40 citations · 43 across the 4 of their papers we have counts for
6 papers
"A Nova Eletricidade: Aplicações, Riscos e Tendências da IA Moderna -- "The New Electricity": Applications, Risks, and Trends in Current AI
Ana L. C. Bazzan, Anderson R. Tavares, André G. Pereira +7
The thought-provoking analogy between AI and electricity, made by computer scientist and entrepreneur Andrew Ng, summarizes the deep transformation that recent advances in Artifici…
Measuring Ethics in AI with AI: A Methodology and Dataset Construction
Pedro H. C. Avelar, Rafael B. Audibert, Anderson R. Tavares +1
Recently, the use of sound measures and metrics in Artificial Intelligence has become the subject of interest of academia, government, and industry. Efforts towards measuring diffe…
Understanding Boolean Function Learnability on Deep Neural Networks: PAC Learning Meets Neurosymbolic Models
Marcio Nicolau, Anderson R. Tavares, Zhiwei Zhang +4
Computational learning theory states that many classes of boolean formulas are learnable in polynomial time. This paper addresses the understudied subject of how, in practice, such…
EPGAT: Gene Essentiality Prediction With Graph Attention Networks
João Schapke, Anderson Tavares, Mariana Recamonde-Mendoza
The identification of essential genes/proteins is a critical step towards a better understanding of human biology and pathology. Computational approaches helped to mitigate experim…
Superpixel Image Classification with Graph Attention Networks
Pedro H. C. Avelar, Anderson R. Tavares, Thiago L. T. da Silveira +2
This paper presents a methodology for image classification using Graph Neural Network (GNN) models. We transform the input images into region adjacency graphs (RAGs), in which regi…
Discrete and Continuous Deep Residual Learning Over Graphs
Pedro H. C. Avelar, Anderson R. Tavares, Marco Gori +1
In this paper we propose the use of continuous residual modules for graph kernels in Graph Neural Networks. We show how both discrete and continuous residual layers allow for more…