44 citations · 50 across the 6 of their papers we have counts for
6 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…
Low-Rank Adapters Initialization via Gradient Surgery for Continual Learning
Joana Pasquali, Ramiro N. Barros, Arthur S. Bianchessi +7
LoRA is widely adopted for continual fine-tuning of Large Language Models due to its parameter efficiency, modularity across tasks, and compatibility with replay strategies. Howeve…
Inference-Time Machine Unlearning via Gated Activation Redirection
Vinícius Conte Turani, Otávio Parraga, João Vitor Boer Abitante +7
Large Language Models (LLMs) memorize vast amounts of training data, raising concerns regarding privacy, copyright infringement, and safety. Machine unlearning seeks to remove the…
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…