activity
20182022
most citedMultitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network

12 citations · 21 across the 6 of their papers we have counts for

collaborators

6 papers

cs.AI2022★ 6 cited

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…

cs.LG2020

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…

cs.LG2019

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…

cs.LG2019★ 2 cited

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…

cs.SI2018★ 12 cited

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…

cs.LG2018★ 1 cited

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…