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A wide range of fundamental machine learning tasks that are addressed by the maximum a posteriori estimation can be reduced to a general minimum conical hull problem.
Maximum likelihood from incomplete data via the EM algorithm
A. P. Dempster, N. M. Laird, and D. B. Rubin · 1977
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Maximum likelihood from incomplete data via the em algorithm
Arthur P Dempster, Nan M Laird, and Donald B Rubin · 1977
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Stochastic relaxation, gibbs distributions, and the bayesian restoration of images
Stuart Geman and Donald Geman · 1984
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Stochastic relaxation, gibbs distributions, and the bayesian restoration of images
Stuart Geman and Donald Geman · 1987
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Latent variable models: An introduction to factor, path, and structural analysis
John C Loehlin · 1987
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Finding maps for belief networks is np-hard
Solomon Eyal Shimony · 1994
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Weak convergence
Aad W Van Der Vaart and Jon A Wellner · 1996
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Learning the parts of objects by non-negative matrix factorization
Daniel D Lee and H Sebastian Seung · 1999
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When does non-negative matrix factorization give a correct decomposition into parts?
David Donoho and Victoria Stodden · 2004
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Gaussian process latent variable models for visualisation of high dimensional data
Neil D Lawrence · 2004
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Pattern recognition and machine learning
Christopher M Bishop · 2006
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Quantum clustering algorithms
Esma Aïmeur, Gilles Brassard, and Sébastien Gambs · 2007
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Probabilistic matrix factorization
Ruslan Salakhutdinov and Andriy Mnih · 2007
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Probabilistic matrix factorization
Andriy Mnih and Ruslan R Salakhutdinov · 2008
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Quantum algorithm for linear systems of equations
Aram W Harrow, Avinatan Hassidim, and Seth Lloyd · 2009
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Bayesian non-negative matrix factorization
Mikkel N Schmidt, Ole Winther, and Lars Kai Hansen · 2009
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Polynomial learning of distribution families
Mikhail Belkin and Kaushik Sinha · 2010
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Bayesian artificial intelligence
Kevin B Korb and Ann E Nicholson · 2010
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Matrix analysis
Roger A Horn and Charles R Johnson · 2012
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Fast conical hull algorithms for near-separable non-negative matrix factorization
Abhishek Kumar, Vikas Sindhwani, and Prabhanjan Kambadur · 2013
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Quantum perceptron models
Ashish Kapoor, Nathan Wiebe, and Krysta Svore · 2016
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Scalable completion of nonnegative matrix with separable structure
Xiyu Yu, Wei Bian, and Dacheng Tao · 2016
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Randomized algorithms in numerical linear algebra
Ravindran Kannan and Santosh Vempala · 2017
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Quantum recommendation systems
Iordanis Kerenidis and Anupam Prakash · 2017
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Fast quantum algorithms for least squares regression and statistic leverage scores
Yang Liu and Shengyu Zhang · 2017
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Quantum-inspired sublinear classical algorithms for solving low-rank linear systems
Nai-Hui Chia, Han-Hsuan Lin, and Chunhao Wang · 2018
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Seth Lloyd, Masoud Mohseni, and Patrick Rebentrost · 2013
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Data mining with big data
Xindong Wu, Xingquan Zhu, Gong-Qing Wu, and Wei Ding · 2013
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Divide-and-conquer anchoring for near-separable nonnegative matrix factorization and completion in high dimensions
Tianyi Zhou, Wei Bian, and Dacheng Tao · 2013
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Quantum principal component analysis
Seth Lloyd, Masoud Mohseni, and Patrick Rebentrost · 2014
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Quantum algorithms for nearest-neighbor methods for supervised and unsupervised learning
Nathan Wiebe, Ashish Kapoor, and Krysta Svore · 2014
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Divide-and-conquer learning by anchoring a conical hull
Tianyi Zhou, Jeff A Bilmes, and Carlos Guestrin · 2014
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Quantum divide-and-conquer anchoring for separable non-negative matrix factorization
Yuxuan Du, Tongliang Liu, Yinan Li, Runyao Duan, and Dacheng Tao · 2018
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Quantum-inspired low-rank stochastic regression with logarithmic dependence on the dimension
András Gilyén, Seth Lloyd, and Ewin Tang · 2018
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A quantum-inspired classical algorithm for recommendation systems
Ewin Tang · 2018
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Quantum-inspired classical algorithms for principal component analysis and supervised clustering
Ewin Tang · 2018
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Quantum-inspired algorithms in practice
Juan Miguel Arrazola, Alain Delgado, Bhaskar Roy Bardhan, and Seth Lloyd · 2019
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Nai-Hui Chia, Tongyang Li, Han-Hsuan Lin, and Chunhao Wang · 2019
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Quantum-inspired support vector machine
Chen Ding, Tian-Yi Bao, and He-Liang Huang · 2019
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