[Todos CMAT] recordatorio: Invitación Seminario IngeMat 2012 - Ignacio Ramírez - viernes 6 de julio - 9:00 hs.

Ana Olivera annao en fing.edu.uy
Jue Jul 5 11:46:33 UYT 2012

  From: Marco Scavino - IMERL 
  Sent: Monday, July 02, 2012 8:11 PM
  Subject: Invitación Seminario IngeMat 2012 - Ignacio Ramírez - viernes 6 de julio - 9:00 hs.

  La Maestría en Ingeniería Matemática tiene el agrado de invitarles a participar de la próxima ponencia 

  del ciclo de Seminario IngeMat 2012, 

  a cargo de Ignacio Ramírez 

  quien presentará el trabajo titulado 
  "An MDL framework for sparse coding and dictionary learning".
  Horario y lugar: viernes 6 de julio, 9:00 horas, Salón Azul del Instituto de Química, Facultad de Ingenieria. 
   ** Resumen **

  The power of sparse signal modeling with learned over-complete
  dictionaries has been demonstrated in a variety of applications and
  fields, from signal processing to statistical inference and machine
  learning. However, the statistical properties of these models, such as
  under-fitting or over-fitting given sets of data, are still not well
  characterized in the literature. As a result, the success of sparse
  modeling depends on hand-tuning critical parameters for each data and
  application. This work aims at addressing this by providing a
  practical and objective characterization of sparse models by means of
  the Minimum Description Length (MDL) principle -- a well established
  information-theoretic approach to model selection in statistical
  inference. The resulting framework derives a family of efficient
  sparse coding and dictionary learning algorithms which, by virtue of
  the MDL principle, are completely parameter free. Furthermore, such
  framework allows to incorporate additional prior information to
  existing models, such as Markovian dependencies, or to define
  completely new problem formulations, including in the matrix analysis
  area, in a natural way. These virtues will be demonstrated with
  parameter-free algorithms for the classic image denoising and
  classification problems, and for low-rank matrix recovery in video

  El trabajo puede ser consultado en 
  paper oficial en IEEE:  http://dx.doi.org/10.1109/TSP.2012.2187203
  preprint arXiv:              http://arxiv.org/abs/1110.2436

  Ignacio Ramírez
  Procesamiento de Señales, Asistente (Gr.2)

  Received the E.E. (2002), and the M.Sc. degree in Electrical
  Engineering (2007) from the Universidad de la República, Uruguay
  (UdelaR), and the Ph.D. degree in Scientific Computation (2012) from
  the University of Minnesota (UofM).
  He was a Research Assistant at the UofM from 2008 to 2012. He also
  held temporary research positions at the UofM in 2007 and
  Hewlett-Packard Laboratories, Palo Alto, in 2004.
  Mr. Ramírez holds an Assistantship with the Department of
  Electrical Engineering at UdelaR since 1999. His main research
  interests are applied information theory statistical signal processing
  abd machine learning, with focus in multimedia data processing. His
  current research focuses in automatic model selection for sparse
  linear models.

  A partir de las 8:45 horas compartiremos café. 
  SCAPA de Ingeniería Matemática

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