H. Kappen

发表

Path integrals and symmetry breaking for optimal control theory

H. Kappen, 2005, physics/0505066.

Linear theory for control of nonlinear stochastic systems.

H. Kappen, 2004, Physical review letters.

An introduction to stochastic control theory, path integrals and reinforcement learning

H. Kappen, 2007 .

A path integral approach to agent planning

H. Kappen, 2022 .

Dynamic Policy Programming with Function Approximation

Vicenç Gómez, Hilbert J. Kappen, Mohammad Gheshlaghi Azar, 2011, AISTATS.

Optimal exploration as a symmetry breaking phenomenon

H. Kappen, S.R.H.M. Tonk, 2010 .

Optimal control theory and the linear bellman equation

H. Kappen, 2011 .

Computer generated higher order expansions

Martijn A. R. Leisink, H. Kappen, 2002, UAI 2002.

Optimal control as a graphical model inference problem

Vicenç Gómez, Hilbert J. Kappen, Manfred Opper, 2009, Machine Learning.

Haplotype Inference in General Pedigrees Using the Cluster Variation Method

H. Kappen, T. Heskes, C. A. Albers, 2007, Genetics.

Mean field theory for asymmetric neural networks.

H. Kappen, J. Spanjers, 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

Graphical Model Inference in Optimal Control of Stochastic Multi-Agent Systems

Hilbert J. Kappen, Wim Wiegerinck, Bart van den Broek, 2008, J. Artif. Intell. Res..

On-line learning processes in artificial neural networks

Hilbert J. Kappen, Tom Heskes, H. Kappen, 1993 .

Path integral control and state-dependent feedback.

H. Kappen, S. Thijssen, Sep Thijssen, 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

Policy Search for Path Integral Control

Vicenç Gómez, Hilbert J. Kappen, Jan Peters, 2014, ECML/PKDD.

Novel iteration schemes for the Cluster Variation Method

Hilbert J. Kappen, Wim Wiegerinck, H. Kappen, 2001, NIPS.

Bound Propagation pdf

Hilbert J. Kappen, Martijn A. R. Leisink, H. Kappen, 2003, J. Artif. Intell. Res..

Speedy Q-Learning

Hilbert J. Kappen, Rémi Munos, Mohammad Ghavamzadeh, 2011, NIPS.

Reinforcement Learning with a Near Optimal Rate of Convergence

H. Kappen, R. Munos, M. Ghavamzadeh, 2011 .

The Cluster Variation Method for Approximate Reasoning in Medical Diagnosis

Hilbert J. Kappen, H. Kappen, 2002 .

A Tighter Bound for Graphical Models

Hilbert J. Kappen, Martijn A. R. Leisink, H. Kappen, 2001, Neural Computation.

Validity Estimates for Loopy Belief Propagation on Binary Real-world Networks

Hilbert J. Kappen, Joris M. Mooij, H. Kappen, 2004, NIPS.

Spin-glass phase transitions on real-world graphs

H. Kappen, J. Mooij, 2004, cond-mat/0408378.

Neural network analysis to predict treatment outcome

H. Kappen, J. Neijt, 1993 .

Risk Sensitive Path Integral Control

Hilbert J. Kappen, Wim Wiegerinck, Bart van den Broek, 2010, UAI.

Stochastic Optimal Control in Continuous Space-Time Multi-Agent Systems pdf

Hilbert J. Kappen, Wim Wiegerinck, Bart van den Broek, 2006, UAI.

Approximate Inference and Constrained Optimization

Hilbert J. Kappen, Tom Heskes, Kees Albers, 2002, UAI.

Efficient Learning in Boltzmann Machines Using Linear Response Theory

Hilbert J. Kappen, Francisco de Borja Rodríguez Ortiz, H. Kappen, 1998, Neural Computation.

Stimulus-dependent correlations in stochastic networks

H. Kappen, 1997 .

Optimal on-line scheduling in stochastic multiagent systems in continuous space-time

Hilbert J. Kappen, Wim Wiegerinck, Bart van den Broek, 2007, AAMAS '07.

