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000917558 1001_ $$0P:(DE-Juel1)179591$$aCramer, Eike$$b0$$ufzj
000917558 245__ $$aNonlinear Isometric Manifold Learning for Injective Normalizing Flows
000917558 260__ $$barXiv$$c2022
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000917558 520__ $$aTo model manifold data using normalizing flows, we propose to employ the isometric autoencoder to design nonlinear encodings with explicit inverses. The isometry allows us to separate manifold learning and density estimation and train both parts to high accuracy. Applied to the MNIST data set, the combined approach generates high-quality images.
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000917558 650_7 $$2Other$$aMachine Learning (cs.LG)
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000917558 7001_ $$0P:(DE-HGF)0$$aRauh, Felix$$b1
000917558 7001_ $$0P:(DE-Juel1)172025$$aMitsos, Alexander$$b2$$ufzj
000917558 7001_ $$0P:(DE-HGF)0$$aTempone, Raúl$$b3
000917558 7001_ $$0P:(DE-Juel1)172097$$aDahmen, Manuel$$b4$$eCorresponding author$$ufzj
000917558 773__ $$a10.48550/ARXIV.2203.03934
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