Jürgen Schmidhuber

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Jürgen H. Schmidhuber, a German computer scientist, researcher and entrepreneur in the field of artificial intelligence, in 2014 co-founder and subsequently chief scientist of the AI company NNAISENSE. His further academic and commercial affiliations include the Faculty of Computer Science, University of Lugano, SUPSI in Manno, the Swiss AI Lab IDSIA, Lugano, and, as student, docent, and from 2004 until 2009 as Professor Extraordinarius, the Technical University of Munich.

Jürgen Schmidhuber is known for his research on machine learning, genetic programming, universal AI along with his former postdoc Marcus Hutter, artificial neural networks in particular recurrent neural networks (RNN) and deep learning, where he along with Sepp Hochreiter coined the term long short-term memory, Zuse's calculating space, Gödel machines, universal search, theory of everything, digital physics, algorithmic information theory, Kolmogorov complexity, and low-complexity art.

=See also=
 * Deep Learning RNNaissance with Jürgen Schmidhuber

=Selected Publications=

1990 ...

 * Jürgen Schmidhuber (1990). Reinforcement Learning in Markovian and Non-Markovian Environments. NIPS 1990, pdf
 * Jürgen Schmidhuber, Rudolf Huber (1991). Learning to Generate Artificial Fovea Trajectories for Target Detection. International Journal of Neural Systems, Vol. 2, No. 1-2, pdf
 * Jürgen Schmidhuber, Rudolf Huber (1991). Using sequential adaptive Neuro-control for efficient Learning of Rotation and Translation Invariance. In Teuvo Kohonen, Kai Mäkisara, Olli Simula, Jari Kangas (eds.) (1991). Artificial Neural Networks. Elsevier
 * Jürgen Schmidhuber (1991). Dynamische neuronale Netze und das fundamentale raumzeitliche Lernproblem (Dynamic Neural Nets and the Fundamental Spatio-Temporal Credit Assignment Problem). Ph.D. thesis
 * Jürgen Schmidhuber (1993). Netzwerkarchitekturen, Zielfunktionen und Kettenregel. Habilitationsschrift, Technische Universität München (German)
 * Sepp Hochreiter, Jürgen Schmidhuber (1995). Simplifying Neural Nets by Discovering Flat Minima. In Gerald Tesauro, David S. Touretzky and Todd K. Leen (eds.), Advances in Neural Information Processing Systems 7, NIPS'7, pages 529-536. MIT Press
 * Sepp Hochreiter, Jürgen Schmidhuber (1997). Long short-term memory. Neural Computation, Vol. 9, No. 8, pdf
 * Jürgen Schmidhuber (1997). Low-Complexity Art. Leonardo, Journal of the International Society for the Arts, Sciences, and Technology, Vol. 30 No. 2, MIT Press
 * Marco Wiering, Jürgen Schmidhuber (1997). HQ-learning. Adaptive Behavior, Vol. 6, No 2
 * Marco Wiering, Jürgen Schmidhuber (1998). Fast online Q (λ). Machine Learning, Vol. 33, No. 1

2000 ...

 * Magdalena Klapper-Rybicka, Nicol N. Schraudolph, Jürgen Schmidhuber (2001). Unsupervised Learning in LSTM Recurrent Neural Networks. ICANN 2001
 * Jürgen Schmidhuber (2003). The New AI:General & Sound & Relevant for Physics. Technical Report IDSIA-04-03
 * Jürgen Schmidhuber (2004). Turing's impact. Nature 429
 * Faustino J. Gomez, Jürgen Schmidhuber (2005). Co-Evolving Recurrent Neurons Learn Deep Memory POMDPs. GECCO 2005, pdf
 * Jürgen Schmidhuber (2007). 2006: Celebrating 75 years of AI - History and Outlook: the Next 25 Years. arXiv:0708.4311
 * Jürgen Schmidhuber (2007). Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity & Creativity. arXiv:0709.0674
 * Jürgen Schmidhuber (2009). Ultimate Cognition à la Gödel. Cognitive Computation, Vol. 1, No. 2, pdf

2010 ...

 * Jürgen Schmidhuber (2010). Formal Theory of Fun and Creativity. ECML/PKDD, pdf
 * Jürgen Schmidhuber (2013). My First Deep Learning System of 1991 + Deep Learning Timeline 1962-2013. arXiv:1312.5548
 * Jürgen Schmidhuber (2014). Deep Learning in Neural Networks: An Overview. arXiv:1404.7828
 * Jürgen Schmidhuber (2015). Deep Learning in Neural Networks: An Overview. Neural Networks, Vol. 61
 * Jürgen Schmidhuber (2015). On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models. arXiv:1511.09249
 * Jürgen Schmidhuber (2018). One Big Net For Everything. arXiv:1802.08864

=External Links=
 * Jürgen Schmidhuber from Wikipedia
 * Jürgen Schmidhuber - The Mathematics Genealogy Project
 * Juergen Schmidhuber - Google Scholar Citations
 * Deep Learning - Scholarpedia by Jürgen Schmidhuber
 * Build An Optimal Scientist, Then Retire, Jürgen Schmidhuber Interview at h+ Magazine by Michael Anissimov, January 5, 2010

Schmidhuber Links
 * Juergen Schmidhuber's home page
 * Learning Robots/ Robot Learning
 * Reinforcement Learning and POMDPs
 * Universal Learning Machines - Optimal Universal AI
 * Very Deep Learning Since 1991
 * Neural Nets for Finance
 * Kurt Gödel by Jürgen Schmidhuber
 * Gödel Machine Home Page


 * Alan Turing by Jürgen Schmidhuber » Alan Turing
 * Konrad Zuse by Jürgen Schmidhuber » Konrad Zuse
 * Zuse's Thesis - Zuse hypothesis - Algorithmic Theory of Everything - Digital Physics, Rechnender Raum (Computing Space, Computing Cosmos) - Computable Universe - The Universe is a Computer - Theory of Everything


 * Femme Fractale: Lady in Red (1997-2010)
 * Formal Theory of Creativity and Fun and Intrinsic Motivation Explains Science, Art, Music, Humor
 * Videos of Juergen Schmidhuber & the Swiss AI Lab IDSIA

=References= Up one level