Data Complexity and Uncertainty-Resilient Deep Variational Learning

Enterprise data present increasing significant characteristics and complexities, such as multi-aspect, heterogeneous and hierarchical features and interactions, and evolving dependencies and multi-distributions.
They significantly challenge the state-of-the-art deep, probabilistic, and neural learning systems, while urgently required for small-to-large enterprise innovations. This research aims to develop a theory of flexible deep variational learning synergizing new deep probabilistic modelling with flexible variational neural learning mechanisms for analytically explainable, complexity-resilient analytics of enterprise data. The outcomes are expected to fill important knowledge gaps and lift innovation competencies in wide domains.