The nonnegativa Boltzmann machine

Advances in Neural Information Processing Systems

Volumn , Issue , 2000, Pages 428-434

  Downs, Oliver B ^a   MacKay, David J C ^b   Lee, Daniel D ^c  

^a PRINCETON UNIVERSITY   (United States)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^c LUCENT TECHNOLOGIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MONTE CARLO METHODS; RECURRENT NEURAL NETWORKS;

BOLTZMANN MACHINES; GENERATIVE MODEL; INVARIANT DISTRIBUTION; MEAN FIELD APPROXIMATION; MONTE CARLO SAMPLING TECHNIQUE; NON-NEGATIVE BOLTZMANN MACHINES; RECURRENT NEURAL NETWORK MODEL; SLICE SAMPLINGS;

MAXIMUM LIKELIHOOD ESTIMATION;

EID: 2442467871     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. Hinton, GE & Sejnowski, TJ (1983). Optimal perceptual learning. IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, 448-453.
2. Ackley, DH, Hinton, GE, & Sejnowski, TJ (1985). A learning algorithm for Boltzmann machines. Cognitive Science 9, 147-169.
3. Socci, ND, Lee, DD, and Seung, HS (1998). The rectified Gaussian distribution. Advances in Neural Information Processing Systems 10, 350-356.
4. MacKay, DJC (1998). Introduction to Monte Carlo Methods. Learning in Graphical Models. Kluwer Academic Press, NATO Science Series, 175-204.
5. Galland, CC (1993). The limitations of deterministic Boltzmann machine learning. Network 4, 355-380.
6. Kappen, HJ & Rodriguez, FB ( 1997). Mean field approach to learning in Boltzmann machines. Pattern Recognition in Practice V), Amsterdam.
7. Neal, RM (1995). Suppressing random walks in Markov chain Monte Carlo using ordered overrelaxation. Technical Report 9508, Dept. of Statistics, University of Toronto.
8. Neal, RM (1997). Markov chain Monte Carlo methods based on "slicing" the density function. Technical Report 9722, Dept. of Statistics, University of Toronto.
9. Ben-Yishai, R, Bar-Or, RL, & Sompolinsky, H (1995). Theory of orientation tuning in visual cortex. Proc. Nat. Acad. Sci. USA 92, 3844-3848.
10. Lee, DD, and Seung, HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401, 788-791.