorrow's robots will need to distinguish useful information from noise when performing
different tasks. A household robot for instance may continuously receive a plethora of …