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Keywords: Bayesian inference, learning and reasoning, stochastic control theory, neural networks, statistical physics

One of the marked differences between computers and animals is the ability of the latter to learn and flexibly adapt to changing situations. Whereas computers need to be programmed with provisions for all possible future circumstances, the brain adapts its 'program' when needed, striking a remarkable balance between flexibility to adapt on the one hand and persistence by re-using pre-learned facts and skills on the other. This is commonly referred to as intelligent behavior. Particular examples of intelligence are pattern recognition, learning and memory, reasoning, planning and motor control.

The question how intelligence arises and how it is computed in the animal brain is not well understood. One can try to reproduce intelligence in artificial systems and the problems of how intelligence is encoded in the brain and how it can be created artificially in computers are clearly related. In addition to being an important intellectual challenge in itself, artificial intelligence research also has clear practical implications. We currently witness an explosion of applications in machine learning - the formal study of how machines learn - in for instance robotics and data analysis.

Due to the essential roles that noise and uncertainty play in perception and learning, a useful way to model intelligence is to use probability models. In the mid 90s, the fields of analog and digital computing as separate approaches to model intelligence, have begun to merge using the idea of Bayesian inference: One can generalize the logic of digital computation to a probabilistic calculus, embodied in a so-called graphical model. Similarly, one can generalize dynamical systems to stochastic dynamical systems that allow for a probabilistic description in terms of a Markov process. The Bayesian paradigm has greatly helped to integrate different schools of thought in particular in the field of artificial intelligence and machine learning but also provides a computational paradigm for neuroscience.

My research is dedicated to the design of efficient and novel computational methods for Bayesian inference and stochastic control theory using ideas and methods from statistical physics. These novel methods are used by me and by others in artificial intelligence research and computational models of brain function.

- The efficient approximate inference methods allow the design of large artificial reasoning systems. Currently, we are designing a diagnostic decision support system for internal medicine consisting of thousands of diagnoses, that should help the doctor during the diagnostic process (in collaboration with Radboud academic hospital).
- Design of high-dimensional Bayesian data analysis methods. The motivation is that Bayesian integration of the pos- terior distribution improves the statistical power of these methods compared to the maximum likelihood approaches. Approximate inference is used to efficiently compute statistics in the posterior distribution. One example is the use of the mean field approximation for sparse L0 regression. Another example is Gaussian Process regression with Monte Carlo sampling. In this case we have shown for yeast data that this method significantly outperforms all other methods and is able to identify novel genetic causes.

Aladin is a software tool for performing efficient linkage analysis of a small number of distantly-related individuals. It estimates multipoint IBD probabilities and parametric LOD scores. Contact Kees Albers for details caa at sanger dot ac dot uk.

2014

Journal of Physics A: Mathematical and Theoretical as a Fast Track Communication,
vol. 47,
no. 2,
pp. 022001,
2014

Proceedings UAI,
vol. 30,
pp. 1-12,
2014

Policy search for path integral control
.

LNAI conference proceedings,
pp. 1-16,
2014
2013

Speedy q-learning: a computationally efficient reinforcement learning algorithm with a near optimal rate of convergence.

Journal of Machine Learning Research,
2013
Machine Learning Journal,
vol. 91,
no. 3,
pp. 325-349,
2013

pp. 1-16,
2013

Frontiers in Computational Neuroscience,
vol. 7,
no. 30,
pp. 1-13,
2013

Handbook on Neural Information Processing,
vol. 49,
pp. 401-431,
2013

Comment: causal entropic forces.

Technical Report,
pp. http://arxiv.org/abs/1312.4185,
2013
Is task selection a solution for bci
illiteracy?.

Journal of Neural Engeneering,
2013
Stochastic path integral control.

International Journal of Control,
2013
2012

Machine Learning,
vol. 87,
no. 2,
pp. 159-182,
2012

Adaptive classification on brain computer interfaces using reinforcement signals.

Neural Computation,
vol. 24,
no. 11,
pp. 2900-2923,
2012
Proceedings of the International Conference on Machine Learning
Learning,
vol. 29 th,
pp. 1-11,
2012

Frontiers in Neuroscience,
2012

Granada Seminar AIP Proceedings 2013,
2012

SmartGrid Comm 2012, Symposium ion Architectures and Models for the SmartGrid,
pp. invited paper,
2012

Plos One,
vol. 7,
no. 3,
pp. e33724,
2012

2011

Neural Networks,
vol. 24,
pp. 1120-1127,
2011

Inference and Learning in Dynamic Models,
pp. 363-387,
2011

Stochastic optimal control predicts human motor behavior in time-constrained
sensorimotor tasks.

