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cs.LG2026
HCPN-GCN: Scaling Hierarchical Prototype Networks with Cone Geometry for Continual Graph Learning
Sammuel R. Silva, Vander L. S. Freitas, Gladston Moreira +2
Continual Graph Learning (CGL) aims to incrementally learn from graph-structured data while preserving knowledge acquired from previous tasks. A major challenge in this setting is…
cs.LG2026
Benford's Law as a Distributional Prior for Post-Training Quantization of Large Language Models
Arthur Negrão, Pedro Silva, Vander L. S. Freitas +2
Post-training quantization (PTQ) is a practical way to reduce the memory footprint of large language models, but low-bit quantization is sensitive to mismatches between the quantiz…
cs.LG2025
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…