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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.

- One line of research concentrates on improving the efficiency of Monte Carlo sampling for path integral control methods, using the idea of importance sampling by using a sampling control. Although any choice of sampling control yields asymptotically correct results in expectation, the asymptotic variance and thus the efficiency of the sampling procedure strongly depends on the sampling control. It can be shown that an optimal, zero variance, sampler is obtained when the sampling control is equal to the optimal control. This idea forms the basis of the design of adaptive sampling methods that starts with arbitrary initial sampling control and improves the sampling control in subsequent iterations.
- The optimal control solution is a mapping from states to actions, providing a feed-back controller. One of the essential problems of stochastic optimal control is the efficient representation of this infinite dimensional object. We are developing linear function approximation methods to construct feed-back controllers with arbitrary complex state dependence. These feedback controllers are also used in the adaptive sampling methods to compute the optimal controls and optimize particle smoothing.
- We jointly develop path integral control methods in collaboration with robotic groups for robot manipulators (with TU Darmstadt, and ATR Japan) and for unmanned aerial vehicles (with UCL). In the latter case, we are able to control up to 20 quadrotors (with 4 dynamical variables each) in simulation, in a coordination task where we compute the state- dependent optimal control for this stochastic non-linear problem in real time. Test flights with real quadrotors is currently being tested.

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

Stochastic optimal control as non-equilibrium statistical mechanics:
calculus of variations over density and current
Journal of Physics A: Mathematical and Theoretical as a Fast Track Communication,
vol. 47,
no. 2,
pp. 022001,
2014
bibtex

Adaptive multi class classification on
bci
Neural Computation,
pp. 1-20,
2014
bibtex

Latent kullback leibler control for continuous-state systems
using probabilistic graphical models
Proceedings UAI,
vol. 30,
pp. 1-12,
2014
bibtex

Policy search for path integral control
LNAI conference proceedings,
pp. 1-16,
2014
bibtex

Explicit solution of relative entropy weighted control
Systems and Control Letters,
pp. 1-16,
2014
bibtex

2013

The variational garrote
Machine Learning Journal,
pp. 1-26,
2013
bibtex

Speedy q-learning: a computationally efficient reinforcement learning algorithm with a near optimal rate of convergence
Journal of Machine Learning Research,
2013
bibtex

Minimax pac bounds on the sample complexity of reinforcement
learning with a generative model
Machine Learning Journal,
vol. 91,
no. 3,
pp. 325-349,
2013
bibtex

Linear pdes and eigenvalue problems corresponding to ergodic stochastic optimization problems on compact manifolds
pp. 1-16,
2013
bibtex

Emerging phenomena in neural networks with dynamic synapses and their computational implications
Frontiers in Computational Neuroscience,
vol. 7,
no. 30,
pp. 1-13,
2013
bibtex

Bayesian networks, introduction and practical applications
Handbook on Neural Information Processing,
vol. 49,
pp. 401-431,
2013
bibtex

Stochastic optimal control and sensori-motor
integration
Technical Report,
pp. invited paper,
2013
bibtex

Comment: causal entropic forces
Technical Report,
pp. http://arxiv.org/abs/1312.4185,
2013
bibtex

Clustered common spatial patterns
TOBI Workshop IV,
pp. 117-119,
2013
bibtex

Is task selection a solution for bci
illiteracy?
Journal of Neural Engeneering,
2013
bibtex

Stochastic path integral control
International Journal of Control,
2013
bibtex

2012

Optimal control as a graphical model inference problem
Machine Learning,
vol. 87,
no. 2,
pp. 159-182,
2012
bibtex

A likelihood-based framework for the analysis of discussion threads
World Wide Web,
pp. 1-31,
2012
bibtex

Adaptive classification on brain computer interfaces using reinforcement signals
Neural Computation,
vol. 24,
no. 11,
pp. 2900-2923,
2012
bibtex

On the sample complexity of reinforcement learning with a generative model
Proceedings of the International Conference on Machine Learning
Learning,
vol. 29 th,
pp. 1-11,
2012
bibtex

Emerging phenomena in neural networks with dynamic synapses and their computational implications
Frontiers in Neuroscience,
2012
bibtex

Short-term synaptic plasticity and heterogeneity in neural systems
Granada Seminar AIP Proceedings 2013,
2012
bibtex

Dynamic policy programming
Journal of Machine Learning Research,
no. 13,
pp. 3207-3245,
2012
bibtex