KL-learning: Online solution of Kullback-Leibler control problems pdf

Hilbert J. Kappen, Joris Bierkens, H. Kappen, 2011, ArXiv.

Learning Higher Order Boltzmann Machines using Linear Response

Martijn A. R. Leisink, H. Kappen, 1998 .

Storage capacity of attractor neural networks with depressing synapses.

Lovorka Pantic, J. J. Torres, Joaquín J Torres, 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

Inference in the Promedas Medical Expert System

Hilbert J. Kappen, Joris M. Mooij, Wim Wiegerinck, 2007, AIME.

On the Sample Complexity of Reinforcement Learning with a

H. Kappen, R. Munos, M. G. Azar, 2012 .

Journal of Machine Learning Research ? ? ? ( ? ? ? ) ? ? ? Submitted ? ? ? ; Published ? ? ? Dynamic Policy Programming

H. Kappen, M. G. Azar, V. Gómez, 2012 .

Effective Connectivity from Single Trial fMRI Data by Sampling Biologically Plausible Models

H. Kappen, H. R. Euler, 2018, 1803.05840.

Dynamic policy programming

Hilbert J. Kappen, Mohammad Gheshlaghi Azar, Vicenc Gomez, 2010, J. Mach. Learn. Res..

Linear PDEs and eigenvalue problems corresponding to ergodic stochastic optimization problems on compact manifolds

H. Kappen, M. Chertkov, J. Bierkens, 2016 .

Linear PDEs and eigenvalue problems corresponding to ergodic stochastic optimization problems on compact manifolds

H. Kappen, M. Chertkov, J. Bierkens, 2013, 1303.0126.

Nonlinear Deconvolution by Sampling Biophysically Plausible Hemodynamic Models.

H. Kappen, H. R. Euler, J. R. F. Marques, 2018, 1803.08797.

Probabilistic solution of relative entropy weighted control

H. Kappen, J. Bierkens, 2012 .

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

H. Kappen, R. Munos, M. Ghavamzadeh, 2013 .

Radial basis Boltzmann machines and learnng with missing values

M. J. Nijman, H. Kappen, 1995 .

Mean field approach to learning Boltzmann Machines

H. Kappen, F. B. Rodriguezt, 2013 .

An Iterative Method for Nonlinear Stochastic Optimal Control Based on Path Integrals

Hilbert J. Kappen, Masami Saeki, Satoshi Satoh, 2017, IEEE Transactions on Automatic Control.

Nonlinear Stochastic Optimal Control with Input Saturation Constraints Based on Path Integrals

H. Kappen, S. Satoh, Satoshi Satoh, 2020, IEEJ Transactions on Electrical and Electronic Engineering.

To appear in Neural Computation 2002 Associative memory with dynamic synapses

J. J. Torres, H. Kappen, S. Gielen, 2019 .

Irregular Dynamics in Up and Down Cortical States

Hilbert J. Kappen, J. J. Torres, Joaquin J. Torres, 2010, PloS one.

Explaining Missing Heritability Using Gaussian Process Regression

Hilbert J. Kappen, Wim Wiegerinck, Cornelis A. Albers, 2016, bioRxiv.

Probabilistic knowledge representation

David Barber, Hilbert J. Kappen, Wim Wiegerinck, 1998 .

Stochastic optimal control theory ICML, Helsinki 2008 tutorial

H. Kappen, 2008 .

1 0 M ay 2 00 5 Path integrals and symmetry breaking for optimal control theory

H. Kappen, 2008 .

Short-term synaptic plasticity and heterogeneity in neural systems

Hilbert J. Kappen, André Longtin, Joaquín J. Torres, 2013 .

