**Selected conference publications on LSTM and other RNNs / feedback networks:**
**52.**
M. Stollenga, W. Beyon, 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.

**51.**
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.

**50.** J. Koutnik, K. Greff, F. Gomez, J. Schmidhuber. A Clockwork RNN. Proc. 31st International Conference on Machine Learning (ICML), p. 1845-1853, Beijing, 2014. Preprint arXiv:1402.3511 [cs.NE].

**49.**
M. Stollenga, J.Masci, F. Gomez, J. Schmidhuber.
Deep Networks with Internal Selective Attention through Feedback Connections.
Preprint arXiv:1407.3068 [cs.CV].
Advances in Neural Information Processing Systems (NIPS), 2014.

**48.**
J. Koutnik, G. Cuccu, J. Schmidhuber, F. Gomez.
Evolving Large-Scale Neural Networks for Vision-Based Reinforcement Learning.
In *Proceedings of the Genetic and Evolutionary
Computation Conference* (GECCO), Amsterdam, 2013.
PDF.

**47.**
J. Koutnik, G. Cuccu, J. Schmidhuber, F. Gomez.
Evolving Large-Scale Neural Networks for Vision-Based TORCS.
In *Foundations of Digital Games* (FDG), Chania, Crete, 2013.
PDF.

**46.**
R. K. Srivastava, B. Steunebrink, M. Stollenga, J. Schmidhuber
Continually Adding Self-Invented Problems to the Repertoire: First Experiments with PowerPlay.
Proc. *IEEE Conference on Development and Learning / EpiRob 2012*
(ICDL-EpiRob'12), San Diego, 2012. PDF.

**45.**
F. Gomez, J. Koutnik, J. Schmidhuber.
Compressed Network Complexity Search.
In C. Coello Coello, V. Cutello, K. Deb, S. Forrest, G. Nicosia, M. Pavone, eds.,
*12th Int. Conf. on Parallel Problem Solving from Nature - PPSN XII,*
Taormina, 2012. *Nominated for best paper award.*
PDF.

**44.**
R. K. Srivastava, F. Gomez, J. Schmidhuber.
Generalized Compressed Network Search.
In C. Coello Coello, V. Cutello, K. Deb, S. Forrest, G. Nicosia, M. Pavone, eds.,
*12th Int. Conf. on Parallel Problem Solving from Nature - PPSN XII,*
Taormina, 2012.
PDF.

**43.**
M. Ring, T. Schaul, J. Schmidhuber.
The Two-Dimensional Organization of Behavior.
In Proc. *Joint IEEE International Conference on Development and Learning (ICDL) and on Epigenetic Robotics (ICDL-EpiRob 2011)*, Frankfurt, 2011.
PDF.

**42.**
J. Schmidhuber, D. Ciresan, U. Meier, J. Masci, A. Graves.
On Fast Deep Nets for AGI Vision.
In Proc. *Fourth Conference on Artificial General Intelligence (AGI-11)*,
Google, Mountain View, California, 2011.
PDF.

**41.**
L. Gisslen, M. Luciw, V. Graziano, J. Schmidhuber.
Sequential Constant Size Compressors and Reinforcement Learning.
In Proc. *Fourth Conference on Artificial General Intelligence (AGI-11)*,
Google, Mountain View, California, 2011.
PDF.
**Kurzweil Prize for Best AGI Paper 2011.**

**40.**
T. Glasmachers, T. Schaul, Sun Yi, D. Wierstra, J. Schmidhuber.
Exponential Natural Evolution Strategies.
*Proceedings of the Genetic and Evolutionary Computation Conference*
(GECCO-2010), Portland, 2010.
PDF.
GECCO 2010 best paper nomination.

**39.**
J. Koutnik, F. Gomez, J. Schmidhuber (2010). Evolving Neural Networks in Compressed Weight Space. *Proceedings of the Genetic and Evolutionary Computation Conference*
(GECCO-2010), Portland, 2010.
PDF.

**38.**
J. Koutnik, F. Gomez, J. Schmidhuber.
Searching for Minimal Neural Networks in Fourier Space.
*The 3rd Conference on Artificial General Intelligence (AGI-10)*, 2010.
PDF.

**37.**
M. Grüttner, F. Sehnke, T. Schaul, J. Schmidhuber.
Multi-Dimensional Deep Memory Go-Player for Parameter Exploring Policy Gradients.
*Proceedings of the International Conference on Artificial Neural Networks (ICANN-2010)*,
Greece, 2010.

**36.**
A. Graves, J. Schmidhuber.
Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks.
*Advances in Neural Information Processing Systems 22, NIPS'22,* p 545-552,
Vancouver, MIT Press, 2009.
PDF.

**35.**
J. Bayer, D. Wierstra, J. Togelius, J. Schmidhuber.
Evolving memory cell structures for sequence learning.
*Proceedings of the 19th International Conference on Artificial Neural Networks (ICANN-09)*,
Cyprus, 2009.
PDF.

