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Diego Granziol
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Cited by
Year
Fast information-theoretic Bayesian optimisation
B Ru, MA Osborne, M McLeod, D Granziol
International Conference on Machine Learning, 4384-4392, 2018
432018
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
202017
Beyond random matrix theory for deep networks
D Granziol
arXiv preprint arXiv:2006.07721, 2020
142020
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
142019
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
132019
Learning rates as a function of batch size: A random matrix theory approach to neural network training
D Granziol, S Zohren, S Roberts
arXiv preprint arXiv:2006.09092, 2020
10*2020
MLRG deep curvature
D Granziol, X Wan, T Garipov, D Vetrov, S Roberts
arXiv preprint arXiv:1912.09656, 2019
102019
Appearance of Random Matrix Theory in deep learning
NP Baskerville, D Granziol, JP Keating
Physica A: Statistical Mechanics and its Applications 590, 126742, 2022
52022
Flatness is a false friend
D Granziol
arXiv preprint arXiv:2006.09091, 2020
42020
Iterate averaging helps: An alternative perspective in deep learning
D Granziol, X Wan, S Roberts
arXiv preprint arXiv:2003.01247, 2020
42020
VBALD-Variational Bayesian approximation of log determinants
D Granziol, E Wagstaff, BX Ru, M Osborne, S Roberts
arXiv preprint arXiv:1802.08054, 2018
32018
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
22021
Applicability of Random Matrix Theory in Deep Learning
NP Baskerville, D Granziol, JP Keating
arXiv preprint arXiv:2102.06740, 2021
22021
Explaining the Adaptive Generalisation Gap
D Granziol, X Wan, S Albanie, S Roberts
arXiv preprint arXiv:2011.08181, 2020
22020
Deep Curvature Suite
D Granziol, X Wan, T Garipov
arXiv preprint arXiv:1912.09656, 2019
22019
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
A random matrix theory approach to damping in deep learning
D Granziol, N Baskerville
Journal of Physics: Complexity 3 (2), 024001, 2022
12022
Universal characteristics of deep neural network loss surfaces from random matrix theory
NP Baskerville, JP Keating, F Mezzadri, J Najnudel, D Granziol
arXiv preprint arXiv:2205.08601, 2022
12022
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
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