Breckenridge, Colorado, December 5 1997
Algorithms that manipulate symbolic information are capable of dealing
with highly structured data. On the other hand, many well known learning
systems, such as feedforward neural nets and mixture models, are limited
to domains in which instances are organized into static data structures,
like records or fixed-size arrays. Restricted classes of dynamical
models have been studied in connectionism. For example, recurrent neural
nets and (input/output) HMMs generalize feedforward nets and mixture models
to sequences, a particular case of dynamically structured data. However,
the range of useful dynamical data structures is clearly not limited to
sequences (i.e., linear chains, from a graphical perspective).
Examples of domains in which instances have a rich structure are
quite numerous. Data in multimedia applications or in hearth sciences have
temporal and spatial dimensions, generalizing sequences to regular multidimensional
grids. Compounds in chemistry and molecular biology are naturally represented
by undirected graphs. Complex graphical structures (such as labeled trees
and webs) are very common in syntactic pattern recognition. Other domains
such as automated reasoning, software engineering or the World Wide Web
also yield instances which are represented by directed graphs.
Unfortunately, connectionist models capable of naturally dealing
with dynamic data structures more general than sequences have received
relatively scarce attention in the literature until recently. A few exceptions
are recursive neural networks and hidden recursive models,
recent extensions of recurrent nets and HMMs that can learn directed acyclic
The aim of the workshop is to reach a unified view of formalisms
and tools for dealing with rich data representations, covering issues such
as computational power of recursive neural networks, probabilistic graphical
models for learning data structures, methods for learning with cyclic and
infinite graphs, methods for learning transduction from graphs to graphs.
The workshop aims to bring together researchers actively involved in this
and related areas. The discussion should address problems and novel potential
achievements related to algorithms and adaptive architectures for dynamical
data structures. A great interest and potential of discussion is expected
from people that have worked in the temporal domain, since sequences are
just a particular case of graphs. It is believed by the organizers that
there is a lot of room for research aiming to extend methods and theoretical
results from sequential spaces to more general graphical spaces.
The workshop will be open by one or two review talks by the organizers,
for introducing the problem of learning data structures, with emphasis
on some well assessed models such as recursive neural networks. Then a
set (about 10) of short talks focusing on specific aspects of dynamical
processing of sequences and data structures will be presented, followed
by open discussion among the participants. The amount of time for presentations
and group discussion will be evenly balanced.
The schedule and abstracts can be found here.
processing of data structures. The page (still under construction) collects
information about connectionist models for learning structured information.
International summer school on Neural Networks has lectures related
to the topics covered in this workshop.
'96 Workshop on Neural Networks and Structured Knowledge (NNSK)
held on August 12, 1996 in Budapest, Hungary, has covered similar topics.
modified: Thu Nov 6 19:26:45 MET 1997