7 papers
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…
Deep Learning for School Dropout Detection: A Comparison of Tabular and Graph-Based Models for Predicting At-Risk Students
Pablo G. Almeida, Guilherme A. L. Silva, Valéria Santos +3
Student dropout is a significant challenge in educational systems worldwide, leading to substantial social and economic costs. Predicting students at risk of dropout allows for tim…
Enhancing Decision Space Diversity in Multi-Objective Evolutionary Optimization for the Diet Problem
Gustavo V. Nascimento, Ivan R. Meneghini, Valéria Santos +2
Multi-objective evolutionary algorithms (MOEAs) are essential for solving complex optimization problems, such as the diet problem, where balancing conflicting objectives, like cost…
Investigating the Impact of Large-Scale Pre-training on Nutritional Content Estimation from 2D Images
Michele Andrade, Guilherme A. L. Silva, Valéria Santos +2
Estimating the nutritional content of food from images is a critical task with significant implications for health and dietary monitoring. This is challenging, especially when rely…
MOPrompt: Multi-objective Semantic Evolution for Prompt Optimization
Sara Câmara, Eduardo Luz, Valéria Carvalho +2
Prompt engineering is crucial for unlocking the potential of Large Language Models (LLMs). Still, since manual prompt design is often complex, non-intuitive, and time-consuming, au…