Ed and Ben: In this paper we explore the importance of representation learning in convolutional neural networks, specifically in the context of an infinite-width limit called the Neural Network Gaussian Process (NNGP) that is often used by theorists. Representation learning refers to the ability of models to learn a transformation of the data that is tailored to the task at hand. This is in contrast to algorithms that use a fixed transformation of the data, e.g. a support vector machine with a fixed kernel function like the RBF kernel. Representation learning is thought to be critical to the success of convolutional neural networks in vision tasks, but networks in the NNGP limit do not perform representation learning, instead transforming the data with a fixed kernel function. A recent modification to the NNGP limit, called the Deep Kernel Machine (DKM), allows one to gradually “add representation learning back in” to the NNGP, using a single hyperparameter that controls the amount of flexibility in the kernel. We extend this algorithm to convolutional architectures, which required us to develop a new sparse inducing point approximation scheme. This allowed us to test on the full CIFAR-10 image classification dataset, where we achieved state-of-the-art test accuracy for kernel methods, with 92.7%.
In the plot below, we see how changing the hyperparameter (x-axis) to reduce flexibility too much harms the performance on unseen data.
We are happy to announce our upcoming applications deadline is 12 June 2023, 23:59 (London UK time zone) for the final few fully funded places to start September 2023. For international applications there are limited scholarship funded places available. Early applications advised.
Compass is offering specific projects for PhD students to study from Sept 2023. We are pleased to announce that there are 4 new project opportunities to study. The full list of the projects/ supervisors has been updated. All the supervisors listed are open to discussion on the projects provided and can also talk to applicants about other project ideas. Please provide a ranked list of 3 projects of interest: 1 = project of highest interest. Project supervisors will be happy to respond to specific questions you have after reading the proposals. Applicants should contact them by email if they wish beforehand.
Application forms will be reviewed based on the 3 ranked projects specified or other proposed topic. Successful applicants will be invited to attend an interview with the Compass admissions tutors and the specific project supervisor. If you are made an offer of PhD study it will be published through the online application system. You will then have 2 weeks to consider the offer before deciding whether to accept or decline.
We welcome applications from all members of our community and are particularly encouraging those from diverse groups, such as members of the LGBT+ and black, Asian and minority ethnic communities, to join us.
Stipend – a generous stipend of £22,622 pa tax free, paid in monthly payments. Plus your own expense budget of £1,000 pa towards travel and research activity.
No fees – all tuition fees are covered by the EPSRC and University of Bristol.
Bespoke training – first year units are designed specifically for the academic needs of each Compass student, which enables students to develop knowledge and capability to pursue cross-disciplinary PhD research.
Supervisors – supervisors from across academic disciplines offer a range of research projects.
Cohort – Compass students benefit from dedicated offices and collaboration spaces, enabling strong cohort links and opportunities for shared learning and research.
About Compass CDT
A 4-year bespoke PhD training programme in the statistical and computational techniques of data science, with partners from across the University of Bristol, industry and government agencies.
The cross-disciplinary programme offers exciting collaborations across medicine, computer science, geography, economics, life and earth sciences, as well as with our external partners who range from government organisations such as the Office for National Statistics, NCSC and the AWE, to industrial partners such as LV, Improbable, IBM Research, EDF, and AstraZeneca.
Students are co-located with the Institute for Statistical Science in the School of Mathematics, which occupies the Fry Building.
Hear from our students about their experience with the programme
Compass has allowed me to advance my statistical knowledge and apply it to a range of exciting applied projects, as well as develop skills that I’m confident will be highly useful for a future career in data science. – Shannon, Cohort 2
With the Compass CDT I feel part of a friendly, interactive environment that is preparing me for whatever I move on to next, whether it be in Academia or Industry. – Sam, Cohort 2
An incredible opportunity to learn the ever-expanding field of data science, statistics and machine learning amongst amazing people. – Danny, Cohort 1
Compass CDT Video
Find out more about what it means to be a part of the Compass programme from our students in this short video.
We are happy to announce our upcoming applications deadline of 16 March 2023 for Compass CDT programme. For international applications there are limited scholarship funded places available for this final recruitment round. Early applications advised.
