Deep Learning of Part-Based Representation of Data Using Sparse Autoencoders With Nonnegativity Constraints
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[1] D. Perrett,et al. Recognition of objects and their component parts: responses of single units in the temporal cortex of the macaque. , 1994, Cerebral cortex.
[2] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[3] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[4] Yoshua. Bengio,et al. Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..
[5] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[6] Anders Krogh,et al. A Simple Weight Decay Can Improve Generalization , 1991, NIPS.
[7] J. van Leeuwen,et al. Neural Networks: Tricks of the Trade , 2002, Lecture Notes in Computer Science.
[8] Samy Bengio,et al. Guest Editors' Introduction: Special Section on Learning Deep Architectures , 2013, IEEE Trans. Pattern Anal. Mach. Intell..
[9] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[10] H. Sebastian Seung,et al. Learning the parts of objects by non-negative matrix factorization , 1999, Nature.
[11] Brendan J. Frey,et al. k-Sparse Autoencoders , 2013, ICLR.
[12] Nitish Srivastava,et al. Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.
[13] Jacek M. Zurada,et al. Learning Understandable Neural Networks With Nonnegative Weight Constraints , 2015, IEEE Transactions on Neural Networks and Learning Systems.
[14] Geoffrey E. Hinton,et al. 3D Object Recognition with Deep Belief Nets , 2009, NIPS.
[15] Li Deng,et al. A tutorial survey of architectures, algorithms, and applications for deep learning , 2014, APSIPA Transactions on Signal and Information Processing.
[16] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[17] David J. Field,et al. Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.
[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] D. V. Essen,et al. Neural mechanisms of form and motion processing in the primate visual system , 1994, Neuron.
[20] Yoshua Bengio,et al. Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.
[21] H. Sebastian Seung,et al. Algorithms for Non-negative Matrix Factorization , 2000, NIPS.
[22] Jacek M. Zurada,et al. Introduction to artificial neural systems , 1992 .
[23] Geoffrey E. Hinton,et al. Autoencoders, Minimum Description Length and Helmholtz Free Energy , 1993, NIPS.
[24] Yoshua Bengio,et al. Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.
[25] Vipin Kumar,et al. Introduction to Data Mining , 2022, Data Mining and Machine Learning Applications.
[26] Jochen J. Steil,et al. Online learning and generalization of parts-based image representations by non-negative sparse autoencoders , 2012, Neural Networks.
[27] Svetha Venkatesh,et al. Learning Parts-based Representations with Nonnegative Restricted Boltzmann Machine , 2013, ACML.
[28] Marc'Aurelio Ranzato,et al. Sparse Feature Learning for Deep Belief Networks , 2007, NIPS.
[29] Karen Spärck Jones. A statistical interpretation of term specificity and its application in retrieval , 2021, J. Documentation.
[30] Jorge Nocedal,et al. A Limited Memory Algorithm for Bound Constrained Optimization , 1995, SIAM J. Sci. Comput..
[31] Dong Yu,et al. Tensor Deep Stacking Networks , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[32] Patrik O. Hoyer,et al. Non-negative Matrix Factorization with Sparseness Constraints , 2004, J. Mach. Learn. Res..
[33] D K Smith,et al. Numerical Optimization , 2001, J. Oper. Res. Soc..
[34] Honglak Lee,et al. Sparse deep belief net model for visual area V2 , 2007, NIPS.
[35] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[36] Yoshua Bengio,et al. Scaling learning algorithms towards AI , 2007 .
[37] Marc'Aurelio Ranzato,et al. Efficient Learning of Sparse Representations with an Energy-Based Model , 2006, NIPS.
[38] Andy Harter,et al. Parameterisation of a stochastic model for human face identification , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.
[39] Rajat Raina,et al. Self-taught learning: transfer learning from unlabeled data , 2007, ICML '07.
[40] Klaus-Robert Müller,et al. Efficient BackProp , 2012, Neural Networks: Tricks of the Trade.
[41] Yoshua Bengio,et al. Why Does Unsupervised Pre-training Help Deep Learning? , 2010, AISTATS.
[42] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .