HyperVAE: A Minimum Description Length Variational Hyper-Encoding Network

We propose a framework called HyperVAE for encoding distributions of distributions. When a target distribution is modeled by a VAE, its neural network parameters \theta is drawn from a distribution p(\theta) which is modeled by a hyper-level VAE. We propose a variational inference using Gaussian mixture models to implicitly encode the parameters \theta into a low dimensional Gaussian distribution. Given a target distribution, we predict the posterior distribution of the latent code, then use a matrix-network decoder to generate a posterior distribution q(\theta). HyperVAE can encode the parameters \theta in full in contrast to common hyper-networks practices, which generate only the scale and bias vectors as target-network parameters. Thus HyperVAE preserves much more information about the model for each task in the latent space. We discuss HyperVAE using the minimum description length (MDL) principle and show that it helps HyperVAE to generalize. We evaluate HyperVAE in density estimation tasks, outlier detection and discovery of novel design classes, demonstrating its efficacy

Developing a Framework using Interpretive Structural Modeling for the Challenges of Digital Financial Services in India

Digital financial services (DFS) can expand the delivery of basic financial services to the poor through innovative technologies like mobile-phone-enabled solutions, electronic money models and digital payment platforms. By 2020, it is estimated that the mobile will have the potential to serve about 250 million people for financial services in India. Yet there remains a long way for India to go in digital finance. Realizing this, the objectives of the current research are to recognize various key challenges of DFS, to find contextual relationships between various challenges and to develop a hierarchy of challenges to promote DFS in India. The findings revealed 45 contextual relationships among the key challenges using experts’ inputs. Implementing interpretive structural modelling (ISM) indicated “Lack of literacy/digital literacy (C4)” and “Universal unavailability of Internet (C8)” as the key driving challenges coming on the way of using DFS