On the relationship between deterministic and probabilistic directed Graphical models: From Bayesian networks to recursive neural networks

Neural Networks - Tập 18 - Trang 1080-1086 - 2005
Pierre Baldi1,2, Michal Rosen-Zvi3
1School of Information and Computer Sciences, University of California, Irvine, CA 92697-3425, USA
2Institute for Genomics and Bioinformatics, University of California, Irvine, CA 92697-3425, USA
3School of Computer Science and Engineering, The Hebrew University of Jerusalem, 91904 Jerusalem, Israel

Tài liệu tham khảo

Baldi, 1996, Hybrid modeling, HMM/NN architectures, and protein applications, Neural Computation, 8, 1541, 10.1162/neco.1996.8.7.1541 Baldi, 2003, The principled design of large-scale recursive neural network architectures—DAG-RNNs and the protein structure prediction problem, Journal of Machine Learn/my Research, 4, 575 Baldi, P., Rosen-Zvi, M. (2005). On the relationship between deterministic and probabilistic directed graphical models: From Bayesian networks to recursive neural networks and back. Technical report, Irvine: Department of Computer Science, University of California. Barber, D. (2000). Dynamic Bayesian networks with determinsitic latent tables. In Advances in neural information processing systems (Vol. 12). Billingsley, 1995 B. Bozhena, R. Dechter. The epsilon-cutset effect in bayesian networks. Technical report, School of Information and Computer Science, University of California, Irvine, 2001 Dechter, 2003 Frasconi, 1998, A general framework for adaptive processing of data structures, IEEE Transactions on Neural Networks, 9, 768, 10.1109/72.712151 Goller, 1996, Learning task-dependent distributed structure-representations by backpropagation through structure, IEEE International Conference on Neural Networks, 347 Heckerman, 1998 LeCun, 1998, Gradient-based learning applied to document recognition, Proceedings of the IEEE, 86, 2278, 10.1109/5.726791 Micheli, 2001, Analysis of the internal representations developed by neural networks for structures applied to quantitative structure-activity relationship studies of benzodiazepines, Journal of Chemical Information and Computer Sciences, 41, 202, 10.1021/ci9903399 Pearl, 1986, Fusion, propagation, and structuring in belief networks, Artificial Intelligence, 29, 241, 10.1016/0004-3702(86)90072-X Pearl, 1988 M. Rosen-Zvi, M. I. Jordan. Approximate inference and the DLR equations. Technical report. Computer Science Division, University of California, Berkeley, 2003. Sperduti, 1997, Supervised neural networks for the classification of structures, IEEE Transactions on Neural Networks, 8, 714, 10.1109/72.572108