2 citations · 5 across the 3 of their papers we have counts for
3 papers
Refining embeddings with fill-tuning: data-efficient generalised performance improvements for materials foundation models
Matthew P. Wilson, Edward O. Pyzer-Knapp, Nicolas Galichet +1
Pretrained foundation models learn embeddings that can be used for a wide range of downstream tasks. These embeddings optimise general performance, and if insufficiently accurate a…
Physics Inspired Approaches To Understanding Gaussian Processes
Maximilian P. Niroomand, Luke Dicks, Edward O. Pyzer-Knapp +1
Prior beliefs about the latent function to shape inductive biases can be incorporated into a Gaussian Process (GP) via the kernel. However, beyond kernel choices, the decision-maki…
Archetypal solution spaces for clustering gene expression datasets in identification of cancer subtypes
Yuchen Wu, Luke Dicks, David J. Wales
Gene expression profiles are essential in identifying different cancer phenotypes. Clustering gene expression datasets can provide accurate identification of cancerous cell lines,…