News

Community Detection in Directed Networks

  • Tuesday, July 26, 2016, 12:00 h. Sala de Grados A, EPS-UAM
  • Dr. Carlos Alaiz (Katolieke Universiteit Leuven)
  • Communities in directed networks have often been characterized as regions with a high density of links, or as sets of nodes with certain patterns of connection. Our approach for community detection combines the optimization of a quality function and a spectral clustering of a deformation of the combinatorial Laplacian, the so-called magnetic Laplacian. The eigenfunctions of the magnetic Laplacian, that we call magnetic eigenmaps, incorporate structural information. Hence, using the magnetic eigenmaps, dense communities including directed cycles can be revealed as well as “role” communities in networks with a running flow, usually discovered thanks to mixture models. Furthermore, in the spirit of the Markov stability method, an approach for studying communities at different energy levels in the network is put forward, based on a quantum mechanical system at finite temperature.
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Data Visualization of Directed Networks

  • Wednesday, July 20, 2016, 15:00 h. Aula C-105, Edif. C, EPS-UAM
  • Dr. Angela Fernandez Pascual (Katolieke Universiteit Leuven)
  • Data visualization is a crucial field for revealing information in a clear and efficient way, being a helpful tool for analyzing data. In this presentation, we will talk about a new method for directed graphs visualization, called Magnetic Eigenmaps, which is based on the analysis of the Magnetic Laplacian, a complex deformation of the well-known combinatorial Laplacian. The main advantage of this method is that it is able to highlight, in a flexible way, groups presented on the network according to the density of links and directionality patterns of the graph, that are revealed through the study of the phases of the first magnetic eigenfunctions.
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Doctoral course: Functional data

Oferta de trabajo

  • El Grupo de Aprendizaje Automático (GAA) de la Escuela Politécnica Superior de la Universidad Autónoma de Madrid [www.eps.uam.es/~gaa] busca candidatos para realizar un proyecto de investigación.
  • Tareas a realizar: Aprendizaje automático aplicado en problemas de geología.
  • Perfil: Licenciado, Ingeniero en Informática o áreas afines. Se valorará experiencia de investigación demostrable en el tema del proyecto.
  • Duración: 1 de septiembre de 2016 a 31 de diciembre de 2016
  • Salario bruto: 1195,83 € /mes
  • Candidatos interesados: Enviar CV (castellano o inglés) y copia del título a alberto.suarez@uam.es [Asunto: Oferta GAA 2015/06]  hasta el jueves 2016/06/30 

Doctoral course: Bayesian Optimization

  • 16-21 December 2015, 11:00-13:00 h, LAB 16, 3rd fl., Bdg. A, EPS-UAM
  • Lecturer: Dr. José Miguel Hernández Lobato (Harvard University)
  • Registration form
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Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks

  • Monday, 21 December 2015, 11:00-13:00 h
  • Dr. José Miguel Hernández Lobato (Harvard University)
  • Large multilayer neural networks trained with backpropagation have recently achieved state-of-the-art results in a wide range of problems. However, using backprop for neural net learning still has some disadvantages, e.g., having to tune a large number of hyperparameters to the data, lack of calibrated probabilistic predictions, and a tendency to overfit the training data. In principle, the Bayesian approach to learning neural networks does not have these problems. However, existing Bayesian techniques lack scalability to large dataset and network sizes. In this work we present a novel scalable method for learning Bayesian neural networks, called probabilistic backpropagation (PBP). Similar to classical backpropagation, PBP works by computing a forward propagation of probabilities through the network and then doing a backward computation of gradients. A series of experiments on ten real-world datasets show that PBP is significantly faster than other techniques, while offering competitive predictive abilities. Our experiments also show that PBP provides accurate estimates of the posterior variance on the network weights.
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Master/PhD/PostDoc Open Position

Shaping Social Activity by Incentivizing Users


Manuel Gómez Rodríguez (Max Planck Institute for Software Systems)

  • November 20, 2014 at 12:00 h
  • Sala de Grados, Escuela Politécnica Superior, Universidad Autónoma de Madrid
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Three reasons why control is hard: learning, planning and representing


Bert Kappen (Radboud University Nijmegen)

  • February 26, 2014 at 11:00 h
  • Sala de Grados, Escuela Politécnica Superior, Universidad Autónoma de Madrid
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Practical Implications of Classification Calibration


Irene Rodríguez Luján (Biocircuits Institute. University of California San Diego)

  • January 13, 2014 at 12:00 h
  • Sala de Grados, Escuela Politécnica Superior, Universidad Autónoma de Madrid
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Training nested functions using auxiliary coordinates


Miguel Á. Carreira-Perpiñán (University of California, Merced)

  • January 8, 2014 at 12:00 h
  • Sala de Grados, Escuela Politécnica Superior, Universidad Autónoma de Madrid
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