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start [2018/02/05 12:30] – [Short summary] jmstart [2018/03/13 11:15] – guilherme
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-====== Geometry, imaGes, learninG and alGorithms ======+====== Geometry, imaGes, learninG, and alGorithms ======
  
 The research group **Geometry, imaGes, learninG and alGorithms** (G4) is a member of the [[http://limos.isima.fr/spip.php?article9|Decision Support Models and Algorithms (MAAD)]] group. We are all part of the [[http://limos.isima.fr/spip.php?article7|LIMOS lab]], in the Cézeaux campus of [[http://uca.fr|Université Clermont Auvergne]], near Clermont Ferrand, France. The research group **Geometry, imaGes, learninG and alGorithms** (G4) is a member of the [[http://limos.isima.fr/spip.php?article9|Decision Support Models and Algorithms (MAAD)]] group. We are all part of the [[http://limos.isima.fr/spip.php?article7|LIMOS lab]], in the Cézeaux campus of [[http://uca.fr|Université Clermont Auvergne]], near Clermont Ferrand, France.
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 The research topics of this group are centered on n-dimensional data modeling and analysis, from both methodology and application points of view. The team's interdisciplinary skills result in projects that cover a wide range of scientific topics including Geometry, Data Mining, Machine Learning, Data Structures, and their interactions for several fields of application. The research topics of this group are centered on n-dimensional data modeling and analysis, from both methodology and application points of view. The team's interdisciplinary skills result in projects that cover a wide range of scientific topics including Geometry, Data Mining, Machine Learning, Data Structures, and their interactions for several fields of application.
  
-For a brief description of our main research areas, we cite: +For a brief description of our main research areas, we mention: 
  
   * **Machine learning**: We address several  aspects of machine learning, from the design of kernel methods to manifold learning and deep learning. Current research include modeling and simulation of spatio-temporal variations and dynamics using manifolds, understanding of the GANs approaches and their link to Optimal Transport, Siamese network for multimodal learning and autoencoders or solving multiclass SVM at the cost of a binary one, and dealing with indefinite kernels or large datasets.   * **Machine learning**: We address several  aspects of machine learning, from the design of kernel methods to manifold learning and deep learning. Current research include modeling and simulation of spatio-temporal variations and dynamics using manifolds, understanding of the GANs approaches and their link to Optimal Transport, Siamese network for multimodal learning and autoencoders or solving multiclass SVM at the cost of a binary one, and dealing with indefinite kernels or large datasets.