Perturbative Interpretation of Adaptive Thouless-Anderson-Palmer Free Energy
نویسندگان
چکیده
منابع مشابه
Perturbative Interpretation of Adaptive Thouless-Anderson-Palmer Free Energy
In conventional well-known derivation methods for the adaptive Thouless-Anderson-Palmer (TAP) free energy, special assumptions that are difficult to mathematically justify except in some mean-field models, must be made. Here, we present a new adaptive TAP free energy derivation method. Using this derivation technique, without any special assumptions, the adaptive TAP free energy can be simply o...
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We study an ill-posed linear inverse problem, where a binary sequence will be reproduced using a sparse matrix. According to the previous study, this model can theoretically provide an optimal compression scheme for an arbitrary distortion level, though the encoding procedure remains an NP-complete problem. In this paper, we focus on the consistency condition for a dynamics model of Markov-type...
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Previous derivation of the Thouless-Anderson-Palmer (TAP) equations for the Hopfield model by the cavity method yielded results that were inconsistent with those of the perturbation theory as well as the results derived by the replica theory of the model. Here we present a derivation of the TAP equation for the Hopfield model by the cavity method and show that it agrees with the form derived by...
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Marylou Gabrié, Eric W. Tramel and Florent Krzakala 1 Laboratoire de Physique Statistique, UMR 8550 CNRS, Department of Physics, École Normale Supérieure and PSL Research University, Rue Lhomond, 75005 Paris, France 2 International Centre for Fundamental Physics and its interfaces at Ecole normale suprieure, 75005 Paris, France 3 Sorbonne Universits, UPMC Univ Paris 06, UMR 8550, LPS, F-75005, ...
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Restricted Boltzmann machines are undirected neural networks which have been shown to be effective in many applications, including serving as initializations for training deep multi-layer neural networks. One of the main reasons for their success is the existence of efficient and practical stochastic algorithms, such as contrastive divergence, for unsupervised training. We propose an alternativ...
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ژورنال
عنوان ژورنال: Journal of the Physical Society of Japan
سال: 2016
ISSN: 0031-9015,1347-4073
DOI: 10.7566/jpsj.85.075001