Sepp Hochreiter

"Hochreiter" redirects here. For people with a similar surname, see Hochreither.
Sepp Hochreiter
Hochreiter in 2012
Born (1967-02-14) 14 February 1967 (age 57)
Mühldorf, West Germany
NationalityGerman
Alma materTechnische Universität München
Scientific career
FieldsMachine learning, bioinformatics
InstitutionsJohannes Kepler University Linz
ThesisGeneralisierung bei neuronalen Netzen geringer Komplexität (1999)
Doctoral advisorWilfried Brauer

Josef "Sepp" Hochreiter (born 14 February 1967) is a German computer scientist. Since 2018 he has led the Institute for Machine Learning at the Johannes Kepler University of Linz after having led the Institute of Bioinformatics from 2006 to 2018. In 2017 he became the head of the Linz Institute of Technology (LIT) AI Lab. Hochreiter is also a founding director of the Institute of Advanced Research in Artificial Intelligence (IARAI).[1] Previously, he was at Technische Universität Berlin, at University of Colorado Boulder, and at the Technical University of Munich. He is a chair of the Critical Assessment of Massive Data Analysis (CAMDA) conference.[2]

Hochreiter has made contributions in the fields of machine learning, deep learning and bioinformatics, most notably the development of the long short-term memory (LSTM) neural network architecture,[3][4] but also in meta-learning,[5] reinforcement learning[6][7] and biclustering with application to bioinformatics data.

  1. ^ "IARAI – INSTITUTE OF ADVANCED RESEARCH IN ARTIFICIAL INTELLIGENCE". www.iarai.ac.at. Retrieved 2021-02-13.
  2. ^ "CAMDA 2021". 20th International Conference on Critical Assessment of Massive Data Analysis. Retrieved 2021-02-13.
  3. ^ Cite error: The named reference thesis was invoked but never defined (see the help page).
  4. ^ Cite error: The named reference Neco was invoked but never defined (see the help page).
  5. ^ 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.
  6. ^ Hochreiter, S. (1991). Implementierung und Anwendung eines neuronalen Echtzeit-Lernalgorithmus für reaktive Umgebungen (PDF) (Report). Technical University Munich, Institute of Computer Science.
  7. ^ Arjona-Medina, J. A.; Gillhofer, M.; Widrich, M.; Unterthiner, T.; Hochreiter, S. (2018). "RUDDER: Return Decomposition for Delayed Rewards". arXiv:1806.07857 [cs.LG].