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We present an algorithm for learning a latent variable generative model via generative adversarial learning where the canonical uniform noise input is replaced by samples from a graphical model.
D. H. Ackley, G. E. Hinton, and T. J. Sejnowski, “A learning algorithm for Boltzmann machines,” in Readings in Computer Vision
1987
Earlier work this paper cites.
M. A. Carreira-Perpinan and G. E. Hinton, “On contrastive divergence learning.,” in Aistats
2005
Earlier work this paper cites.
D. Koller, N. Friedman, L. Getoor, and B. Taskar, “Graphical models in a nutshell,” Introduction to statistical relational learning
2007
Earlier work this paper cites.
N. Le Roux and Y. Bengio, “Representational power of restricted Boltzmann machines and deep belief networks,” Neural computation
2008
Earlier work this paper cites.
V. Choi, “Minor-embedding in adiabatic quantum computation: Ii. minor-universal graph design,” Quantum Information Processing
2011
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems
2014
Earlier work this paper cites.
L. Deng, D. Yu, et al
2014
Earlier work this paper cites.
T. F. Rønnow, Z. Wang, J. Job, S. Boixo, S. V. Isakov, D. Wecker, J. M. Martinis, D. A. Lidar, and M. Troyer, “Defining and detecting quantum speedup,” Science
2014
Earlier work this paper cites.
H. G. Katzgraber, F. Hamze, and R. S. Andrist, “Glassy chimeras could be blind to quantum speedup: Designing better benchmarks for quantum annealing machines,” Physical Review X
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
M. H. Amin, “Searching for quantum speedup in quasistatic quantum annealers,” Physical Review A
2015
Earlier work this paper cites.
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al
2016
Earlier work this paper cites.
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, et al
2016
Earlier work this paper cites.
2016
Cited alongside, same era.
T. Arici and A. Celikyilmaz, “Associative adversarial networks,” arXiv preprint arXiv:1611.06953
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, “Improved training of wasserstein gans,” in Advances in Neural Information Processing Systems
2017
Later among the works it cites.
M. Benedetti, J. Realpe-Gómez, R. Biswas, and A. Perdomo-Ortiz, “Quantum-assisted learning of hardware-embedded probabilistic graphical models,” Physical Review X
2017
Later among the works it cites.
J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, “Quantum machine learning,” Nature
2017
Later among the works it cites.
K. Wang, C. Gou, Y. Duan, Y. Lin, X. Zheng, and F.-Y. Wang, “Generative adversarial networks: introduction and outlook,” IEEE/CAA Journal of Automatica Sinica
2017
Later among the works it cites.
R. Biswas, Z. Jiang, K. Kechezhi, S. Knysh, S. Mandra, B. O’Gorman, A. Perdomo-Ortiz, A. Petukhov, J. Realpe-Gómez, E. Rieffel, et al
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2016
Cited alongside, same era.
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, “Improved techniques for training gans,” in Advances in Neural Information Processing Systems
2016
Cited alongside, same era.
M. Benedetti, J. Realpe-Gómez, R. Biswas, and A. Perdomo-Ortiz, “Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning,” Physical Review A
2016
Cited alongside, same era.
J. Raymond, S. Yarkoni, and E. Andriyash, “Global warming: Temperature estimation in annealers,” Frontiers in ICT
2016
Cited alongside, same era.
C. Doersch, “Tutorial on variational autoencoders,” arXiv preprint arXiv:1606.05908
2016
Cited alongside, same era.
MIT Press, 2016
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning · 2016
Cited alongside, same era.
T. R. Society, “Machine learning: the power and promise of computers that learn by example,” The Royal Society
2017
Cited alongside, same era.
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” arXiv preprint
2017
Cited alongside, same era.
2017
Later among the works it cites.
2017
Later among the works it cites.
C. Ciliberto, M. Herbster, A. D. Ialongo, M. Pontil, A. Rocchetto, S. Severini, and L. Wossnig, “Quantum machine learning: a classical perspective,” Proc. R. Soc. A
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
M. Benedetti, J. R. Gómez, and A. Perdomo-Ortiz, “Quantum-assisted helmholtz machines: A quantum-classical deep learning framework for industrial datasets in near-term devices,” Quantum Science and Technology
2018
Later among the works it cites.