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Author (up) Anna Esposito; Italia Cirillo; Antonietta Esposito; Leopoldina Fortunati; Gian Luca Foresti; Sergio Escalera; Nikolaos Bourbakis edit  openurl
  Title Impairments in decoding facial and vocal emotional expressions in high functioning autistic adults and adolescents Type Conference Article
  Year 2020 Publication Faces and Gestures in E-health and welfare workshop Abbreviated Journal  
  Volume Issue Pages 667-674  
  Keywords  
  Abstract  
  Address Virtual; November 2020  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference FGW  
  Notes HUPBA Approved no  
  Call Number Admin @ si @ ECE2020 Serial 3516  
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Author (up) Anna Esposito; Terry Amorese; Nelson Maldonato; Alessandro Vinciarelli; Maria Ines Torres; Sergio Escalera; Gennaro Cordasco edit  openurl
  Title Seniors’ ability to decode differently aged facial emotional expressions Type Conference Article
  Year 2020 Publication Faces and Gestures in E-health and welfare workshop Abbreviated Journal  
  Volume Issue Pages 716-722  
  Keywords  
  Abstract  
  Address Virtual; November 2020  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference FGW  
  Notes HUPBA Approved no  
  Call Number Admin @ si @ EAM2020 Serial 3515  
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Author (up) Ciprian Corneanu; Meysam Madadi; Sergio Escalera; Aleix Martinez edit   pdf
openurl 
  Title Explainable Early Stopping for Action Unit Recognition Type Conference Article
  Year 2020 Publication Faces and Gestures in E-health and welfare workshop Abbreviated Journal  
  Volume Issue Pages 693-699  
  Keywords  
  Abstract A common technique to avoid overfitting when training deep neural networks (DNN) is to monitor the performance in a dedicated validation data partition and to stop
training as soon as it saturates. This only focuses on what the model does, while completely ignoring what happens inside it.
In this work, we open the “black-box” of DNN in order to perform early stopping. We propose to use a novel theoretical framework that analyses meso-scale patterns in the topology of the functional graph of a network while it trains. Based on it,
we decide when it transitions from learning towards overfitting in a more explainable way. We exemplify the benefits of this approach on a state-of-the art custom DNN that jointly learns local representations and label structure employing an ensemble of dedicated subnetworks. We show that it is practically equivalent in performance to early stopping with patience, the standard early stopping algorithm in the literature. This proves beneficial for AU recognition performance and provides new insights into how learning of AUs occurs in DNNs.		  
  Address Virtual; November 2020  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference FGW  
  Notes HUPBA; Approved no  
  Call Number Admin @ si @ CME2020 Serial 3514  
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