  Understanding and quantifying uncertainty in black box Neural Networks (NNs)
is critical when deployed in real-world settings such as healthcare. Recent
works using Bayesian and non-Bayesian methods have shown how a unified
predictive uncertainty can be modelled for NNs. Decomposing this uncertainty to
disentangle the granular sources of heteroscedasticity in data provides rich
information about its underlying causes. We propose a conceptually simple
non-Bayesian approach, deep split ensemble, to disentangle the predictive
uncertainties using a multivariate Gaussian mixture model. The NNs are trained
with clusters of input features, for uncertainty estimates per cluster. We
evaluate our approach on a series of benchmark regression datasets, while also
comparing with unified uncertainty methods. Extensive analyses using dataset
shits and empirical rule highlight our inherently well-calibrated models. Our
work further demonstrates its applicability in a multi-modal setting using a
benchmark Alzheimer's dataset and also shows how deep split ensembles can
highlight hidden modality-specific biases. The minimal changes required to NNs
and the training procedure, and the high flexibility to group features into
clusters makes it readily deployable and useful. The source code is available
at https://github.com/wazeerzulfikar/deep-split-ensembles

Machine Learning;Machine Learning