2 citations · 2 across the 2 of their papers we have counts for
3 papers
MXNorm: Reusing MXFP block scales for efficient tensor normalisation
Callum McLean, Luke Y. Prince, Alexandre Payot +2
Matrix multiplication performance has long been the major bottleneck to scaling deep learning workloads, which has stimulated the design of new accelerators that use increasingly l…
: A Parameter-Efficient Foundation Model for Molecular Learning
Kerstin Kläser, Błażej Banaszewski, Samuel Maddrell-Mander +5
In biological tasks, data is rarely plentiful as it is generated from hard-to-gather measurements. Therefore, pre-training foundation models on large quantities of available data a…
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Dominique Beaini, Shenyang Huang, Joao Alex Cunha +32
Recently, pre-trained foundation models have enabled significant advancements in multiple fields. In molecular machine learning, however, where datasets are often hand-curated, and…