An atomic Boltzmann machine capable of self-adaption

H. Kappen, B. Kiraly, A. Khajetoorians, 2020, Nature Nanotechnology.

Tunable glassiness on a two-dimensional atomic spin array

H. Kappen, Alex Kolmus, M. Katsnelson, 2018, 1804.08093.

A daptive Multi-class classification for Brain Computer Interfaces

H. Kappen, V. Gómez, A. Llera, 2022 .

Decision support for medical diagnosis

H. Kappen, W. Wiegerinck, E. T. Braak, 2002 .

Implementing perceptron models with qubits

H. Kappen, R. Wiersema, 2019, Physical Review A.

Implementing perceptron models with qubits

H. Kappen, R. Wiersema, 2019, Physical Review A.

06 72 8 v 1 [ qu an tph ] 1 6 M ay 2 01 9 Implementing perceptron models with qubits

H. Kappen, R. Wiersema, 2022 .

qu an tph ] 2 2 A ug 2 01 9 Implementing perceptron models with qubits

H. Kappen, R. Wiersema, 2022 .

Time-Integrated Position Error Accounts for Sensorimotor Behavior in Time-Constrained Tasks

H. Kappen, W. Wiegerinck, S. Gielen, 2012, PloS one.

The Binary Garrote pdf

Hilbert J. Kappen, H. Kappen, 2011, ArXiv.

LEARNING ACTIVE VISION

Hilbert J. Kappen, M. J. Nijman, T. van Moorsel, 1998 .

Approximate reasoning: real world applications of graphical models

Ali Taylan Cemgil, Hilbert J. Kappen, Wim Wiegerinck, 2001 .

Neurocomputing research in The Netherlands

Hilbert J. Kappen, C. Gielen, H. Kappen, 1990, Neurocomputing.

An introduction to stochastic neural networks

Hilbert J. Kappen, H. Kappen, 2001 .

Confidence values for neural networks

Hilbert J. Kappen, Tom Heskes, C.C.A.M. Gielen, 1995 .

Predicting newspaper sales: JED system 'weathers' the test

Hilbert J. Kappen, Tom Heskes, H. Kappen, 2000 .

Learning rules, stochastic processes, and local minima

Hilbert J. Kappen, Tom Heskes, H. Kappen, 1992 .

Spin-glass phase transitions on real-world graphs

H. Kappen, J. Mooij, 2004, cond-mat/0408378.

Linear theory for control of nonlinear stochastic systems.

H. Kappen, 2004, Physical review letters.

Approximate Inference Methods for Genetic Linkage Analysis

Hilbert J. Kappen, Cornelis A. Albers, H. Kappen, 2001 .

Consistent Adaptive Multiple Importance Sampling and Controlled Diffusions

H. Kappen, S. Thijssen, 2018, 1803.07966.

Survey propagation at finite temperature: application to a Sourlas code as a toy model

H. Kappen, B. Wemmenhove, 2005, cond-mat/0508586.

Bonaparte: Bayesian Networks to Give Victims back their Names

Hilbert J. Kappen, Wim Wiegerinck, H. Kappen, 2016, ERCIM News.

Why adiabatic quantum annealing is unlikely to yield speed-up

H. Kappen, Aarón Villanueva, Peyman Najafi, 2022, 2212.13649.

Efficient inference in the transverse field Ising model

H. Kappen, E. Dom'inguez, 2022, 2210.11193.

On Cavity Approximations for Graphical Models

T Rizzo, B Wemmenhove, H J Kappen, 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

Sufficient Conditions for Convergence of the Sum–Product Algorithm

Hilbert J. Kappen, Joris M. Mooij, H. Kappen, 2005, IEEE Transactions on Information Theory.

Approximate Inference on Planar Graphs using Loop Calculus and Belief Propagation

Michael Chertkov, Vicenç Gómez, Hilbert J. Kappen, 2009, J. Mach. Learn. Res..

Coincidence detection with dynamic synapses.

Lovorka Pantic, J. J. Torres, Joaquín J Torres, 2003, Network.

On the role of dynamical synapses in coincidence detection

Hilbert J. Kappen, Joaquín J. Torres, Lovorka Pantic, 2001, Neurocomputing.

Efficient inference in the transverse field Ising model

H. Kappen, E. Dom'inguez, 2022, 2210.11193.

A generative model for music transcription

David Barber, Ali Taylan Cemgil, Hilbert J. Kappen, 2006, IEEE Transactions on Audio, Speech, and Language Processing.

