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It is well established that neural networks with deep architectures perform better than shallow networks for many tasks in machine learning.
Statistics of two-dimensional ferromagnet
Hendrick A. Kramers and Gregory H. Wanniers · 1941
Earlier work this paper cites.
Crystal statistics. i. a two-dimensional model with an order-disorder transition
Lars Onsager · 1944
Earlier work this paper cites.
Critical exponents in 3.99 dimensions
Kenneth G. Wilson and Michael E. Fisher · 1972
Earlier work this paper cites.
A learning algorithm for boltzmann machines
David H. Ackley, Geoffrey E. Hinton, and Terrence J. Sejnowski · 1985
Earlier work this paper cites.
Information Processing in Dynamical Systems: Foundations of Harmony Theory ; CU-CS-321-86
Paul Smolensky · 1986
Earlier work this paper cites.
The “wake-sleep” algorithm for unsupervised neural networks
Geoffrey E. Hinton, Peter Dayan, Brendan J. Frey, and Radford M. Neal · 1995
Earlier work this paper cites.
Monte Carlo Methods in Statistical Physics
Mark E.J. Newman and Gerard T. Barkema · 1999
Earlier work this paper cites.
Training products of experts by minimizing contrastive divergence
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Earlier work this paper cites.
Reducing the dimensionality of data with neural networks
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Earlier work this paper cites.
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Earlier work this paper cites.
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