Learning the Parameters of Deep Convolutional Networks with Geodesics – In this paper, a non-parametric method to measure the uncertainty of the neural network model can be presented. To this end, we study how to accurately predict the posterior distribution of the neural network models. We compare the posterior distribution of the neural network models with a standard Gaussian model for different applications. Our results, comparing three different nonparametric approaches, show that a neural network model may show more uncertainty than the Gaussian model for different real-world applications.

The objective of this paper is to propose an algorithm for computing a Bayesian stochastic model that is linear in the model parameters, rather than stochastic in their parameters. The proposed algorithm takes as input the model parameter values and performs a Bayesian search for the parameters at each time step. Since the Bayesian search involves an infinite loop, an algorithm based on the proposed algorithm could be used to automatically identify the optimal model. The paper discusses several Bayesian search problems from the literature.

# Learning the Parameters of Deep Convolutional Networks with Geodesics

The Information Bottleneck Problem with Finite Mixture ModelsThe objective of this paper is to propose an algorithm for computing a Bayesian stochastic model that is linear in the model parameters, rather than stochastic in their parameters. The proposed algorithm takes as input the model parameter values and performs a Bayesian search for the parameters at each time step. Since the Bayesian search involves an infinite loop, an algorithm based on the proposed algorithm could be used to automatically identify the optimal model. The paper discusses several Bayesian search problems from the literature.