Diego Granziol
Title
Cited by
Cited by
Year
Fast information-theoretic Bayesian optimisation
B Ru, MA Osborne, M McLeod, D Granziol
International Conference on Machine Learning, 4384-4392, 2018
302018
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
162017
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
112019
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
92019
Beyond random matrix theory for deep networks
D Granziol
arXiv preprint arXiv:2006.07721, 2020
72020
MLRG deep curvature
D Granziol, X Wan, T Garipov, D Vetrov, S Roberts
arXiv preprint arXiv:1912.09656, 2019
72019
Learning Rates as a Function of Batch Size: A Random Matrix Theory Approach to Neural Network Training
D Granziol, S Zohren, S Roberts
arXiv e-prints, arXiv: 2006.09092, 2020
52020
Iterate averaging helps: An alternative perspective in deep learning
D Granziol, X Wan, S Roberts
arXiv preprint arXiv:2003.01247, 2020
32020
VBALD-Variational Bayesian approximation of log determinants
D Granziol, E Wagstaff, BX Ru, M Osborne, S Roberts
arXiv preprint arXiv:1802.08054, 2018
32018
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
22018
Entropic determinants of massive matrices
D Granziol, S Roberts
2017 IEEE International Conference on Big Data (Big Data), 88-93, 2017
22017
Applicability of Random Matrix Theory in Deep Learning
NP Baskerville, D Granziol, JP Keating
arXiv preprint arXiv:2102.06740, 2021
12021
Explaining the Adaptive Generalisation Gap
D Granziol, X Wan, S Albanie, S Roberts
arXiv preprint arXiv:2011.08181, 2020
12020
Flatness is a False Friend
D Granziol
arXiv preprint arXiv:2006.09091, 2020
12020
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
12019
MLRG deep curvature: An open-source package to analyse and visualise neural network curvature and loss surface
D Granziol, X Wan, T Garipov, DP Vetrov, S Roberts
MLRG Deep Curvature, 2019
12019
The Deep Learning Limit: are negative neural network eigenvalues just noise?
D Granziol, T Garipov, S Zohren, D Vetrov, S Roberts, AG Wilson
ICML 2019 Workshop on Theoretical Physics for Deep Learning, 2019
12019
An information and field theoretic approach to the grand canonical ensemble
D Granziol, S Roberts
arXiv preprint arXiv:1703.10099, 2017
12017
Ranker-agnostic Contextual Position Bias Estimation
OB Mayor, V Bellini, A Buchholz, G Di Benedetto, DM Granziol, M Ruffini, ...
arXiv preprint arXiv:2107.13327, 2021
2021
Ranker-agnostic Contextual Position Bias Estimation
O Barbany Mayor, V Bellini, A Buchholz, G Di Benedetto, DM Granziol, ...
arXiv e-prints, arXiv: 2107.13327, 2021
2021
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