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In linear algebra one usually studies the inverse problem, that is, given a matrix, one tries to find the eigenvalues and eigenvectors.
References
D.H. Ackley, G.E. Hinton, T.J. Sejnowski. A Learning Algorithm for Boltzmann Machines. Cogn. Sci. 9(1):147–169 (Elsevier Science, Amsterdam, 1985)
J.A. Anderson, E. Rosenfeld, Neurocomputing: Foundations of Research (MIT Press, Cambridge, 1988)
C. Andrieu, N. De Freitas, A. Doucet, M.I. Jordan. An introduction to MCMC for machine Learning. Mach. Learn. 50:5–43 (Kluwer, Dordrecht, 2003)
Y. Freund, D. Haussler. Unsupervised Learning of Distributions on Binary Vectors using Two Layer Networks. Advances in Neural Information Processing Systems 4 (Morgan Kaufmann, San Mateo, 1992), pp. 912–919
W. Greiner, L. Neise, H. Stöcker. Thermodynamik und Statistische Mechanik (Series: Theoretische Physik). Verlag Harri Deutsch, Thun, Frankfurt am Main, Germany, English edition: Thermodynamics and Statistical Physics (Springer, Berlin, 1987) (2000)
S. Haykin, Neural Networks and Learning Machines (Prentice Hall, Englewood Cliffs, 2008)
D.O. Hebb. The Organization of Behaviour (Wiley, New York, 1949). Chap. 4: The First Stage of Perception: Growth of an Assembly reprinted in [Anderson und Rosenfeld 1988], pp. 45–56
G. Hinton, T.J. Sejnowski, in Learning and Relearning in Boltzmann Machines, ed. by D.E. Rumelhart, J.L. McClelland (1986), pp. 282–317
G.E. Hinton. Training products of experts by minimizing contrastive divergence. Neural Comput. 14(8):1771–1800 (MIT Press, Cambridge, 2002)
G.E. Hinton, S. Osindero, Y.W. Teh. A fast learning algorithm for deep belief nets. Neural Comput. 18(7):1527–1554 (MIT Press, Cambridge, 2006)
G.E. Hinton. A Practical Guide to Training Restricted Boltzmann Machines. Technical report 2010-003, Department of Computer Science, University of Toronto, Canada (2010)
J.J. Hopfield. Neural networks and physical systems with emergent collective computational abilities. Proc. Nat. Acad. Sci. 79:2554–2558(1982)
J.J. Hopfield. Neurons with Graded response have collective computational properties like those of two-state neurons. Proc. Nat. Acad. Sci. 81:3088–3092 (1984)
J. Hopfield, D. Tank. “Neural” Computation of decisions in optimization problems. Biol. Cybern. 52:141–152 (Springer, Heidelberg, 1985)
E. Ising. Beitrag zur Theorie des Ferromagnetismus. Zeitschrift für Physik 31(253) (1925)
S. Kirkpatrick, C.D. Gelatt, M.P. Vercchi. Optimization by simulated annealing. Science 220:671–680 (High Wire Press, Stanford, 1983)
S. Kullback, R.A. Leibler. On information and sufficiency. Ann. Math. Stat. 22:79–86 (Institute of Mathematical Statistics, Hayward, 1951)
N. Metropolis, N. Rosenblut, A. Teller, E. Teller. Equation of state calculations for fast computing machines. J. Chem. Phys. 21:1087–1092 (American Institute of Physics, Melville, 1953)
R. Rojas, Theorie der neuronalen Netze – Eine systematische Einführung (Springer, Berlin, 1996)
D.E. Rumelhart, J.L. McClelland (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations (MIT Press, Cambridge, 1986)
P. Smolensky, Information Processing in Dynamical Systems: Foundations of Harmony Theory, ed. by Rumelhart, D.E, McClelland, J.L (1986) pp. 194–281
T. Tieleman, Training restricted Boltzmann machines, using approximations to the likelihood gradient. Proceedings 21st International Conference on Machine Learning (ICML, Helsinki, Finland) (ACM Press, New York, 2008)
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Kruse, R., Borgelt, C., Braune, C., Mostaghim, S., Steinbrecher, M. (2016). Hopfield Networks. In: Computational Intelligence. Texts in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-7296-3_8
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