machine-learning

Synergising Bayesian inference and Riemannian geometry for control in multimodal dynamical systems.

This talk presented recent work synergising Bayesian inference and probabilistic Riemannian geometries to control multimodal dynamical …

This work presents a two-stage method to perform trajectory optimisation in multimodal dynamical systems with unknown nonlinear stochastic transition dynamics. The method finds trajectories that remain in a preferred dynamics mode where possible and in regions of the transition dynamics model that have been observed and can be predicted confidently.

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.

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

I am in the process of creating Jupyter notebooks for several probabilistic models (Bayesian linear regression, Gaussian process regression) and approximate inference algorithms. Particular focus has been put on providing detailed theory as well as easy to follow code.

As part of the FARSCOPE CDT program I worked in a team to develop a solution to Amazon’s picking challenge. This involved designing a robotic pick-and-place system that was capable of recognising and grasping both known and novel objects in cluttered environments.