Fast information-theoretic Bayesian optimisation B Ru, MA Osborne, M McLeod, D Granziol International Conference on Machine Learning, 4384-4392, 2018 | 43 | 2018 |

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 | 20 | 2017 |

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 | 14 | 2019 |

Beyond random matrix theory for deep networks D Granziol arXiv preprint arXiv:2006.07721, 2020 | 13 | 2020 |

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 | 13 | 2019 |

MLRG deep curvature D Granziol, X Wan, T Garipov, D Vetrov, S Roberts arXiv preprint arXiv:1912.09656, 2019 | 10 | 2019 |

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 | 5 | 2022 |

Flatness is a false friend D Granziol arXiv preprint arXiv:2006.09091, 2020 | 4 | 2020 |

Iterate averaging helps: An alternative perspective in deep learning D Granziol, X Wan, S Roberts arXiv preprint arXiv:2003.01247, 2020 | 4 | 2020 |

VBALD-Variational Bayesian approximation of log determinants D Granziol, E Wagstaff, BX Ru, M Osborne, S Roberts arXiv preprint arXiv:1802.08054, 2018 | 3 | 2018 |

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 | 2 | 2021 |

Applicability of Random Matrix Theory in Deep Learning NP Baskerville, D Granziol, JP Keating arXiv preprint arXiv:2102.06740, 2021 | 2 | 2021 |

Explaining the Adaptive Generalisation Gap D Granziol, X Wan, S Albanie, S Roberts arXiv preprint arXiv:2011.08181, 2020 | 2 | 2020 |

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 | 2 | 2018 |

Entropic determinants of massive matrices D Granziol, S Roberts 2017 IEEE International Conference on Big Data (Big Data), 88-93, 2017 | 2 | 2017 |

A random matrix theory approach to damping in deep learning D Granziol, N Baskerville Journal of Physics: Complexity 3 (2), 024001, 2022 | 1 | 2022 |

Deep Curvature Suite D Granziol, X Wan, T Garipov arXiv preprint arXiv:1912.09656, 2019 | 1 | 2019 |

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 | 1 | 2019 |

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 | 1 | 2019 |