Compass is offering specific projects for PhD students to study from Sept 2023. The projects are listed in the research section of our website. All the supervisors listed are open to discussion on the projects provided and can also talk to applicants about other project ideas. Please provide a ranked list of 3 projects of interest: 1 = project of highest interest. Project supervisors will be happy to respond to specific questions you have after reading the proposals. Applicants should contact them by email if they wish beforehand.
Also, we are pleased to announce a new projectGenetic Similarity Based Cohort Building that has been added to the list for September 2023 start funded by Roche, one of the world’s largest biotech companies, as well as a leading provider of in-vitro diagnostics and a global supplier of transformative innovative solutions across major disease areas.
PhD Project Allocation Process
Application forms will be reviewed based on the 3 ranked projects specified or other proposed topic. Successful applicants will be invited to attend an interview with the Compass admissions tutors and the specific project supervisor. If you are made an offer of PhD study it will be published through the online application system. You will then have 2 weeks to consider the offer before deciding whether to accept or decline.
We welcome applications from all members of our community and are particularly encouraging those from diverse groups, such as members of the LGBT+ and black, Asian and minority ethnic communities, to join us.
Stipend – a generous stipend of £21,668 pa tax free, paid in monthly payments. Plus your own expense budget of £1,000 pa towards travel and research activity.
No fees – all tuition fees are covered by the EPSRC and University of Bristol.
Bespoke training – first year units are designed specifically for the academic needs of each Compass student, which enables students to develop knowledge and capability to pursue cross-disciplinary PhD research.
Supervisors – supervisors from across academic disciplines offer a range of research projects.
Cohort – Compass students benefit from dedicated offices and collaboration spaces, enabling strong cohort links and opportunities for shared learning and research.
About Compass CDT
A 4-year bespoke PhD training programme in the statistical and computational techniques of data science, with partners from across the University of Bristol, industry and government agencies.
The cross-disciplinary programme offers exciting collaborations across medicine, computer science, geography, economics, life and earth sciences, as well as with our external partners who range from government organisations such as the Office for National Statistics, NCSC and the AWE, to industrial partners such as LV, Improbable, IBM Research, EDF, and AstraZeneca.
Students are co-located with the Institute for Statistical Science in the School of Mathematics, which occupies the Fry Building.
Hear from our students about their experience with the programme
Compass has allowed me to advance my statistical knowledge and apply it to a range of exciting applied projects, as well as develop skills that I’m confident will be highly useful for a future career in data science. – Shannon, Cohort 2
With the Compass CDT I feel part of a friendly, interactive environment that is preparing me for whatever I move on to next, whether it be in Academia or Industry. – Sam, Cohort 2
An incredible opportunity to learn the ever-expanding field of data science, statistics and machine learning amongst amazing people. – Danny, Cohort 1
Compass CDT Video
Find out more about what it means to be a part of the Compass programme from our students in this short video.
This blog describes an approach being developed to deliver rapid classification of farmer strategies. The data comes from a survey conducted with two groups of smallholder farmers (see image 2), one group living in the Taita Hills area of southern Kenya and the other in Yebelo, southern Ethiopia. This work would not have been possible without the support of my supervisors James Hammond, from the International Livestock Research Institute (ILRI) (and developer of the Rural Household Multi Indicator Survey, RHoMIS, used in this research), as well as Andrew Dowsey, Levi Wolf and Kate Robson Brown from the University of Bristol.
Image 2: Measuring a Cows Heart Girth as Part of the Farm Surveys
Aims of the project
The goal of my PhD is to contribute a landscape approach to analysing agricultural systems. On-farm practices are an important part of an agricultural system and are one of the trilogy of components that make-up what Rizzo et al (2022) call ‘agricultural landscape dynamics’ – the other two components being Natural Resources and Landscape Patterns. To understand how a farm interacts with and responds to Natural Resources and Landscape Patterns it seems sensible to try and understand not just each farms inputs and outputs but its overall strategy and component practices. (more…)
“Gaussian processes are a highly flexible class of non-parametric Bayesian models used in a variety of applications. In their exact form they provide principled uncertainty representations, at the expense of poor scalability (O(n^3)) with the number of training points. As a result, many approximate methods have been proposed to try and address this. We raise the question of how to assess the performance of such methods. The most obvious approach is to generate data from the exact GP model and then benchmark performance metrics of the approximations against the data generating process. Unfortunately, generating data from an exact GP is also in general an O(n^3) problem. We address this limitation by demonstrating how tunable parameters controlling the fidelity of inexact methods of drawing samples can be chosen to ensure that their samples are, with high probability, indistinguishable from genuine data from the exact GP.”
