5 papers
Leveraging graph neural networks and mobility data for COVID-19 forecasting
Fernando H. O. Duarte, Gladston J. P. Moreira, Eduardo J. S. Luz +2
The COVID-19 pandemic has claimed millions of lives, spurring the development of diverse forecasting models. In this context, the true utility of complex spatio-temporal architectu…
Benford's Law as a Distributional Prior for Post-Training Quantization of Large Language Models
Arthur Negrão, Pedro Silva, Vander L. S. Freitas +2
The rapid growth of Large Language Models (LLMs) intensifies the need for effective compression, with weight quantization being the most widely adopted technique. Standard uniform…
PD-Loss: Proxy-Decidability for Efficient Metric Learning
Pedro Silva, Guilherme A. L. Silva, Pablo Coelho +4
Deep Metric Learning (DML) aims to learn embedding functions that map semantically similar inputs to proximate points in a metric space while separating dissimilar ones. Existing m…
A Systematic Review of ECG Arrhythmia Classification: Adherence to Standards, Fair Evaluation, and Embedded Feasibility
Guilherme Silva, Pedro Silva, Gladston Moreira +3
The classification of electrocardiogram (ECG) signals is crucial for early detection of arrhythmias and other cardiac conditions. However, despite advances in machine learning, man…
Leveraging Visibility Graphs for Enhanced Arrhythmia Classification with Graph Convolutional Networks
Rafael F. Oliveira, Gladston J. P. Moreira, Vander L. S. Freitas +1
Arrhythmias, detectable through electrocardiograms (ECGs), pose significant health risks, underscoring the need for accurate and efficient automated detection techniques. While rec…