most citedEffective Integration of Symbolic and Connectionist Approaches through a Hybrid Representation

1 citations · 1 across the 3 of their papers we have counts for

collaborators

7 papers

cs.DB2020

Managing Data Lineage of O&G Machine Learning Models: The Sweet Spot for Shale Use Case

Raphael Thiago, Renan Souza, L. Azevedo +7

Machine Learning (ML) has increased its role, becoming essential in several industries. However, questions around training data lineage, such as "where has the dataset used to trai…

cs.AI20191 cited

Effective Integration of Symbolic and Connectionist Approaches through a Hybrid Representation

Marcio Moreno, Daniel Civitarese, Rafael Brandao +1

In this paper, we present our position for a neuralsymbolic integration strategy, arguing in favor of a hybrid representation to promote an effective integration. Such description…

cs.AI2019

Bridging the Gap between Semantics and Multimedia Processing

Marcio Ferreira Moreno, Guilherme Lima, Rodrigo Costa Mesquita Santos +2

In this paper, we give an overview of the semantic gap problem in multimedia and discuss how machine learning and symbolic AI can be combined to narrow this gap. We describe the ga…

cs.AI2019

An Introduction to Symbolic Artificial Intelligence Applied to Multimedia

Guilherme Lima, Rodrigo Costa, Marcio Ferreira Moreno

In this chapter, we give an introduction to symbolic artificial intelligence (AI) and discuss its relation and application to multimedia. We begin by defining what symbolic AI is,…

cs.AI2019

Multimedia Search and Temporal Reasoning

Marcio Ferreira Moreno, Rodrigo Costa Mesquita Santos, Wallas Henrique Sousa dos Santos +2

Properly modelling dynamic information that changes over time still is an open issue. Most modern knowledge bases are unable to represent relationships that are valid only during a…

cs.DC2019

Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering

Renan Souza, Leonardo Azevedo, Vítor Lourenço +10

Machine Learning (ML) has become essential in several industries. In Computational Science and Engineering (CSE), the complexity of the ML lifecycle comes from the large variety of…