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.
1a.
J. Schmidhuber.
Neural sequence chunkers.
Technical Report FKI-148-91, Institut für Informatik, Technische
Universität München, April 1991.