Classification, Regression, and Feature Selection on Matrix Data _{Tech. Report 2004}

Author(s):	S. Hochreiter - K. Obermayer
Published:	Technical Report No. 2004/02, Revised, Elektrotechnik und Informatik, Technical University Berlin, 2004
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Gene Selection for Microarray Data _{Kernel Methods in Computational Biology 2004}

Author(s):	S. Hochreiter - K. Obermayer
Published:	In Kernel Methods in Computational Biology, pp. 319-355, MIT Press, 2004
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Learning quadratic forms by density estimation and its application to image coding _{Neurocomputing 2004}

Author(s):	H. Bartsch - S. Hochreiter - K. Obermayer
Published:	Neurocomputing, 2004

Classification and Feature Selection on Matrix Data with Application to Gene-Expression Analysis _ISI 2003

Author(s):	S. Hochreiter - K. Obermayer
Published:	54th Session of the International Statistical Institute, 2003
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Gene Selection with Support Vector Machines _{Workshop Kernel Methods in Computational Biology 2003}

Author(s):	S. Hochreiter - K. Obermayer
Published:	Research in Computational Molecular Biology, Workshop Kernel Methods in Computational Biology, 2003
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Learning Quadratic Forms by Density Estimation and its Application to Image Coding _CNS 2003

Author(s):	H. Bartsch - S. Hochreiter - K. Obermayer
Published:	Computational Neuroscience Meeting (CNS), 2003
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Feature Selection and Classification on Matrix Data: From Large Margins To Small Covering Numbers _NIPS 2003

Author(s):	S. Hochreiter - K. Obermayer
Published:	Neural Information Processing Systems 15, pp. 913-920, 2003
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Coulomb Classifiers: Generalizing Support Vector Machines via an Analogy to Electrostatic Systems _NIPS 2003

Author(s):	S. Hochreiter - M.C. Mozer - K. Obermayer
Published:	Neural Information Processing Systems 15, pp. 561-568, 2003
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Classification of Pairwise Proximity Data with Support Vectors _{The Learning Workshop 2002}

Author(s):	S. Hochreiter - K. Obermayer
Published:	The Learning Workshop, Snowbird, USA, 2002
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Coulomb Classifiers: Reinterpreting SVMs as Electrostatic Systems _{Tech. Report 2001}

Author(s):	S. Hochreiter - M.C. Mozer
Published:	Technical Report CU-CS-921-01, 2001
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Monaural separation and classification of non-linear transformed and mixed independent signals: An SVM perspective _ICA 2001

Author(s):	S. Hochreiter - M.C. Mozer
Published:	Proceedings of the 3rd International Conference on Independent Component Analysis and Blind Signal Separation, San Diego, CA, USA, 2001
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Learning to Learn Using Gradient Descent _ICANN 2001

Author(s):	S. Hochreiter - A. S. Younger - P. R. Conwell
Published:	Proceedings of the International Conference on Artificial Neural Networks (ICANN), Vienna, Austria, pp. 87-94, 2001
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A Discrete Probabilistic Memory Model for Discovering Dependencies in Time _ICANN 2001

Author(s):	S. Hochreiter - M.C. Mozer
Published:	Proceedings of the International Conference on Artificial Neural Networks (ICANN), Vienna, Austria, pp. 661-668, 2001
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Meta-Learning with Backpropagation _{IEEE Neural Networks 2001}

Author(s):	A. S. Younger - S. Hochreiter - P. R. Conwell
Published:	Proceedings of the International Joint Conference on Neural Networks (IEEE-2001), pp. 2001-2006, 2001
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Beyond maximum likelihood and density estimation: A sample-based criterion for unsupervised learning of complex models _NIPS 2001

Author(s):	S. Hochreiter - M.C. Mozer
Published:	Advances in Neural Information Processing Systems 13, pp. 535-541, 2001
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Evaluating benchmark problems by random guessing _{Field Guide to Dynamical Recurrent Networks 2000}

Author(s):	J. Schmidhuber - S. Hochreiter - Y. Bengio
Published:	in A Field Guide to Dynamical Recurrent Networks, J. F. Kolen and S. C. Kremer (eds.), IEEE Press New York City, 2000, pp. 231-236
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Gradient flow in recurrent nets: the difficulty of learning long-term dependencies _{Field Guide to Dynamical Recurrent Networks 2000}

Author(s):	S. Hochreiter - Y. Bengio - P. Frasconi - J. Schmidhuber
Published:	in A Field Guide to Dynamical Recurrent Networks, J. F. Kolen and S. C. Kremer (eds.), IEEE Press New York City, 2000, pp. 237-244
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An electric field approach to independent component analysis _ICA 2000

Author(s):	S. Hochreiter - M.C. Mozer
Published:	Proceedings of the Second International Workshop on Independent Component Analysis and Blind Signal Separation, Helsinki, Finland, pp. 45-50, 2000
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Nonlinear ICA through low-complexity autoencoders _{IEEE Circuits and Systems 1999}

