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ORCIDDiego Granziol
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2020 – today
- 2024
[j4]Diego Granziol, Nicholas P. Baskerville, Xingchen Wan, Samuel Albanie, Stephen Roberts:
Iterate Averaging in the Quest for Best Test Error. J. Mach. Learn. Res. 25: 20:1-20:55 (2024)- 2022
- [j3]Diego Granziol
, Binxin Ru, Xiaowen Dong, Stefan Zohren, Michael A. Osborne, Stephen J. Roberts:
Maximum Entropy Approach to Massive Graph Spectrum Learning with Applications. Algorithms 15(6): 209 (2022) - [j2]Diego Granziol, Stefan Zohren, Stephen Roberts:
Learning Rates as a Function of Batch Size: A Random Matrix Theory Approach to Neural Network Training. J. Mach. Learn. Res. 23: 173:1-173:65 (2022) - [i13]Nicholas P. Baskerville, Jonathan P. Keating, Francesco Mezzadri, Joseph Najnudel, Diego Granziol:
Universal characteristics of deep neural network loss surfaces from random matrix theory. CoRR abs/2205.08601 (2022) - 2021
- [i12]Nicholas P. Baskerville, Diego Granziol, Jonathan P. Keating:
Applicability of Random Matrix Theory in Deep Learning. CoRR abs/2102.06740 (2021) - 2020
- [b1]Diego Granziol:
Spectral machine learning. University of Oxford, UK, 2020 - [i11]Diego Granziol, Xingchen Wan, Stephen Roberts:
Iterate Averaging Helps: An Alternative Perspective in Deep Learning. CoRR abs/2003.01247 (2020) - [i10]Diego Granziol:
Beyond Random Matrix Theory for Deep Networks. CoRR abs/2006.07721 (2020) - [i9]Diego Granziol:
Flatness is a False Friend. CoRR abs/2006.09091 (2020) - [i8]Diego Granziol:
Curvature is Key: Sub-Sampled Loss Surfaces and the Implications for Large Batch Training. CoRR abs/2006.09092 (2020) - [i7]Diego Granziol, Samuel Albanie, Xingchen Wan, Stephen J. Roberts:
Explaining the Adaptive Generalisation Gap. CoRR abs/2011.08181 (2020)
2010 – 2019
- 2019
- [j1]Diego Granziol, Bin Xin Ru, Stefan Zohren, Xiaowen Dong, Michael A. Osborne, Stephen J. Roberts:
MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning. Entropy 21(6): 551 (2019) - [i6]Diego Granziol, Bin Xin Ru, Stefan Zohren, Xiaowen Dong, Michael A. Osborne, Stephen J. Roberts:
MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning. CoRR abs/1906.01101 (2019) - [i5]Diego Granziol, Robin Ru, Stefan Zohren, Xiaowen Dong, Michael A. Osborne, Stephen J. Roberts:
A Maximum Entropy approach to Massive Graph Spectra. CoRR abs/1912.09068 (2019) - [i4]Diego Granziol, Xingchen Wan, Timur Garipov, Dmitry P. Vetrov, Stephen Roberts:
MLRG Deep Curvature. CoRR abs/1912.09656 (2019) - 2018
- [c3]Bin Xin Ru, Mark McLeod, Diego Granziol, Michael A. Osborne:
Fast Information-theoretic Bayesian Optimisation. ICML 2018: 4381-4389 - [i3]Diego Granziol, Edward Wagstaff, Bin Xin Ru, Michael A. Osborne, Stephen J. Roberts:
VBALD - Variational Bayesian Approximation of Log Determinants. CoRR abs/1802.08054 (2018) - [i2]Diego Granziol, Bin Xin Ru, Stefan Zohren, Xiaowen Dong, Michael A. Osborne, Stephen J. Roberts:
Entropic Spectral Learning in Large Scale Networks. CoRR abs/1804.06802 (2018) - 2017
- [c2]Diego Granziol, Stephen J. Roberts:
Entropic determinants of massive matrices. IEEE BigData 2017: 88-93 - [c1]Jack K. Fitzsimons, Diego Granziol, Kurt Cutajar, Michael A. Osborne, Maurizio Filippone, Stephen J. Roberts:
Entropic Trace Estimates for Log Determinants. ECML/PKDD (1) 2017: 323-338 - [i1]Jack K. Fitzsimons, Diego Granziol, Kurt Cutajar, Michael A. Osborne, Maurizio Filippone, Stephen J. Roberts:
Entropic Trace Estimates for Log Determinants. CoRR abs/1704.07223 (2017)
Coauthor Index
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