… to previous Bayesian compression approaches, our BAMC utilizes the Dirichlet process
mixture models which provide a systematic way for learning the layerwise compression policies. …

… In this paper we will use the variational Bayesian approximation for Bayesian inference
which has also been explicitly interpreted in terms of model compression [27]. By employing …

… A recent line of work [10–12] proposes to solve this issue by reformulating it as a model
compression problem: we treat a single datum as a continuous signal that maps coordinates to …

… -intensive HMM model in one of the cases. We thus conclude that the Bayesian technique …
time/space tradeoff, compressing better than the faster 4-state models, and using significantly …

… and safety of mechanical compression devices and manual compression in patients with …
a Bayesian network meta-analysis to compare AutoPulse, LUCAS and manual compression …

… of compressive measurements from a Bayesian perspective. … are observed from compressive
measurements, and the … Section III-B), the Bayesian formalism, more importantly, provides a …

… compression through the Bayesian framework. We show that Bayesian neural networks
automatically discover redundancy in model parameters, thus enabling self-compression, which …

… However, directly applying tensor compression in the training process is a … -rank Bayesian
tensorized neural network. Our Bayesian method performs automatic model compression via …

… Compressing a dense neural network offers many advantages including lower computation
… of Bayesian model selection in a deep learning setup. Given a fully connected Bayesian …

… In Chapter 4, I consider the probiem of how to efficiently compress data using Bayesian
networks with hidden variables. When t here are hidden variables, a Bayesian network may …