Harri Valpola
Academy Research Fellow (2005-2010), Dr.Tech.
- Postal Address:
- Aalto University
- School of Science and Technology
- Dep. of Biomedical Engineering and Computatonal Science
- P.O. Box 12200
- FI-00076 AALTO, FINLAND
- Visiting Address:
- Rakentajanaukio 2, Espoo
- 3rd floor, Room F303
- Tel:
- +358 9 470 25724
- Fax:
- +358 9 470 23182
- Email:
- Harri.Valpola [at] tkk.fi
I have moved to ZenRobotics
Ltd.. I'm still working part time at Aalto University, but I went back
to ICS.
Between November 2004 and November 2010, I run the
Computational Neuroscience
group in the LCE at
TKK.
Before coming to the LCE, I worked for 14
months for the AI Lab at
the University of Zurich and for more than ten years at the Neural Networks Research
Centre at TKK.
Research Activities
In NNRC I was working on many methods for unsupervised learning. In
order to widen my scope, I went to AI Lab to work with robots. I'm
interested in natural and artificial intelligence and my current
research is about "building a brain" (not the brain) for a
robot. By brain I mean here an integrated control architecture which
is capable of autonomous behaviour and learning. The motivation for
this approach is that high-level cognitive control uses many of the
same mechanisms as motor control. It is easier to study and understand
navigation, manipulation and very basic (emotional) communication than
planning, reasoning and symbolic language, but the same mechanisms are
at work: we grasp objects and ideas, we find places and solutions.
I believe that to get a complete picture of intelligence, one has to
consider the whole behaviour and the interactions between individual
adaptive systems that create it.
My speciality is learning representations (abstractions, concepts,
etc.). Suitable internal representations are the essential ingredient
for the transition from immediate motor control to high-level
cognition. At the moment, one of my main interests is task-adapted
sensory processing which includes development of behaviourally
meaningful representations and attention (see
self-organization of invariant representations and
attention).
Of course it is possible to study task-adapted sensory processing
only if there is a task. Since the brain has evolved to control
movements, it is easiest to understand the brain by considering the
challenges posed by autonomous robots and motor control. I've been
studying mainly
predictive motor control and reinforcement learning.
Here is more information about my research.
Publications
Below is a list of my recent publications. Click the titles for
descriptions of the publications and related
links. The topic of most of my publications is (broadly speaking)
either cognitive architecture,
denoising source separation or
variational Bayesian learning.
2010
- A cognitive architecture for developing sensory and motor
abstractions
- H. Valpola
- A presentation given at the First International Conference
on Biologically Inspired Cognitive Architectures, BICA 2010.
- Oscillatory neural network for image
segmentation with biased competition for
attention
- T. Raiko and H. Valpola.
- In the Brain Inspired Cognitive Systems (BICS 2010) symposium,
Madrid, Spain, 14-16 July, 2010.
2009
- Selective attention improves learning
- A. Yli-Krekola, J. Särelä and H. Valpola.
- In Proceedings of the 19th International Conference of Artificial
Neural Networks, ICANN 2009, Limassol, Cyprus, Part II, pp. 285-294,
2009.
2008
- From raw data to abstract concepts
- H. Valpola
- Keynote presentation in AKRR'08: International and
Interdisciplinary Conference on Adaptive Knowledge Representation
and Reasoning, Porvoo, Finland, September 17-19, 2008.
- The engine of thought -- a bio-insipred mechanism for distributed selection of useful information
- H. Valpola.
- Nokia Workshop on Machine Consciousness, Helsinki, Finland,
pp. 27-31, 2008.
2007
- Computational model of co-operating covert attention and learning.
- A. Yli-Krekola and H. Valpola.
- Fifth Nordic Neuroinformatics Workshop, Espoo, Finland, p. 34, 2007.
- A model of cerebellar automation of voluntary basal-ganglia control.
- M. Pihlaja and H. Valpola.
- Fifth Nordic Neuroinformatics Workshop, Espoo, Finland, p. 29, 2007.
