Add to ORKGThe Bayesian analysis of neural networks is dicult because a sim-ple prior over weights implies a complex prior distribution over functions. In this paper we investigate the use of Gaussian process priors over functions, which permit the predictive Bayesian anal-ysis for xed values of hyperparameters to be carried out exactly using matrix operations. Two methods, using optimization and av-eraging (via Hybrid Monte Carlo) over hyperparameters have been tested on a number of challenging problems and have produced excellent results.
International audienceIn this paper, we introduce the notion of Gaussian processes indexed by probab...
We consider the problem of assigning an input vector bfx to one of m classes by predicting P(c|bfx) ...
Bayesian optimization (BO) has become a popular strategy for global optimization of many expensive r...
The Bayesian analysis of neural networks is difficult because a simple prior over weights implies a ...
The Bayesian analysis of neural networks is difficult because the prior over functions has a complex...
The full Bayesian method for applying neural networks to a prediction problem is to set up the prior...
The goal of this thesis was to implement a practical tool for optimizing hy- perparameters of neural...
d.barber~aston.ac.uk c.k.i.williams~aston.ac.uk The full Bayesian method for applying neural network...
Abstract. Gaussian processes are a natural way of dening prior distributions over func-tions of one ...
Gaussian Processes are powerful regression models specified by parametrized mean and covariance func...
Abstract. Gaussian processes are a powerful tool for non-parametric re-gression. Training can be rea...
This paper will discuss how a Gaussian process, which describes a probability distribution over an i...
grantor: University of TorontoThis thesis develops two Bayesian learning methods relying o...
This dissertation aims at introducing Gaussian process priors on the regression to capture features ...
Gaussian process models constitute a class of probabilistic statistical models in which a Gaussian p...
International audienceIn this paper, we introduce the notion of Gaussian processes indexed by probab...
We consider the problem of assigning an input vector bfx to one of m classes by predicting P(c|bfx) ...
Bayesian optimization (BO) has become a popular strategy for global optimization of many expensive r...
The Bayesian analysis of neural networks is difficult because a simple prior over weights implies a ...
The Bayesian analysis of neural networks is difficult because the prior over functions has a complex...
The full Bayesian method for applying neural networks to a prediction problem is to set up the prior...
The goal of this thesis was to implement a practical tool for optimizing hy- perparameters of neural...
d.barber~aston.ac.uk c.k.i.williams~aston.ac.uk The full Bayesian method for applying neural network...
Abstract. Gaussian processes are a natural way of dening prior distributions over func-tions of one ...
Gaussian Processes are powerful regression models specified by parametrized mean and covariance func...
Abstract. Gaussian processes are a powerful tool for non-parametric re-gression. Training can be rea...
This paper will discuss how a Gaussian process, which describes a probability distribution over an i...
grantor: University of TorontoThis thesis develops two Bayesian learning methods relying o...
This dissertation aims at introducing Gaussian process priors on the regression to capture features ...
Gaussian process models constitute a class of probabilistic statistical models in which a Gaussian p...
International audienceIn this paper, we introduce the notion of Gaussian processes indexed by probab...
We consider the problem of assigning an input vector bfx to one of m classes by predicting P(c|bfx) ...
Bayesian optimization (BO) has become a popular strategy for global optimization of many expensive r...