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 …

Abstract Recently, Vision Transformer (ViT) has continuously established new milestones in
the computer vision field, while the high computation and memory cost makes its …

Channel pruning has been broadly recognized as an effective technique to reduce the
computation and memory cost of deep convolutional neural networks. However …

Federated learning (FL) enables distribution of machine learning workloads from the cloud
to resource-limited edge devices. Unfortunately, current deep networks remain not only too …

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 …

In response to recent data regulation requirements, machine unlearning (MU) has emerged
as a critical process to remove the influence of specific examples from a given model …

The lottery ticket hypothesis (LTH) has shown that dense models contain highly sparse
subnetworks (ie, winning tickets) that can be trained in isolation to match full accuracy …

Recently, bilevel optimization (BLO) has taken center stage in some very exciting
developments in the area of signal processing (SP) and machine learning (ML). Roughly …

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 & …