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Probabilistic clustering models (or equivalently, mixture models) are basic building blocks in countless statistical models and involve latent random variables over discrete spaces.
Funzione caratteristica di un fenomeno aleatorio
de Finetti, B · 1931
Earlier work this paper cites.
Symmetric measures on cartesian products
Hewitt, E. and Savage, L. J · 1955
Earlier work this paper cites.
Finite forms of de Finetti’s theorem on exchangeability
Diaconis, P · 1977
Earlier work this paper cites.
Finite exchangeable sequences
Diaconis, P. and Freedman, D · 1980
Earlier work this paper cites.
Mixture models: Inference and applications to clustering , volume 84
McLachlan, G. J. and Basford, K. E · 1988
Earlier work this paper cites.
The” wake-sleep” algorithm for unsupervised neural networks
Hinton, G. E., Dayan, P., Frey, B. J., and Neal, R. M · 1995
Earlier work this paper cites.
Markov chain sampling methods for Dirichlet process mixture models
Neal, R. M · 2000
Earlier work this paper cites.
Expectation propagation for approximate Bayesian inference
Minka, T. P · 2001
Earlier work this paper cites.
Functional asymmetries in on and off ganglion cells of primate retina
Chichilnisky, E. J. and Kalmar, R. S · 2002
Earlier work this paper cites.
Variational Methods for the Dirichlet Process
Blei, D. M. and Jordan, M. I · 2004
Earlier work this paper cites.
Getting it right: Joint distribution tests of posterior simulators
Geweke, J · 2004
Earlier work this paper cites.
A split-merge Markov chain Monte Carlo procedure for the Dirichlet process mixture model
Jain, S. and Neal, R. M · 2004
Earlier work this paper cites.
Collapsed Variational Dirichlet Process Mixture Models
Kurihara, K., Welling, M., and Teh, Y. W · 2007
Earlier work this paper cites.
A nonparametric bayesian alternative to spike sorting
Wood, F. and Black, M. J · 2008
Earlier work this paper cites.
Finite de Finetti theorem for conditional probability distributions describing physical theories
Christandl, M. and Toner, B · 2009
Earlier work this paper cites.
Clustering: A neural network approach
Du, K.-L · 2010
Earlier work this paper cites.
Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance
Vinh, N. X., Epps, J., and Bailey, J · 2010
Earlier work this paper cites.
Kalman filter mixture model for spike sorting of non-stationary data
Calabrese, A. and Paninski, L · 2011
Earlier work this paper cites.
Sequence transduction with recurrent neural networks
Graves, A · 2012
Earlier work this paper cites.
Multichannel electrophysiological spike sorting via joint dictionary learning and mixture modeling
Carlson, D. E., Vogelstein, J. T., Wu, Q., Lian, W., Zhou, M., Stoetzner, C. R., Kipke, D., Weber, D., Dunson, D. B., and Carin, L · 2013
Earlier work this paper cites.
Memoized online variational inference for dirichlet process mixture models
Hughes, M. C. and Sudderth, E · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
NONPARAMETRIC BAYESIAN INFERENCE
Rodriguez, A. and Mueller, P · 2013
Earlier work this paper cites.
Learning stochastic inverses
Stuhlmüller, A., Taylor, J., and Goodman, N · 2013
Cited alongside, same era.
Amortized inference in probabilistic reasoning
Gershman, S. and Goodman, N · 2014
Cited alongside, same era.
Sequence to sequence learning with neural networks
Sutskever, I., Vinyals, O., and Le, Q. V · 2014
Cited alongside, same era.
Importance weighted autoencoders
Burda, Y., Grosse, R., and Salakhutdinov, R · 2015
Cited alongside, same era.
Reliable and scalable variational inference for the hierarchical Dirichlet process
Hughes, M., Kim, D. I., and Sudderth, E · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
Cited alongside, same era.
Model-based spike sorting with a mixture of drifting t-distributions
Shan, K. Q., Lubenov, E. V., and Siapas, A. G · 2017
Later among the works it cites.
Deep sets
Zaheer, M., Kottur, S., Ravanbakhsh, S., Póczos, B., Salakhutdinov, R., and Smola, A. J · 2017
Later among the works it cites.
Clustering with Deep Learning: Taxonomy and New Methods
Aljalbout, E., Golkov, V., Siddiqui, Y., and Cremers, D · 2018
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A novel and fully automatic spike-sorting implementation with variable number of features
Chaure, F. J., Rey, H. G., and Quian Quiroga, R · 2018
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Neural processes
Garnelo, M., Schwarz, J., Rosenbaum, D., Viola, F., Rezende, D. J., Eslami, S., and Teh, Y. W · 2018
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Bruno: A deep recurrent model for exchangeable data
Korshunova, I., Degrave, J., Huszar, F., Gal, Y., Gretton, A., and Dambre, J · 2018
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