Print Email Facebook Twitter Multiset-equivariant set prediction with approximate implicit differentiation Title Multiset-equivariant set prediction with approximate implicit differentiation Author Zhang, Y. Zhang, D.W. Lacoste-Julien, S. Burghouts, G.J. Snoek, C.G.M. Publication year 2022 Abstract Most set prediction models in deep learning use set-equivariant operations, but they actually operate on multisets. We show that set-equivariant functions cannot represent certain functions on multisets, so we introduce the more appropriate notion of multiset-equivariance. We identify that the existing Deep Set Prediction Network (DSPN) can be multiset-equivariant without being hindered by set-equivariance and improve it with approximate implicit differentiation, allowing for better optimization while being faster and saving memory. In a range of toy experiments, we show that the perspective of multiset-equivariance is beneficial and that our changes to DSPN achieve better results in most cases. On CLEVR object property prediction, we substantially improve over the state-of-the-art Slot Attention from 8% to 77% in one of the strictest evaluation metrics because of the benefits made possible by implicit differentiation. (C) 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved. Subject Deep learningToysEquivarianceEvaluation metricsMulti-setsObject propertyOptimisationsPrediction modellingProperty predictionsState of the artForecasting To reference this document use: http://resolver.tudelft.nl/uuid:ee3867c1-2efc-4656-8b2e-78d53922748b TNO identifier 985095 Publisher International Conference on Learning Representations, ICLR Source ICLR 2022 - 10th International Conference on Learning Representations Document type conference paper Files To receive the publication files, please send an e-mail request to TNO Library.