Biological Cybernetics,
pp. xx,
2011
NIPS 2011, Advances in Neural Information Processing Systems 24,
vol. 25,
pp. 2411--2419,
2011

NIPS 2011, 4th International Workshop on Optimization for Machine Learning,
vol. 25,
pp. 1-6,
2011

Proceedings of the 22nd ACM Conference on Hypertext and Hypermedia,
vol. 22,
pp. 181-190,
2011

Stochastic optimal control of state constrained systems.

International Journal of Control,
vol. 84,
no. 3,
pp. 597-615,
2011
JMLR: Workshop and Conference Proceedings: AISTATS 2011,
vol. 15,
pp. 119-127,
2011

Donders Institute for Brain, Cognition and Behaviour,
no. 17,
pp. 4-5,
2011

2010

Interactive Collaborative Information Systems,
vol. SCI 281,
pp. 547-578,
2010

Expert Systems With Applications,
vol. 37,
no. 12,
pp. 7526-7532,
2010

Journal for Machine Learning Research (JMLR),
vol. 11,
pp. 1273-1296,
2010

2009

Proceedings UAI,
vol. 25,
pp. no pages,
2009

Advances in Neural Information Processing Systems,
vol. 22,
pp. 513-520,
2009

Neural Information Processing Systems,
vol. 22,
pp. 1105-1113,
2009

Proceedings of ABCI workshop,
vol. xx,
no. xx,
pp. xxx,
2009

Bio-Inspired Systems: Computational and Ambient Intelligence,
pp. 17-23,
2009

PASCAL Computational Statistics Workshop,
pp. Presentation,
2009

Dynamic policy programming with kl-divergence minimization.

NIPS Workshop on Probabilistic Approaches for Stochastic Optimal Control and Robotics,
2009
2008

Journal of Artificial Intelligence Research,
vol. 32,
pp. 95-122,
2008

Adaptive Agents and Multi-Agent Systems III. Adaptation and Multi-Agent Learning,
vol. 4865,
pp. 15-26,
2008

Alamas 07, Maastricht 2-3 April.,
pp. 9-20,
2008

Multipoint approximations of identity-by-descent probabilities for accurate linkage analysis of distantly-related individuals.

American Journal of Human Genetics,
vol. 82,
no. web only,
pp. 607-622,
2008
UAI,
vol. Website Only,
pp. 1-8,
2008

2007

Modeling linkage disequilibrium in exact linkage.

BMC Proceedings of the genetic Analysis Workshop 15: Gene Expression Analysis and Approaches to Detecting Multiple Functional,
vol. 1,
pp. S159,
2007
Journal for Machine Learning Research (JMLR),
vol. 8,
pp. 1987-2016,
2007

AAMAS'07,
vol. website,
pp. 1-8,
2007

In 9th Granada seminar on Computational Physics: Computational and
Mathematical Modeling of Cooperative Behavior in Neural Systems.,
pp. 149-181,
2007

Attractor neural networks with activity-dependent synapses: the role
of synaptic facilitation..

Neurocomputing,
vol. 70,
no. 10-12,
pp. 2022-2025,
2007
Physical Review E, section Statistical physics,
vol. Rev. E 76,
no. 011102,
pp. 9 pages,
2007

Proceedings of the 11th Conference on Artificial Intelligence in
Medicine (AIME 07),
vol. 4594,
pp. 456-460,
2007

Neural Computation,
vol. 19,
pp. 1739-1765,
2007

Neural Computation,
vol. 19,
pp. 2739-2755,
2007

IEEE Transactions
on Information Theory,
vol. 53,
no. 12,
pp. 4422-4437,
2007

Journal of Machine Learning Research,
vol. 8,
pp. 1113-1143,
2007

Genetics,
no. 177,
pp. 1101-1116,
2007

2006

Neural Computation,
vol. 18,
pp. 614-633,
2006

BMC Bioinformatics, Special issue on Machine Learning in Computational Biology,
vol. 7(Suppl 1),
pp. S1,
2006

J. Phys. A: Math. Gen.,
vol. 39,
pp. 1265-1283,
2006

IEEE Transactions on Speech and Audio
Processing,
vol. 14,
pp. 679-694,
2006

UAI,
vol. 22 th,
pp. 528-535,
2006

Op zoek naar de ziel.

Zelfdenkende pillen en andere technologie die ons leven zal veranderen,
pp. 217-223,
2006
Neural automata: the effect of microdynamics on unstable solutions.

