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Researchers at NeurIPS have developed a neural network architecture for set feature vectors as input, with set vector representations as output. This Deep Set architecture has both the permutation-invariant and permutation-equivariant characteristics, making it applicable to datasets with data types of sets. The input data does not need to have an arbitrary ordering.