Sepp Hochreiter (English Wikipedia)

Arjona-Medina, J. A.; Gillhofer, M.; Widrich, M.; Unterthiner, T.; Hochreiter, S. (2018). "RUDDER: Return Decomposition for Delayed Rewards". arXiv:1806.07857 [cs.LG].
Clevert, D.-A.; Mayr, A.; Unterthiner, T.; Hochreiter, S. (2015). "Rectified Factor Networks". arXiv:1502.06464v2 [cs.LG].
Clevert, D.-A.; Mayr, A.; Unterthiner, T.; Hochreiter, S. (2015). Rectified Factor Networks. Advances in Neural Information Processing Systems 29. arXiv:1502.06464.
Ramsauer, H.; Schäfl, B.; Lehner, J.; Seidl, P.; Widrich, M.; Gruber, L.; Holzleitner, M.; Pavlović, M.; Sandve, G. K.; Greiff, V.; Kreil, D.; Kopp, M.; Klambauer, G.; Brandstetter, J.; Hochreiter, S. (2020). "Hopfield Networks is All You Need". arXiv:2008.02217 [cs.NE].
Widrich, M.; Schäfl, B.; Ramsauer, H.; Pavlović, M.; Gruber, L.; Holzleitner, M.; Brandstetter, J.; Sandve, G. K.; Greiff, V.; Hochreiter, S.; Klambauer, G. (2020). "Modern Hopfield Networks and Attention for Immune Repertoire Classification". arXiv:2007.13505 [cs.LG].

camda.info

"CAMDA 2021". 20th International Conference on Critical Assessment of Massive Data Analysis. Retrieved 2021-02-13.

doi.org

Hochreiter, S.; Schmidhuber, J. (1997). "Long Short-Term Memory". Neural Computation. 9 (8): 1735–1780. doi:10.1162/neco.1997.9.8.1735. PMID 9377276. S2CID 1915014.
Hochreiter, S.; Younger, A. S.; Conwell, P. R. (2001). "Learning to Learn Using Gradient Descent". Artificial Neural Networks — ICANN 2001 (PDF). Lecture Notes in Computer Science. Vol. 2130. pp. 87–94. CiteSeerX 10.1.1.5.323. doi:10.1007/3-540-44668-0_13. ISBN 978-3-540-42486-4. ISSN 0302-9743. S2CID 52872549.
Hochreiter, S. (1998). "The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 06 (2): 107–116. doi:10.1142/S0218488598000094. ISSN 0218-4885. S2CID 18452318.
Hochreiter, S.; Heusel, M.; Obermayer, K. (2007). "Fast model-based protein homology detection without alignment". Bioinformatics. 23 (14): 1728–1736. doi:10.1093/bioinformatics/btm247. PMID 17488755.
Hochreiter, S.; Schmidhuber, J. (1997). "Flat Minima". Neural Computation. 9 (1): 1–42. doi:10.1162/neco.1997.9.1.1. PMID 9117894. S2CID 733161.
Clevert, D.-A.; Unterthiner, T.; Povysil, G.; Hochreiter, S. (2017). "Rectified factor networks for biclustering of omics data". Bioinformatics. 33 (14): i59 – i66. doi:10.1093/bioinformatics/btx226. PMC 5870657. PMID 28881961.
Hochreiter, Sepp; Bodenhofer, Ulrich; Heusel, Martin; Mayr, Andreas; Mitterecker, Andreas; Kasim, Adetayo; Khamiakova, Tatsiana; Van Sanden, Suzy; Lin, Dan; Talloen, Willem; Bijnens, Luc; Göhlmann, Hinrich W. H.; Shkedy, Ziv; Clevert, Djork-Arné (2010-06-15). "FABIA: factor analysis for bicluster acquisition". Bioinformatics. 26 (12): 1520–1527. doi:10.1093/bioinformatics/btq227. PMC 2881408. PMID 20418340.
Hochreiter, S. (2013). "HapFABIA: Identification of very short segments of identity by descent characterized by rare variants in large sequencing data". Nucleic Acids Research. 41 (22): e202. doi:10.1093/nar/gkt1013. PMC 3905877. PMID 24174545.
Hochreiter, S.; Clevert, D.-A.; Obermayer, K. (2006). "A new summarization method for affymetrix probe level data". Bioinformatics. 22 (8): 943–949. doi:10.1093/bioinformatics/btl033. PMID 16473874.
Hochreiter, S.; Obermayer, K. (2006). "Support Vector Machines for Dyadic Data". Neural Computation. 18 (6): 1472–1510. CiteSeerX 10.1.1.228.5244. doi:10.1162/neco.2006.18.6.1472. PMID 16764511. S2CID 26201227.
Hochreiter, S.; Obermayer, K. (2006). Nonlinear Feature Selection with the Potential Support Vector Machine. Feature Extraction, Studies in Fuzziness and Soft Computing. pp. 419–438. doi:10.1007/978-3-540-35488-8_20. ISBN 978-3-540-35487-1.
Hochreiter, S.; Obermayer, K. (2004). "Gene Selection for Microarray Data". Kernel Methods in Computational Biology. MIT Press: 319–355. doi:10.7551/mitpress/4057.003.0020. ISBN 978-0-262-25692-6. Archived from the original on 2012-03-25.

