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The variational auto-encoder (VAE) is a popular method for learning a generative model and embeddings of the data.
A note on the generation of random normal deviates
Box, G. E. P. and Muller, M. E. (1958) · 1958
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Retrieval time from semantic memory
Collins, A. and Quillian, M. (1969) · 1969
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Mardia, K. V. (1975) · 1975
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Semantic and Conceptual Development: An Ontological Perspective
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Adaptive rejection sampling for gibbs sampling
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Pattern Classification (2Nd Edition)
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Learning annotated hierarchies from relational data
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The human disease network
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Bayesian agglomerative clustering with coalescents
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A gyrovector space approach to hyperbolic geometry
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Automatic differentiation in pytorch
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Explorations in Homeomorphic Variational Auto-Encoding
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Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry
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Class2str: End to end latent hierarchy learning
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Representation tradeoffs for hyperbolic embeddings
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Wasserstein auto-encoders
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Adversarial autoencoders with constant-curvature latent manifolds
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