74 citations · 111 across the 13 of their papers we have counts for
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cs.LG2023
Cluster Flow: how a hierarchical clustering layer make allows deep-NNs more resilient to hacking, more human-like and easily implements relational reasoning
Ella Gale, Oliver Matthews
Despite the huge recent breakthroughs in neural networks (NNs) for artificial intelligence (specifically deep convolutional networks) such NNs do not achieve human-level performanc…
cs.LG2023
Icospherical Chemical Objects (ICOs) allow for chemical data augmentation and maintain rotational, translation and permutation invariance
Ella Gale
Dataset augmentation is a common way to deal with small datasets; Chemistry datasets are often small. Spherical convolutional neural networks (SphNNs) and Icosahedral neural networ…
cs.LG2023
Shape is (almost) all!: Persistent homology features (PHFs) are an information rich input for efficient molecular machine learning
Ella Gale
3-D shape is important to chemistry, but how important? Machine learning works best when the inputs are simple and match the problem well. Chemistry datasets tend to be very small…