Learning price-elasticity of smart consumers in power distribution systems.
SmartGrid Comm 2012, Symposium ion Architectures and Models for the SmartGrid,
pp. invited paper,
2012
bibtex

Time-integrated position error accounts for sensorimotor behavior in time-constr ained tasks.
Plos One,
vol. 7,
no. 3,
pp. e33724,
2012
bibtex

2011

On the use of interaction error potentials for adaptive brain computer interfaces
Neural Networks,
vol. 24,
pp. 1120-1127,
2011
bibtex

Optimal control theory and the linear bellman equation
Inference and Learning in Dynamic Models,
pp. 363-387,
2011
bibtex

Stochastic optimal control predicts human motor behavior in time-constrained
sensorimotor tasks
Biological Cybernetics,
pp. xx,
2011
bibtex

Speedy q-learning
NIPS 2011, Advances in Neural Information Processing Systems 24,
vol. 25,
pp. 2411--2419,
2011
bibtex

Online solution of the average cost kullback-leibler optimization problem
NIPS 2011, 4th International Workshop on Optimization for Machine Learning,
vol. 25,
pp. 1-6,
2011
bibtex

Modeling the structure and evolution of discussion cascades.
Proceedings of the 22nd ACM Conference on Hypertext and Hypermedia,
vol. 22,
pp. 181-190,
2011
bibtex

Stochastic optimal control of state constrained systems
International Journal of Control,
vol. 84,
no. 3,
pp. 597-615,
2011
bibtex

Dynamic policy programming with function approximation
JMLR: Workshop and Conference Proceedings: AISTATS 2011,
vol. 15,
pp. 119-127,
2011
bibtex

Machines - now with intelligence
Donders Institute for Brain, Cognition and Behaviour,
no. 17,
pp. 4-5,
2011
bibtex

2010

Bayesian networks for expert systems, theory
and practical applications
Interactive Collaborative Information Systems,
vol. SCI 281,
pp. 547-578,
2010
bibtex

A bayesian petrophysical decision support system for estimation of reservoir compositions
Expert Systems With Applications,
vol. 37,
no. 12,
pp. 7526-7532,
2010
bibtex

Approximate inference on planar graphs using loop calculus and belief propagation
Journal for Machine Learning Research (JMLR),
vol. 11,
pp. 1273-1296,
2010
bibtex

Ep for efficient stochastic control with obstacles
ECAI,
pp. 1-6,
2010
bibtex

Optimal exploration as a symmetry breaking phenomenon
no. TR1001,
pp. 1-5,
2010
bibtex

Risk sensitive path integral control
UAI,
vol. 26,
pp. 1-8,
2010
bibtex

Irregular dynamics in up and down cortical states
Plos One,
vol. 5,
no. 11,
pp. 1-13,
2010
bibtex

2009

Approximate inference on planar graphs using loop calculus and belief propagation
Proceedings UAI,
vol. 25,
pp. no pages,
2009
bibtex

Self-organization using synaptic plasticity
Advances in Neural Information Processing Systems,
vol. 22,
pp. 513-520,
2009
bibtex

Bounds on marginal probability distributions
Neural Information Processing Systems,
vol. 22,
pp. 1105-1113,
2009
bibtex

Novel bounds on marginal probabilities
Journal for Machine Learning Research (JMLR),
2009
bibtex

Sparse matrix factorization for brain computer interfaces_
Proceedings of ABCI workshop,
vol. xx,
no. xx,
pp. xxx,
2009
bibtex

Switching dynamics of neural systems in the presence of
multiplicative colored noise
Bio-Inspired Systems: Computational and Ambient Intelligence,
pp. 17-23,
2009
bibtex

Bayesian construction of perceptrons to predict phenotypes from 584k snp data.
PASCAL Computational Statistics Workshop,
pp. Presentation,
2009
bibtex

Dynamic policy programming with kl-divergence minimization
NIPS Workshop on Probabilistic Approaches for Stochastic Optimal Control and Robotics,
2009
bibtex

2008

Graphical model inference in optimal control of stochastic multi-agent systems
Journal of Artificial Intelligence Research,
vol. 32,
pp. 95-122,
2008
bibtex

Optimal control in large stochastic multi-agent systems
Adaptive Agents and Multi-Agent Systems III. Adaptation and Multi-Agent Learning,
vol. 4865,
pp. 15-26,
2008
bibtex

