Parameter Sharing for Spatio-Temporal Process Models

While probabilistic models such as Markov random fields can be highly beneficial for spatio-temporal data, they often suffer from over- fitting and have limited use in memory-constrained systems. We present a novel method to compress trained models based on temporal parameter sharing, which reduces redundancies in the parameters.