Sparse Bayesian deep learning for dynamic system identification

A Bayesian approach to sparse dynamic network identification

Modeling and identification for high dimensional (i.e. signals with many components) data sets poses severe challenges to off-the-shelf techniques for system identification. This is particularly so when relatively small data sets, as compared to the number signal components, have to be used. It is often the case that each component of the measured signal can be described in terms of few other m...

Dynamic System Identification using HDMR-Bayesian Technique

This paper presents a novel HDMR-Bayesian technique for dynamic system identification in a Bayesian framework. This technique is based on Bayesian Inference. Direct application of Bayesian inference in estimation and identification involves evaluation of multi-dimensional integrals in multidimensional systems. This is computationally complex and tedious. The proposed technique addresses this ch...

Clustered Sparse Bayesian Learning

Many machine learning and signal processing tasks involve computing sparse representations using an overcomplete set of features or basis vectors, with compressive sensing-based applications a notable example. While traditionally such problems have been solved individually for different tasks, this strategy ignores strong correlations that may be present in real world data. Consequently there h...

Bayesian Compression for Deep Learning

Compression and computational efficiency in deep learning have become a problem of great significance. In this work, we argue that the most principled and effective way to attack this problem is by taking a Bayesian point of view, where through sparsity inducing priors we prune large parts of the network. We introduce two novelties in this paper: 1) we use hierarchical priors to prune nodes ins...

Bayesian Deep Learning | Uncertainty in Deep Learning