Status:
+44 1865 270508
Research groups:
Address
University of Oxford
Andrew Wiles Building
Radcliffe Observatory Quarter
Woodstock Road
Oxford
OX2 6GG
Research interests:
The main focus of my research is on Theories of Deep Learning with the aim of applying them to the automatisation of the appraisal of real-estates with RE5Q.
Initially, I focused on the application of traditional derivative-free optimisation algorithms to generate adversarial examples for deep nets. This lead to a comprehensive review of how different optimisation techniques compare in this task.
Currently, I am focusing on the use of Information Theory to develop initialisations of neural networks that allow an optimal transmission of information through the layers.
Further details:
InFoMM CDT DPhil Student (Cohort 4)
Teaching:
Tutor for:
- C.6.5 Theories of Deep Learning (MT20)
- B.5.6 Nonlinear Systems (HT20)
- C.6.5 Theories of Deep Learning (MT19)
Teaching Assistant for:
- C.7.7 Random Matrix Theory (HT21)
- Oxford Internet Institute - Machine Learning (MT20)
- C.6.5 Theories of Deep Learning (HT19)
- B.8.4 Information Theory (MT18)
Major / recent publications:
G Ughi, V Abrol, J Tanner
Optimization and Engineering, Springer (2021)
Mutual Information of Neural Network Initialisations: Mean Field Approximations
J Tanner, G Ughi
2021 IEEE International Symposium on Information Theory (ISIT 2021)
A Model-Based Derivative-Free Approach to Black-Box Adversarial Examples: BOBYQA
G Ughi, V Abrol, J Tanner
NeurIPS 2019 Workshop: Beyond First Order Methods in ML (2019)