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Sepp Hochreiter | Andreas Mayr | Djork-Arné Clevert | Thomas Unterthiner | S. Hochreiter | Thomas Unterthiner | Djork-Arné Clevert | Andreas Mayr
[1] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[2] E. M.,et al. Statistical Mechanics , 2021, On Complementarity.
[3] R. A. Leibler,et al. On Information and Sufficiency , 1951 .
[4] J. B. Rosen. The gradient projection method for nonlinear programming: Part II , 1961 .
[5] E. M. L. Beale,et al. Nonlinear Programming: A Unified Approach. , 1970 .
[6] D. Bertsekas. On the Goldstein-Levitin-Polyak gradient projection method , 1974, CDC 1974.
[7] Rajnikant V. Patel,et al. Trace inequalities involving Hermitian matrices , 1979 .
[8] D. Bertsekas. Projected Newton methods for optimization problems with simple constraints , 1981, 1981 20th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.
[9] D. Dowson,et al. The Fréchet distance between multivariate normal distributions , 1982 .
[10] New York Dover,et al. ON THE CONVERGENCE PROPERTIES OF THE EM ALGORITHM , 1983 .
[11] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[12] D. Chakrabarti,et al. A fast fixed - point algorithm for independent component analysis , 1997 .
[13] Geoffrey E. Hinton,et al. Generative models for discovering sparse distributed representations. , 1997, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.
[14] Geoffrey E. Hinton,et al. A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.
[15] Brendan J. Frey,et al. Variational Learning in Nonlinear Gaussian Belief Networks , 1999, Neural Computation.
[16] Carl Tim Kelley,et al. Iterative methods for optimization , 1999, Frontiers in applied mathematics.
[17] Geoffrey E. Hinton,et al. Variational Learning for Switching State-Space Models , 2000, Neural Computation.
[18] José Mario Martínez,et al. Nonmonotone Spectral Projected Gradient Methods on Convex Sets , 1999, SIAM J. Optim..
[19] Mark A. Girolami,et al. A Variational Method for Learning Sparse and Overcomplete Representations , 2001, Neural Computation.
[20] Arkadi Nemirovski,et al. 6. Interior Point Polynomial Time Methods for Linear Programming, Conic Quadratic Programming, and Semidefinite Programming , 2001 .
[21] Matthew J. Beal. Variational algorithms for approximate Bayesian inference , 2003 .
[22] Michael I. Jordan,et al. An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.
[23] Nathan Srebro,et al. Learning with matrix factorizations , 2004 .
[24] Patrik O. Hoyer,et al. Non-negative Matrix Factorization with Sparseness Constraints , 2004, J. Mach. Learn. Res..
[25] 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..
[26] Bhaskar D. Rao,et al. Variational EM Algorithms for Non-Gaussian Latent Variable Models , 2005, NIPS.
[27] R. Steele,et al. Optimization , 2005, Encyclopedia of Biometrics.
[28] Luca Zanni,et al. Gradient projection methods for quadratic programs and applications in training support vector machines , 2005, Optim. Methods Softw..
[29] A. Kabán,et al. A variational Bayesian method for rectified factor analysis , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[30] William J. Byrne,et al. Convergence Theorems for Generalized Alternating Minimization Procedures , 2005, J. Mach. Learn. Res..
[31] I. Dhillon,et al. A New Projected Quasi-Newton Approach for the Nonnegative Least Squares Problem , 2006 .
[32] Yoshua Bengio,et al. Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.
[33] Klaus Obermayer,et al. A new summarization method for affymetrix probe level data , 2006, Bioinform..
[34] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[35] Ben Taskar,et al. Expectation Maximization and Posterior Constraints , 2007, NIPS.
[36] Yoshua Bengio,et al. An empirical evaluation of deep architectures on problems with many factors of variation , 2007, ICML '07.
[37] Markus Harva,et al. Variational learning for rectified factor analysis , 2007, Signal Process..
[38] Richard G. Baraniuk,et al. Sparse Coding via Thresholding and Local Competition in Neural Circuits , 2008, Neural Computation.
[39] Koby Crammer,et al. Confidence-weighted linear classification , 2008, ICML '08.
[40] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[41] Ben Taskar,et al. Posterior vs Parameter Sparsity in Latent Variable Models , 2009, NIPS.
[42] Ulrich Bodenhofer,et al. FABIA: factor analysis for bicluster acquisition , 2010, Bioinform..
[43] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[44] Pascal Vincent,et al. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..
[45] Ben Taskar,et al. Posterior Regularization for Structured Latent Variable Models , 2010, J. Mach. Learn. Res..
[46] Yann LeCun,et al. Learning Fast Approximations of Sparse Coding , 2010, ICML.
[47] Yoshua Bengio,et al. Deep Sparse Rectifier Neural Networks , 2011, AISTATS.
[48] P. Cochat,et al. Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.
[49] Nitish Srivastava,et al. Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.
[50] Jürgen Schmidhuber,et al. Multi-column deep neural networks for image classification , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[51] Karl J. Friston,et al. A Free Energy Principle for Biological Systems. , 2012, Entropy.
[52] 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.
[53] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[54] Koby Crammer,et al. Confidence-Weighted Linear Classification for Text Categorization , 2012, J. Mach. Learn. Res..
[55] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[56] Geoffrey E. Hinton,et al. Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[57] Geoffrey E. Hinton,et al. On rectified linear units for speech processing , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[58] S. Hochreiter,et al. HapFABIA: Identification of very short segments of identity by descent characterized by rare variants in large sequencing data , 2013, Nucleic acids research.
[59] Yoshua Bengio,et al. Maxout Networks , 2013, ICML.
[60] Pierre Baldi,et al. The dropout learning algorithm , 2014, Artif. Intell..
[61] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[62] Benjamin Graham,et al. Fractional Max-Pooling , 2014, ArXiv.
[63] Qiang Chen,et al. Network In Network , 2013, ICLR.
[64] Yoshua Bengio,et al. An empirical analysis of dropout in piecewise linear networks , 2013, ICLR.
[65] Quoc V. Le,et al. Sequence to Sequence Learning with Neural Networks , 2014, NIPS.
[66] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[67] Bie M. P. Verbist,et al. Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project. , 2015, Drug discovery today.
[68] Zhuowen Tu,et al. Deeply-Supervised Nets , 2014, AISTATS.
[69] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..