On Nonparametric Guidance for Learning Autoencoder Representations
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[1] Geoffrey E. Hinton,et al. Binary coding of speech spectrograms using a deep auto-encoder , 2010, INTERSPEECH.
[2] Neil D. Lawrence,et al. Non-linear matrix factorization with Gaussian processes , 2009, ICML '09.
[3] David J. Fleet,et al. Erratum: "Gaussian process dynamical models for human motion" (IEEE Transactions on Pattern analysis and Machine Intelligenc (292)) , 2008 .
[4] Geoffrey E. Hinton,et al. 3D Object Recognition with Deep Belief Nets , 2009, NIPS.
[5] Hugo Larochelle,et al. Efficient Learning of Deep Boltzmann Machines , 2010, AISTATS.
[6] Rajesh P. N. Rao,et al. Learning Shared Latent Structure for Image Synthesis and Robotic Imitation , 2005, NIPS.
[7] Trevor Darrell,et al. Discriminative Gaussian process latent variable model for classification , 2007, ICML '07.
[8] Thomas Hofmann,et al. Greedy Layer-Wise Training of Deep Networks , 2007 .
[9] Christopher K. I. Williams. Computation with Infinite Neural Networks , 1998, Neural Computation.
[10] C. Bishop,et al. Analysis of multiphase flows using dual-energy gamma densitometry and neural networks , 1993 .
[11] Yann LeCun,et al. Dimensionality Reduction by Learning an Invariant Mapping , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[12] Geoffrey E. Hinton,et al. Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure , 2007, AISTATS.
[13] Joaquin Quiñonero Candela,et al. Local distance preservation in the GP-LVM through back constraints , 2006, ICML.
[14] Marc'Aurelio Ranzato,et al. Semi-supervised learning of compact document representations with deep networks , 2008, ICML '08.
[15] David J. Fleet,et al. Multifactor Gaussian process models for style-content separation , 2007, ICML '07.
[16] Geoffrey E. Hinton,et al. Bayesian Learning for Neural Networks , 1995 .
[17] David J. Fleet,et al. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Gaussian Process Dynamical Model , 2007 .
[18] Y. LeCun,et al. Learning methods for generic object recognition with invariance to pose and lighting , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..
[19] M WangJack,et al. Gaussian Process Dynamical Models for Human Motion , 2008 .
[20] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[21] Michael I. Jordan,et al. Tree-Structured Stick Breaking for Hierarchical Data , 2010, NIPS.
[22] Geoffrey E. Hinton,et al. Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes , 2007, NIPS.
[23] Neil D. Lawrence,et al. Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models , 2005, J. Mach. Learn. Res..
[24] Yoshua Bengio,et al. Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.
[25] Yoshua Bengio,et al. An empirical evaluation of deep architectures on problems with many factors of variation , 2007, ICML '07.
[26] D. Mackay,et al. Bayesian neural networks and density networks , 1995 .