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

Over the past few years, larger and deeper neural network models, particularly convolutional
neural networks (CNNs), have consistently advanced state-of-the-art performance across …

Edge Intelligence (EI) offers an attractive approach for local AI processing at the network
edge for privacy protection and reduced transmission, but deploying resource-intensive …

Ensemble learning is a method that leverages weak learners to produce a strong learner.
However, obtaining a large number of base learners requires substantial time and …

This study explores the learning dynamics of neural networks by analyzing the singular
value decomposition (SVD) of their weights throughout training. Our investigation reveals …

Abstract One-shot Network Pruning at Initialization (OPaI) is acknowledged as a highly cost-
effective strategy for network pruning. However, it has been observed that OPaI models tend …

Code summary generation is the task of writing natural language descriptions of a section of
source code. Recent advances in Large Language Models (LLMs) and other AI-based …

Bandwidth constraints during signal acquisition frequently impede real-time detection
applications. Hyperspectral data is a notable example, whose vast volume compromises …

Assessment of histology presented within tissue slides is an essential expert task
undertaken by pathologists to determine diagnosis and the aggressiveness of a tumour thus …

Pruning at initialization (PaI) reduces training costs by removing weights before training,
which becomes increasingly crucial with the growing network size. However, current PaI …