Symmetry Breaking and Training from Incomplete Data with Radial Basis Boltzmann Machines

Hilbert J. Kappen, Marcel J. Nijman, M. J. Nijman, 1997, Int. J. Neural Syst..

Integrating Tempo Tracking and Quantization using Particle Filtering

Ali Taylan Cemgil, Hilbert J. Kappen, H. Kappen, 2002, ICMC.

Monte Carlo Methods for Tempo Tracking and Rhythm Quantization pdf

Ali Taylan Cemgil, Hilbert J. Kappen, H. Kappen, 2011, J. Artif. Intell. Res..

Minimal navigation solution for a swarm of tiny flying robots to explore an unknown environment

G. C. H. E. de Croon, K. N. McGuire, C. De Wagter, 2019, Science Robotics.

Using Boltzmann Machines for probability estimation

Hilbert J. Kappen, H. Kappen, 1993 .

Atom-by-atom construction of attractors in a tunable finite size spin array

H. Kappen, M. Katsnelson, A. Khajetoorians, 2018, New Journal of Physics.

Input-Driven Oscillations in Networks with Excitatory and Inhibitory Neurons with Dynamic Synapses

Hilbert J. Kappen, Daniele Marinazzo, Stan C. A. M. Gielen, 2007, Neural Computation.

Chapter 13 An introduction to stochastic neural networks

H. J. Kappen, H. Kappen, 2001 .

A two-dimensional ensemble coding model for spatial-temporal transformation of saccades in monkey superior colliculus

Hilbert J. Kappen, A. J. van Opstal, H. Kappen, 1993 .

On tempo tracking: Tempogram Representation and Kalman filtering

Peter Desain, Bert Kappen, Henkjan Honing, 2000, ICMC.

Advanced ovarian cancer. Neural network analysis to predict treatment outcome.

H. Kappen, J. Neijt, 1993, Annals of oncology : official journal of the European Society for Medical Oncology.

Adaptive Importance Sampling for Control and Inference

Hilbert J. Kappen, H. Kappen, 2015, ArXiv.

Adaptive Importance Sampling for Control and Inference

H. Kappen, H. Ruiz, 2016, Journal of Statistical Physics.

On the Sample Complexity of Reinforcement Learning with a Generative Model

Hilbert J. Kappen, Rémi Munos, Mohammad Gheshlaghi Azar, 2012, ICML.

Competition Between Synaptic Depression and Facilitation in Attractor Neural Networks

Hilbert J. Kappen, Joaquín J. Torres, J. J. Torres, 2006, Neural Computation.

Emerging phenomena in neural networks with dynamic synapses and their computational implications

Hilbert J. Kappen, Joaquín J. Torres, J. J. Torres, 2013, Front. Comput. Neurosci..

Hysteresis and bistability in a realistic model for IP3-driven Ca2+ oscillations

J. J. Torres, H. Kappen, P. Willems, 2001 .

Learning active vision : industrial application processing systems

Hilbert J. Kappen, M. J. Nijman, T. van Moorsel, 1995 .

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

Hilbert J. Kappen, Joaquín J. Torres, J. J. Torres, 2007, Neurocomputing.

Learning effective state-feedback controllers through efficient multilevel importance samplers

Hilbert J. Kappen, S. A. Menchón, H. Kappen, 2019, Int. J. Control.

Training Quantum Boltzmann Machines with the $\beta$-Variational Quantum Eigensolver

Onno E Huijgen, H. Kappen, L. Coopmans, 2023, 2304.08631.

An overview of neural network applications

Hilbert J. Kappen, H. Kappen, 1996 .

Efficient Optical Flow and Stereo Vision for Velocity Estimation and Obstacle Avoidance on an Autonomous Pocket Drone

Hilbert J. Kappen, Guido C. H. E. de Croon, Karl Tuyls, 2016, IEEE Robotics and Automation Letters.

Bayesian neural networks with variable selection for prediction of genotypic values

H. Kappen, M. Calus, C. A. Albers, 2020, Genetics Selection Evolution.

Explicit solution of relative entropy weighted control

Hilbert J. Kappen, Joris Bierkens, H. Kappen, 2012, Syst. Control. Lett..

