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Deep neural networks with discrete latent variables offer the promise of better symbolic reasoning, and learning abstractions that are more useful to new tasks.
Some methods for classification and analysis of multivariate observations
James MacQueen et al · 1967
Earlier work this paper cites.
Maximum likelihood from incomplete data via the em algorithm
Arthur P Dempster, Nan M Laird, and Donald B Rubin · 1977
Earlier work this paper cites.
A monte carlo implementation of the em algorithm and the poor man’s data augmentation algorithms
Greg CG Wei and Martin A Tanner · 1990
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
Earlier work this paper cites.
Convergence properties of the k-means algorithms
Leon Bottou and Yoshua Bengio · 1995
Earlier work this paper cites.
On-line em algorithm for the normalized gaussian network
Masa-Aki Sato and Shin Ishii · 2000
Earlier work this paper cites.
Online em for unsupervised models
Percy Liang and Dan Klein · 2009
Earlier work this paper cites.
Web-scale k-means clustering
David Sculley · 2010
Earlier work this paper cites.
Cartesian k-means
Mohammad Norouzi and David J Fleet · 2013
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
Earlier work this paper cites.
Learning phrase representations using RNN encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
Earlier work this paper cites.
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Earlier work this paper cites.
Adam: A method for stochastic optimization
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Regularizing neural networks by penalizing confident output distributions
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Attention is all you need
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