In this paper, we provide a systematic investigation of SO(2) theory to machine learning interatomic potentials (MLIPs) and identify the limitations of conventional SO(2) Linear architectures relative to SO(3) Clebsch-Gordan Tensor Products (CGTP). Building on these insights, we…
The rapid advancement of traditional machine learning has opened up new avenues within the medical field. For the automated screening of paediatric pneumonia via chest radiographs, this paper proposes a ‘deep features + heterogeneous ensemble’ framework. Utilising Kaggle’s datase…