To improve the ability of variational auto-encoders (VAE) to disentangle in the latent space, existing works mostly focus on enforcing the independence among the learned latent factors. However, the ability of these models to disentangle often decreases as the complexity of the generative factors increases. In this paper, we investigate the little-explored effect of the modeling capacity of a posterior density on the disentangling ability of the VAE. We note that the independence within and the complexity of the latent density are two different properties we constrain when regularizing the posterior density: while the former promotes the disentangling ability of VAE, the latter - if overly limited - creates an unnecessary competition with the data reconstruction objective in VAE. Therefore, if we preserve the independence but allow richer modeling capacity in the posterior density, we will lift this competition and thereby allow improved independence and data reconstruction at the same time. We investigate this theoretical intuition with a VAE that utilizes a non-parametric latent factor model, the Indian Buffet Process (IBP), as a latent density that is able to grow with the complexity of the data. Across two widely-used benchmark data sets (MNIST and dSprites) and two clinical data sets little explored for disentangled learning, we qualitatively and quantitatively demonstrated the improved disentangling performance of IBP-VAE over the state of the art. In the latter two clinical data sets riddled with complex factors of variations, we further demonstrated that unsupervised disentangling of nuisance factors via IBP-VAE - when combined with a supervised objective - can not only improve task accuracy in comparison to relevant supervised deep architectures, but also facilitate knowledge discovery related to task decision-making.

Three-dimensional design software is challenging for novices and non-experts; when working with objects that already exist, the task becomes even more difficult. We present Printy3D, a system that enables children to design customized containers for electronic modules using tangible interaction and spatially augmented reality feedback. Our system allows users to position physical objects in three dimensions relative to a virtual container, providing feedback on placement location and validity. We implemented two different interaction styles and conducted a user study with 26 participants, 23 of them children. We detail the results of our study and suggest implications for design as well as opportunities for future research for systems of this kind.