6 papers
When to ASK: Uncertainty-Gated Language Assistance for Reinforcement Learning
Juarez Monteiro, Nathan Gavenski, Gianlucca Zuin +1
Reinforcement learning (RL) agents often struggle with out-of-distribution (OOD) scenarios, leading to high uncertainty and random behavior. While language models (LMs) contain val…
Enhancing Authorship Attribution with Synthetic Paintings
Clarissa Loures, Caio Hosken, Luan Oliveira +2
Attributing authorship to paintings is a historically complex task, and one of its main challenges is the limited availability of real artworks for training computational models. T…
Navigating Time's Possibilities: Plausible Counterfactual Explanations for Multivariate Time-Series Forecast through Genetic Algorithms
Gianlucca Zuin, Adriano Veloso
Counterfactual learning has become promising for understanding and modeling causality in complex and dynamic systems. This paper presents a novel method for counterfactual learning…
"A 6 or a 9?": Ensemble Learning Through the Multiplicity of Performant Models and Explanations
Gianlucca Zuin, Adriano Veloso
Creating models from past observations and ensuring their effectiveness on new data is the essence of machine learning. However, selecting models that generalize well remains a cha…
Leveraging Large Language Models for Tacit Knowledge Discovery in Organizational Contexts
Gianlucca Zuin, Saulo Mastelini, Túlio Loures +1
Documenting tacit knowledge in organizations can be a challenging task due to incomplete initial information, difficulty in identifying knowledgeable individuals, the interplay of…
Automatic Tag Recommendation for Painting Artworks Using Diachronic Descriptions
Gianlucca Zuin, Adriano Veloso, João Cândido Portinari +1
In this paper, we deal with the problem of automatic tag recommendation for painting artworks. Diachronic descriptions containing deviations on the vocabulary used to describe each…