SUMMARY OF JUERGEN SCHMIDHUBER'S PROFESSIONAL ACADEMIC ACTIVITIES IN 2015

PUBLICATIONS OF 2015 IN SCHMIDHIUBER's GROUP
    (for pre-2015 and post-2015 articles, PDFs/HTMLs/overviews see http://www.idsia.ch/~juergen/onlinepub.html)

JOURNALS 2015
5. J. Schmidhuber. Deep Learning in Neural Networks: An Overview. Neural Networks, Volume 61, January 2015, Pages 85-117 (DOI: 10.1016/j.neunet.2014.09.003), published online in 2014. 
4. J. Schmidhuber. Deep Learning. Scholarpedia, 10(11):32832, 2015.
3. A. Giusti, J. Guzzi, D. Ciresan, F. Lin He, J. P. Rodriguez, F. Fontana, M. Faessler, C. Forster, J. Schmidhuber, G. A. Di Caro, D. Scaramuzza, L. Gambardella. A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots. IEEE Robotics and Automation Letters, 2015.
2. I. Arganda-Carreras, S. C. Turaga, D. R. Berger, D. Ciresan, A. Giusti, L. M. Gambardella, J. Schmidhuber, D. Laptev, S. Dwivedi, J. M. Buhmann, T. Liu, M. Seyedhosseini, T. Tasdizen, L. Kamentsky, R. Burget, V. Uher, X. Tan, C. Sun, T. Pham, E. Bas, M. G. Uzunbas, A. Cardona, J. Schindelin, H. S. Seung. Crowdsourcing the creation of image segmentation algorithms for connectomics. Frontiers in Neuroanatomy, 2015. Link.
1. V. R. Kompella, M. Stollenga, M. Luciw, J. Schmidhuber. Continual curiosity-driven skill acquisition from high-dimensional video inputs for humanoid robots. Artificial Intelligence, 2015, Doi:10.1016/j.artint.2015.02.001. 

CONFERENCES 2015 
6. K. Greff, R. K. Srivastava, J. Schmidhuber. Training Very Deep Networks. Advances in Neural Information Processing Systems (NIPS), 2015, in press. Preprint: arxiv:1505.00387.
5. M. Stollenga, W. Byeon, M. Liwicki, J. Schmidhuber. Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation. Advances in Neural Information Processing Systems (NIPS), 2015, in press. Preprint: arxiv:1506.07452.
4. R. K. Srivastava, J. Masci, F. Gomez, J. Schmidhuber: Understanding Locally Competitive Networks. International Conference on Learning Representations ICLR 2015. Preprint: arxiv:1410.1165.
3. P. Toufiq, D. Ciresan, A. Giusti - Efficient Classifier Training to Reduce False Merges in Electron Microscopy Segmentation (ICCV 2015)
2. D. Ciresan, U. Meier - Multi-Column Deep Neural Networks for offline handwritten Chinese character classification (IJCNN 2015)
1. E. Nivel, K.R. Thorisson, B.R. Steunebrink, J. Schmidhuber. Anytime Bounded Rationality. In Proceedings of the 8th Conference on Artificial General Intelligence (AGI 2015), LNAI 9205, pages 121-130. Springer, Heidelberg, 2015. 

FORMER DOCTORAL STUDENTS OF JS WHO DEFENDED THEIR THESIS IN 2015
1. Jonathan Masci
2. Mikhail 'Kail' Frank
3. Juergen 'Juxi' Leitner
4. Somayeh Danafar
5. Varun Kompella
6. Justin Bayer

LARGE IT COMPANIES USING ALGORITHMS DEVELOPED IN SCHMIDHUBER'S LAB
Google / BAIDU / Microsoft / IBM and others used our RNNs and other NNs to improve speech processing, machine translation, computer vision, etc  

SELECTED INVITED TALKS 2014
Dec 3-16, NYC (RenTec) & Montreal (3 NIPS workshops)
Nov 4-5, London: Financial Times, Deep Learning Meetup
Oct 28, Stuttgart
Oct 27, Zurich
Oct 15, Delft Robotics
Oct 5, 7, 9, Munich: MICCAI and Deep Learning meetup 
Sep 14, Bern: Keynote for Swiss eHealth summit
Aug 17-20 Seattle: IEEE distinguished lecture / Microsoft Research, Amazon
Aug 7-16, San Francisco: Deep Learning WS & Plenary @ INNS BigData / Apple Inc. & others
Aug 3-6: Talks at MIT
Jul 23, Berlin: AGI Keynote
Jun 12, Munich: Big Techday
Jun 9-10, Frankfurt: Deep Learning for Finance
May 27, Oslo: Telenor
May 19-21, Shenzhen / Hongkong: Huawei STW
May 5-8, L.A.: TED-style talk for XPRIZE (Future of Mind)
May 1-4, NYC

TEACHING 2015
WS 2015/2016: Masters Course Intelligent Systems (Machine Learning)

SCIENTIFIC SERVICE 2015
JS reviewed scientific grant proposals and papers, and served on the editorial boards of various journals.