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Gaussian Mixture Models (GMMs) are one of the most potent parametric density models used extensively in many applications.
Maximum likelihood from incomplete data via the EM algorithm
Arthur P Dempster, Nan M Laird, and Donald B Rubin · 1977
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Mixture densities, maximum likelihood and the EM algorithm
Richard A Redner and Homer F Walker · 1984
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Hierarchical mixtures of experts and the EM algorithm
Michael I Jordan and Robert A Jacobs · 1994
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A database for handwritten text recognition research
Jonathan J. Hull · 1994
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Gaussian parsimonious clustering models
Gilles Celeux and Gérard Govaert · 1995
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A view of the EM algorithm that justifies incremental, sparse, and other variants
Radford M Neal and Geoffrey E Hinton · 1998
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Natural gradient works efficiently in learning
Shun-Ichi Amari · 1998
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Semi-tied covariance matrices for hidden Markov models
Mark JF Gales · 1999
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On formulating semidefinite programming problems as smooth convex nonlinear optimization problems
Robert J Vanderbei and Hande Yurttan Benson · 2000
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Optimization with EM and expectation-conjugate-gradient
Ruslan Salakhutdinov, Sam T Roweis, and Zoubin Ghahramani · 2003
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Methods for multidimensional event classification: a case study using images from a Cherenkov Gamma-ray telescope
Rudolf K Bock, Markus Chilingarian, Ashot Agassiand Gaug, Frantisek Hakl, Thomas Hengstebeck, Marcel Jiřina, Jan Klaschka, Emil Kotrč, Petr Savickỳ, Sherry Towers, Anthony Vaiciulis, and Wittek Wolfgang · 2004
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Using unsupervised learning of a finite Dirichlet mixture model to improve pattern recognition applications
Nizar Bouguila and Djemel Ziou · 2005
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Model-based cluster and discriminant analysis with the MIXMOD software
Christophe Biernacki, Gilles Celeux, Gérard Govaert, and Florent Langrognet · 2006
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The EM algorithm and extensions
Geoffrey J McLachlan and Thriyambakam Krishnan · 2007
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CETO, a carbon free wave power energy provider of the future
Laurence D Mann, Alan R Burns, and Michael E Ottaviano · 2007
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A stochastic version of expectation maximization algorithm for better estimation of hidden Markov model
Shamsul Huda, John Yearwood, and Roberto Togneri · 2009
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Optimization algorithms on matrix manifolds
Pierre-Antoine Absil, Robert Mahony, and Rodolphe Sepulchre · 2009
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Learning stable nonlinear dynamical systems with Gaussian mixture models
Seyed Mohammad Khansari-Zadeh and Aude Billard · 2011
Local maxima in the likelihood of Gaussian mixture models: Structural results and algorithmic consequences
Chi Jin, Yuchen Zhang, Sivaraman Balakrishnan, Martin J Wainwright, and Michael I Jordan · 2016
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Sliced Wasserstein distance for learning Gaussian mixture models
Soheil Kolouri, Gustavo K Rohde, and Heiko Hoffmann · 2018
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On GANs and GMMs
Eitan Richardson and Yair Weiss · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
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Finite mixture models
Geoffrey J McLachlan, Sharon X Lee, and Suren I Rathnayake · 2019
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Training faster by separating modes of variation in batch-normalized models
Mahdi M Kalayeh and Mubarak Shah · 2019
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
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Stochastic gradient descent on Riemannian manifolds
Silvere Bonnabel · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Matrix manifold optimization for Gaussian mixtures
Reshad Hosseini and Suvrit Sra · 2015
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Estimation of Gaussian mixture models via tensor moments with application to online learning
Donya Rahmani, Mahesan Niranjan, Damien Fay, Akiko Takeda, and Jacek Brodzki · 2020
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An alternative to EM for Gaussian mixture models: batch and stochastic Riemannian optimization
Reshad Hosseini and Suvrit Sra · 2020
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Why are adaptive methods good for attention models?
Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank Reddi, Sanjiv Kumar, and Suvrit Sra · 2020
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Efficient Riemannian optimization on the Stiefel manifold via the Cayley transform
Jun Li, Li Fuxin, and Sinisa Todorovic · 2020
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A new EM algorithm for flexibly tied GMMs with large number of components
Hadi Asheri, Reshad Hosseini, and Babak Nadjar Araabi · 2021
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