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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
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
Beyond random matrix theory for deep networks
D Granziol
arXiv preprint arXiv:2006.07721, 2020
132020
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
MLRG deep curvature
D Granziol, X Wan, T Garipov, D Vetrov, S Roberts
arXiv preprint arXiv:1912.09656, 2019
102019
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
9*2020
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
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
Deep Curvature Suite
D Granziol, X Wan, T Garipov
arXiv preprint arXiv:1912.09656, 2019
12019
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
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
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Articles 1–20