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We introduce a novel training principle for probabilistic models that is an alternative to maximum likelihood.
Perturbation theory and finite markov chains
Schweitzer, Paul J · 1968
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Learning continuous attractors in recurrent networks
Seung, Sebastian H · 1998
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Comparison of perturbation bounds for the stationary distribution of a markov chain
Cho, Grace E., Meyer, Carl D., Carl, and Meyer, D · 2000
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Dependency networks for inference, collaborative filtering, and data visualization
Heckerman, David, Chickering, David Maxwell, Meek, Christopher, Rounthwaite, Robert, and Kadie, Carl · 2000
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Greedy layer-wise training of deep networks
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Efficient sparse coding algorithms
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Efficient learning of sparse representations with an energy-based model
Ranzato, M., Poultney, C., Chopra, S., and LeCun, Y · 2007
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Krizhevsky, A., Sutskever, I., and Hinton, G · 2012
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Goodfellow, Ian J., Mirza, Mehdi, Courville, Aaron, and Bengio, Yoshua · 2013
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