- Cerebellar model tested in control of a load-carrying robot.
- I. Aaltonen and H. Valpola.
- Fifth Nordic Neuroinformatics Workshop, Espoo, Finland, p. 16, 2007.
- Cerebellar model for coordination.
- T. J. Lukka and H. Valpola.
- Fifth Nordic Neuroinformatics Workshop, Espoo, Finland, p. 25, 2007.
- Compact modeling of data using independent variable group analysis.
- E. Alhoniemi, A. Honkela, K. Lagus, J. Seppä, P. Wagner and H.
Valpola.
- IEEE Transactions on Neural Networks, 18(6):1762-1776, 2007.
- Blind separation of nonlinear mixtures by variational Bayesian
learning.
- A. Honkela, H. Valpola, A. Ilin and J. Karhunen.
- Digital Signal Processing, 17(5):914-934, 2007.
- Building blocks for variational Bayesian learning
of latent variable models.
- T. Raiko, H. Valpola, M. Harva and J. Karhunen.
- Journal of Machine Learning Research, 8:155-201, 2007.
- Finding interesting climate phenomena by exploratory statistical
techniques.
- A. Ilin, H. Valpola and E. Oja.
- In Proceedings of the Fifth Conference on Artificial
Intelligence Applications to Environmental Science, 5AI, as part of
the 87th Annual Meeting of the American Meteorological Society, San
Antonio, TX, USA, January 2007.
2006
- Hyperparameter adaptation in variational Bayes for the gamma
distribution.
- H. Valpola and A. Honkela.
- Helsinki University of Technology, Publications in Computer and
Information Science, Espoo, Finland, Tech. Rep. E6, 2006.
- Learning anticipatory behaviour using a simple cerebellar model.
- H. Valpola.
- In Proceedings of the Ninth Scandinavian Conference on Artificial
Intelligence, SCAI 2006, Espoo, Finland, pp. 135-142, 2006.
- Extraction of climate components with structured variance.
- A. Ilin, H. Valpola and E. Oja.
- In Proceedings of the IEEE World Congress on Computational
Intelligence, WCCI 2006, Vancouver, BC, Canada, pp. 10528-10535, 2006.
- Exploratory analysis of climate data using source separation methods.
- A. Ilin, H. Valpola and E. Oja.
- Neural Networks, 19(2):155-167, 2006.
- Separation of nonlinear image mixtures by denoising source separation.
- M. S. C. Almeida, H. Valpola and J. Särelä.
- In Proceedings of the 6th International Conference on Independent
Component Analysis and Blind Signal Separation, ICA 2006,
Charleston, SC, USA, pp. 8-15, 2006.
2005
- Frequency-based separation of climate signals.
- A. Ilin and H. Valpola.
- In Proceedings of the 9th European Conference on Principles and Practice
of Knowledge Discovery in Databases (PKDD 2005), Porto, Portugal,
pp. 519-526, 2005.
- On the effect of the form of the posterior approximation in
variational learning of ICA models.
- A. Ilin and H. Valpola.
- Neural Processing Letters 22(2):183-204, 2005.
- Semiblind source separation of climate data detects El Niño as
the component with the highest interannual variability.
- A. Ilin, H. Valpola and E. Oja.
- In Proceedings of the International Joint Conference on Neural
Networks (IJCNN 2005), Montréal, Québec, Canada, pp. 1722-1727, 2005.
- Bayes Blocks: An implementation of the variational Bayesian
building blocks framework.
- M. Harva, T. Raiko, A. Honkela, H. Valpola and J. Karhunen.
- In Proceedings of the 21st Conference on Uncertainty in Artificial
Intelligence (UAI 2005), Edinburgh, Scotland, pp. 259-266, 2005.
- Development of representations, categories and concepts--a hypothesis.
- H. Valpola.
- In Proceedings of the 6th IEEE International Symposium on
Computational Intelligence in Robotics and Automation, CIRA 2005, Espoo,
Finland, pp. 593-599, 2005.