googleblog.com

ai.googleblog.com

Khaitan, Pranav (May 18, 2016). "Chat Smarter with Allo". Google AI Blog. Retrieved 2021-10-20.

googleresearch.blogspot.co.at

"The neural networks behind Google Voice transcription". 11 August 2015.

googleresearch.blogspot.co.uk

"Google voice search: faster and more accurate". 24 September 2015.

iarai.ac.at

"IARAI – INSTITUTE OF ADVANCED RESEARCH IN ARTIFICIAL INTELLIGENCE". www.iarai.ac.at. Retrieved 2021-02-13.
"Sepp Hochreiter receives IEEE CIS Neural Networks Pioneer Award 2021 - IARAI". www.iarai.ac.at. 24 July 2020. Retrieved 3 June 2021.

idsia.ch

people.idsia.ch

Schmidhuber, J. (1990). Making the world differentiable: On Using Fully Recurrent Self-Supervised Neural Networks for Dynamic Reinforcement Learning and Planning in Non-Stationary Environments (PDF) (Technical report). Technical University Munich, Institute of Computer Science. FKI-126-90 (revised).

jku.at

bioinf.jku.at

Hochreiter, S. (1991). Untersuchungen zu dynamischen neuronalen Netzen (PDF) (diploma thesis). Technical University Munich, Institute of Computer Science.
Hochreiter, S.; Younger, A. S.; Conwell, P. R. (2001). "Learning to Learn Using Gradient Descent". Artificial Neural Networks — ICANN 2001 (PDF). Lecture Notes in Computer Science. Vol. 2130. pp. 87–94. CiteSeerX 10.1.1.5.323. doi:10.1007/3-540-44668-0_13. ISBN 978-3-540-42486-4. ISSN 0302-9743. S2CID 52872549.
Hochreiter, S. (1991). Implementierung und Anwendung eines neuronalen Echtzeit-Lernalgorithmus für reaktive Umgebungen (PDF) (Report). Technical University Munich, Institute of Computer Science.
Hochreiter, S.; Obermayer, K. (2003). "Classification and Feature Selection on Matrix Data with Application to Gene-Expression Analysis". 54th Session of the International Statistical Institute. Archived from the original on 2012-03-25.
Hochreiter, S.; Obermayer, K. (2004). "Gene Selection for Microarray Data". Kernel Methods in Computational Biology. MIT Press: 319–355. doi:10.7551/mitpress/4057.003.0020. ISBN 978-0-262-25692-6. Archived from the original on 2012-03-25.

nih.gov

pubmed.ncbi.nlm.nih.gov

Hochreiter, S.; Schmidhuber, J. (1997). "Long Short-Term Memory". Neural Computation. 9 (8): 1735–1780. doi:10.1162/neco.1997.9.8.1735. PMID 9377276. S2CID 1915014.
Hochreiter, S.; Heusel, M.; Obermayer, K. (2007). "Fast model-based protein homology detection without alignment". Bioinformatics. 23 (14): 1728–1736. doi:10.1093/bioinformatics/btm247. PMID 17488755.
Hochreiter, S.; Schmidhuber, J. (1997). "Flat Minima". Neural Computation. 9 (1): 1–42. doi:10.1162/neco.1997.9.1.1. PMID 9117894. S2CID 733161.
Clevert, D.-A.; Unterthiner, T.; Povysil, G.; Hochreiter, S. (2017). "Rectified factor networks for biclustering of omics data". Bioinformatics. 33 (14): i59 – i66. doi:10.1093/bioinformatics/btx226. PMC 5870657. PMID 28881961.
Hochreiter, Sepp; Bodenhofer, Ulrich; Heusel, Martin; Mayr, Andreas; Mitterecker, Andreas; Kasim, Adetayo; Khamiakova, Tatsiana; Van Sanden, Suzy; Lin, Dan; Talloen, Willem; Bijnens, Luc; Göhlmann, Hinrich W. H.; Shkedy, Ziv; Clevert, Djork-Arné (2010-06-15). "FABIA: factor analysis for bicluster acquisition". Bioinformatics. 26 (12): 1520–1527. doi:10.1093/bioinformatics/btq227. PMC 2881408. PMID 20418340.
Hochreiter, S. (2013). "HapFABIA: Identification of very short segments of identity by descent characterized by rare variants in large sequencing data". Nucleic Acids Research. 41 (22): e202. doi:10.1093/nar/gkt1013. PMC 3905877. PMID 24174545.
Hochreiter, S.; Clevert, D.-A.; Obermayer, K. (2006). "A new summarization method for affymetrix probe level data". Bioinformatics. 22 (8): 943–949. doi:10.1093/bioinformatics/btl033. PMID 16473874.
Hochreiter, S.; Obermayer, K. (2006). "Support Vector Machines for Dyadic Data". Neural Computation. 18 (6): 1472–1510. CiteSeerX 10.1.1.228.5244. doi:10.1162/neco.2006.18.6.1472. PMID 16764511. S2CID 26201227.

