Abstract
Building robust classifiers trained on data susceptible to
group or subject-specific variations is a challenging pattern
recognition problem. We develop hierarchical Bayesian
neural networks to capture subject-specific variations and
share statistical strength across subjects. Leveraging recent work on learning Bayesian neural networks, we build
fast, scalable algorithms for inferring the posterior distribution over all network weights in the hierarchy. We also
develop methods for adapting our model to new subjects
when a small number of subject-specific personalization
data is available. Finally, we investigate active learning algorithms for interactively labeling personalization data in
resource-constrained scenarios. Focusing on the problem of
gesture recognition where inter-subject variations are commonplace, we demonstrate the effectiveness of our proposed
techniques. We test our framework on three widely used gesture recognition datasets, achieving personalization performance competitive with the state-of-the-art