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Normalizing flows are among the most popular paradigms in generative modeling, especially for images, primarily because we can efficiently evaluate the likelihood of a data point.
A comparison of signalling alphabets
Edgar N Gilbert · 1952
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Estimate of the number of signals in error correcting codes
Rom Rubenovich Varshamov · 1957
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Calyampudi Radhakrishna Rao, Calyampudi Radhakrishna Rao, Mathematischer Statistiker, Calyampudi Radhakrishna Rao, and Calyampudi Radhakrishna Rao · 1973
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The total variation distance between high-dimensional gaussians
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Glow: Generative flow with invertible 1x1 convolutions
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High-dimensional probability: An introduction with applications in data science , volume 47
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George Papamakarios, Theo Pavlakou, and Iain Murray · 2017
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Jens Behrmann, Will Grathwohl, Ricky TQ Chen, David Duvenaud, and Jörn-Henrik Jacobsen
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On the invertibility of invertible neural networks
Jens Behrmann, Paul Vicol, Kuan-Chieh Wang, Roger B Grosse, and Jörn-Henrik Jacobsen
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Emilien Dupont, Arnaud Doucet, and Yee Whye Teh · 2019
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Augmented normalizing flows: Bridging the gap between generative flows and latent variable models
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