- Unsupervised variational Bayesian learning of nonlinear models.
- A. Honkela and H. Valpola.
- In L. Saul, Y. Weis and L. Bottous, eds., Advances in
Neural Information Processing Systems 17 (NIPS 2004),
pp. 593-600, 2005.
- Denoising source separation: a novel approach to ICA and feature
extraction using denoising and Hebbian learning.
- J. Särelä and H. Valpola.
- In AI 2005 special session on correlation learning, pp. 45-56, 2005.
- Denoising source separation.
- J. Särelä and H. Valpola.
- Journal of Machine Learning Research 6:233-272, 2005.
2004
- Accurate, fast and stable denoising source separation algorithms.
- H. Valpola and J. Särelä.
- In Proceedings of the 5th International Conference on Independent
Component Analysis and Blind Signal Separation (ICA 2004), Granada, Spain,
pp. 65-72, 2004.
- Using kernel PCA for initialisation of variational Bayesian nonlinear
blind source separation method.
- A. Honkela, S. Harmeling, L. Lundqvist and H. Valpola
- In Proceedings of the 5th International Conference on Independent
Component Analysis and Blind Signal Separation (ICA 2004), Granada, Spain,
pp. 65-72, 2004.
- Denoising source separation: from temporal to contextual invariance.
- H. Valpola and J. Särelä.
- Presented in Early Cognitive Vision Workshop, Isle of Skye,
Scotland, 2004.
- Behaviourally meaningful representations from normalisation and
context-guided denoising.
- H. Valpola.
- AI Lab technical report, University of Zurich, 2004.
- Variational learning and bits-back coding: an information theoretic
view to Bayesian learning.
- A. Honkela and H. Valpola.
- IEEE Transactions on Neural Networks, 15(4):800-810, 2004.
- Nonlinear dynamical factor analysis for state change detection.
- A. Ilin, H. Valpola and E. Oja.
- IEEE Transactions on Neural Networks, 15(3):559-575, 2004.
- Hierarchical models of variance sources.
- H. Valpola, M. Harva and J. Karhunen.
- Signal Processing, 84(2):267-282, 2004.
1995-2003
Most of my publications up to 2003 are available only at
my NNRC page.
- An unsupervised ensemble learning method for nonlinear dynamic
state-space models.
- H. Valpola and J. Karhunen.
- Neural Computation, 14(11):2647-2692, 2002.
- A fast semi-blind source separation algorithm.
- H. Valpola and J. Särelä.
- In Publications in Computer and Information Science, Report A66,
Helsinki University of Technology, Espoo, Finland, 4 p., 2002.
- Bayesian ensemble learning for nonlinear factor analysis.
- H. Valpola.
- PhD thesis, Helsinki University of Technology, Espoo, 2000.
- Published in Acta Polytechnica Scandinavica, Mathematics and
Computing Series No. 108, 2000.
- Fast algorithms for Bayesian independent component analysis.
- H. Valpola and P. Pajunen.
- In Proceedings of the Second International Workshop on Independent
Component Analysis and Blind Signal Separation, ICA 2000, Helsinki,
Finland, pp. 233-237, 2000.
Teaching
I'm now lecturing the course
Tfy-99.3730
Information processing in the brain.
In spring 2008, I gave two lectures for the course
Tfy-99.4247 Ihmisaivojen
rakenne ja toiminta (in Finnish)
In autumn 2006, I lectured the course
S-114.3812 Computational Neuroscience in collaboration with
Jarmo Hurri from Helsinki University.
In spring 2006, I lectured the following course:
S-114.4220 Research Seminar on Computational Science.
The topic was: Principles of brain evolution.
In spring 2005, I lectured the following course:
S-114.220 Research Seminar on Computational Science.
The topic was neurorobotics.
I'm also involved in
Synthetic Brain which is a project started by people who were
involved in NeuroHel, a multidisciplinary
neuroscience study group.
Other Info
Harri Valpola
Last modified: Tue Nov 10 15:15:51 EET 2009