This research aimed to investigate the synergies between deep learning and heterogeneous graph-based scenario modeling. The candidate has thoroughly studied the state-of-the-art (SOTA) techniques that combine deep learning to heterogeneous graph (het-graph) modeled scenarios. Two main paradigms of collaboration have been identified: 1. Deep learning enhances the scalability and the representation power of graph algorithms and shallow machine learning approaches for graph analysis. 2. Het-graph modeled scenarios help design solution-space exploration biases for deep learning-based optimization algorithms. Moreover, the candidate has chosen two important research fields from industry and academia to identify two open problems where the studied synergisms could be helpful. These open problems were: 1. The online optimization of service function chain deployment in virtualized content delivery networks for live-streaming, 2. The inference of developmental regulatory mechanisms between genes and cis-regulatory elements. Finally, the candidate demonstrated his proficiency in the research field by applying the synergisms identified in the first phase of the research to solve these open problems.

Deep learning applications over heterogeneous networks: from multimedia to genes

CEVALLOS MOREN, JESUS FERNANDO
2022-09-26

Abstract

This research aimed to investigate the synergies between deep learning and heterogeneous graph-based scenario modeling. The candidate has thoroughly studied the state-of-the-art (SOTA) techniques that combine deep learning to heterogeneous graph (het-graph) modeled scenarios. Two main paradigms of collaboration have been identified: 1. Deep learning enhances the scalability and the representation power of graph algorithms and shallow machine learning approaches for graph analysis. 2. Het-graph modeled scenarios help design solution-space exploration biases for deep learning-based optimization algorithms. Moreover, the candidate has chosen two important research fields from industry and academia to identify two open problems where the studied synergisms could be helpful. These open problems were: 1. The online optimization of service function chain deployment in virtualized content delivery networks for live-streaming, 2. The inference of developmental regulatory mechanisms between genes and cis-regulatory elements. Finally, the candidate demonstrated his proficiency in the research field by applying the synergisms identified in the first phase of the research to solve these open problems.
File allegati a questo prodotto
File Dimensione Formato  
Tesi_dottorato_CevallosMoreno.pdf

accesso aperto

Note: Deep Learning applications over Heterogeneous Networks: from Multimedia to Genes - A thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science Engineering
Tipologia: Tesi di dottorato
Licenza: Creative commons
Dimensione 12.34 MB
Formato Adobe PDF
12.34 MB Adobe PDF Visualizza/Apri PDF

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/1654723
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact