Modern deep neural networks, particularly recent large language models, come with
massive model sizes that require significant computational and storage resources. To …

Neural network pruning typically removes connections or neurons from a pretrained
converged model; while a new pruning paradigm, pruning at initialization (PaI), attempts to …

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 …

Can models with particular structure avoid being biased towards spurious correlation in out-
of-distribution (OOD) generalization? Peters et al.(2016) provides a positive answer for …

The deployment constraints in practical applications necessitate the pruning of large-scale
deep learning models, ie, promoting their weight sparsity. As illustrated by the Lottery Ticket …

Pruning large neural networks to create high-quality, independently trainable sparse masks,
which can maintain similar performance to their dense counterparts, is very desirable due to …

Pruning, the task of sparsifying deep neural networks, received increasing attention recently.
Although state-of-the-art pruning methods extract highly sparse models, they neglect two …

Large neural networks can be pruned to a small fraction of their original size, with little loss
in accuracy, by following a time-consuming" train, prune, re-train" approach. Frankle & …

Railway bridges exposed to extreme environmental conditions can gradually lose their
effective cross-section at critical locations and cause catastrophic failure. This paper has …

Random masks define surprisingly effective sparse neural network models, as has been
shown empirically. The resulting sparse networks can often compete with dense …