Learning Universal Computations with Spikes

Hilbert J. Kappen, Raoul-Martin Memmesheimer, Marvin Uhlmann, 2015, PLoS Comput. Biol..

Adaptive Smoothing Path Integral Control

Hilbert J. Kappen, Vicencc G'omez, Dominik Thalmeier, 2020, ArXiv.

Nonmonotonic Generalization Bias of Gaussian Mixture Models

Hilbert J. Kappen, Shotaro Akaho, H. Kappen, 2000, Neural Computation.

Bounds on marginal probability distributions

Hilbert J. Kappen, Joris M. Mooij, H. Kappen, 2008, NIPS.

Approximations of Bayesian networks through KL minimisation

Hilbert J. Kappen, Wim Wiegerinck, H. Kappen, 2000, New Generation Computing.

Mean field approach to learning in Boltzmann Machine

Hilbert J. Kappen, Francisco de Borja Rodríguez Ortiz, H. Kappen, 1997, Pattern Recognit. Lett..

Variational methods for approximate reasoning in graphical models

David Barber, Tom Heskes, Hilbert J. Kappen, 2000 .

On the properties of the Bethe approximation and loopy belief propagation on binary networks

Hilbert J. Kappen, Joris M. Mooij, H. Kappen, 2004 .

Learning quantum models from quantum or classical data

Hilbert J Kappen, H. Kappen, 2018, Journal of Physics A: Mathematical and Theoretical.

On the role of synaptic stochasticity in training low-precision neural networks

Hilbert J. Kappen, Carlo Baldassi, Carlo Lucibello, 2017, Physical review letters.

A likelihood-based framework for the analysis of discussion threads

H. Kappen, N. Litvak, V. Gómez, 2012, World Wide Web.

Approximate algorithms for neural-Bayesian approaches

Hilbert J. Kappen, Tom Heskes, Bart Bakker, 2002, Theor. Comput. Sci..

Online solution of the average cost kullback-leibler optimization problem.

Hilbert J. Kappen, G.N.J.C. Bierkens, H. Kappen, 2011 .

Real-Time Stochastic Optimal Control for Multi-Agent Quadrotor Systems

Vicenç Gómez, Hilbert J. Kappen, Stephen Hailes, 2015, ICAPS.

Comment: Causal entropic forces pdf

Hilbert J. Kappen, H. Kappen, 2013, ArXiv.

Sufficient Conditions for Convergence of Loopy Belief Propagation

Hilbert J. Kappen, Joris M. Mooij, H. Kappen, 2005, UAI.

Mean field theory for graphical models

Hilbert J. Kappen, Wim Wiegerinck, H. Kappen, 2001 .

Modeling the structure and evolution of discussion cascades

Vicenç Gómez, Hilbert J. Kappen, Andreas Kaltenbrunner, 2010, HT '11.

Minimax PAC bounds on the sample complexity of reinforcement learning with a generative model

H. Kappen, R. Munos, M. G. Azar, 2013, Machine Learning.

The Variational Garrote

Vicenç Gómez, Hilbert J. Kappen, H. Kappen, 2011, Machine Learning.

EP for Efficient Stochastic Control with Obstacles

Hilbert J. Kappen, Thomas Mensink, Jakob J. Verbeek, 2010, ECAI.

Effects of Fast Presynaptic Noise in Attractor Neural Networks

Hilbert J. Kappen, Joaquín J. Torres, Jesús M. Cortés, 2005, Neural Computation.

Associative Memory with Dynamic Synapses

Hilbert J. Kappen, Joaquín J. Torres, Stan C. A. M. Gielen, 2002, Neural Computation.

Truncating the Loop Series Expansion for Belief Propagation

Vicenç Gómez, Hilbert J. Kappen, Joris M. Mooij, 2006, J. Mach. Learn. Res..

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

Melanie Bahlo, Hilbert J Kappen, Russell Thomson, 2008, American journal of human genetics.

