44 citations · 50 across the 8 of their papers we have counts for
8 papers
Latent Fact-Checking: Detecting Misinformation through Activation Engineering
Pedro T. Barcelos, Otávio Parraga, Marcelo M. Delucis +3
The proliferation of misinformation online has driven demand for scalable detection systems. While most existing approaches rely on surface-level linguistic features or external kn…
Continual Learning for Sequential Personalization of Small Language Models: A Stability Monitoring Analysis
Thomas S. Paula, Lucas S. Kupssinskü, Rodrigo C. Barros
Small Language Models (SLMs) are increasingly being considered for deployment on edge devices such as laptops, enabling private, low-latency, and locally personalized applications.…
Performance Evaluation of GraphCast for Medium-Range Weather Forecasting over Brazil
Wolfgang R. Rowell, Lucas S. Kupssinskü
The paradigm of global weather forecasting is rapidly shifting with the emergence of Machine Learning Weather Prediction models (MLWP). While these data-driven architectures demons…
Quantization-Robust LLM Unlearning via Low-Rank Adaptation
João Vitor Boer Abitante, Joana Meneguzzo Pasquali, Luan Fonseca Garcia +4
Large Language Model (LLM) unlearning aims to remove targeted knowledge from a trained model, but practical deployments often require post-training quantization (PTQ) for efficient…
A Framework for Benchmarking Fairness-Utility Trade-offs in Text-to-Image Models via Pareto Frontiers
Marco N. Bochernitsan, Rodrigo C. Barros, Lucas S. Kupssinskü
Achieving fairness in text-to-image generation demands mitigating social biases without compromising visual fidelity, a challenge critical to responsible AI. Current fairness evalu…
Inference Time Debiasing Concepts in Diffusion Models
Lucas S. Kupssinskü, Marco N. Bochernitsan, Jordan Kopper +2
We propose DeCoDi, a debiasing procedure for text-to-image diffusion-based models that changes the inference procedure, does not significantly change image quality, has negligible…
Zero-shot performance of the Segment Anything Model (SAM) in 2D medical imaging: A comprehensive evaluation and practical guidelines
Christian Mattjie, Luis Vinicius de Moura, Rafaela Cappelari Ravazio +4
Segmentation in medical imaging is a critical component for the diagnosis, monitoring, and treatment of various diseases and medical conditions. Presently, the medical segmentation…
Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Survey
Otávio Parraga, Martin D. More, Christian M. Oliveira +6
Despite being responsible for state-of-the-art results in several computer vision and natural language processing tasks, neural networks have faced harsh criticism due to some of t…