Diego Granziol

Publication Activity (10 Years)

Years Active: 2017-2022
Publications (10 Years): 19

Top Topics

Joint Estimation

Random Matrix Theory

Maximum Entropy

Top Venues

J. Mach. Learn. Res.

Publications

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 (2022)
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) (2022)
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 (2022)
Nicholas P. Baskerville, Diego Granziol, Jonathan P. Keating
Applicability of Random Matrix Theory in Deep Learning. CoRR (2021)
Diego Granziol, Xingchen Wan, Stephen Roberts
Iterate Averaging Helps: An Alternative Perspective in Deep Learning. CoRR (2020)
Diego Granziol
Beyond Random Matrix Theory for Deep Networks. CoRR (2020)
Diego Granziol
Flatness is a False Friend. CoRR (2020)
Diego Granziol
Curvature is Key: Sub-Sampled Loss Surfaces and the Implications for Large Batch Training. CoRR (2020)
Diego Granziol, Samuel Albanie, Xingchen Wan, Stephen J. Roberts
Explaining the Adaptive Generalisation Gap. CoRR (2020)
Diego Granziol, Xingchen Wan, Timur Garipov, Dmitry P. Vetrov, Stephen Roberts
MLRG Deep Curvature. CoRR (2019)
Diego Granziol, Robin Ru, Stefan Zohren, Xiaowen Dong, Michael A. Osborne, Stephen J. Roberts
A Maximum Entropy approach to Massive Graph Spectra. CoRR (2019)
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) (2019)
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 (2019)
Diego Granziol, Bin Xin Ru, Stefan Zohren, Xiaowen Dong, Michael A. Osborne, Stephen J. Roberts
Entropic Spectral Learning in Large Scale Networks. CoRR (2018)
Bin Xin Ru, Mark McLeod, Diego Granziol, Michael A. Osborne
Fast Information-theoretic Bayesian Optimisation. ICML (2018)
Diego Granziol, Edward Wagstaff, Bin Xin Ru, Michael A. Osborne, Stephen J. Roberts
VBALD - Variational Bayesian Approximation of Log Determinants. CoRR (2018)
Jack K. Fitzsimons, Diego Granziol, Kurt Cutajar, Michael A. Osborne, Maurizio Filippone, Stephen J. Roberts
Entropic Trace Estimates for Log Determinants. CoRR (2017)
Diego Granziol, Stephen J. Roberts
Entropic determinants of massive matrices. IEEE BigData (2017)
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)