Learning Causally Linked Markov Random Fields.
作者： Geoffrey E. Hinton , Simon Osindero , Kejie Bao
DOI:
关键词: Artificial intelligence 、 Markov model 、 Examples of Markov chains 、 Markov kernel 、 Markov chain 、 Theoretical computer science 、 Machine learning 、 Mathematics 、 Variable-order Markov model 、 Markov blanket 、 Markov renewal process 、 Markov property
摘要:
参考文章(11)
Wray L. Buntine, Chain graphs for learning uncertainty in artificial intelligence. pp. 46- 54 ,(1995)
M. Weber, M. Welling, P. Perona, Unsupervised Learning of Models for Recognition Computer Vision - ECCV 2000. pp. 18- 32 ,(2000) , 10.1007/3-540-45054-8_2
G. Hinton, P Dayan, B. Frey, R. Neal, The "Wake-Sleep" Algorithm for Unsupervised Neural Networks Science. ,vol. 268, pp. 1158- 1161 ,(1995) , 10.1126/SCIENCE.7761831
S. L. Lauritzen, N. Wermuth, Graphical Models for Associations between Variables, some of which are Qualitative and some Quantitative Annals of Statistics. ,vol. 17, pp. 31- 57 ,(1989) , 10.1214/AOS/1176347003
Steffen L. Lauritzen, Thomas S. Richardson, Chain graph models and their causal interpretations Journal of The Royal Statistical Society Series B-statistical Methodology. ,vol. 64, pp. 321- 348 ,(2002) , 10.1111/1467-9868.00340
Geoffrey E. Hinton, Yee Whye Teh, Rate-coded Restricted Boltzmann Machines for Face Recognition neural information processing systems. ,vol. 13, pp. 908- 914 ,(2000)
Geoffrey E. Hinton, Training products of experts by minimizing contrastive divergence Neural Computation. ,vol. 14, pp. 1771- 1800 ,(2002) , 10.1162/089976602760128018
J.-F. Cardoso, Infomax and maximum likelihood for blind source separation IEEE Signal Processing Letters. ,vol. 4, pp. 112- 114 ,(1997) , 10.1109/97.566704
Judea Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference ,(1988)
Radford M. Neal, Geoffrey E. Hinton, A view of the EM algorithm that justifies incremental, sparse, and other variants Proceedings of the NATO Advanced Study Institute on Learning in graphical models. pp. 355- 368 ,(1998) , 10.1007/978-94-011-5014-9_12