Revisiting Small Batch Training for Deep Neural Networks. - Dominic Masters; Carlo Luschi (CoRR, 2018)

Proxy-Normalizing Activations to Match Batch Normalization while Removing Batch Dependence. - Antoine Labatie; Dominic Masters; Zach Eaton-Rosen; Carlo Luschi (NeurIPS, 2021)

Making EfficientNet More Efficient: Exploring Batch-Independent Normalization, Group Convolutions and Reduced Resolution Training. - Dominic Masters; Antoine Labatie; Zach Eaton-Rosen; Carlo Luschi (CoRR, 2021)

8-bit Numerical Formats for Deep Neural Networks. - Badreddine Noune; Philip Jones; Daniel Justus; Dominic Masters; Carlo Luschi (CoRR, 2022)

GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction. - Dominic Masters; Josef Dean; Kerstin Klaser 001; Zhiyi Li; Sam Maddrell-Mander; Adam Sanders; Hatem Helal; Deniz Beker; Ladislav Rampásek; Dominique Beaini (CoRR, 2022)

Generating QM1B with PySCFIPU. - Alexander Mathiasen; Hatem Helal; Kerstin Klaser 001; Paul Balanca; Josef Dean; Carlo Luschi; Dominique Beaini; Andrew W. Fitzgibbon; Dominic Masters (NeurIPS, 2023)

PopSparse: Accelerated block sparse matrix multiplication on IPU. - Zhiyi Li; Douglas Orr; Valeriu Ohan; Godfrey Da Costa; Tom Murray; Adam Sanders; Deniz Beker; Dominic Masters (CoRR, 2023)

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets. - Dominique Beaini; Shenyang Huang; Joao Alex Cunha; Zhiyi Li; Gabriela Moisescu-Pareja; Oleksandr Dymov; Samuel Maddrell-Mander; Callum McLean; Frederik Wenkel; Luis Müller; Jama Hussein Mohamud; Ali Parviz; et al. (CoRR, 2023)

GPS++: Reviving the Art of Message Passing for Molecular Property Prediction. - Dominic Masters; Josef Dean; Kerstin Kläser 001; Zhiyi Li; Samuel Maddrell-Mander; Adam Sanders; Hatem Helal; Deniz Beker; Andrew W. Fitzgibbon; Shenyang Huang; Ladislav Rampásek; Dominique Beaini (Trans. Mach. Learn. Res., 2023)

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets. - Dominique Beaini; Shenyang Huang; Joao Alex Cunha; Zhiyi Li; Gabriela Moisescu-Pareja; Oleksandr Dymov; Samuel Maddrell-Mander; Callum McLean; Frederik Wenkel; Luis Müller; Jama Hussein Mohamud; Ali Parviz; et al. (ICLR, 2024)

Probabilistic weather forecasting with machine learning. - Ilan Price; Alvaro Sanchez-Gonzalez; Ferran Alet; Tom R. Andersson; Andrew El-Kadi; Dominic Masters; Timo Ewalds; Jacklynn Stott; Shakir Mohamed; Peter W. Battaglia; Rémi R. Lam; Matthew Willson (Nat., 2025)

Skillful joint probabilistic weather forecasting from marginals. - Ferran Alet; Ilan Price; Andrew El-Kadi; Dominic Masters; Stratis Markou; Tom R. Andersson; Jacklynn Stott; Rémi Lam; Matthew Willson; Alvaro Sanchez-Gonzalez; Peter W. Battaglia (CoRR, 2025)