119 citations · 157 across the 13 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…