Optimal control in large stochastic multi-agent systems
Alamas 07, Maastricht 2-3 April.,
pp. 9-20,
2008
bibtex

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
bibtex

Hybrid variational / gibbs collapsed inference in topic models
UAI,
vol. Website Only,
pp. 1-8,
2008
bibtex

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
bibtex

Truncating the loop series expansion for bp.
Journal for Machine Learning Research (JMLR),
vol. 8,
pp. 1987-2016,
2007
bibtex

Optimal on-line scheduling in stochastic multi-agent systems in continuous space and time
AAMAS'07,
vol. website,
pp. 1-8,
2007
bibtex

An introduction to stochastic control theory, path integrals and reinforcement learning.
In 9th Granada seminar on Computational Physics: Computational and
Mathematical Modeling of Cooperative Behavior in Neural Systems.,
pp. 149-181,
2007
bibtex

Attractor neural networks with activity-dependent synapses: the role
of synaptic facilitation.
Neurocomputing,
vol. 70,
no. 10-12,
pp. 2022-2025,
2007
bibtex

Cavity approximation for graphical models
Physical Review E, section Statistical physics,
vol. Rev. E 76,
no. 011102,
pp. 9 pages,
2007
bibtex

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

Input-driven oscillations in networs with excitatory and inhibitory neurons with dynamic synapses
Neural Computation,
vol. 19,
pp. 1739-1765,
2007
bibtex

Loop corrected belief propagation
Proceedings AISTATS 2007,
vol. Website,
pp. 8 pages,
2007
bibtex

Competition between synaptic depression and facilitation in attractor neural networks
Neural Computation,
vol. 19,
pp. 2739-2755,
2007
bibtex

Sufficient conditions for convergence of the sum-product algorithm
IEEE Transactions
on Information Theory,
vol. 53,
no. 12,
pp. 4422-4437,
2007
bibtex

Loop corrections for approximate inference on
factor graphs
Journal of Machine Learning Research,
vol. 8,
pp. 1113-1143,
2007
bibtex

Haplotype inference in general pedigrees using the cluster variation
method
Genetics,
no. 177,
pp. 1101-1116,
2007
bibtex

2006

Effects of fast presynaptic noise in attractor neural networks
Neural Computation,
vol. 18,
pp. 614-633,
2006
bibtex

The cluster variation method for efficient linkage analysis on extended pedigrees
BMC Bioinformatics, Special issue on Machine Learning in Computational Biology,
vol. 7(Suppl 1),
pp. S1,
2006
bibtex

Survey propagation at finite temperature: application
to a sourlas code as a toy model
J. Phys. A: Math. Gen.,
vol. 39,
pp. 1265-1283,
2006
bibtex

A generative model for music transcription
IEEE Transactions on Speech and Audio
Processing,
vol. 14,
pp. 679-694,
2006
bibtex

Computer stelt straks medische diagnose: zeg eens @
Intermediair,
pp. 51-53,
2006
bibtex

Stochastic optimal control in continuous space-time multi-agent systems
UAI,
vol. 22 th,
pp. 528-535,
2006
bibtex

Op zoek naar de ziel
Zelfdenkende pillen en andere technologie die ons leven zal veranderen,
pp. 217-223,
2006
bibtex

Neural automata: the effect of microdynamics on unstable solutions
2006
bibtex

2005

Sufficient conditions for convergence of loopy belief propagation
Uncertainty in Artificial Intelligence,
pp. 396-403,
2005
bibtex

Intelligente machines
Inaugurele rede,
pp. 1-23,
2005
bibtex

Validity estimates for loopy belief propagation on binary real-world networks
Advances in Neural Information Processing Systems 17,
vol. 17,
pp. 945-952,
2005
bibtex

Algorithms for identification and categorization
Proceedings of the AIP Conference,
vol. 779,
pp. 178-184,
2005
bibtex

A linear theory for control of non-linear stochastic systems
Physical Review Letters,
vol. 95,
pp. 200201,
2005
bibtex

Path integrals and symmetry breaking for optimal control theory
Journal of Statistical Mechanics: Theory and Experiment,
pp. P11011,
2005
bibtex

On the properties of the bethe approximation and loopy belief propagation on binary networks
Journal of Statistical Mechanics: Theory and Experiment,
pp. P110-12,
2005
bibtex

2004

Improving cox survival analysis with a neural-bayesian approach
Statistics in Medicine,
pp. 2989-3012,
2004
bibtex

