Training Neural Networks with Implicit Variance
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[1] Nitish Srivastava,et al. Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.
[2] Stefan Schaal,et al. Incremental Online Learning in High Dimensions , 2005, Neural Computation.
[3] Geoffrey E. Hinton,et al. Training Recurrent Neural Networks , 2013 .
[4] Geoffrey E. Hinton,et al. On the importance of initialization and momentum in deep learning , 2013, ICML.
[5] Jürgen Schmidhuber,et al. Multi-column deep neural networks for image classification , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[6] Dong Yu,et al. Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition , 2012, IEEE Transactions on Audio, Speech, and Language Processing.
[7] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[8] Steve Renals,et al. Deep Architectures for Articulatory Inversion , 2012, INTERSPEECH.
[9] Jeffrey K. Uhlmann,et al. New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.
[10] P. Werbos,et al. Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .
[11] Klaus-Robert Müller,et al. Efficient BackProp , 2012, Neural Networks: Tricks of the Trade.
[12] Yoshua Bengio,et al. Estimating or Propagating Gradients Through Stochastic Neurons , 2013, ArXiv.
[13] Geoffrey E. Hinton,et al. Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes , 2007, NIPS.
[14] Christopher M. Bishop,et al. Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .
[15] C. Bishop. Mixture density networks , 1994 .
[16] Ruslan Salakhutdinov,et al. Learning Stochastic Feedforward Neural Networks , 2013, NIPS.
[17] R. Salakhutdinov,et al. A New Learning Algorithm for Stochastic Feedforward Neural Nets , 2013 .
[18] Christopher D. Manning,et al. Fast dropout training , 2013, ICML.
[19] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[20] Alexander J. Smola,et al. Heteroscedastic Gaussian process regression , 2005, ICML.
[21] Yoshua Bengio,et al. An empirical evaluation of deep architectures on problems with many factors of variation , 2007, ICML '07.
[22] Radford M. Neal. Connectionist Learning of Belief Networks , 1992, Artif. Intell..
[23] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[24] Grgoire Montavon,et al. Neural Networks: Tricks of the Trade , 2012, Lecture Notes in Computer Science.
[25] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[26] Y. L. Cun. Learning Process in an Asymmetric Threshold Network , 1986 .
[27] Françoise Fogelman-Soulié,et al. Disordered Systems and Biological Organization , 1986, NATO ASI Series.
[28] Geoffrey E. Hinton,et al. On rectified linear units for speech processing , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[29] Yoshua Bengio,et al. Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..
[30] Yoshua Bengio,et al. Deep Generative Stochastic Networks Trainable by Backprop , 2013, ICML.