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 …

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 …

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 …

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 …

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 …

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 …

A fundamental task for artificial intelligence is learning. Deep Neural Networks have proven
to cope perfectly with all learning paradigms, ie supervised, unsupervised, and …

Major complications arise from the recent increase in the amount of high-dimensional data,
including high computational costs and memory requirements. Feature selection, which …

Feature selection algorithms aim to select a subset of informative features from a dataset to
reduce the data dimensionality, consequently saving resource consumption and improving …

This paper does not describe a novel method. Instead, it studies an incremental, yet must-
know baseline given the recent progress in sparse neural network training and Generative …