PERMUTATION-INVARIANT OPTIMIZATION METRICS FOR NEURAL NETWORKS

摘要

Permutation-invariant neural networks are trained by calculating a pairwise distance between each of a plurality of elements of a first data and each of a plurality of elements of a second data, normalizing each pairwise distance with a normalizing function to obtain a normalized value corresponding to each pairwise distance, de-normalizing a summation of the normalized values of all pairwise distances between a single element of the second data and each element of the first data with a de-normalizing function to obtain a first value, for each element of the second data, estimating a summation of the first values for all elements of the second data, and training a neural network by using at least the summation of the first values for an optimization metric.