2003

Approximate inference and constrained optimization.
In: Proceedings UAI-2003,
pp. 313-320,
2003
bibtex

A dynamical bayesian network for tempo and polyphonic pitch tracking
Proceedings of the International Conference on Artificial Neural Networks,
pp. CD,
2003
bibtex

Coincidence detection with dynamic synapses
Network: Computation in Neural Systems,
vol. 14,
pp. 17-33,
2003
bibtex

Application of cluster variation method to genetic linkage analysis
Proceedings BNAIC,
vol. 15,
pp. 11-18,
2003
bibtex

On the role of synaptic depression in the performance of attractor neural networks
AIP Conference Proceedings 661,
pp. 174-180,
2003
bibtex

Monte carlo methods for tempo tracking and rhythm quantization
Journal of Artificial Intelligence Research,
vol. 18,
pp. 45-81,
2003
bibtex

Bound propagation
Journal of Artificial Intelligence Research,
pp. 139-154,
2003
bibtex

A generative model based polyphonic music transcription
In Proceedings IEEE WASPAA, Workshop on Applications of Signal Processing to Audio and Acoustics.,
2003
bibtex

2002

Linkage analysis: a bayesian approach
ICANN 2002, LNCS 2415,
pp. 595-600,
2002
bibtex

Integrating tempo tracking and quantization using particle filtering
Proceedings of 2002 International Computer Music Conference, Gothenburg/Sweden,
pp. 419-422,
2002
bibtex

Decision support for medical diagnosis
Dealing with the data flood. Mining data, text and multimedia,
pp. 111-121,
2002
bibtex

Approximate algorithms for neural-bayesian approaches
Theoretical Computer Science,
vol. 287,
no. 1,
pp. 219-238,
2002
bibtex

Means, correlations and bounds
In: Advances in Neural Information Processing Systems, 14,
vol. 14-1,
pp. 455-462,
2002
bibtex

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
bibtex

Associative memory with dynamic synapses
Neural Computation,
vol. 14,
pp. 2903-2923,
2002
bibtex

The cluster variation method for approximate reasoning in medical diagnosis
Modelling Bio-Medical Signals,
pp. 3-16,
2002
bibtex

Novel iteration schemes for the cluster variation method
Advances in Neural Information Processing Systems 14,
vol. 14,
pp. 415-422,
2002
bibtex

General lower bounds based on computer generated higher order expansions.
In: Proceedings Uncertainty in AI 2002,
vol. 18,
pp. 293-210,
2002
bibtex

On the storage capacity of attractor neural networks with depressing synapses
Physical Review E,
vol. 66,
pp. 061910,
2002
bibtex

2001

Bayesian real-time adaptation for interactive performance systems
Proceedings of 2001 International Computer Music Conference, Havana/Cuba,
pp. 147-150,
2001
bibtex

Second order approximations for probability models
Advances in Neural Information Processing Systems 11,
vol. 13,
pp. 238-244,
2001
bibtex

Bayesbuilder
Software Support for Bayesian Analysis Systems Proceedings,
pp. 11-13,
2001
bibtex

Mean field theory for graphical models
Advanced mean field theory,
pp. 37-49,
2001
bibtex

On tempo tracking: tempogram representation and kalman filtering
Journal of New Music Research,
vol. 29,
pp. 259-273,
2001
bibtex

An introduction to stochastic neural networks
In: Handbook of Biological Physics, Neuro-informatics and Neural Modelling,
vol. 4,
pp. 517-552,
2001
bibtex

A tighter bound for graphical models
Neural Computation,
vol. 13,
no. 9,
pp. 2149--2171,
2001
bibtex

A novel iteration scheme for the cluster variation method
Neural Information Processing Systems,
vol. 13,
pp. 415-422,
2001
bibtex

Approximate reasoning: real world applications of graphical models
Foundations of Real-World Intelligence,
pp. 73-121,
2001
bibtex

A dynamic belief network implementation for realtime music transcription
Proceedings of the 13th Belgian-Dutch Conference on Artificial Intelligence,
pp. 473-474,
2001
bibtex

Hysteresis and bistability in a realistic model for ip3-driven ca oscillations
Europhysics Letters,
vol. 55,
no. 5,
pp. 746-752,
2001
bibtex

Mathematical model for calcium oscillations in non-excitable cells
Biophysical Journal,
vol. 80,
pp. 613A, part 2,
2001
bibtex