2006
2005

Uncertainty in Artificial Intelligence,
pp. 396-403,
2005

Advances in Neural Information Processing Systems 17,
vol. 17,
pp. 945-952,
2005

Proceedings of the AIP Conference,
vol. 779,
pp. 178-184,
2005

Physical Review Letters,
vol. 95,
pp. 200201,
2005

Journal of Statistical Mechanics: Theory and Experiment,
pp. P11011,
2005

Journal of Statistical Mechanics: Theory and Experiment,
pp. P110-12,
2005

2004

Statistics in Medicine,
pp. 2989-3012,
2004

2003

Proceedings of the International Conference on Artificial Neural Networks,
pp. CD,
2003

Network: Computation in Neural Systems,
vol. 14,
pp. 17-33,
2003

Proceedings BNAIC,
vol. 15,
pp. 11-18,
2003

AIP Conference Proceedings 661,
pp. 174-180,
2003

Journal of Artificial Intelligence Research,
vol. 18,
pp. 45-81,
2003

In Proceedings IEEE WASPAA, Workshop on Applications of Signal Processing to Audio and Acoustics.,
2003

2002

Proceedings of 2002 International Computer Music Conference, Gothenburg/Sweden,
pp. 419-422,
2002

Dealing with the data flood. Mining data, text and multimedia,
pp. 111-121,
2002

Theoretical Computer Science,
vol. 287,
no. 1,
pp. 219-238,
2002

In: Advances in Neural Information Processing Systems, 14,
vol. 14-1,
pp. 455-462,
2002

Tempo tracking and rhythm quantization by sequential monte carlo.

Advances in Neural Information Processing Systems 14, Part VIII Applications,
vol. 14-2,
pp. 1361-1368,
2002
Modelling Bio-Medical Signals,
pp. 3-16,
2002

Advances in Neural Information Processing Systems 14,
vol. 14,
pp. 415-422,
2002

In: Proceedings Uncertainty in AI 2002,
vol. 18,
pp. 293-210,
2002

Physical Review E,
vol. 66,
pp. 061910,
2002

2001

Proceedings of 2001 International Computer Music Conference, Havana/Cuba,
pp. 147-150,
2001

Advances in Neural Information Processing Systems 11,
vol. 13,
pp. 238-244,
2001

Journal of New Music Research,
vol. 29,
pp. 259-273,
2001

In: Handbook of Biological Physics, Neuro-informatics and Neural Modelling,
vol. 4,
pp. 517-552,
2001

A novel iteration scheme for the cluster variation method.

Neural Information Processing Systems,
vol. 13,
pp. 415-422,
2001
Approximate reasoning: real world applications of graphical models.

Foundations of Real-World Intelligence,
pp. 73-121,
2001
A dynamic belief network implementation for realtime music transcription.

Proceedings of the 13th Belgian-Dutch Conference on Artificial Intelligence,
pp. 473-474,
2001
Europhysics Letters,
vol. 55,
no. 5,
pp. 746-752,
2001

Biophysical Journal,
vol. 80,
pp. 613A, part 2,
2001

2000

Neural Networks,
vol. 13 - 3,
no. 3,
pp. 329-335,
2000

Prerational Intelligence: Adaptive Behavior and Intelligent Systems Without Symbols and Logic.,
vol. 2,
pp. 157-164,
2000

In Neural Information Processing Systems, NIPS 2000, Denver, USA,
vol. 13,
pp. 266-272,
2000

Ned tijdschrift voor Natuurkunde,
vol. 66,
pp. 14-19,
2000

New Generation Computing,
vol. 18,
no. 2,
pp. 167-175,
2000

RWC'2000 symposium, Tokyo, Japan,
pp. 265-270,
2000

Proceedings Artificial Neural Networks in Medicine and Biology,
pp. 162-167,
2000

A graphical model for music transcription.

In Neural Information Processing System, NIPS 2000, Denver, USA,
2000
Stochastic dynamics with dominant self-coupling.

Presentation "Learning",,
2000
An application of linear response learning.

Proceedings of the 12th Belgium-Netherlands Artificial Intelligence Conference,
pp. 117-124,
2000
Int Computer Music Conf., Berlin, Sept. 2000,
pp. 352-355,
2000

Coincidence detection with dynamic synapses.

Abstract Workshop NIPS'00,
vol. 13,
pp. workshop,
2000
Proceedings of CNS'2000, Neurocomputing,
vol. 38-40,
pp. 285-291,
2000

Predicting newspaper sales: jed system 'weathers' the tests.

