
Giuseppe Ughi
Address
Mathematical Institute
University of Oxford
Andrew Wiles Building
Radcliffe Observatory Quarter
Woodstock Road
Oxford
OX2 6GG
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.
O O'Donoghue, P Duckworth, G Ughi, L Scheibenreif, K Khezeli, A Hoarfrost, S Budd, P Foley, N Chia, J Kalantari, G Mackintosh, F Soboczenski, L Sanders
NeurIPS 2021 Workshop Machine Learning for Health (2021)
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)
Post-Doctoral Research Associate within the CIMDA-Oxford collaboration.