6 citations · 6 across the 4 of their papers we have counts for
4 papers
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…
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…
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…
Fragility, Robustness and Antifragility in Deep Learning
Chandresh Pravin, Ivan Martino, Giuseppe Nicosia +1
We propose a systematic analysis of deep neural networks (DNNs) based on a signal processing technique for network parameter removal, in the form of synaptic filters that identifie…