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Hyperbolic space is a geometry that is known to be well-suited for representation learning of data with an underlying hierarchical structure.
WordNet: An electronic lexical database
Miller, G · 1998
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A kernel two-sample test
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A · 2012
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Low distortion delaunay embedding of trees in hyperbolic plane
Sarkar, R · 2012
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Distributed representations of words and phrases and their compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J · 2013
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
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Stochastic backpropagation and approximate inference in deep generative models
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Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al · 2015
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Variational inference with normalizing flows
Rezende, D. J. and Mohamed, S · 2015
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Word representations via gaussian embedding
Vilnis, L. and McCallum, A · 2015
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Importance weighted autoencoders
Burda, Y., Grosse, R. B., and Salakhutdinov, R · 2016
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Improved variational inference with inverse autoregressive flow
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2016
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β \beta -vae: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2017
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Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B · 2017
Hyperspherical variational auto-encoders
Davidson, T. R., Falorsi, L., De Cao, N., Kipf, T., and Tomczak, J. M · 2018
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Adversarial autoencoders with constant-curvature latent manifolds
Grattarola, D., Livi, L., and Alippi, C · 2018
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Learning continuous hierarchies in the lorentz model of hyperbolic geometry
Nickel, M. and Kiela, D · 2018
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Representation tradeoffs for hyperbolic embeddings
Sala, F., De Sa, C., Gu, A., and Re, C · 2018
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The loracs prior for vaes: Letting the trees speak for the data
Vikram, S., Hoffman, M. D., and Johnson, M. J · 2018
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Hyperbolic attention networks
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Poincaré embeddings for learning hierarchical representations
Nickel, M. and Kiela, D · 2017
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Discrete variational autoencoders
Rolfe, J. T · 2017
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Hyperbolic entailment cones for learning hierarchical embeddings
Ganea, O., Bécigneul, G., and Hofmann, T
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In Advances in Neural Information Processing Systems 31 , pp. 5350–5360, 2018b
Ganea, O., Bécigneul, G., and Hofmann, T
Cited in the paper.
Gülçehre, Ç., Denil, M., Malinowski, M., Razavi, A., Pascanu, R., Hermann, K. M., Battaglia, P., Bapst, V., Raposo, D., Santoro, A., and de Freitas, N · 2019
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Poincaré wasserstein autoencoder
Ovinnikov, I · 2019
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