General Lower Bounds based on Computer Generated Higher Order Expansions pdf

Hilbert J. Kappen, Martijn A. R. Leisink, H. Kappen, 2012, UAI.

Quantitative analysis of task selection for brain–computer interfaces

Vicenç Gómez, Hilbert J Kappen, Alberto Llera, 2014, Journal of neural engineering.

Adaptive Multiclass Classification for Brain Computer Interfaces

Vicenç Gómez, Hilbert J. Kappen, A. Llera, 2014, Neural Computation.

Latent Kullback Leibler Control for Continuous-State Systems using Probabilistic Graphical Models

Takamitsu Matsubara, Vicenç Gómez, Hilbert J. Kappen, 2014, UAI.

Monte Carlo Methods for Tempo Tracking and Rhythm Quantization

H. Kappen, A. Cemgil, 2011, J. Artif. Intell. Res..

Bayesian Networks, Introduction and Practical Applications

Hilbert J. Kappen, Wim Wiegerinck, Willem Burgers, 2013, Handbook on Neural Information Processing.

Error potentials for self-organization

Hilbert J. Kappen, Tom Heskes, H. Kappen, 1993, IEEE International Conference on Neural Networks.

Speedy Q-Learning

H. Kappen, R. Munos, M. Ghavamzadeh, 2011, NIPS.

Stochastic optimal control of state constrained systems

Hilbert J. Kappen, Wim Wiegerinck, Bart van den Broek, 2011, Int. J. Control.

Consistent Adaptive Multiple Importance Sampling and Controlled Diffusions

Sep Thijssen, H. J. Kappen, H. Kappen, 2018, 1803.07966.

Particle Smoothing for Hidden Diffusion Processes: Adaptive Path Integral Smoother

Hilbert J. Kappen, Hans-Christian Ruiz, H. Kappen, 2016, IEEE Transactions on Signal Processing.

Adaptive Smoothing for Path Integral Control

H. Kappen, V. Gómez, Dominik Thalmeier, 2020, J. Mach. Learn. Res..

Post-operative Delirium after Intracranial Surgery: a retrospective cohort study.

H. Kappen, E. Andrinopoulou, C. Dirven, 2023, World neurosurgery.

An atomic Boltzmann machine capable of self-adaption

H. Kappen, B. Kiraly, A. Khajetoorians, 2020, Nature Nanotechnology.

Correcting Multivariate Auto-Regressive Models for the Influence of Unobserved Common Input

Vicenç Gómez, Hilbert J. Kappen, Mohammad Gheshlaghi Azar, 2016, CCIA.

Bayesian Real-time Adaptation for Interactive Performance Systems

Ali Taylan Cemgil, Hilbert J. Kappen, H. Kappen, 2001, ICMC.

Rhythm Quantization for Transcription

Peter Desain, Hilbert J. Kappen, Ali Taylan Cemgil, 2000, Computer Music Journal.

M ay 2 00 7 Sufficient conditions for convergence of the Sum-Product Algorithm

H. Kappen, Joris M. Mooij, 2007 .

Why adiabatic quantum annealing is unlikely to yield speed-up

H. Kappen, Aarón Villanueva, Peyman Najafi, 2022, Journal of Physics A: Mathematical and Theoretical.

Adaptive Classification on Brain-Computer Interfaces Using Reinforcement Signals

H. Kappen, V. Gómez, A. Llera, 2012, Neural Computation.

Bayesian neural networks with variable selection for prediction of genotypic values

H. Kappen, M. Calus, C. A. Albers, 2020, Genetics Selection Evolution.

Boltzmann Machine Learning Using Mean Field Theory and Linear Response Correction

Hilbert J. Kappen, Francisco de Borja Rodríguez Ortiz, H. Kappen, 1997, NIPS.

The Cluster Variation Method for Efficient Linkage Analysis on Extended Pedigrees

Hilbert J. Kappen, Martijn A. R. Leisink, Cornelis A. Albers, 2006, BMC Bioinformatics.

An atomic Boltzmann machine capable of on-chip learning

H. Kappen, B. Kiraly, A. Khajetoorians, 2020 .