BESS: Balanced Entity Sampling and Sharing for Large-Scale Knowledge Graph Completion. - Alberto Cattaneo; Daniel Justus; Harry Mellor; Douglas Orr; Jérôme Maloberti; Zhenying Liu; Thorin Farnsworth; Andrew W. Fitzgibbon; Blazej Banaszewski; Carlo Luschi (CoRR, 2022)

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)

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)

Reducing the Cost of Quantum Chemical Data By Backpropagating Through Density Functional Theory. - Alexander Mathiasen; Hatem Helal; Paul Balanca; Adam Krzywaniak; Ali Parviz; Frederik Hvilshøj; Blazej Banaszewski; Carlo Luschi; Andrew William Fitzgibbon (CoRR, 2024)

MiniMol: A Parameter-Efficient Foundation Model for Molecular Learning. - Kerstin Kläser 001; Blazej Banaszewski; Samuel Maddrell-Mander; Callum McLean; Luis Müller; Ali Parviz; Shenyang Huang; Andrew W. Fitzgibbon (CoRR, 2024)