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

With the increasing demand for intelligent services, the sixth-generation (6G) wireless
networks will shift from a traditional architecture that focuses solely on a high transmission …

Structural pruning enables model acceleration by removing structurally-grouped parameters
from neural networks. However, the parameter-grou** patterns vary widely across …

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 …

Diffusion models are powerful, but they require a lot of time and data to train. We propose
Patch Diffusion, a generic patch-wise training framework, to significantly reduce the training …

Text-to-image diffusion models can create stunning images from natural language
descriptions that rival the work of professional artists and photographers. However, these …

On-device training enables the model to adapt to new data collected from the sensors by
fine-tuning a pre-trained model. Users can benefit from customized AI models without having …

Abstract Convolutional Neural Networks (CNNs) have achieved remarkable performance in
various computer vision tasks but this comes at the cost of tremendous computational …

The popularity of LLaMA (Touvron et al., 2023a; b) and other recently emerged moderate-
sized large language models (LLMs) highlights the potential of building smaller yet powerful …

High-resolution dense prediction enables many appealing real-world applications, such as
computational photography, autonomous driving, etc. However, the vast computational cost …