Information

Scope

Deep learning is an artificial intelligence (AI) function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network.

The objective of the workshop on Deep Learning Applications is an opportunity for researchers to provide further insight into the problems solved at this stage, advantages and disadvantages of the various approaches used, lessons learned, and meaningful contributions to enhance applications based on deep learning. In this sense, the first workshop on Deep Learning Applications (DeLA) will provide a forum for the presentation and discussion of novel research ideas or actual deployments focused on the development of advanced applications based on Deep Learning.


Topics

The topics of interest for this special session include, but are not limited to:

  • Chatbots
  • Natural Language Processing (NLP);
  • Computer Vision;
  • Sentiment Analysis;
  • Speech Recognition;
  • Autonomous Vehicles;
  • Robotics;
  • Image Detection and Object Classification;
  • Deep Learning Business Applications;
  • Healthcare
  • Entertainment
  • Composing Music
  • Pattern Recognition.
  • Action Recognition
  • Virtual Assistants
  • Facial Recognition Systems
  • Fraud Detection
  • Forecasting Solutions
  • eXplainable Artificial Intelligence (XAI)

Committee

Organizing Committee

  • Dalila Durães, University of Minho (Portugal)
  • Cleber Zanchetin, Northwestern University (EUA)
  • Leonardo Matos, Federal University of Sergipe (Brazil)
  • Flávio Santos, University of Minho, Braga (Portugal)


Program Committee

  • Ricardo Matsumura Araujo, Federal University of Pelotas (Brazil)
  • Adriano Lorena Inacio de Oliveira, Federal University of Pernambuco (Brazil)
  • Francisco Marcondes, University of Minho (Portugal)
  • Bruno Fernandes, University of Minho (Portugal)
  • Tiago Oliveira, Tokyo Medical and Dental Univerity (Japan)
  • Ângelo Costa, Technical University of Valencia (Spain)
  • Hector Moretón, University of Leon (Spain)
  • Javier Bajo, Technical University of Madrid (Spain)
  • Davide Carneiro, ESTG, Polytechnic Institute of Porto (Portugal)
  • Paulo Novais, University of Minho (Portugal)

Contact

Dalila Durães
dalila.duraes@algoritmi.uminho.pt