Newspaper Techniques,
2000
Learning higher order boltzmann machines using linear response.

Neural Networks,
vol. 13,
pp. 329--335,
2000
Ned tijdschrift voor Natuurkunde,
vol. 12,
pp. 1411ÃÂƒÂ¢Ã¯Â¿Â½Ã¯Â¿Â½1427,
2000

1999

Proceedings International Conference on Artificial Neural Networks 9,
vol. 2,
pp. 425-431,
1999

Proceedings International Conference on Artificial Neural Networks 9,
vol. 2,
pp. 732-737,
1999

Pattern Recognition Letters,
vol. 20,
pp. 1231-1239,
1999

On the validity of mean field theory for finite size boltzmann machines.

Proceedings Snowbird 99, Utah, USA,
1999
Promedas. a probabilistic medical diagnostic advisory system.

Presentatie project Promedas,
1999
Promedas. a probabilistic medical diagnostic advisory system.

AIM'99,
pp. 16,
1999
A variational approach to bayesian survival analysis.

Advances in Neural Information Processing Systems 11,
1999
1998

Artificial Neural Networks 8,
vol. 2,
pp. 511-517,
1998

Advances in Neural Information Processing Systems 10,
pp. 280-286,
1998

Just enough delivery.

INMA Ideas Magazine,
pp. 23,
1998
1997

Symmetry breaking and training from incomplete data with radial basis boltzmann machines.

International Journal of Neural Systems A,
vol. 8,
pp. 301-316,
1997
Efficient learning in sparsely connected boltzmann machines.

Proceedings RWC'97,
pp. 406-409,
1997
Physical Review E,
vol. 55,
pp. 5849-5858,
1997

Neural Computation,
vol. 10,
pp. 1137-1156,
1997

Pattern Recognition in Practice V,
vol. 18,
no. 11-13,
pp. 1317-1322,
1997

An advisory system for anaemia based on boltzmann machines.

5th Eurpean Congres on Intelligent Techniques and Soft Computing,
vol. 1,
pp. 364-368,
1997
A polynomial time algorithm for boltzmann machine learning.

Workshop Cambridge,
1997
Voorspelling van frisdrankverkoop.

1997
Neural networks: best practice in europe.

Neural Networks: Best Practice in Europe,
pp. 209,
1997
Accelerated learning in boltzmann machines using mean field theory.

Artificial Neural Networks 7,
pp. 301-306,
1997
Lab-test selection in diagnosis of anaemia.

Neural Networks: Best Practice in Europe,
pp. 179-181,
1997
Practical confidence and prediction intervals for prediction tasks.

Neural Networks: Best Practice in Europe,
pp. 128-135,
1997
Stimulation initiative for european neural applications (siena).

Neural Networks: Best Practice in Europe,
pp. 1-8,
1997
1996

Artificial Neural Networks 5, Session 1,
pp. 433-436,
1996

Efficient learning in sparsely connected boltzmann machines.

Artificial Neural Networks 6,
pp. 41-46,
1996
Classification with inquiry.

1996
Active decision.

Neural Networks,
1996
Weersafhankelijkheid losse verkoop van kranten en tijdschriften in badplaatsen.

1996
Efficient learning in sparselyconnected boltzmann machines.

NIPS,
1996
Lab-test selection in diagnosis of anaemia.

Proceedings RWC, Japan,
pp. 83-88,
1996
Voorspelling van verkoop en inzet van personeel.

1996
Efficient estimation of the partition function of anisotropic spin systems.

Physical Review Letters,
1996
Artificial Neural Networks 6,
pp. 101-106,
1996

Automatiserings Gids,
vol. 30,
pp. 17,
1996

Siena: stimulation initiative for european neural applications.

Proceedings EUFIT'96,
pp. 280-281,
1996
Proceedings 6th International Congress for Cumputer Technology in Agriculture,
pp. 75-79,
1996

Using neural networks to predict consumer behaviour.

Proceedings EUFIT'96,
pp. 2149-2150,
1996
1995

Confidence intervals for neural networks.

1995
World Conference on Neural Network,
vol. 1,
pp. 72-75,
1995

Confidence intervals for neural networks.

Proceedings of the International Conference on Digital Signal Processing,
vol. 1,
pp. 396-401,
1995
Self-organization and nonparametric regression.

Artificial Neural Networks 5,
vol. 1,
pp. 81-86,
1995
Radial basis boltzmann machines and incomplete data.

1995
Brain Processes, Theories and models. Proceedings W.S. McCullock: 25 years in memoriam.,
pp. 294-299,
1995

Active perception and cognition.

