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

Most existing dynamic or runtime channel pruning methods have to store all weights to
achieve efficient inference which brings extra storage costs. Static pruning methods can …

Channel pruning has been widely studied as a prevailing method that effectively reduces
both computational cost and memory footprint of the original network while kee** a …

Deep neural networks (DNNs) have achieved great success in many object detection tasks.
However, such DNNS-based large object detection models are generally computationally …

Vision transformers have recently achieved competitive results across various vision tasks
but still suffer from heavy computation costs when processing a large number of tokens …

One fascinating aspect of pre-trained vision-language models~(VLMs) learning under
language supervision is their impressive zero-shot generalization capability. However, this …

Recently, federated learning (FL) as a new learning paradigm allows multi-party to
collaboratively train a shared global model with privacy protection. However, vanilla FL …

Lithology identification is a necessary task for activities of reservoir evaluation and
underground engineering construction. Recently, the intelligent lithology identification …

Distributed machine learning on edges is widely used in intelligent transportation, smart
home, industrial manufacturing, and underground pipe network monitoring to achieve low …

For the application of deep learning (DL) models in the field of remaining useful life (RUL)
prediction and predictive maintenance (PdM) of complex equipment, the insufficient training …