Seminar Computational Intelligence A (708.111)

WS 2013

Institut für Grundlagen der Informationsverarbeitung (708)

Lecturer:
O.Univ.-Prof. Dr. Wolfgang Maass

Office hours: by appointment (via e-mail)

E-mail: maass@igi.tugraz.at
Homepage: www.igi.tugraz.at/maass/


Assoc. Prof. Dr. Robert Legenstein

Office hours: by appointment (via e-mail)

E-mail: robert.legenstein@igi.tugraz.at
Homepage: www.igi.tugraz.at/legi/




Location: IGI-seminar room, Inffeldgasse 16b/I, 8010 Graz
Date: starting from October 14th  2013, every Monday, 16.15 - 18.00 p.m.


Content of the seminar:

Various topics about principles of brain computation and machine learning, including novel ideas in artificial neural networks and reinforcement learning.
Each student will present some research results from a research article (length: 35 to 40 min).







Options for Talks:

Papers related to Principles of Brain Computation :

  • S. Habenschuss, Z. Jonke, and W. Maass. Stochastic Computations in Cortical Microcircuit Models. PLOS CB, 2013: in press. pdf
  • A. Zylberberg, S. Dehaene, P. R. Roelfsema, and M. Sigman. The human Turing machine: a neural framework for mental programs. Trends in Cognitive Sciences, 15(7):293-300, 2011. pdf
  • R. Stickgold and M. Walker. Sleep-dependent memory triage: evolving generalization through selective processing. Nature Neuroscience, 16(2):139-145, 2013.pdf
  • O. Sporns. Network attributes for segregation and integration in the human brain. Current Opinion in Neurobiology, 23:162-171, 2013. pdf
  • M. Larkum. A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex. Trends in Neurosciences, 36(3):141-151, 2013. pdf

Papers related to machine learning :

  • Salakhutdinov R. (2010). Learning Deep Boltzmann Machines using Adaptive MCMC. Proc. of the 27th International Conference on Machine Learning. pdf
  • Hinton, G. E., Srivastava, N., Krizhevsky, A.,  Sutskever, I.  and Salakhutdinov, R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. Technical Report. pdf
  • A. M. Zaslavsky (2003). Hierarchical Bayesian Modeling. Ch.14 in Subjective and Objective Bayesian Statistics: Principles, Models, and Applications. pdf
  • KR Canini, MM Shashkov, TL Griffiths (2010). Modeling Transfer Learning in Human Categorization with the Hierarchical Dirichlet Process. ICML. pdf
  • T. Hester, P Stone (2012). Learning and using models.  Reinforcement Learning, 2011 - Springer. pdf
  • ME Taylor, P Stone (2009). Transfer learning for reinforcement learning domains: A survey.   The Journal of Machine Learning Research, 2009. pdf                                             

Talks:

Date
Speaker Paper

Nov 18th, 2013   

Gernot Riegler

Hinton, G. E., Srivastava, N., Krizhevsky, A.,  Sutskever, I. and Salakhutdinov, R. (2012).
Improving neural networks by preventing co-adaptation of feature detectors. Technical Report. pdf
SLIDES

Dec 02nd, 2013

Miran Lever

T. Hester, P Stone (2012). Learning and using models. Reinforcement Learning, 2011 - Springer. pdf
SLIDES




Dec 16th, 2013



Martin Tappler

A. M. Zaslavsky (2003). Hierarchical Bayesian Modeling.
Ch.14 in Subjective and Objective Bayesian Statistics: Principles, Models, and Applications. pdf
SLIDES



Jan 13th, 2014


Wolfgang Roth
N. M. Oliver, B. Rosario, and A. P. Pentland (2000).
A Bayesian computer vision system for modeling
human interactions. IEEE Transactions on Pattern Analysis and Machine Intelligence. pdf
SLIDES