Stacks of convolutional Restricted Boltzmann Machines for shift-invariant feature learning
暂无分享,去创建一个
[1] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[2] Marc'Aurelio Ranzato,et al. Efficient Learning of Sparse Representations with an Energy-Based Model , 2006, NIPS.
[3] G LoweDavid,et al. Distinctive Image Features from Scale-Invariant Keypoints , 2004 .
[4] Yali Amit,et al. POP: Patchwork of Parts Models for Object Recognition , 2007, International Journal of Computer Vision.
[5] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[6] Ching Y. Suen,et al. A trainable feature extractor for handwritten digit recognition , 2007, Pattern Recognit..
[7] Alan F. Murray,et al. Continuous restricted Boltzmann machine with an implementable training algorithm , 2003 .
[8] William T. Freeman,et al. What makes a good model of natural images? , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[9] Subhransu Maji,et al. Classification using intersection kernel support vector machines is efficient , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[10] Honglak Lee,et al. Sparse deep belief net model for visual area V2 , 2007, NIPS.
[11] Michael J. Black,et al. Fields of Experts: a framework for learning image priors , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[12] Thomas Hofmann,et al. Efficient Learning of Sparse Representations with an Energy-Based Model , 2007 .
[13] Yee Whye Teh,et al. Energy-Based Models for Sparse Overcomplete Representations , 2003, J. Mach. Learn. Res..
[14] James L. McClelland,et al. Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .
[15] G. Kane. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol 1: Foundations, vol 2: Psychological and Biological Models , 1994 .
[16] Fatih Murat Porikli,et al. Pedestrian Detection via Classification on Riemannian Manifolds , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[17] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[18] Thomas Serre,et al. Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[19] Yoshua Bengio,et al. Classification using discriminative restricted Boltzmann machines , 2008, ICML '08.
[20] Geoffrey E. Hinton,et al. Learning Sparse Topographic Representations with Products of Student-t Distributions , 2002, NIPS.
[21] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[22] Bill Triggs,et al. Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[23] Kunihiko Fukushima,et al. Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position , 1982, Pattern Recognit..
[24] David G. Lowe,et al. Multiclass Object Recognition with Sparse, Localized Features , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[25] Paul Smolensky,et al. Information processing in dynamical systems: foundations of harmony theory , 1986 .
[26] Marc'Aurelio Ranzato,et al. Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.