On weight and variance uncertainty in neural networks for regression tasks

• URL: http://arxiv.org/abs/2501.04272v1
• Date: Wed, 08 Jan 2025 04:44:47 GMT
• Title: On weight and variance uncertainty in neural networks for regression tasks
• Authors: Moein Monemi, Morteza Amini, S. Mahmoud Taheri, Mohammad Arashi,
• Abstract summary: We show that including the variance uncertainty can improve the prediction performance of the Bayesian NN. We explore fully connected dense networks and dropout NNs with Gaussian and spike-and-slab priors, respectively, for the network weights.
• Score: 1.6649383443094408
• License:
• Abstract: We consider the problem of weight uncertainty proposed by [Blundell et al. (2015). Weight uncertainty in neural network. In International conference on machine learning, 1613-1622, PMLR.] in neural networks {(NNs)} specialized for regression tasks. {We further} investigate the effect of variance uncertainty in {their model}. We show that including the variance uncertainty can improve the prediction performance of the Bayesian {NN}. Variance uncertainty enhances the generalization of the model {by} considering the posterior distribution over the variance parameter. { We examine the generalization ability of the proposed model using a function approximation} example and {further illustrate it with} the riboflavin genetic data set. {We explore fully connected dense networks and dropout NNs with} Gaussian and spike-and-slab priors, respectively, for the network weights.

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