Set Transformer: A Framework for Attentionbased PermutationInvariant Neural Networks
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Proceedings of the 36th International Conference on Machine Learning, PMLR 97:37443753, 2019.
Abstract
Many machine learning tasks such as multiple instance learning, 3D shape recognition, and fewshot image classification are defined on sets of instances. Since solutions to such problems do not depend on the order of elements of the set, models used to address them should be permutation invariant. We present an attentionbased neural network module, the Set Transformer, specifically designed to model interactions among elements in the input set. The model consists of an encoder and a decoder, both of which rely on attention mechanisms. In an effort to reduce computational complexity, we introduce an attention scheme inspired by inducing point methods from sparse Gaussian process literature. It reduces the computation time of selfattention from quadratic to linear in the number of elements in the set. We show that our model is theoretically attractive and we evaluate it on a range of tasks, demonstrating the stateoftheart performance compared to recent methods for setstructured data.
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