2000

Linear response for higher order boltzmann machines
Neural Networks,
vol. 13 - 3,
no. 3,
pp. 329-335,
2000
bibtex

Constructing modular architectures with boltzmann machines
Prerational Intelligence: Adaptive Behavior and Intelligent Systems Without Symbols and Logic.,
vol. 2,
pp. 157-164,
2000
bibtex

A tighter bound for graphical models
In Neural Information Processing Systems, NIPS 2000, Denver, USA,
vol. 13,
pp. 266-272,
2000
bibtex

Kansrijke methoden voor kunstmatig leren en redeneren
Ned tijdschrift voor Natuurkunde,
vol. 66,
pp. 14-19,
2000
bibtex

Rhythm quantization for transcription
Computer Music Journal,
vol. 24,
no. 2,
pp. 60-75,
2000
bibtex

Approximations of bayesian networks through kl minimisation
New Generation Computing,
vol. 18,
no. 2,
pp. 167-175,
2000
bibtex

Mean field theory for asymmetric neural networks
Physical Review E,
vol. 61,
pp. 5658-5663,
2000
bibtex

Variational methods for approximate reasoning in graphical models
RWC'2000 symposium, Tokyo, Japan,
pp. 265-270,
2000
bibtex

Survival analysis: a neural-bayesian approach
Proceedings Artificial Neural Networks in Medicine and Biology,
pp. 162-167,
2000
bibtex

A graphical model for music transcription
In Neural Information Processing System, NIPS 2000, Denver, USA,
2000
bibtex

A bridge between mean field theory and exact inference in probabilistic graphical models
2000
bibtex

Stochastic dynamics with dominant self-coupling
Presentation "Learning",,
2000
bibtex

An application of linear response learning
Proceedings of the 12th Belgium-Netherlands Artificial Intelligence Conference,
pp. 117-124,
2000
bibtex

On tempo tracking: tempogram representation and kalman filtering
Int Computer Music Conf., Berlin, Sept. 2000,
pp. 352-355,
2000
bibtex

Coincidence detection with dynamic synapses
Abstract Workshop NIPS'00,
vol. 13,
pp. workshop,
2000
bibtex

On the role of dynamical synapses in coincidence detection
Proceedings of CNS'2000, Neurocomputing,
vol. 38-40,
pp. 285-291,
2000
bibtex

Predicting newspaper sales: jed system 'weathers' the tests
Newspaper Techniques,
2000
bibtex

Learning higher order boltzmann machines using linear response
Neural Networks,
vol. 13,
pp. 329--335,
2000
bibtex

Nonmonotonic generalization bias of gaussian mixture models
Ned tijdschrift voor Natuurkunde,
vol. 12,
pp. 1411ÃÂÂ¢Ã¯Â¿Â½Ã¯Â¿Â½1427,
2000
bibtex

1999

Validity of tap equations in neural network
Proceedings International Conference on Artificial Neural Networks 9,
vol. 2,
pp. 425-431,
1999
bibtex

Stimulus segmentation in a stochastic neural network with exogenous signals
Proceedings International Conference on Artificial Neural Networks 9,
vol. 2,
pp. 732-737,
1999
bibtex

Approximate inference for medical diagnosis
Pattern Recognition Letters,
vol. 20,
pp. 1231-1239,
1999
bibtex

On the validity of mean field theory for finite size boltzmann machines
Proceedings Snowbird 99, Utah, USA,
1999
bibtex

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

Rhythm quantization for transcription
Proceedings of the AISB'99 Convention,
pp. 140-146,
1999
bibtex

Promedas. a probabilistic medical diagnostic advisory system
AIM'99,
pp. 16,
1999
bibtex

Glauber machines
Technical Report SNN, NIPS'99 rejected,
1999
bibtex

A variational approach to bayesian survival analysis
Advances in Neural Information Processing Systems 11,
1999
bibtex

An application of linear response learning
IJCNN'2000,
1999
bibtex

1998

Probabilistic knowledge representation
RWC'98, Tokyo, Japan,
pp. 285-292,
1998
bibtex

Learning higher order boltzmann machines using linear response
Artificial Neural Networks 8,
vol. 2,
pp. 511-517,
1998
bibtex

Nonmonotonic generalization bias of gaussian mixture models
Neural Computation,
1998
bibtex

Boltzmann machine learning using mean field theory and linear response correction
Advances in Neural Information Processing Systems 10,
pp. 280-286,
1998
bibtex

