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The Restricted Boltzmann Machine (RBM) is a stochastic neural network capable of solving a variety of difficult tasks such as NP-Hard combinatorial optimization problems and integer factorization.
The complexity of theorem-proving procedures
Cook, S. A. & A., S · 1971
Earlier work this paper cites.
Reducibility among Combinatorial Problems
Karp, R. M · 1972
Earlier work this paper cites.
On the computational complexity of ising spin glass models
Barahona, F · 1982
Earlier work this paper cites.
Optimization by simulated annealing
Kirkpatrick, S., Gelatt, C. D. & Vecchi, M. P · 1983
Earlier work this paper cites.
A learning algorithm for boltzmann machines
Ackley, D. H., Hinton, G. E. & Sejnowski, T. J · 1985
Earlier work this paper cites.
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Geman, S. & Geman, D · 1987
Earlier work this paper cites.
Combinatorial optimization on a Boltzmann machine
Korst, J. H. & Aarts, E. H · 1989
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Analog CMOS Deterministic Boltzmann Circuits
Schneider, C. R. & Card, H. C · 1993
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Gibbs Fields and Monte Carlo Simulation
Brémaud, P · 1999
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Training products of experts by minimizing contrastive divergence
Hinton, G. E · 2002
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Stochastic Local Search (Elsevier, 2005)
Hoos, H. H. & Stützle, T · 2005
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On contrastive divergence learning
Carreira-Perpiñán, M. Ã. & Hinton, G. E · 2005
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Training restricted boltzmann machines using approximations to the likelihood gradient
Tieleman, T · 2008
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Neural Networks on GPUs: Restricted Boltzmann Machines
Ly, D., Paprotski, V. & Yen, D · 2008
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Using fast weights to improve persistent contrastive divergence
Tieleman, T. & Hinton, G · 2009
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A high-performance FPGA architecture for Restricted Boltzmann Machines
Ly, D. L. & Chow, P · 2009
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A highly scalable restricted Boltzmann machine FPGA implementation
Kim, S. K., McAfee, L. C., McMahon, P. L. & Olukotun, K · 2009
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A multi-FPGA architecture for stochastic Restricted Boltzmann Machines
Ly, D. L. & Chow, P · 2009
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Janus: An FPGA-based system for high-performance scientific computing
Belletti, F. et al · 2009
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Deep Boltzmann machines
Salakhutdinov, R. & Hinton, G · 2009
Cited alongside, same era.
A large-scale architecture for restricted Boltzmann machines
Kim, S. K., McMahon, P. L. & Olukotun, K · 2010
Cited alongside, same era.
An FPGA implementation of a Restricted Boltzmann Machine classifier using stochastic bit streams
Li, B., Najafi, M. H. & Lilja, D. J · 2015
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More Than Moore
Waldrop, M. M · 2016
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Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding
Han, S., Mao, H. & Dally, W. J · 2016
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A 20k-spin Ising chip to solve combinatorial optimization problems with CMOS annealing
Yamaoka, M. et al · 2016
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Memristive boltzmann machine: A hardware accelerator for combinatorial optimization and deep learning
Bojnordi, M. N. & Ipek, E · 2016
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A fully programmable 100-spin coherent Ising machine with all-to-all connections
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Efficient learning of Deep Boltzmann Machines
Salakhutdinov, R. & Larochelle, H · 2010
Cited alongside, same era.
Building a multi-FPGA virtualized Restricted Boltzmann Machine architecture using embedded MPI
Lo, C. & Chow, P · 2011
Cited alongside, same era.
Resource efficient arithmetic effects on RBM neural network solution quality using MNIST
Savich, A. W. & Moussa, M · 2011
Cited alongside, same era.
The chip design game at the end of Moore’s law
Colwell, R · 2013
Cited alongside, same era.
Coherent Ising machine based on degenerate optical parametric oscillators
Wang, Z., Marandi, A., Wen, K., Byer, R. L. & Yamamoto, Y · 2013
Cited alongside, same era.
Ising formulations of many NP problems
Lucas, A · 2014
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McMahon, P. L. et al · 2016
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Stochastic p -Bits for Invertible Logic
Camsari, K. Y., Faria, R., Sutton, B. M. & Datta, S · 2017
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In-Datacenter Performance Analysis of a Tensor Processing Unit
Jouppi, N. P. et al · 2017
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Prime factorization using quantum annealing and computational algebraic geometry
Dridi, R. & Alghassi, H · 2017
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Implementing p-bits with Embedded MTJ
Camsari, K. Y., Salahuddin, S. & Datta, S · 2017
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Silicon chip delivers quantum speeds [News]
Boyd, J · 2018
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Soft weight-sharing for neural network compression
Ullrich, K., Welling, M. & Meeds, E · 2019
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Flexgibbs: Reconfigurable parallel gibbs sampling accelerator for structured graphs
Ko, G. G., Chai, Y., Rutenbar, R. A., Brooks, D. & Wei, G. Y · 2019
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Integer factorization using stochastic magnetic tunnel junctions
Borders, W. A. et al · 2019
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A 74 TMACS/W CMOS-RRAM Neurosynaptic Core with Dynamically Reconfigurable Dataflow and In-situ Transposable Weights for Probabilistic Graphical Models
Wan, W. et al · 2020
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