Diego Granziol
Cited by
Cited by
Fast information-theoretic Bayesian optimisation
B Ru, MA Osborne, M McLeod, D Granziol
International Conference on Machine Learning, 4384-4392, 2018
Entropic trace estimates for log determinants
J Fitzsimons, D Granziol, K Cutajar, M Osborne, M Filippone, S Roberts
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2017
MEMe: An accurate maximum entropy method for efficient approximations in large-scale machine learning
D Granziol, B Ru, S Zohren, X Dong, M Osborne, S Roberts
Entropy 21 (6), 551, 2019
Towards understanding the true loss surface of deep neural networks using random matrix theory and iterative spectral methods
D Granziol, T Garipov, D Vetrov, S Zohren, S Roberts, AG Wilson
MLRG deep curvature
D Granziol, X Wan, T Garipov, D Vetrov, S Roberts
arXiv preprint arXiv:1912.09656, 2019
Beyond random matrix theory for deep networks
D Granziol
arXiv preprint arXiv:2006.07721, 2020
Iterate averaging helps: An alternative perspective in deep learning
D Granziol, X Wan, S Roberts
arXiv preprint arXiv:2003.01247, 2020
Curvature is key: Sub-sampled loss surfaces and the implications for large batch training
D Granziol
arXiv preprint arXiv:2006.09092, 2020
VBALD-Variational Bayesian approximation of log determinants
D Granziol, E Wagstaff, BX Ru, M Osborne, S Roberts
arXiv preprint arXiv:1802.08054, 2018
Entropic determinants of massive matrices
D Granziol, S Roberts
2017 IEEE International Conference on Big Data (Big Data), 88-93, 2017
A Maximum Entropy approach to Massive Graph Spectra
D Granziol, R Ru, S Zohren, X Dong, M Osborne, S Roberts
arXiv preprint arXiv:1912.09068, 2019
Entropic Spectral Learning for Large-Scale Graphs
D Granziol, B Ru, S Zohren, X Dong, M Osborne, S Roberts
arXiv preprint arXiv:1804.06802, 2018
MLRG Deep Curvature: An Open-source Package to Analyse and Visualise Neural Network Curvature and Loss Surface
D Granziol, X Wan, T Garipov, D Vetrov, S Roberts
An information and field theoretic approach to the grand canonical ensemble
D Granziol, S Roberts
arXiv preprint arXiv:1703.10099, 2017
Applicability of Random Matrix Theory in Deep Learning
NP Baskerville, D Granziol, JP Keating
arXiv preprint arXiv:2102.06740, 2021
Explaining the Adaptive Generalisation Gap
D Granziol, S Albanie, X Wan, S Roberts
arXiv preprint arXiv:2011.08181, 2020
Learning Rates as a Function of Batch Size: A Random Matrix Theory Approach to Neural Network Training
D Granziol, S Zohren, S Roberts
stat 1050, 6, 2020
Flatness is a False Friend
D Granziol
arXiv preprint arXiv:2006.09091, 2020
Gadam: Combining Adaptivity with Iterate Averaging Gives Greater Generalisation
D Granziol, X Wan, S Roberts
stat 1050, 10, 2020
Deep Curvature Suite
D Granziol, X Wan, T Garipov
arXiv preprint arXiv:1912.09656, 2019
The system can't perform the operation now. Try again later.
Articles 1–20