119 citations · 157 across the 13 of their papers we have counts for
15 papers
Solving the Kidney-Exchange Problem via Graph Neural Networks with No Supervision
Pedro Foletto Pimenta, Pedro H. C. Avelar, Luis C. Lamb
This paper introduces a new learning-based approach for approximately solving the Kidney-Exchange Problem (KEP), an NP-hard problem on graphs. The problem consists of, given a pool…
On the Evolution of A.I. and Machine Learning: Towards a Meta-level Measuring and Understanding Impact, Influence, and Leadership at Premier A.I. Conferences
Rafael B. Audibert, Henrique Lemos, Pedro Avelar +2
Artificial Intelligence is now recognized as a general-purpose technology with ample impact on human life. This work aims at understanding the evolution of AI and, in particular Ma…
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…
Weekly sequential Bayesian updating improves prediction of deaths at an early epidemic stage
Pedro Henrique da Costa Avelar, Natalia Del Coco, Luis C. Lamb +2
Background: Following the outbreak of the coronavirus epidemic in early 2020, municipalities, regional governments and policymakers worldwide had to plan their Non-Pharmaceutical I…
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…
Neural-Symbolic Relational Reasoning on Graph Models: Effective Link Inference and Computation from Knowledge Bases
Henrique Lemos, Pedro Avelar, Marcelo Prates +2
The recent developments and growing interest in neural-symbolic models has shown that hybrid approaches can offer richer models for Artificial Intelligence. The integration of effe…
Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective
Luis C. Lamb, Artur Garcez, Marco Gori +3
Neural-symbolic computing has now become the subject of interest of both academic and industry research laboratories. Graph Neural Networks (GNN) have been widely used in relationa…
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…
Graph Colouring Meets Deep Learning: Effective Graph Neural Network Models for Combinatorial Problems
Henrique Lemos, Marcelo Prates, Pedro Avelar +1
Deep learning has consistently defied state-of-the-art techniques in many fields over the last decade. However, we are just beginning to understand the capabilities of neural learn…
Typed Graph Networks
Marcelo O. R. Prates, Pedro H. C. Avelar, Henrique Lemos +2
Recently, the deep learning community has given growing attention to neural architectures engineered to learn problems in relational domains. Convolutional Neural Networks employ p…
On Quantifying and Understanding the Role of Ethics in AI Research: A Historical Account of Flagship Conferences and Journals
Marcelo Prates, Pedro Avelar, Luis C. Lamb
Recent developments in AI, Machine Learning and Robotics have raised concerns about the ethical consequences of both academic and industrial AI research. Leading academics, busines…
Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network
Pedro H. C. Avelar, Henrique Lemos, Marcelo O. R. Prates +1
The application of deep learning to symbolic domains remains an active research endeavour. Graph neural networks (GNN), consisting of trained neural modules which can be arranged i…
Learning to Solve NP-Complete Problems - A Graph Neural Network for Decision TSP
Marcelo O. R. Prates, Pedro H. C. Avelar, Henrique Lemos +2
Graph Neural Networks (GNN) are a promising technique for bridging differential programming and combinatorial domains. GNNs employ trainable modules which can be assembled in diffe…
Assessing Gender Bias in Machine Translation -- A Case Study with Google Translate
Marcelo O. R. Prates, Pedro H. C. Avelar, Luis Lamb
Recently there has been a growing concern about machine bias, where trained statistical models grow to reflect controversial societal asymmetries, such as gender or racial bias. A…