Learning active vision
Industrial Applications of Neural Networks,
pp. 193-202,
1998
bibtex

Just enough delivery
INMA Ideas Magazine,
pp. 23,
1998
bibtex

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
bibtex

Efficient learning in sparsely connected boltzmann machines
Proceedings RWC'97,
pp. 406-409,
1997
bibtex

Stimulus dependent correlations in stochastic networks
Physical Review E,
vol. 55,
pp. 5849-5858,
1997
bibtex

Efficient learning in boltzmann machines using linear response theory
Neural Computation,
vol. 10,
pp. 1137-1156,
1997
bibtex

Mean field approach to learning in boltzmann machines
Pattern Recognition in Practice V,
vol. 18,
no. 11-13,
pp. 1317-1322,
1997
bibtex

An advisory system for anaemia based on boltzmann machines
5th Eurpean Congres on Intelligent Techniques and Soft Computing,
vol. 1,
pp. 364-368,
1997
bibtex

A polynomial time algorithm for boltzmann machine learning
Workshop Cambridge,
1997
bibtex

Voorspelling van frisdrankverkoop
1997
bibtex

Neural networks: best practice in europe
Neural Networks: Best Practice in Europe,
pp. 209,
1997
bibtex

Accelerated learning in boltzmann machines using mean field theory
Artificial Neural Networks 7,
pp. 301-306,
1997
bibtex

Lab-test selection in diagnosis of anaemia
Neural Networks: Best Practice in Europe,
pp. 179-181,
1997
bibtex

Practical confidence and prediction intervals for prediction tasks
Neural Networks: Best Practice in Europe,
pp. 128-135,
1997
bibtex

Stimulation initiative for european neural applications (siena)
Neural Networks: Best Practice in Europe,
pp. 1-8,
1997
bibtex

1996

Neural network analysis to predict outcome in patients with ovarian cancer
Artificial Neural Networks 5, Session 1,
pp. 433-436,
1996
bibtex

Learning structure with many-take-all networks
Artificial Neural Networks 6,
pp. 95-101,
1996
bibtex

Efficient learning in sparsely connected boltzmann machines
Artificial Neural Networks 6,
pp. 41-46,
1996
bibtex

Classification with inquiry
1996
bibtex

Active decision
Neural Networks,
1996
bibtex

Weersafhankelijkheid losse verkoop van kranten en tijdschriften in badplaatsen
1996
bibtex

Efficient learning in sparselyconnected boltzmann machines
NIPS,
1996
bibtex

Lab-test selection in diagnosis of anaemia
Proceedings RWC, Japan,
pp. 83-88,
1996
bibtex

Voorspelling van verkoop en inzet van personeel
1996
bibtex

Efficient estimation of the partition function of anisotropic spin systems
Physical Review Letters,
1996
bibtex

Dynamic feature linking in stochastic networks with short range interactions
Artificial Neural Networks 6,
pp. 101-106,
1996
bibtex

Doorbraak neurale netwerken afhankelijk van standaardisatie
Automatiserings Gids,
vol. 30,
pp. 17,
1996
bibtex

Siena: stimulation initiative for european neural applications
Proceedings EUFIT'96,
pp. 280-281,
1996
bibtex

An overview of neural network applications
Proceedings 6th International Congress for Cumputer Technology in Agriculture,
pp. 75-79,
1996
bibtex

Using neural networks to predict consumer behaviour
Proceedings EUFIT'96,
pp. 2149-2150,
1996
bibtex

1995

Deterministic learning rules for boltzmann machines
Neural Networks,
vol. 8,
pp. 537-548,
1995
bibtex

Confidence intervals for neural networks
1995
bibtex

Radial basis boltzmann machines and learning with missing values
World Conference on Neural Network,
vol. 1,
pp. 72-75,
1995
bibtex

Confidence intervals for neural networks
Proceedings of the International Conference on Digital Signal Processing,
vol. 1,
pp. 396-401,
1995
bibtex

Self-organization and nonparametric regression
Artificial Neural Networks 5,
vol. 1,
pp. 81-86,
1995
bibtex

Radial basis boltzmann machines and incomplete data
1995
bibtex

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

Active perception and cognition
RWC'95, Tokyo, Japan,
pp. 13-14,
1995
bibtex

Stochastic resonance and multimodal firing patterns in single-neuron models
Neural networks: artificial intelligence and industrial applications,
pp. 63-66,
1995
bibtex

