Large pre-trained models (LPMs), such as LLaMA and GLM, have shown exceptional
performance across various tasks through fine-tuning. Although low-rank adaption (LoRA) …

Practitioners frequently observe that pruning improves model generalization. A long-
standing hypothesis based on bias-variance trade-off attributes this generalization …

The sheer size of modern neural networks makes model serving a serious computational
challenge. A popular class of compression techniques overcomes this challenge by pruning …

The leap in performance in state-of-the-art computer vision methods is attributed to the
development of deep neural networks. However it often comes at a computational price …

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

Unstructured sparse neural networks are an important class of machine learning (ML)
models, as they compact model size and reduce floating point operations. The execution …

As the deep learning models have been widely used in recent years, there is a high demand
for reducing the model size in terms of memory and computation without much compromise …

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 …

As neural networks grow in size and complexity, inference speeds decline. To combat this,
one of the most effective compression techniques–channel pruning–removes channels from …

Structural model pruning is a prominent approach used for reducing the computational cost
of Convolutional Neural Networks (CNNs) before their deployment on resource-constrained …