Author(s):	S. Hochreiter - J. Schmidhuber
Published:	Proceedings of the 1999 IEEE International Symposium on Circuits and Systems (ISCAS'99), IEEE, vol. 5, pp. 53-56, 1999
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LOCOCODE performs nonlinear ICA without knowing the number of sources _ICA 1999

Author(s):	S. Hochreiter - J. Schmidhuber
Published:	Proceedings of the First International Workshop on Independent Component Analysis and Signal Separation, Aussois, France, pp. 149-154, 1999
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Feature extraction through LOCOCODE _{Neural Computation 1999}

Author(s):	S. Hochreiter - J. Schmidhuber
Published:	Neural Computation, vol. 11, nr. 3, pp. 679-714, 1999
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Source Separation as a By-product of Regularization _{NIPS 1999}

Author(s):	S. Hochreiter - J. Schmidhuber
Published:	Advances in Neural Information Processing Systems 12, pp. 459-465, 1999
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The vanishing gradient problem during learning recurrent neural nets and problem solutions _{Journal of Uncertainty 1998}

Author(s):	S. Hochreiter
Published:	International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 6, nr. 2, pp. 107-116, 1998
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LOCOCODE versus PCA and ICA _{ICANN 1998}

Author(s):	S. Hochreiter - J. Schmidhuber
Published:	In Proc. International Conference on Artificial Neural Networks 98, pp. 669-674, 1998
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LOCOCODE _{Tech. Report 1997}

Author(s):	S. Hochreiter - J. Schmidhuber
Published:	Technical report FKI-222-97, 1997 (revised January 1998)
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Long Short-Term Memory _{Neural Computation 1997}

Author(s):	S. Hochreiter - J. Schmidhuber
Published:	Neural Computation, vol. 9, nr. 8, pp. 1735-1780, 1997
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Low-complexity coding and decoding _{TANC 1997}

Author(s):	S. Hochreiter - J. Schmidhuber
Published:	Theoretical Aspects of Neural Computation (TANC 97), Hong Kong, Springer, pp. 297-306, 1997
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Unsupervised coding with Lococode _{ICANN 1997}

Author(s):	S. Hochreiter - J. Schmidhuber
Published:	In Proc. International Conference on Artificial Neural Networks 97, pp. 655-660, 1997
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Recurrent Neural Net Learning and Vanishing Gradient _{Fuzzy-Neuro Workshop 1997}

Author(s):	S. Hochreiter
Published:	In Proc. Fuzzy-Neuro Workshop, Soest, Germany, pp. 130-137, 1997
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LSTM can solve hard long time lag problems _{NIPS 1997}

Author(s):	S. Hochreiter - J. Schmidhuber
Published:	In Proc. Neural Information Processing Systems 97, pp. 473-479, 1997
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Bridging Long Time Lags by Weight Guessing and Long Short-Term Memory _{Spatiotemporal models in biological and artificial systems 1996}

Author(s):	S. Hochreiter - J. Schmidhuber
Published:	In Proc. Spatiotemporal models in biological and artificial systems. IOS Press, Amsterdam, Netherlands. Pages 65-72, 1996
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Guessing can outperform many long time lag algorithms _{Tech. Note 1996}

Author(s):	J. Schmidhuber - S. Hochreiter
Published:	Technical Note IDSIA-19-96, 1996
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Flat minima _{Tech. Report 1995}

Author(s):	S. Hochreiter - J. Schmidhuber
Published:	Neural Computation, vol. 9, nr. 1, pp. 1-42, 1997
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Reinforcement driven information acquisition in non-deterministic environments _{ICANN 1995}

Author(s):	J. Storck - S. Hochreiter - J. Schmidhuber
Published:	In Proc. International Conference on Artificial Neural Networks 95, pp. 159-164, 1995
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Long short term memory _{Tech. Report 1995}

Author(s):	S. Hochreiter - J. Schmidhuber
Published:	Technical report FKI-207-95, 1995
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Simplifying nets by discovering flat minima _{NIPS 1995}

Author(s):	S. Hochreiter - J. Schmidhuber
Published:	In Proc. Neural Information Processing Systems 7, pp. 529-536, 1995
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Flat minimum search finds simple nets _{Tech. Report 1994}

Author(s):	S. Hochreiter - J. Schmidhuber
Published:	Technical report FKI-200-94, 1994
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Untersuchungen zu dynamischen neuronalen Netzen _{Diploma Thesis 1991}

Author(s):	S. Hochreiter
Published:	Diploma thesis. Institut für Informatik, Lehrstuhl Prof. Brauer, Technische Universität München, 1991
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Implementierung und Anwendung eines ‘neuronalen’ Echtzeit-Lernalgorithmus für reaktive Umgebungen _{Prac. Work 1990}

Author(s):	S. Hochreiter
Published:	Practical work. Institut für Informatik, Lehrstuhl Prof. Brauer, Technische Universität München, 1990
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