postdoctoral thesis
Juergen Schmidhuber
schmidhu at informatik.tu-muenchen.de
Tue Feb 15 04:06:19 EST 1994
---------------- postdoctoral thesis ----------------
Juergen Schmidhuber
Technische Universitaet Muenchen
(submitted April 1993, accepted October 1993)
-----------------------------------------------------
NETZWERKARCHITEKTUREN, ZIELFUNKTIONEN UND KETTENREGEL
Es gibt relativ neuartige, auf R"uckkopplung basierende
k"unstliche neuronale Netze (KNN), deren F"ahigkeiten
betr"achtlich "uber simple Musterassoziation hinausge-
hen. Diese KNN gestatten im Prinzip die Implementierung
beliebiger auf einem herk"ommlichen sequentiell arbei-
tenden Digitalrechner berechenbarer Funktionen. Im Ge-
gensatz zu herk"ommlichen Rechnern l"a"st sich dabei
jedoch die Qualit"at der Ausgaben (formal spezifiziert
durch eine sinnvolle Zielfunktion) bez"uglich der
``Software'' (bei KNN die Gewichtsmatrix) mathematisch
differenzieren, was die Anwendung der Kettenregel zur
Herleitung gradientenbasierter Software"anderungsalgo-
rithmen erm"oglicht. Die Arbeit verdeutlicht dies durch
formale Herleitung einer Reihe neuartiger Lernalgorith-
men aus folgenden Bereichen: (1) "uberwachtes Lernen
sequentiellen Ein/Ausgabeverhaltens mit zyklischen und
azyklischen Architekturen, (2) ``Reinforcement Lernen''
und Subzielgenerierung ohne informierten Lehrer, (3)
un"uberwachtes Lernen zur Redundanzextraktion aus Ein-
gaben und Eingabestr"omen. Zahlreiche Experimente zei-
gen M"oglichkeiten und Schranken dieser Lernalgorithmen
auf. Zum Abschluss wird ein ``selbstreferentielles''
neuronales Netzwerk pr"asentiert, welches theoretisch
lernen kann, seinen eigenen Software"anderungsalgorith-
mus zu "andern.
-----------------------------------------------------
The postdoctoral thesis above is now available (in unrevised form)
via ftp. To obtain a copy, follow the instructions at the end of
this message.
Here is additional information for those who are interested
but don't understand German (or are unfamiliar with Germany's
academic system): The postdoctoral thesis is part of a process
called ``Habilitation'' which is seen as a qualification for
tenure. The thesis is about learning algorithms derived by the
chain rule. It addresses supervised sequence learning, variants
of reinforcement learning, and unsupervised learning (for
redundancy reduction). Unlike some previous papers of mine,
it contains lots of experiments and lots of figures. Here is
a very brief summary based on pointers to recent English
publications upon which the thesis elaborates:
Chapters 2 and 3 are on supervised sequence learning and extend
publications [1] and [4]. Chapter 4 is on variants of learning
with a ``distal teacher'' and extends publication [7] (robot
experiments in chapter 4 were conducted by Eldracher and Baginski,
see e.g. [9]). Chapters 5, 6 and 7 describe unsupervised learning
algorithms based on detection of redundant information in input
patterns and pattern sequences: Chapter 5 elaborates on publication
[5], and chapter 6 extends publication [3]. Chapter 6 includes a
result by Peter Dayan, Richard Zemel and A. Pouget (SALK Institute)
who demonstrated that equation (4.3) in [3] with $\beta = 0, \alpha =
= \gamma =1$ is essentially equivalent to equation (5.1). Chapter
6 also includes experiments conducted by Stefanie Lindstaedt who
successfully applied the method in [3] to redundant images of
letters presented according to the probabilities of English
language, see [10]. Chapter 7 extends publications [2] and [8].
Experiments show how sequence processing neural nets using algorithms
for redundancy reduction can learn to bridge time lags (between
correlated events) of more than 1000 discrete time steps. Other
experiments use neural nets for text compression and compare them
to standard data compression algorithms. Finally, chapter 8
elaborates on publication [6].
-------------------------- References -------------------------------
[1] J. H. Schmidhuber. A fixed size storage O(n^3) time complexity
learning algorithm for fully recurrent continually running networks.
Neural Computation, 4(2):243--248, 1992.
[2] J. H. Schmidhuber. Learning complex, extended sequences using the
principle of history compression. Neural Computation, 4(2):234--242, 1992.
[3] J. H. Schmidhuber. Learning factorial codes by predictability
minimization. Neural Computation, 4(6):863--879, 1992.
[4] J. H. Schmidhuber. Learning to control fast-weight memories: An
alternative to recurrent nets. Neural Computation, 4(1):131--139, 1992.
[5] J. H. Schmidhuber and D. Prelinger. Discovering predictable
classifications. Neural Computation, 5(4):625--635, 1993.
[6] J. H. Schmidhuber. A self-referential weight matrix. In Proc. of
the Int. Conf. on Artificial Neural Networks, Amsterdam, pages 446--451.
Springer, 1993.
[7] J. H. Schmidhuber and R. Wahnsiedler. Planning simple trajectories
using neural subgoal generators. In J. A. Meyer, H. L. Roitblat, and S. W.
Wilson, editors, Proc. of the 2nd Int. Conf. on Simulation of Adaptive
Behavior, pages 196--202. MIT Press, 1992.
[8] J. H. Schmidhuber, M. C. Mozer, and D. Prelinger. Continuous history
compression. In H. Huening, S. Neuhauser, M. Raus, and W. Ritschel,
editors, Proc. of Intl. Workshop on Neural Networks, RWTH Aachen,
pages 87--95. Augustinus, 1993.
[9] M. Eldracher and B. Baginski. Neural subgoal generation using
backpropagation. In George G. Lendaris, Stephen Grossberg and Bart
Kosko, editors, Proc. of WCNN'93, Lawrence Erlbaum Associates, Inc.,
Hillsdale, pages = III-145--III-148, 1993.
[10] S. Lindstaedt. Comparison of unsupervised neural networks for
redundancy reduction. In M. C. Mozer, P. Smolensky, D. S. Touretzky,
J. L. Elman and A. S. Weigend, editors, Proc. of the 1993 Connectionist
Models Summer School, pages 308-315. Hillsdale, NJ: Erlbaum Associates,
1993.
----------------------------------------------------------------------
The thesis comes in three parts. To obtain a copy, do:
unix> ftp 131.159.8.35
Name: anonymous
Password: (your email address, please)
ftp> binary
ftp> cd pub/fki
ftp> get schmidhuber.habil.1.ps.Z
ftp> get schmidhuber.habil.2.ps.Z
ftp> get schmidhuber.habil.3.ps.Z
ftp> bye
unix> uncompress schmidhuber.habil.1.ps.Z
unix> lpr schmidhuber.habil.1.ps
.
.
.
Note: The layout is designed for conventional
European DINA4 format. Expect 145 pages.
----------------------------------------------------------------------
Dr. habil. J. H. Schmidhuber, Fakultaet fuer Informatik,
Technische Universitaet Muenchen, 80290 Muenchen, Germany
schmidhu at informatik.tu-muenchen.de
--------- postdoctoral thesis (unrevised) -----------
NETZWERKARCHITEKTUREN, ZIELFUNKTIONEN UND KETTENREGEL
Juergen Schmidhuber, TUM
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