Approximate inference on planar graphs using Loop Calculus and Belief Propagation (2009)
Gómez, V., Kappen, H. J., Chertkov, M.
We introduce novel results for approximate inference on planar graphical models using the loop calculus framework. The loop calculus (Chertkov and Chernyak, 2006) allows to express the exact...
The cluster variation method for approximate reasoning in medical diagnosis (2007)
In this paper, we discuss the rule based and probabilistic approaches to computer aided medical diagnosis. We conclude that the probabilistic approach is superior to the rule based approach, but due...
Truncating the loop series expansion for Belief Propagation (2006)
Gomez, Vicenc, Mooij, J. M., Kappen, H. J.
Recently, M. Chertkov and V.Y. Chernyak derived an exact expression for the partition sum (normalization constant) corresponding to a graphical model, which is an expansion around the Belief...
On Cavity Approximations for Graphical Models (2006)
Rizzo, T., Wemmenhove, B., Kappen, H. J.
We reformulate the Cavity Approximation (CA), a class of algorithms recently introduced for improving the Bethe approximation estimates of marginals in graphical models. In our new formulation, which...
Algorithms for identification and categorization (2006)
Cortes, J. M., Garrido, P. L., Kappen, H. J., Marro, J., Morillas, C., Navidad, D., ...
The main features of a family of efficient algorithms for recognition and classification of complex patterns are briefly reviewed. They are inspired in the observation that fast synaptic noise is...
Competition between synaptic depression and facilitation in attractor neural networks (2006)
Torres, J. J., Cortes, J. M., Marro, J., Kappen, H. J.
We study the effect of competition between short-term synaptic depression and facilitation on the dynamical properties of attractor neural networks, using Monte Carlo simulation and a mean field...
A generative model for music transcription (2006)
Cemgil, A.T., Kappen, H.J., Barber, D.
In this paper, we present a graphical model for polyphonic music transcription. Our model, formulated as a dynamical Bayesian network, embodies a transparent and computationally tractable approach to...
Survey propagation at finite temperature: application to a Sourlas code as a toy model (2005)
In this paper we investigate a finite temperature generalization of survey propagation, by applying it to the problem of finite temperature decoding of a biased finite connectivity Sourlas code for...
Effects of fast presynaptic noise in attractor neural networks (2005)
Cortes, J. M., Torres, J. J., Marro, J., Garrido, P. L., Kappen, H. J.
We study both analytically and numerically the effect of presynaptic noise on the transmission of information in attractor neural networks. The noise occurs on a very short-time scale compared to...
Path integrals and symmetry breaking for optimal control theory (2005)
This paper considers linear-quadratic control of a non-linear dynamical system subject to arbitrary cost. I show that for this class of stochastic control problems the non-linear...
A linear theory for control of non-linear stochastic systems (2004)
We address the role of noise and the issue of efficient computation in stochastic optimal control problems. We consider a class of non-linear control problems that can be formulated as a path...
A development protocol for a diagnostic DSS (1999)
Yl, O., Burg, W.J. Ter, Braak, E. Ter, Neijt, J.P., Wiegerinck, W.A., Nijman, M.J., ...
Approximate Inference for Medical Diagnosis (1999)
H.J. Kappen, M.J. Nijman, Y.L. O, ...
Computer-based diagnostic decision support systems (DSS) will play an increasingly important role in health care. Due to the inherent probabilistic nature of medical diagnosis, a DSS should...
Validity of TAP Equations in Neural Networks (1999)
The statistics of a Boltzmann machine can be approximated using the tap equations combined with linear response theory. We discuss the validity of the tap equations, in particular for finite size...
Learning Higher Order Boltzmann Machines using Linear Response (1998)
Boltzmann machines are able to represent some probability distribution but the exact learning algorithm needs a time that is exponential in the number of neurons. The approximation method called...
Boltzmann Machine learning using mean field theory and linear response correction (1998)
H.J. Kappen, F.B. Rodríguez, F. B. Rodr'iguez
We present a new approximate learning algorithm for Boltzmann Machines, using a systematic expansion of the Gibbs free energy to second order in the weights. The linear response correction to the...
Stimulus Dependent Correlations in Stochastic Networks (1997)
It has been observed that cortical neurons display synchronous firing for some stimuli and not for others. The resulting synchronous cell assemblies are thought to form the basis of object...
Learning Structure with Many-Take-All networks (1996)
. It is shown that by restricting the number of active neurons in a layer of a Boltzmann machine, a sparse distributed coding of the input data can be learned. Unlike Winner-Take-All, this coding...
Neural network analysis to predict treatment outcome (1993)
Background Quantitative methods for the analysis of prognostic information are important in order to use this knowledge optimally. The neural network is a new quantitative method where the...