Abstract:
A processor-implemented method of pre-processing for deep neural network compilation comprising receiving a representation of an artificial neural network (ANN) model. The ANN includes multiple nodes coupled by edges. Position information is determined for each node of the ANN. An operator embedding is generated to represent operators of the ANN model in an embedding space based on the position information. A graph neural network (GNN) processes the operator embedding to generate a graph embedding corresponding to the ANN model according to a learned distance metric and based on the position information. The GNN determines a set of hyperparameters for the ANN model based on the graph embedding.
Abstract:
Techniques are described for combining two or more images that are taken with varying brightness degrees to generate a composite image. In one embodiment, at least two weight masks are generated based on one or more characteristics of the two or more images. A first image A first weight mask is used to combine color channels of one or more pixels of the two or more images and a second weight mask is used to combine intensity channels of the one or more pixels of the two or more images.