RWC'95, Tokyo, Japan,
pp. 13-14,
1995
Neural networks: artificial intelligence and industrial applications,
pp. 63-66,
1995

Learning active vision: industrial application processing systems,.

Artificial Neural Networks 5, Session 7, Robotics,
pp. 193-202,
1995
Neural networks: artificial intelligence and industrial applications.

Proceedings of the 3rd SNN symposium,
1995
Neurale netwerken en voorspelling losse verkoop.

1995
Automatisering van neurale netwerken; een direct-mailing applicatie.

1995
1994

Neural network analysis to predict treatment outcome in patients with gynaecological cancer.

1994
Using neural networks for survival prediction.

Proceedings Interregional Dutch-German Biometric Meeting,
1994
Neural network analysis to predict treatment outcome in patients with gynaecological cancer.

1994
Proceedings Pattern Recognition in Practice IV,
pp. 299-312,
1994

Voorspelling samenstelling vliegas m.b.v neurale netwerken en symbolische methodes.

1994
Korte termijn voorspelling van vliegas m.b.v. neurale netwerken en symbolsch inductie methodes.

1994
Neurale netwerken voor toepassingen op grote databases.

1994
Neural network analysis to predict treatment outcome in patients with gynaecological cancer.

1994
Informatie en Informatiebeleid, winter,
vol. 12,
no. 4,
pp. 75-81,
1994

1993

On-line learning processes in artificial neural networks.

vol. 51,
pp. 199-234,
1993
Neural network analysis to predict treatment outcome.

Annuals of Oncology,
vol. 4,
pp. 31-34,
1993
Using boltzmann machines as perceptrons.

IEEE Trans. Neural Networks,
1993
Optimizing the architecture of multi-layer
perceptrons for one-dimensional classification.

Artificial Neural Networks 3,
pp. 558-561,
1993
Using boltzmann machines for probability estimation.

Artificial Neural Networks 3,
pp. 521-526,
1993
Neural network analysis for prediction of treatment outcome in ovarian cancer.

EWOC-3,
1993
Neural representation of saccadic eye movements in monkey superior colliculus.

Artificial Neural Networks 3,
pp. 88-93,
1993
Learning processes in neural networks.

1993
Error potentials for self-organization.

International Conference on Neural Networks, San Francisco,
vol. 3,
pp. 1219-1223,
1993
Cooling schedules for learning in neural networks.

Physical Review E,
vol. 47,
pp. 4457-4464,
1993
Neurale netwerken, fuzzy rules en artificiele intelligentie.

Klinische Fysica,
vol. 1,
pp. 13-16,
1993
A two-dimensional model for spatial-temporal transformation of saccades in monkey superior colliculus.

Network,
vol. 4,
pp. 19-38,
1993
Proceedings of the international confidence icann'93.

Artificial Neural Networks 3,
1993
1992

Learning in neural networks with local minima.

Physical Review A,
vol. 46,
pp. 5221-5231,
1992
Learning parameter adjustment in neural networks.

Physical Review A,
vol. 45,
pp. 8885-8893,
1992
Learning rules, stochastic processes, and local minima.

Artificial Neural Networks 2,
vol. 1,
pp. 71-78,
1992
Neural network analysis for prediction of treatment
outcome in ovarian cancer.

ASCO,
1992
Proceedings symposium on neural networks.

Proceedings of 2nd SNN Conference on Neural Networks,
vol. 2,
1992
1991

Learning processes in neural networks.

Physical Review A,
vol. 44,
pp. 2718-2726,
1991
Neural networks learning in a changing environment.

Artificial Neural Networks 1,
vol. 1,
pp. 15-20,
1991
Een computersimulatie van hetbilocale correlator model.

1991
Quantitative model for spatio-temporal transformation of oculomotor signals in monkey superior colliculus..

Eur. J. Neurosci. (Suppl),
vol. 14,
pp. 56,
1991
Neural networks learning in achanging environment.

International Joint Conference on Neural Networks, Seattle,
vol. 1,
pp. 823-828,
1991
Learning at a constant rate.

1991
Neurale netwerken: verslag van een symposium.

Informatie,
vol. 33,
pp. 435-438,
1991
Proceedings symposium on neural networks.

Proceedings of 1st SNN symposium,
vol. 1,
1991
1990

Neurocomputing research in thenetherlands.

Neurocomputing,
vol. 2,
pp. 35-38,
1990
Latent kullback leibler control for continuous-state systems
using probabilistic graphical models.

Proceedings UAI,
vol. 30,