Add to ORKGIn this paper, we propose a full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. We then propose a reversible jump Markov chain Monte Carlo (MCMC) method to perform the necessary computations. We find that the results are not only better than the previously reported ones, but also appear to be robust with respect to the prior specification. Moreover, we present a geometric convergence theorem for the algorithm
In this work, I will focus on ways in which we can build machine learning models that appropriately ...
Bayesian statistics is a powerful framework for modeling the world and reasoning over uncertainty. I...
Bayesian neural networks (BNNs), a family of neural networks with a probability distribution placed ...
In this paper, we propose a full Bayesian model for neural networks. This model treats the model dim...
We propose a hierarchical full Bayesian model for radial basis networks. This model treats the model...
We propose a hierarchical full Bayesian model for radial basis networks. This model treats the model...
Summary The application of the Bayesian learning paradigm to neural networks results in a flexi-ble ...
This thesis puts forward methods for computing local robustness of probabilistic neural networks, s...
We propose a novel reversible jump Markov chain Monte Carlo (MCMC) simulated annealing algorithm to ...
Conventional training methods for neural networks involve starting al a random location in the solut...
Trans-dimensional Bayesian inference for multi-layer perceptron architectures of varying size by rev...
We introduce a probabilistic robustness measure for Bayesian Neural Networks (BNNs), defined as the ...
We give a short review on Bayesian techniques for neural networks and demonstrate the advantages of ...
It is generally assumed when using Bayesian inference methods for neural networks that the input dat...
AbstractWe describe two specific examples of neural-Bayesian approaches for complex modeling tasks: ...
In this work, I will focus on ways in which we can build machine learning models that appropriately ...
Bayesian statistics is a powerful framework for modeling the world and reasoning over uncertainty. I...
Bayesian neural networks (BNNs), a family of neural networks with a probability distribution placed ...
In this paper, we propose a full Bayesian model for neural networks. This model treats the model dim...
We propose a hierarchical full Bayesian model for radial basis networks. This model treats the model...
We propose a hierarchical full Bayesian model for radial basis networks. This model treats the model...
Summary The application of the Bayesian learning paradigm to neural networks results in a flexi-ble ...
This thesis puts forward methods for computing local robustness of probabilistic neural networks, s...
We propose a novel reversible jump Markov chain Monte Carlo (MCMC) simulated annealing algorithm to ...
Conventional training methods for neural networks involve starting al a random location in the solut...
Trans-dimensional Bayesian inference for multi-layer perceptron architectures of varying size by rev...
We introduce a probabilistic robustness measure for Bayesian Neural Networks (BNNs), defined as the ...
We give a short review on Bayesian techniques for neural networks and demonstrate the advantages of ...
It is generally assumed when using Bayesian inference methods for neural networks that the input dat...
AbstractWe describe two specific examples of neural-Bayesian approaches for complex modeling tasks: ...
In this work, I will focus on ways in which we can build machine learning models that appropriately ...
Bayesian statistics is a powerful framework for modeling the world and reasoning over uncertainty. I...
Bayesian neural networks (BNNs), a family of neural networks with a probability distribution placed ...