Learning active vision: industrial application processing systems,
Artificial Neural Networks 5, Session 7, Robotics,
pp. 193-202,
1995
bibtex

Neural networks: artificial intelligence and industrial applications
Proceedings of the 3rd SNN symposium,
1995
bibtex

Neurale netwerken en voorspelling losse verkoop
1995
bibtex

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

1994

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

Using neural networks for survival prediction
Proceedings Interregional Dutch-German Biometric Meeting,
1994
bibtex

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

Using boltzmann machines for probability estimation: a general framework for neural network learning
Proceedings Pattern Recognition in Practice IV,
pp. 299-312,
1994
bibtex

Voorspelling samenstelling vliegas m.b.v neurale netwerken en symbolische methodes
1994
bibtex

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

Neurale netwerken voor toepassingen op grote databases
1994
bibtex

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

Neurale netwerken en hun toepassingen
Informatie en Informatiebeleid, winter,
vol. 12,
no. 4,
pp. 75-81,
1994
bibtex

1993

On-line learning processes in artificial neural networks
vol. 51,
pp. 199-234,
1993
bibtex

Neural network analysis to predict treatment outcome
Annuals of Oncology,
vol. 4,
pp. 31-34,
1993
bibtex

Using boltzmann machines as perceptrons
IEEE Trans. Neural Networks,
1993
bibtex

Optimizing the architecture of multi-layer
perceptrons for one-dimensional classification
Artificial Neural Networks 3,
pp. 558-561,
1993
bibtex

Using boltzmann machines for probability estimation
Artificial Neural Networks 3,
pp. 521-526,
1993
bibtex

Neural network analysis for prediction of treatment outcome in ovarian cancer
EWOC-3,
1993
bibtex

Neural representation of saccadic eye movements in monkey superior colliculus
Artificial Neural Networks 3,
pp. 88-93,
1993
bibtex

Learning processes in neural networks
1993
bibtex

Error potentials for self-organization
International Conference on Neural Networks, San Francisco,
vol. 3,
pp. 1219-1223,
1993
bibtex

Cooling schedules for learning in neural networks
Physical Review E,
vol. 47,
pp. 4457-4464,
1993
bibtex

Neurale netwerken, fuzzy rules en artificiele intelligentie
Klinische Fysica,
vol. 1,
pp. 13-16,
1993
bibtex

A two-dimensional model for spatial-temporal transformation of saccades in monkey superior colliculus
Network,
vol. 4,
pp. 19-38,
1993
bibtex

Proceedings of the international confidence icann'93
Artificial Neural Networks 3,
1993
bibtex

1992

Learning in neural networks with local minima
Physical Review A,
vol. 46,
pp. 5221-5231,
1992
bibtex

Learning parameter adjustment in neural networks
Physical Review A,
vol. 45,
pp. 8885-8893,
1992
bibtex

Learning rules, stochastic processes, and local minima
Artificial Neural Networks 2,
vol. 1,
pp. 71-78,
1992
bibtex

Neural network analysis for prediction of treatment
outcome in ovarian cancer
ASCO,
1992
bibtex

Proceedings symposium on neural networks
Proceedings of 2nd SNN Conference on Neural Networks,
vol. 2,
1992
bibtex

1991

Learning processes in neural networks
Physical Review A,
vol. 44,
pp. 2718-2726,
1991
bibtex

Neural networks learning in a changing environment
Artificial Neural Networks 1,
vol. 1,
pp. 15-20,
1991
bibtex

Een computersimulatie van hetbilocale correlator model
1991
bibtex

Quantitative model for spatio-temporal transformation of oculomotor signals in monkey superior colliculus.
Eur. J. Neurosci. (Suppl),
vol. 14,
pp. 56,
1991
bibtex

Neural networks learning in achanging environment
International Joint Conference on Neural Networks, Seattle,
vol. 1,
pp. 823-828,
1991
bibtex

Learning at a constant rate
1991
bibtex

Neurale netwerken: verslag van een symposium
Informatie,
vol. 33,
pp. 435-438,
1991
bibtex

Proceedings symposium on neural networks
Proceedings of 1st SNN symposium,
vol. 1,
1991
bibtex

1990

Neurocomputing research in thenetherlands
Neurocomputing,
vol. 2,
pp. 35-38,
1990
bibtex

Latent kullback leibler control for continuous-state systems
using probabilistic graphical models
Proceedings UAI,
vol. 30,
bibtex