Abstract: A computer-implemented method for auto-splitting and classifying an input document into one or more sub-documents using a machine learning system is described. The machine learning system includes a visual splitting neural network, an optical character recognition sub-system, a title classifier, a document classifier, and a grouper sub-system. The method includes: receiving a visual input representing a plurality of pages of the input document; classifying, using the visual splitting neural network, each page of the input document into a respective template of a plurality of templates; for each page of the input document, determining a final document type that the page belongs to; and grouping, using a grouper sub-system, the plurality of pages of the input document into one or more sub-documents based on (i) the respective template of each page and (ii) the respective final document type that each page belongs to.

Abstract: A system including one or more computers and one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to implement a group-equivariant convolutional neural network configured to process a network input including a set of 3D points to generate an output tensor representing transformed features of the network input is described. The group-equivariant convolutional neural network includes a grouping layer, a switching layer, a pre-processing layer, a group-equivariant layer, a first subnetwork, an average pooling layer, a second subnetwork, an output subnetwork, and a final output layer.