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.

Sparse training has received an upsurging interest in machine learning due to its tantalizing saving potential for the entire training process as well as inference.

RL benchmark tailored for embodied pixel-based RL.

Fairness in machine learning has attracted increasing attention in recent years.

Thus, becoming convoluted and difficult to analyze.