  Multiparameter persistent homology has been largely neglected as an input to
machine learning algorithms. We consider the use of lattice-based convolutional
neural network layers as a tool for the analysis of features arising from
multiparameter persistence modules. We find that these show promise as an
alternative to convolutions for the classification of multidimensional
persistence modules.
