approximate-inference

Function-space Parameterization of Neural Networks for Sequential Learning

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

Aidan Scannell, Riccardo Mereu, Paul Chang, Ella Tamir, Joni Pajarinen, Arno Solin

Sparse Function-space Representation of Neural Networks

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

Aidan Scannell, Riccardo Mereu, Paul Chang, Ella Tamir, Joni Pajarinen, Arno Solin

Sparse Function-space Representation of Neural Networks

Mode-constrained Model-based Reinforcement Learning via Gaussian Processes

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.

Aidan Scannell, Carl Henrik Ek, Arthur Richards

Bayesian Learning for Control in Multimodal Dynamical Systems

Mode remaining navigation (and exploration) in unknown multimodal dynamical systems via model-based reinforcement learning.

Aidan Scannell

Bayesian Learning for Control in Multimodal Dynamical Systems

Approximate Inference

This work implements and compares a variety of approximate inference techniques for the tasks of image de-noising (restoration) and image segmentation.

Aidan Scannell

Approximate Inference