Practical Variational Inference for Neural Networks
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[1] J. Rissanen,et al. Modeling By Shortest Data Description* , 1978, Autom..
[2] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[3] Geoffrey E. Hinton,et al. Experiments on Learning by Back Propagation. , 1986 .
[4] Paul Smolensky,et al. Information processing in dynamical systems: foundations of harmony theory , 1986 .
[5] Ian H. Witten,et al. Arithmetic coding for data compression , 1987, CACM.
[6] Hsiao-Wuen Hon,et al. Speaker-independent phone recognition using hidden Markov models , 1989, IEEE Trans. Acoust. Speech Signal Process..
[7] Yann LeCun,et al. Optimal Brain Damage , 1989, NIPS.
[8] C. S. Wallace,et al. Classification by Minimum-Message-Length Inference , 1991, ICCI.
[9] Geoffrey E. Hinton,et al. Simplifying Neural Networks by Soft Weight-Sharing , 1992, Neural Computation.
[10] Geoffrey E. Hinton,et al. Keeping the neural networks simple by minimizing the description length of the weights , 1993, COLT '93.
[11] Martin A. Riedmiller,et al. A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.
[12] Jonathan G. Fiscus,et al. DARPA TIMIT:: acoustic-phonetic continuous speech corpus CD-ROM, NIST speech disc 1-1.1 , 1993 .
[13] C. Lee Giles,et al. Pruning recurrent neural networks for improved generalization performance , 1994, IEEE Trans. Neural Networks.
[14] David Mackay,et al. Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks , 1995 .
[15] C. Lee Giles,et al. An analysis of noise in recurrent neural networks: convergence and generalization , 1996, IEEE Trans. Neural Networks.
[16] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[17] David Barber,et al. Radial Basis Functions: A Bayesian Treatment , 1997, NIPS.
[18] Brendan J. Frey,et al. Graphical Models for Machine Learning and Digital Communication , 1998 .
[19] Charles M. Bishop,et al. Ensemble learning in Bayesian neural networks , 1998 .
[20] Antti Honkela,et al. Variational learning and bits-back coding: an information-theoretic view to Bayesian learning , 2004, IEEE Transactions on Neural Networks.
[21] Sang Joon Kim,et al. A Mathematical Theory of Communication , 2006 .
[22] Jürgen Schmidhuber,et al. Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks , 2006, ICML.
[23] T. Munich,et al. Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks , 2008, NIPS.
[24] Manfred Opper,et al. The Variational Gaussian Approximation Revisited , 2009, Neural Computation.
[25] Geoffrey E. Hinton,et al. Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine , 2010, NIPS.