Computing Upper and Lower Bounds on Likelihoods in Intractable Networks (1996)

by Tommi S. Jaakkola , Michael Jordan , Michael I
Citations:41 - 10 self

Active Bibliography

116 Mean Field Theory for Sigmoid Belief Networks – Lawrence K. Saul, Tommi Jaakkola, Michael I. Jordan - 1996
2 Variational inference for continuous sigmoidal Bayesian networks – Brendan J. Frey - 1996
73 Tutorial on Variational Approximation Methods – Tommi S. Jaakkola - 2000
3 Recognition in hierarchical models – Peter Dayan - 1997
84 Markovian Models for Sequential Data – Yoshua Bengio - 1996
172 A Guide to the Literature on Learning Probabilistic Networks From Data – Wray Buntine - 1996
Graphical Models And Variational Approximation – Michael Jordan, Zoubin Ghahramani, Tommi Jaakkola, Marina Meila, Lawrence Saul - 1998
2 Mean-field methods for a special class of Belief Networks – Chiranjib Bhattacharyya, S. Sathiya Keerthi - 2001
488 The Infinite Hidden Markov Model – Matthew J. Beal, Zoubin Ghahramani, Carl E. Rasmussen - 2002
49 Ensemble learning for independent component analysis – James W. Miskin - 2000
4 Recurrent Sampling Models for the Helmholtz Machine – Peter Dayan - 1999
18 A hierarchical model of binocular rivalry – Peter Dayan - 1998
193 The Helmholtz Machine – Peter Dayan, Geoffrey E. Hinton, Radford M. Neal, Richard S. Zemel - 1995
11 A Mean Field Learning Algorithm For Unsupervised Neural Networks – Lawrence Saul, Michael Jordan - 1999
17 Variational learning in non-linear Gaussian belief networks – Brendan J. Frey, Geoffrey E. Hinton - 1999
9 Continuous Sigmoidal Belief Networks Trained Using Slice Sampling – Brendan Frey
8 An Introduction to Variational Methods for Graphical Methods – Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, Lawrence K. Saul - 1998
8 Variational Learning in Mixed-State Dynamic Graphical Models – Vladimir Pavlovic, Brendan J. Frey, Thomas S. Huang - 1999
10 Attractor Dynamics in Feedforward Neural Networks – Lawrence K. Saul, Michael I. Jordan