Seminar Computational Intelligence E (708.115)

  SS 2013

Institut für Grundlagen der Informationsverarbeitung (708)
 

Leaders of the Seminar:
Assoc. Prof. Dr. DI Robert Legenstein
O.Univ.-Prof. Dr. Wolfgang Maass

Office hours: by appointment (via e-mail)

E-mail:
maass@igi.tugraz.at
robert.legenstein@igi.tugraz.at

Homepage: www.igi.tugraz.at/maass/

   

Location: IGI-seminar room, Inffeldgasse 16b/I, 8010 Graz
time: Monday  4.15 pm
starting on: March 11th
, 2013

Content of the seminar:

This seminar will introduce into research related to three research areas of Computational Intelligence:

1. The new EU-Flagship Project "Human Brain Project",
http://www.humanbrainproject.eu/
http://www.igi.tugraz.at/maass/nonexperts.html
in particular into the research area "Principles of Brain Computation" in this project, for which our Institute is responsible.

2. New research in machine learning and artificial neural networks (e.g. deep belief nets).

3. Machine learning and robotics research for the EU-project AMARSi http://www.amarsi-project.eu/

This seminar is designed for master students in their second year, or also in the first year, if they take simultaneously NN B (or have already
taken one of the courses listed below).

The papers and talks will be on an introductory level, and will be accessible to an audience with minimal technical background. One didactical goal
of the seminar is to provide experience in giving presentation, which can be practiced here in a relaxed setting.
In addition, students can explore research topics in which they might be interested for a master thesis or -project.

One of the courses NN A, NN B, ML A, ML B suffices as background.



Talks:

29. 04. 2013    Michael Rath, slides
10 .06. 2013    Florian Hofer, slides
10 .06. 2013    Wolfgang Roth, slides

Options for talks:

Papers related to Principles of Brain Computation/Human Brain Projec

1.       Eliasmith, C., Stewart, T. C., Choo, X., Bekolay, T., DeWolf, T., Tang, C., & Rasmussen, D. (2012). A large-scale model of the functioning brain. Science, 338(6111), 1202-1205.
http://clm.utexas.edu/compjclub/papers/Eliasmith2012.pdf

Supplementary Material   PDF                     

Commentary:  Machens, C. K. (2012). Building the Human Brain. Science, 338(6111), 1156-1157. PDF 

Three presentations should cover this material.

2.       A model for stochastic computation in cortical microcircuits  (new paper by Habenschuss,  Jonke, Maass),  preprint will be made available to seminar participants

This paper should be covered by 2 talks (one on stationary distributions of network states, one on Sudoku application).

3.       Larkum, M. (2012). A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex. Trends in Neurosciences. PDF 

One talk can cover this paper.

4.       Sporns, Olaf. "Network attributes for segregation and integration in the human brain." Current opinion in neurobiology (2013). PDF

One talk can cover this paper.

5.       S. Haeusler and W. Maass. A statistical analysis of information processing properties of lamina-specific cortical microcircuit models. Cerebral Cortex, 17(1):149-162,2007.
http://www.igi.tugraz.at/maass/psfiles/162.pdf

One or two talks can be given on this paper. (Possibly the talks can cover also some related new results for a column model of the Human Brain Project)

 

Papers related to machine learning and artificial neural networks:

1.   Neal, R. (1992). Connectionist learning of belief networks. Artificial Intelligence, 56: 71-113

       http://www.sciencedirect.com/science/article/pii/0004370292900656

This paper introduces sigmoidal belief networks and relates them to Boltzmann machines. This talk should be the basis for a later talk on deep belief networks. (The PDF is not freely available. It will be provided to seminar participants)

2.   Hinton, G. E., Osindero, S. and Teh, Y. (2006). A fast learning algorithm for deep belief nets. Neural Computation 18, pp 1527-1554. 2006.

http://www.cs.toronto.edu/%7Ehinton/absps/ncfast.pdf

Introduces greedy-layerwise training in deep belief networks.

