  We present SrvfNet, a generative deep learning framework for the joint
multiple alignment of large collections of functional data comprising
square-root velocity functions (SRVF) to their templates. Our proposed
framework is fully unsupervised and is capable of aligning to a predefined
template as well as jointly predicting an optimal template from data while
simultaneously achieving alignment. Our network is constructed as a generative
encoder-decoder architecture comprising fully-connected layers capable of
producing a distribution space of the warping functions. We demonstrate the
strength of our framework by validating it on synthetic data as well as
diffusion profiles from magnetic resonance imaging (MRI) data.
