Machine-Learning

Trajectory Optimisation in Learned Multimodal Dynamical Systems via Latent-ODE Collocation featured image

Trajectory Optimisation in Learned Multimodal Dynamical Systems via Latent-ODE Collocation

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

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Aidan Scannell
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Trajectory Optimisation in Learned Multimodal Dynamical Systems featured image

Trajectory Optimisation in Learned Multimodal Dynamical Systems

This work presents a two-stage method to perform trajectory optimisation in multimodal dynamical systems with unknown nonlinear stochastic transition dynamics. The method finds …

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Aidan Scannell
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Identifiable Mixtures of Sparse Variational Gaussian Process Experts featured image

Identifiable Mixtures of Sparse Variational Gaussian Process Experts

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 …

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Aidan Scannell
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Gaussian Process Regression

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

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Aidan Scannell
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Probabilistic Modelling

I am in the process of creating Jupyter notebooks for several probabilistic models (Bayesian linear regression, Gaussian process regression) and approximate inference algorithms. …

Aidan Scannell
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Model-Based Reinforcement Learning with Gaussian Processes

In this work I re-implemented the PILCO algorithm in python using Tensorflow and GPflow. This work was mainly carried out for personal development and some of the implementation is …

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Aidan Scannell
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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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Amazon Picking Challenge

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 …

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