Semi-supervised auto-encoder based on manifold learning
暂无分享,去创建一个
Changyin Sun | Yawei Li | A. Kai Qin | Yew-Soon Ong | Lizuo Jin | Tong Cui
[1] Yoshua Bengio,et al. What regularized auto-encoders learn from the data-generating distribution , 2012, J. Mach. Learn. Res..
[2] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[3] Pascal Vincent,et al. The Manifold Tangent Classifier , 2011, NIPS.
[4] Geoffrey E. Hinton,et al. Deep Boltzmann Machines , 2009, AISTATS.
[5] Geoffrey Zweig,et al. Recent advances in deep learning for speech research at Microsoft , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[6] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[7] Yann LeCun,et al. Unsupervised Learning of Sparse Features for Scalable Audio Classification , 2011, ISMIR.
[8] Jason Weston,et al. Deep learning via semi-supervised embedding , 2008, ICML '08.
[9] Pascal Vincent,et al. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..
[10] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[11] Aaron C. Courville,et al. Understanding Representations Learned in Deep Architectures , 2010 .
[12] Yichuan Tang,et al. Deep Learning using Linear Support Vector Machines , 2013, 1306.0239.
[13] J. van Leeuwen,et al. Neural Networks: Tricks of the Trade , 2002, Lecture Notes in Computer Science.
[14] Honglak Lee,et al. An Analysis of Single-Layer Networks in Unsupervised Feature Learning , 2011, AISTATS.
[15] Christopher K. I. Williams. On a Connection between Kernel PCA and Metric Multidimensional Scaling , 2004, Machine Learning.
[16] Yoshua Bengio,et al. Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.
[17] Harm de Vries,et al. RMSProp and equilibrated adaptive learning rates for non-convex optimization. , 2015 .
[18] Mikhail Belkin,et al. Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..
[19] Yoshua Bengio,et al. Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.
[20] Yoshua Bengio,et al. Implicit Density Estimation by Local Moment Matching to Sample from Auto-Encoders , 2012, ArXiv.
[21] Pascal Vincent,et al. Adding noise to the input of a model trained with a regularized objective , 2011, ArXiv.
[22] Jing J. Liang,et al. Performance Evaluation of Multiagent Genetic Algorithm , 2006, Natural Computing.
[23] David A Clausi,et al. MAGIC: MAp-Guided Ice Classification System , 2010 .
[24] P.N. Suganthan,et al. Generalized null space uncorrelated Fisher discriminant analysis for linear dimensionality reduction , 2006, Pattern Recognit..
[25] E. Parzen. On Estimation of a Probability Density Function and Mode , 1962 .
[26] Thomas Hofmann,et al. Efficient Learning of Sparse Representations with an Energy-Based Model , 2007 .
[27] Pascal Vincent,et al. Contractive Auto-Encoders: Explicit Invariance During Feature Extraction , 2011, ICML.
[28] Mikhail Belkin,et al. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.
[29] Yoshua Bengio,et al. Semi-supervised Learning by Entropy Minimization , 2004, CAP.
[30] Dong-Hyun Lee,et al. Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks , 2013 .
[31] A. Kai Qin,et al. Rapid and brief communication Uncorrelated heteroscedastic LDAbasedon theweightedpairwise Chernoff criterion , 2004 .
[32] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[33] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[34] A. Kai Qin,et al. Enhanced neural gas network for prototype-based clustering , 2005, Pattern Recognit..
[35] Yoshua Bengio,et al. Marginalized Denoising Auto-encoders for Nonlinear Representations , 2014, ICML.
[36] Yoshua. Bengio,et al. Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..
[37] Peter Glöckner,et al. Why Does Unsupervised Pre-training Help Deep Learning? , 2013 .
[38] J. Tenenbaum,et al. A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.
[39] Marco Wiering,et al. 2011 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) , 2011, IJCNN 2011.
[40] Xiaofei He,et al. Locality Preserving Projections , 2003, NIPS.