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Deep generative models are universal tools for learning data distributions on high dimensional data spaces via a mapping to lower dimensional latent spaces.
Area cartograms: their use and creation
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Dan Jurafsky · 2000
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Diffusion-based method for producing density-equalizing maps
Michael T Gastner and M E J Newman · 2004
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Scikit-learn: Machine learning in Python
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2013
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Learning phrase representations using RNN encoder-decoder for statistical machine translation
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Latent space oddity: on the curvature of deep generative models
Georgios Arvanitidis, Lars Kai Hansen, and Søren Hauberg · 2017
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GLSR-VAE: geodesic latent space regularization for variational autoencoder architectures
Gaëtan Hadjeres, Frank Nielsen, and François Pachet · 2017
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An explanatory analysis of the geometry of latent variables learned by variational Auto-Encoders
Alexandra Peste, Luigi Malagò, and Septimia Sârbu · 2017
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Improved variational autoencoders for text modeling using dilated convolutions
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Metrics for deep generative models
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Wavenet: A generative model for raw audio
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The riemannian geometry of deep generative models
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Feature-based metrics for exploring the latent space of generative models
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Fast flow-based algorithm for creating density-equalizing map projections
Michael T. Gastner, Vivien Seguy, and Pratyush More · 2018
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