3 papers
cs.LG2026
Systematic Characterization of Minimal Deep Learning Architectures: A Unified Analysis of Convergence, Pruning, and Quantization
Ziwei Zheng, Huizhi Liang, Vaclav Snasel +4
Deep learning networks excel at classification, yet identifying minimal architectures that reliably solve a task remains challenging. We present a computational methodology for sys…
cs.LG2024
On Learnable Parameters of Optimal and Suboptimal Deep Learning Models
Ziwei Zheng, Huizhi Liang, Vaclav Snasel +4
We scrutinize the structural and operational aspects of deep learning models, particularly focusing on the nuances of learnable parameters (weight) statistics, distribution, node i…
cs.LG2024
Deep Neural Networks via Complex Network Theory: a Perspective
Emanuele La Malfa, Gabriele La Malfa, Giuseppe Nicosia +1
Deep Neural Networks (DNNs) can be represented as graphs whose links and vertices iteratively process data and solve tasks sub-optimally. Complex Network Theory (CNT), merging stat…