3.   Salakhutdinov R. (2010). Learning Deep Boltzmann Machines using Adaptive MCMC. Proc. of the 27th International Conference on Machine Learning.

      http://icml2010.haifa.il.ibm.com/papers/441.pdf

Introduces a novel way to learn deep Boltzmann machines (as opposed to DBNs).

4.   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.

      http://arxiv.org/abs/1207.0580

Introduces a novel technique to prevent overfitting in deep feed-forward neural networks.

5.   G. M. Hoerzer, R. Legenstein, and Wolfgang Maass (2012). Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learning. Cerebral Cortex.

      http://www.igi.tugraz.at/maass/psfiles/214_incl_suppl.pdf

This paper is related to Echo State Networks that were discussed briefly in NNA, but it is extended and discussed in a biological context.

 

Papers related to machine learning and robotics research for the EU-project AMARSi:


1.   Ijspeert, A.;Nakanishi, J.; Pastor, P; Hoffmann, H.; Schaal, S. (2013). Dynamical Movement
      Primitives: Learning Attractor Models for Motor Behaviors, Neural Computation, 25, pp.328–373.

      http://www-clmc.usc.edu/publications/I/ijspeert-NC2013.pdf

This journal paper provides a detailed discussion on parametrized elementary movements using dynamical systems. This approach is the most widely used movement primitive representation in robotics.


2.   Muelling, K.; Kober, J.; Kroemer, O.; Peters, J. (2013). Learning to Select and Generalize Striking
      Movements in Robot Table Tennis, International Journal of Robotics Research

      http://www.ias.informatik.tu-darmstadt.de/uploads/Publications/Muelling_IJRR_2013.pdf

This paper presents a motor skill learning approach implementing a movement primitive library. In impressing exeriments the authors show how a robot learns to play table tennis.


3.   Matthew Botvinick, Marc Toussaint (2012): Planning as Inference. Trends in Cognitive Sciences,
      16(10), 485-488, 2012.

      http://ipvs.informatik.uni-stuttgart.de/mlr/marc/publications/12-BotvinickToussaint-TICS.pdf

Paper on how the human brain may implement motor planning. The authors discuss recent advances in motor planning using probabilistic models.

Can be combined with a more theoretical machine learning paper, i.e. Konrad Rawlik, Marc Toussaint, Sethu Vijayakumar: On stochastic optimal control and reinforcement learning by approximate inference. Int. Conf. on Robotics Science and Systems (R:SS 2012). Best paper runner up award. http://ipvs.informatik.uni-stuttgart.de/mlr/marc/publications/12-rawlik-toussaint-vijayakumar-RSS.pdf


4.   Quadrupet Robots and Motor Control Approaches: The following three papers should be
      combined. The discuss robot modelling appraoches and trajectory formation strategies:

Sproewitz A, Kuechler L, Tuleu A, Ajallooeian M, D’Haene M, Moeckel R, Ijspeert AJ. (2011): Oncilla Robot, A Light-weight Bio-inspired Quadruped Robot for Fast Locomotion in Rough Terrain. In: Symposium on Adaptive Motion of Animals and Machines (AMAM2011). Symposium on Adaptive Motion of Animals and Machines (AMAM2011). ; 2011. p. 63-64.

http://infoscience.epfl.ch/record/182313/files/s323.pdf

A. Sproewitz, M. Fremerey, K. Karakasiliotis, S. Rutishauser and L. Righetti (2009). Compliant Leg Design for a Quadruped Robot. Dynamic Walking 2009, Vancouver, Canada.

http://infoscience.epfl.ch/record/142736/files/Sproewitz08_dw_preprint.pdf
http://www.dynamicwalking.org/dw2009/sites/default/files/u8/abstracts/89.pdf

S. Rutishauser, A. Sproewitz, L. Righetti and A. J. Ijspeert (2008). Passive compliant quadruped robot using central pattern generators for locomotion control. International Conference on Biomedical Robotics and Biomechatronics, Scottsdale, 2008.

http://infoscience.epfl.ch/record/130727/files/sRutishauser08.pdf

 



      



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