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Universality of neural networks describes the ability to approximate arbitrary function, and is a key ingredient to keep the method effective.
Sur les ensembles de fonctions et les opérations linéaires
Maurice Fréchet · 1904
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Über lineare gleichungssysteme in linearen räumen
Hans Hahn · 1927
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Algorithm 630: Bbvscg–a variable-storage algorithm for function minimization
Albert Buckley and A LeNir · 1985
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Approximation by superpositions of a sigmoidal function
G. Cybenko · 1989
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Approximation capabilities of multilayer feedforward networks
Kurt Hornik · 1991
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An artificial neuron with quantum mechanical properties
Dan Ventura and Tony Martinez · 1998
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Pattern recognition and machine learning
Christopher M Bishop · 2006
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Large-scale machine learning with stochastic gradient descent
Léon Bottou · 2010
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Mnist handwritten digit database
Yann LeCun and Corinna Cortes · 2010
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Extensions of recurrent neural network language model
T. Mikolov, S. Kombrink, L. Burget, J. Černocký, and S. Khudanpur · 2011
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Quantum-state preparation with universal gate decompositions
Martin Plesch and Časlav Brukner · 2011
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Quantum algorithm for data fitting
Nathan Wiebe, Daniel Braun, and Seth Lloyd · 2012
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Quantum algorithms for supervised and unsupervised machine learning
Seth Lloyd, Masoud Mohseni, and Patrick Rebentrost · 2013
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A convolutional neural network for modelling sentences
Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom · 2014
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Quantum support vector machine for big data classification
Patrick Rebentrost, Masoud Mohseni, and Seth Lloyd · 2014
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Quantum principal component analysis
Seth Lloyd, Masoud Mohseni, and Patrick Rebentrost · 2014
Quantum neuron: an elementary building block for machine learning on quantum computers
Yudong Cao, Gian Giacomo Guerreschi, and Alán Aspuru-Guzik · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Quantum boltzmann machine
Mohammad H. Amin, Evgeny Andriyash, Jason Rolfe, Bohdan Kulchytskyy, and Roger Melko · 2018
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Quantum circuit learning
Kosuke Mitarai, Makoto Negoro, Masahiro Kitagawa, and Keisuke Fujii · 2018
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Continuous-variable quantum neural networks
Nathan Killoran, Thomas R Bromley, Juan Miguel Arrazola, Maria Schuld, Nicolás Quesada, and Seth Lloyd · 2019
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2019
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End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al · 2016
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Deep learning , volume 1
Goodfellow Ian, Bengio Yoshua, and Courville Aaron · 2016
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Quantum machine learning
Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd · 2017
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Quantum convolutional neural networks
Iris Cong, Soonwon Choi, and Mikhail D Lukin · 2019
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Circuit-centric quantum classifiers
Maria Schuld, Alex Bocharov, Krysta M Svore, and Nathan Wiebe · 2020
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Power of data in quantum machine learning
Hsin-Yuan Huang, Michael Broughton, Masoud Mohseni, Ryan Babbush, Sergio Boixo, Hartmut Neven, and Jarrod R. McClean · 2041
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