Mean Field Theory for Sigmoid Belief Networks (1996)

by Lawrence K. Saul , Tommi Jaakkola , Michael I. Jordan
Venue:Journal of Artificial Intelligence Research
Citations:116 - 12 self

Active Bibliography

10 Attractor Dynamics in Feedforward Neural Networks – Lawrence K. Saul, Michael I. Jordan
12 Towards Perceptual Intelligence: Statistical Modeling of Human Individual and Interactive Behaviors – Nuria M. Oliver - 1995
488 The Infinite Hidden Markov Model – Matthew J. Beal, Zoubin Ghahramani, Carl E. Rasmussen - 2002
8 An Introduction to Variational Methods for Graphical Methods – Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, Lawrence K. Saul - 1998
73 Tutorial on Variational Approximation Methods – Tommi S. Jaakkola - 2000
2 Learning to Parse Images – Yee Whye Teh - 2000
2 Mean-field methods for a special class of Belief Networks – Chiranjib Bhattacharyya, S. Sathiya Keerthi - 2001
84 Markovian Models for Sequential Data – Yoshua Bengio - 1996
2 Variational inference for continuous sigmoidal Bayesian networks – Brendan J. Frey - 1996
11 A Mean Field Learning Algorithm For Unsupervised Neural Networks – Lawrence Saul, Michael Jordan - 1999
20 Low Entropy Coding with Unsupervised Neural Networks – George Francis Harpur
18 Unsupervised learning – Zoubin Ghahramani - 2004
564 Dynamic Bayesian Networks: Representation, Inference and Learning – Kevin Patrick Murphy - 2002
42 Latent Variable Models for Neural Data Analysis – Maneesh Sahani - 1999
Graphical Models And Variational Approximation – Michael Jordan, Zoubin Ghahramani, Tommi Jaakkola, Marina Meila, Lawrence Saul - 1998
42 Computing Upper and Lower Bounds on Likelihoods in Intractable Networks – Tommi S. Jaakkola, Michael Jordan, Michael I - 1996
249 Operations for Learning with Graphical Models – Wray L. Buntine - 1994
48 Variational Approximations between Mean Field Theory and the Junction Tree Algorithm – Wim Wiegerinck - 2000
194 The Helmholtz Machine – Peter Dayan, Geoffrey E. Hinton, Radford M. Neal, Richard S. Zemel - 1995