Approximate inference and Bayesian decision theory
Lead Research Organisation:
UNIVERSITY OF CAMBRIDGE
Department Name: Engineering
Abstract
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Organisations
People |
ORCID iD |
| Richard Turner (Primary Supervisor) | |
| Wessel Bruinsma (Student) |
Publications
Bruinsma W. P.
(2020)
Scalable Exact Inference in Multi-Output Gaussian Processes
Bruinsma W. P.
(2021)
The Gaussian Neural Process
James Requeima
(2019)
The Gaussian Process Autoregressive Regression Model (GPAR)
Jonathan Gordon
(2020)
Convolutional Conditional Neural Processes
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509620/1 | 30/09/2016 | 29/09/2022 | |||
| 1971216 | Studentship | EP/N509620/1 | 05/01/2018 | 29/06/2021 | Wessel Bruinsma |
| Description | The key findings so far are academic and can be summarised as follows: 1. A Gaussian process is a particular model for time series, which is typically expensive to apply to problems with many outputs. We found that they can be cheaply applied to many outputs, without requiring approximation. 2. It is possible to build in symmetry into a particular family of neural networks (conditional neural processes), which significantly improves their predictive performance. |
| Exploitation Route | As the key findings are mostly academic, they may be taken forward by further research. Alternatively, the developed techniques can be used directly by companies for time series prediction problems. |
| Sectors | Digital/Communication/Information Technologies (including Software) Other |
| Description | Research on modelling time series with multiple outputs is in use by a company. |