Compass CDT is now recruiting for its fully funded places to start September 2023.
We are happy to announce that The University of Bristol online application system is open, and we are receiving applications for Compass CDT programme for September 2023 start. Early application is advised.
For 2023/34 entry, applicants must review the projects on offer. The projects are listed in the research section of our website. You will need to provide a Research Statement in your application documents with a ranked list of 3 projects of interest to you: 1 being the project of highest interest.
PhD Project Allocation Process
Application forms will be reviewed based on the 3 ranked projects specified. Successful applicants will be invited to attend an interview with the Compass admissions tutors and the specific project supervisor. If you are made an offer of PhD study it will be published through the online application system. You will then have 2 weeks to consider the offer before deciding whether to accept or decline.
The next review of applications for 2023 funded places will take place after
We welcome applications from all members of our community and are particularly encouraging those from diverse groups, such as members of the LGBT+ and black, Asian and minority ethnic communities, to join us.
Advantages of being a Compass Student
Stipend – a generous stipend of £21,668 pa tax free, paid in monthly payments. Plus your own expense budget of £1,000 pa towards travel and research activity.
No fees – all tuition fees are covered by the EPSRC and University of Bristol.
Bespoke training – first year units are designed specifically for the academic needs of each Compass student, which enables students to develop knowledge and capability to pursue cross-disciplinary PhD research.
Supervisors – supervisors from across academic disciplines offer a range of research projects.
Cohort – Compass students benefit from dedicated offices and collaboration spaces, enabling strong cohort links and opportunities for shared learning and research.
About Compass CDT
A 4-year bespoke PhD training programme in the statistical and computational techniques of data science, with partners from across the University of Bristol, industry and government agencies.
The cross-disciplinary programme offers exciting collaborations across medicine, computer science, geography, economics, life and earth sciences, as well as with our external partners who range from government organisations such as the Office for National Statistics, NCSC and the AWE, to industrial partners such as LV, Improbable, IBM Research, EDF, and AstraZeneca.
Students are co-located with the Institute for Statistical Science in the School of Mathematics, which occupies the Fry Building.
Hear from our students about their experience with the programme
Compass has allowed me to advance my statistical knowledge and apply it to a range of exciting applied projects, as well as develop skills that I’m confident will be highly useful for a future career in data science. – Shannon, Cohort 2
With the Compass CDT I feel part of a friendly, interactive environment that is preparing me for whatever I move on to next, whether it be in Academia or Industry. – Sam, Cohort 2
An incredible opportunity to learn the ever-expanding field of data science, statistics and machine learning amongst amazing people. – Danny, Cohort 1
APPLY BEFORE:
Wednesday 4 January 2023, 5pm (London, UK time zone)
Compass student Dan Milner and his academic supervisors have published an article in Frontiers, one of the most cited and largest research publishers in the world. Dan’s work is funded in collaboration with ILRI (International Livestock Research Institute). (more…)
Between 4th and 8th of April 2022 Compass CDT students are attending APTS Week 2 in Durham.
Academy for PhD Training in Statistics (APTS) organises, through a collaboration between major UK statistics research groups, four residential weeks of training each year for first-year PhD students in statistics and applied probability nationally. Compass students attend all four APTS courses hosted by prestigious UK Universities.
For their APTS Week in Durham Compass students will be attending the following modules:
Applied Stochastic Processes (Nicholas Georgiou and Matt Roberts): This module will introduce students to two important notions in stochastic processes — reversibility and martingales — identifying the basic ideas, outlining the main results and giving a flavour of some of the important ways in which these notions are used in statistics.
Statistical Modelling (Helen Ogden): The aim of this module is to introduce important aspects of statistical modelling, including model selection, various extensions to generalised linear models, and non-linear models.
We are delighted to announce the confirmed DataScience@work seminars for 2022. Huge thanks to our invited speakers who will be joining us in person and online over the coming months!
The Compass DataScience@work seminar invites speakers from industry, government and third-sector to provide our PhD students with their perspective on the realities of being a data scientist in industry: from the methods and techniques they use to build applications, to working as part of a wider organisation, and how to build a career in their sector.
Find out more on our DataScience@work seminar here.