**34.**
J. Unkelbach, S. Yi, J. Schmidhuber.
An EM based training algorithm for recurrent neural networks.
*Proceedings of the 19th International Conference on Artificial Neural Networks (ICANN-09)*,
Cyprus, 2009.
PDF.

**33.**
A. Graves, S. Fernandez,M. Liwicki, H. Bunke, J. Schmidhuber.
Unconstrained online handwriting recognition with recurrent neural networks.
*Advances in Neural Information Processing Systems 21, NIPS'21,*
p 577-584, 2008, MIT Press,
Cambridge, MA, 2008.
PDF.

**32.**
T. Rückstiess, M. Felder, J. Schmidhuber.
State-Dependent Exploration for Policy Gradient Methods.
*19th European Conference on Machine Learning ECML,*
2008.
PDF.

**31.**
T. Schaul and J. Schmidhuber. A Scalable Neural Network Architecture for Board Games.
Proceedings of the *2008 IEEE Symposium on Computational Intelligence in Games* CIG-2008,
Perth, Australia, 2008, in press.
PDF.

**30.**
F. Sehnke, C. Osendorfer, T. Rückstiess, A. Graves, J. Peters, and J. Schmidhuber.
Policy gradients with parameter-based exploration for control.
In J. Koutnik V. Kurkova, R. Neruda, editors,
Proceedings of the
*International Conference on Artificial Neural Networks ICANN-2008*
ICANN 2008, Prague, LNCS 5163, pages 387-396. Springer-Verlag Berlin Heidelberg, 2008.
PDF.

**29.**
D. Wierstra, T. Schaul, J. Peters, J. Schmidhuber. Fitness Expectation Maximization.
*Proceedings of Parallel Problem Solving from Nature PPSN-2008*, Dortmund, 2008.
PDF.

**28.**
D. Wierstra, T. Schaul, J. Peters, J. Schmidhuber.
Natural Evolution Strategies.
Proceedings of *IEEE Congress on Evolutionary Computation CEC-2008*, Hongkong, 2008.
PDF.

**27.**
D. Wierstra, J. Schmidhuber.
Policy Gradient Critics.
*18th European Conference on Machine Learning ECML,*
Warszaw, 2007.
PDF.

**26.**
M. Liwicki, A. Graves, H. Bunke, J. Schmidhuber. A novel approach
to on-line handwriting recognition based on bidirectional
long short-term memory networks. *9th International Conference
on Document Analysis and Recognition,* 2007.
PDF.

**25.**
S. Fernandez, A. Graves, J. Schmidhuber. An application of
recurrent neural networks to discriminative keyword
spotting.
*Intl. Conf. on Artificial Neural Networks ICANN'07,*
2007.
PDF.

**24.**
A. Graves, S. Fernandez, J. Schmidhuber. Multi-Dimensional Recurrent
Neural Networks.
*Intl. Conf. on Artificial Neural Networks ICANN'07,*
2007.
Preprint: arxiv:0705.2011.
PDF.

**23.**
D. Wierstra, A. Foerster, J. Schmidhuber. Solving Deep Memory POMDPs
with Recurrent Policy Gradients.
*Intl. Conf. on Artificial Neural Networks ICANN'07,*
2007.

**22.**
A. Foerster, A. Graves, J. Schmidhuber.
RNN-based Learning of Compact Maps for Efficient Robot Localization.
*
15th European Symposium on Artificial Neural Networks,
ESANN, Bruges, Belgium, 2007
PDF.
*

**21.**
S. Fernandez, A. Graves, J. Schmidhuber.
Sequence labelling in structured domains with
hierarchical recurrent neural networks. In Proc.
*20th International Joint Conference on Artificial Intelligence*
(IJCAI 07), p. 774-779, Hyderabad, India, 2007 (talk).
PDF.

**20.**
F. Gomez, J. Schmidhuber, and R. Miikkulainen (2006).
Efficient Non-Linear Control through Neuroevolution.
Proceedings of the *European Conference
on Machine Learning* (ECML-06, Berlin).
PDF.

**19.**
H. Mayer, F. Gomez, D. Wierstra, I. Nagy, A. Knoll, and J. Schmidhuber (2006).
A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent
Neural Networks. Proceedings of the *International Conference on
Intelligent Robotics and Systems* (IROS-06, Beijing).
PDF.

**18.**
A. Graves, S. Fernandez, F. Gomez, J. Schmidhuber.
Connectionist Temporal Classification: Labelling Unsegmented Sequence
Data with Recurrent Neural Networks. Proceedings of
the International Conference on Machine Learning (ICML-06, Pittsburgh), 2006.
PDF.

**17.**
J. Schmidhuber and D. Wierstra and F. J. Gomez.
Hybrid Neuroevolution/Regression Search for Sequence Prediction.
*Proceedings of the 19th International Joint Conference
on Artificial Intelligence (IJCAI),*
2005.
PDF.

**16.**
D. Wierstra and F. Gomez and J. Schmidhuber.
Modeling systems with internal state using Evolino.
In *Proc. of the 2005 conference on genetic and
evolutionary computation (GECCO)*, Washington, D. C.,
pp. 1795-1802, ACM Press, New York, NY, USA, 2005.
(Got a GECCO best paper award).
PDF.