ncbi.nlm.nih.gov

Clevert, D.-A.; Unterthiner, T.; Povysil, G.; Hochreiter, S. (2017). "Rectified factor networks for biclustering of omics data". Bioinformatics. 33 (14): i59 – i66. doi:10.1093/bioinformatics/btx226. PMC 5870657. PMID 28881961.
Hochreiter, Sepp; Bodenhofer, Ulrich; Heusel, Martin; Mayr, Andreas; Mitterecker, Andreas; Kasim, Adetayo; Khamiakova, Tatsiana; Van Sanden, Suzy; Lin, Dan; Talloen, Willem; Bijnens, Luc; Göhlmann, Hinrich W. H.; Shkedy, Ziv; Clevert, Djork-Arné (2010-06-15). "FABIA: factor analysis for bicluster acquisition". Bioinformatics. 26 (12): 1520–1527. doi:10.1093/bioinformatics/btq227. PMC 2881408. PMID 20418340.
Hochreiter, S. (2013). "HapFABIA: Identification of very short segments of identity by descent characterized by rare variants in large sequencing data". Nucleic Acids Research. 41 (22): e202. doi:10.1093/nar/gkt1013. PMC 3905877. PMID 24174545.

psu.edu

citeseerx.ist.psu.edu

Hochreiter, S.; Younger, A. S.; Conwell, P. R. (2001). "Learning to Learn Using Gradient Descent". Artificial Neural Networks — ICANN 2001 (PDF). Lecture Notes in Computer Science. Vol. 2130. pp. 87–94. CiteSeerX 10.1.1.5.323. doi:10.1007/3-540-44668-0_13. ISBN 978-3-540-42486-4. ISSN 0302-9743. S2CID 52872549.
Hochreiter, S.; Bengio, Y.; Frasconi, P.; Schmidhuber, J. (2000). Kolen, J. F.; Kremer, S. C. (eds.). Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A Field Guide to Dynamical Recurrent Networks. New York City: IEEE Press. pp. 237–244. CiteSeerX 10.1.1.24.7321.
Hochreiter, S.; Obermayer, K. (2006). "Support Vector Machines for Dyadic Data". Neural Computation. 18 (6): 1472–1510. CiteSeerX 10.1.1.228.5244. doi:10.1162/neco.2006.18.6.1472. PMID 16764511. S2CID 26201227.

semanticscholar.org

api.semanticscholar.org

Hochreiter, S.; Schmidhuber, J. (1997). "Long Short-Term Memory". Neural Computation. 9 (8): 1735–1780. doi:10.1162/neco.1997.9.8.1735. PMID 9377276. S2CID 1915014.
Hochreiter, S.; Younger, A. S.; Conwell, P. R. (2001). "Learning to Learn Using Gradient Descent". Artificial Neural Networks — ICANN 2001 (PDF). Lecture Notes in Computer Science. Vol. 2130. pp. 87–94. CiteSeerX 10.1.1.5.323. doi:10.1007/3-540-44668-0_13. ISBN 978-3-540-42486-4. ISSN 0302-9743. S2CID 52872549.
Hochreiter, S. (1998). "The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 06 (2): 107–116. doi:10.1142/S0218488598000094. ISSN 0218-4885. S2CID 18452318.
Hochreiter, S.; Schmidhuber, J. (1997). "Flat Minima". Neural Computation. 9 (1): 1–42. doi:10.1162/neco.1997.9.1.1. PMID 9117894. S2CID 733161.
Hochreiter, S.; Obermayer, K. (2006). "Support Vector Machines for Dyadic Data". Neural Computation. 18 (6): 1472–1510. CiteSeerX 10.1.1.228.5244. doi:10.1162/neco.2006.18.6.1472. PMID 16764511. S2CID 26201227.

web.archive.org

Hochreiter, S.; Obermayer, K. (2003). "Classification and Feature Selection on Matrix Data with Application to Gene-Expression Analysis". 54th Session of the International Statistical Institute. Archived from the original on 2012-03-25.
Hochreiter, S.; Obermayer, K. (2004). "Gene Selection for Microarray Data". Kernel Methods in Computational Biology. MIT Press: 319–355. doi:10.7551/mitpress/4057.003.0020. ISBN 978-0-262-25692-6. Archived from the original on 2012-03-25.

worldcat.org

search.worldcat.org

Hochreiter, S.; Younger, A. S.; Conwell, P. R. (2001). "Learning to Learn Using Gradient Descent". Artificial Neural Networks — ICANN 2001 (PDF). Lecture Notes in Computer Science. Vol. 2130. pp. 87–94. CiteSeerX 10.1.1.5.323. doi:10.1007/3-540-44668-0_13. ISBN 978-3-540-42486-4. ISSN 0302-9743. S2CID 52872549.
Hochreiter, S. (1998). "The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 06 (2): 107–116. doi:10.1142/S0218488598000094. ISSN 0218-4885. S2CID 18452318.