As their size increases, Large Languages Models (LLMs) are natural candidates for network
pruning methods: approaches that drop a subset of network weights while striving to …

Given the generational gap in available hardware between lay practitioners and the most
endowed institutions, LLMs are becoming increasingly inaccessible as they grow in size …

The increasing computational demands of modern neural networks present deployment
challenges on resource-constrained devices. Network pruning offers a solution to reduce …

While excellent in transfer learning Vision-Language models (VLMs) come with high
computational costs due to their large number of parameters. To address this issue …

Despite the ongoing success of artificial intelligence applications, the deployment of deep
learning models on end devices remains challenging due to the limited onboard …

The rising footprint of machine learning has led to a focus on imposing model sparsity as a
means of reducing computational and memory costs. For deep neural networks (DNNs), the …

The recent paradigm shift to large-scale foundation models has brought about a new era for
deep learning that, while has found great success in practice, has also been plagued by …

We present L0Learn: an open-source package for sparse linear regression and
classification using ℓ0 regularization. L0Learn implements scalable, approximate …

Neural network pruning has become increasingly crucial due to the complexity of neural
network models and their widespread use in various fields. Existing pruning algorithms often …

Recent works show that reducing the number of layers in a convolutional neural network can
enhance efficiency while maintaining the performance of the network. Existing depth …