**15.**
F. Gomez and J. Schmidhuber.
Co-evolving recurrent neurons learn deep memory POMDPs.
In *Proc. of the 2005 conference on genetic and
evolutionary computation (GECCO)*, Washington, D. C.,
pp. 1795-1802, ACM Press, New York, NY, USA, 2005.
(Nominated for a best paper award).
PDF.

**14.**
F. J. Gomez and J. Schmidhuber.
Evolving modular fast-weight networks for control.
In W. Duch et al. (Eds.):
*Proc. Intl. Conf. on Artificial Neural Networks ICANN'05,*
LNCS 3697, pp. 383-389, Springer-Verlag Berlin Heidelberg, 2005.
*Featuring a 3-wheeled reinforcement learning robot with distance sensors
that learns without a teacher to balance a jointed pole
indefinitely in a confined 3D environment.*
PDF.

**13.**
A. Graves, S. Fernandez, and J. Schmidhuber.
Bidirectional LSTM Networks for Improved Phoneme Classification and Recognition.
In W. Duch et al. (Eds.):
*Proc. Intl. Conf. on Artificial Neural Networks ICANN'05,*
LNCS 3697, pp. 799-804, Springer-Verlag Berlin Heidelberg, 2005.
PDF.

**12.**
N. Beringer and A. Graves and F. Schiel and J. Schmidhuber.
Classifying unprompted speech by retraining LSTM Nets.
In W. Duch et al. (Eds.):
*Proc. Intl. Conf. on Artificial Neural Networks ICANN'05,*
LNCS 3696, pp. 575-581, Springer-Verlag Berlin Heidelberg, 2005.
PDF.

**11.**
A. Graves and J. Schmidhuber.
Framewise Phoneme Classification with Bidirectional LSTM Networks.
In Proc. International Joint Conference on Neural Networks
IJCNN'05, 2005.
PDF.

**10.**
A. Graves, D. Eck and N. Beringer, J. Schmidhuber.
Biologically Plausible Speech Recognition with LSTM Neural Nets.
In J. Ijspeert (Ed.),
* First Intl. Workshop on Biologically
Inspired Approaches to Advanced Information Technology, *
Bio-ADIT 2004, Lausanne, Switzerland, p. 175-184, 2004.
PDF .

**9.**
A. Graves, N. Beringer, J. Schmidhuber.
A Comparison Between Spiking and Differentiable Recurrent
Neural Networks on Spoken Digit Recognition.
In * Proc. 23rd International Conference on modelling, identification,
and control * (IASTED), 2004.
PDF .

**8.**
B. Bakker, V. Zhumatiy, G. Gruener, and J. Schmidhuber.
A Robot that Reinforcement-Learns to Identify and Memorize Important
Previous Observations
(PDF).
In Proceedings of the 2003 IEEE/RSJ
International Conference on Intelligent Robots and Systems, IROS2003, 2003.

**7.**
B. Bakker and J. Schmidhuber.
Hierarchical Reinforcement
Learning Based on Subgoal Discovery and Subpolicy Specialization
(PDF).
In F. Groen, N. Amato, A. Bonarini, E. Yoshida, and B. Kröse (Eds.),
*
Proceedings of the 8-th Conference on Intelligent Autonomous Systems,
IAS-8, * Amsterdam, The Netherlands, p. 438-445, 2004.

**6.**
B. Bakker, F. Linaker, J. Schmidhuber.
Reinforcement Learning in Partially Observable Mobile Robot
Domains Using Unsupervised Event Extraction.
In Proceedings of the 2002
IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS 2002), Lausanne, 2002.
PDF .

**5.**
B. Bakker.
Reinforcement Learning with Long Short-Term Memory.
Advances in Neural Information Processing
Systems 13 (NIPS'13), 2002.
(On J. Schmidhuber's CSEM grant 2002.)

**4.**
D. Eck and J. Schmidhuber.
Learning The Long-Term Structure of the Blues.
In J. Dorronsoro, ed.,
Proceedings of Int. Conf. on Artificial Neural Networks
ICANN'02, Madrid, pages 284-289, Springer, Berlin, 2002.
PDF.

**3.**
M. Klapper-Rybicka, N. N. Schraudolph, J. Schmidhuber.
Unsupervised Learning in LSTM Recurrent Neural Networks.
In G. Dorffner, H. Bischof, K. Hornik, eds.,
Proceedings of Int. Conf. on Artificial Neural Networks
ICANN'01, Vienna, LNCS 2130, pages 684-691, Springer, 2001.
PDF.

**2.**
S. Hochreiter and J. Schmidhuber.
LSTM can solve hard long time lag problems.
In M. C. Mozer, M. I. Jordan, T. Petsche, eds.,
*Advances in Neural Information Processing Systems 9, NIPS'9,*
pages 473-479, MIT Press, Cambridge MA, 1997.
PDF .
HTML.