bayesian-inference

Sequential learning paradigms pose challenges for gradient-based deep learning due to difficulties incorporating new data and retaining …

Deep neural networks (NNs) are known to lack uncertainty estimates and struggle to incorporate new data. We present a method that …

We present a model-based RL algorithm that constrains training to a single dynamic mode with high probability. This is a difficult problem because the mode constraint is a hidden variable associated with the environment’s dynamics. As such, it is 1) unknown a priori and 2) we do not observe its output from the environment, so cannot learn it with supervised learning.

Mixture models are inherently unidentifiable as different combinations of component distributions and mixture weights can generate the …

This work introduces a variational lower bound for the Mixture of Gaussian Process Experts model with a GP-based gating network based on sparse GPs. The model (and inference) are implemented in GPflow/TensorFlow.

This post introduces the theory underpinning Gaussian process regression and provides a basic walk-through in python.