  Memory-efficient continuous Sign Language Translation is a significant
challenge for the development of assisted technologies with real-time
applicability for the deaf. In this work, we introduce a paradigm of designing
recurrent deep networks whereby the output of the recurrent layer is derived
from appropriate arguments from nonparametric statistics. A novel variational
Bayesian sequence-to-sequence network architecture is proposed that consists of
a) a full Gaussian posterior distribution for data-driven memory compression
and b) a nonparametric Indian Buffet Process prior for regularization applied
on the Gated Recurrent Unit non-gate weights. We dub our approach
Stick-Breaking Recurrent network and show that it can achieve a substantial
weight compression without diminishing modeling performance.
