18.
S. Hochreiter and J. Schmidhuber.
Source separation as a by-product of regularization.
In M. S. Kearns, S. A. Solla, D. A. Cohn, eds.,
*Advances in Neural Information Processing Systems 11, NIPS'11,*
p. 459-465, MIT Press, Cambridge MA, 1999.
PDF .
HTML.

17.
S. Hochreiter and J. Schmidhuber.
LOCOCODE performs nonlinear ICA without knowing the
number of sources.
In J.-F. Cardoso and C. Jutten and P. Loubaton, eds.,
Proceedings of the First International Workshop on
Independent Component Analysis and Signal Separation
(ICA'99), 149-154, Aussois, France, 1999.

16.
S. Hochreiter and J. Schmidhuber.
Feature extraction through LOCOCODE.
PDF
.
HTML (some pictures missing).
Neural Computation 11(3): 679-714, 1999
(28 pages, 20 figures, 703 K, 4.9 M gunzipped).

15.
S. Hochreiter and J. Schmidhuber.
LOCOCODE versus PCA and ICA.
In L. Niklasson and M. Boden and T. Ziemke, eds.,
*Proceedings of the International Conference on
Artificial Neural Networks, Sweden*,
p. 669-674,
Springer, London, 1998.

14.
J. Schmidhuber.
Neural predictors for detecting and removing redundant information.
In H. Cruse, J. Dean, and H. Ritter, editors, *Adaptive Behavior
and Learning*. Kluwer, 1998.
PDF .
HTML.

13.
N. N. Schraudolph, M. Eldracher, J. Schmidhuber.
Processing Images by Semi-Linear Predictability Minimization.
Network, 10(2): 133-169, 1999 (1766 K).
PDF
.

12.
S. Hochreiter and J. Schmidhuber.
Low-complexity coding and decoding. In
K. M. Wong, I. King, D. Yeung, eds.,
*Theoretical Aspects of Neural Computation: a Multidisciplinary Perspective*,
pages 297-306, Springer, 1997.

11.
S. Hochreiter and J. Schmidhuber.
Unsupervised coding with LOCOCODE.
In W. Gerstner, A. Germond, M. Hasler, J.-D. Nicoud, eds.,
*Proceedings of the International Conference on
Artificial Neural Networks, Lausanne, Switzerland*,
Springer, 655-660, 1997.

10.
J. Schmidhuber and M. Eldracher and B. Foltin.
Semilinear predictability minimzation produces well-known
feature detectors.
Neural Computation, 8(4):773-786, 1996 (260 K).
PDF .
HTML.

9.
J. Schmidhuber.
The Neural Heat Exchanger.
In S. Amari, L. Xu, L. Chan, I. King, K. Leung, eds.,
*Progress in Neural Information
Processing: Proceedings of the Intl. Conference
on Neural Information Processing*, pages 194-197,
Springer, Hongkong, 1996. Earlier presentations
in talks at universities since 1990.
PDF .
HTML.

8.
J. Schmidhuber and B. Foltin.
Semilinear predictability minimization produces orientation
sensitive edge detectors.
Technical Report FKI-201-94, Fakultät für Informatik,
Technische Universität München, December 1994.

7.
J. Schmidhuber and D. Prelinger.
Discovering
predictable classifications.
Neural Computation, 5(4):625-635, 1993 (51 K).
PDF.
HTML.

6.
J. Schmidhuber and D. Prelinger.
Unsupervised extraction of predictable abstract features.
In *Proceedings of the International Conference on Artificial
Neural Networks, Amsterdam*, pages 601-604. Springer, 1993.

5.
J. Schmidhuber and D. Prelinger.
A novel unsupervised classification method.
In *Proc. of the Intl. Conf. on Artificial Neural Networks,
Brighton*, pages 91-96. IEE, 1993.

4.
J. Schmidhuber, M. C. Mozer, and D. Prelinger.
Continuous history compression.
In H. Hüning, S. Neuhauser,
M. Raus, and W. Ritschel, editors,
*Proc. of Intl. Workshop on Neural Networks, RWTH Aachen*, pages 87-95.
Augustinus, 1993.

3.
J. Schmidhuber.
Learning factorial
codes by predictability minimization.
Neural Computation, 4(6):863-879, 1992 (53 K).
PDF.
HTML.

2.
J. Schmidhuber and D. Prelinger.
Discovering predictable classifications.
Technical Report CU-CS-626-92, Dept. of Comp. Sci., University of
Colorado at Boulder, November 1992.

1.
J. Schmidhuber.
Learning factorial codes by predictability minimization.
Technical Report CU-CS-565-91, Dept. of Comp. Sci., University of
Colorado at Boulder, December 1991.

**
SEQUENCE COMPRESSION
**

Adaptive methods for sequence compression and sequence coding
are important instances of redundancy reduction
and unsupervised learning
(compare section above and work on
recurrent networks).

1j.
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.

1i.
J. Schmidhuber and S. Heil.
Compressing texts with neural nets. In
Dale, Moisl and Somers, eds.,
*Handbook of Natural Language Processing*,
Marcel Dekker, Inc.,
1998.

1h.
J. Schmidhuber and S. Heil.
Sequential neural text compression.
IEEE Transactions on Neural Networks,
7(1):142-146, 1996 (68 K).
PDF.
HTML.

1g.
J. Schmidhuber and S. Heil.
Predictive coding with neural nets: Application to text compression.
In G. Tesauro, D. S. Touretzky and T. K. Leen, eds.,
*Advances in Neural Information Processing Systems 7*, pages 1047-1054.
MIT Press, Cambridge MA, 1995.

1f.
J. Schmidhuber, M. C. Mozer, and D. Prelinger.
Continuous history compression.
In H. Hüning, S.
Neuhauser, M. Raus, and W. Ritschel, editors,
*Proc. of Intl. Workshop on Neural Networks, RWTH Aachen*, pages 87-95.
Augustinus, 1993.

1e.
J. Schmidhuber.
Learning complex,
extended sequences using the principle of history compression.
Neural Computation, 4(2):234-242, 1992 (41 K).
PDF.
HTML.

1d.
J. Schmidhuber.
Learning unambiguous reduced sequence descriptions.
In J. E. Moody, S. J. Hanson, and R. P. Lippman, editors, *
Advances in Neural Information Processing Systems 4, NIPS'4*, pages 291-298. San
Mateo, CA: Morgan Kaufmann, 1992.

1c.
J. Schmidhuber.
Adaptive history compression for learning to divide and conquer.
In *Proc. International Joint Conference on Neural Networks,
Singapore*, volume 2, pages 1130-1135. IEEE, 1991.

1b.
J. Schmidhuber.
Adaptive decomposition of time.
In T. Kohonen, K. Mäkisara,
O. Simula, and J. Kangas, editors,
*Artificial Neural Networks*, pages 909-914. Elsevier Science Publishers
B.V., North-Holland, 1991.