Function-space Parameterization of Neural Networks for Sequential Learning
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 …
PhD Thesis: Bayesian Learning for Control in Multimodal Dynamical Systems
Mode remaining navigation (and exploration) in unknown multimodal dynamical systems via model-based reinforcement learning.
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
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1 min read