The remarkable performance of deep Convolutional neural networks (CNNs) is generally
attributed to their deeper and wider architectures, which can come with significant …

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

Federated Learning (FL) is a machine learning setting where many devices collaboratively
train a machine learning model while keeping the training data decentralized. In most of the …

Filter pruning has been widely used for neural network compression because of its enabled
practical acceleration. To date, most of the existing filter pruning works explore the …

Federated learning (FL) allows model training from local data collected by edge/mobile
devices while preserving data privacy, which has wide applicability to image and vision …

Federated Learning is a machine learning scheme in which a shared prediction model can
be collaboratively learned by a number of distributed nodes using their locally stored data. It …

Recent pretrained language models extend from millions to billions of parameters. Thus the
need to fine-tune an extremely large pretrained model with a limited training corpus arises in …

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 …

Abstract Sparsity in Deep Neural Networks (DNNs) is studied extensively with the focus of
maximizing prediction accuracy given an overall parameter budget. Existing methods rely on …