Calls for Participation: NIPS*98 Workshops
Sue Becker
becker at curie.psychology.mcmaster.ca
Mon Aug 31 16:25:37 EDT 1998
Dear Connectionists,
Below are brief annoucements of the 20 NIPS*98 workshops taking place
in Breckenridge, Colorado on December 4-5 following the main
conference in Denver. Many of these have published web pages with
further details. See
http://www.cs.cmu.edu/Groups/NIPS/1998/Workshops.html
and the URLs listed below.
Rich Zemel and Sue Becker, NIPS*98 Workshops Co-chairs
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DYNAMICS IN NETWORKS OF SPIKING NEURONS
http://diwww.epfl.ch/w3lami/team/gerstner/NIPS_works.html
Organizer: W. Gerstner (Lausanne, Switzerland)
Networks of spiking neurons have several interesting dynamic properties,
for example very rapid and characteristic transients, synchronous firing
and asynchronous states. A better understanding of typical phenomena
has important implications for problems associated with neuronal coding
(spikes or rates). For example, the population activity is a rate-type
quantity, but does not need temporal averaging - which suggests fast
rate coding as a potential strategy. The idea of the workshop is to
start from mathematical models of network dynamics, see what is known in
terms of results, and then try to find out what the implications for
'coding' in the most general sense could be.
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POPULATION CODING
Organizers:
Glen D. Brown, The Salk Institute
Kechen Zhang, The Salk Institute
We will explore experimental approaches to population coding in three
parts. First, we will examine techniques for recording from populations of
neurons including electrode arrays and optical methods. Next, we will
discuss spike-sorting and other issues in data analysis. Finally, we will
examine strategies for interpreting population data, including population
recordings from the hippocampus. To facilitate discussion, we are
establishing a data base of neuronal-population recordings that will be
available for analysis and interpretation.
For more information, please contact
Glen Brown (glen at salk.edu) or Kechen Zhang (zhang at salk.edu)
Computational Neurobiology Laboratory
The Salk Institute for Biological Studies
10010 North Torrey Pines Road
La Jolla, CA 92037
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TEMPORAL CODING: IS THERE EVIDENCE FOR IT AND WHAT IS ITS FUNCTION?
http://www.cs.cmu.edu/Groups/NIPS/1998/Workshop-CFParticipation/hatsopoulos.html
Organizers:
Nicho Hatsopoulos and Harel Shouval
Brown University
Departments of Neuroscience and Physics
One of the most fundamental issues in neuroscience concerns the exact
nature of neural coding or representation. The standard view is that
information is represented in the firing rates of single or populations
of neurons. Recently, a growing body of research has provided evidence
for coding strategies based on more precise temporal relationships among
spikes. These are some of the questions that the workshop intends to
address:
1. What do we mean by temporal coding? What time resolution constitutes
a temporal code?
2. What evidence is there for temporal coding in the nervous system.
3. What functional role does it play? What computational problem can
it solve that firing rate cannot?
4. Is it feasible to implement given the properties of neurons and
their interactions?
We intend to organize it as a debate with formal presentations and
informal discussion with some of the major figures in the field.
Different views regarding this subject will be presented. We will
invite speakers doing work in a variety of areas including both
vertebrate and invertebrate systems.
----------------------------------------------------------------------
OPTICAL IMAGING OF THE VISUAL CORTEX
http://camelot.mssm.edu/~udi
Organizers: Ehud Kaplan, Gary Blasdel
It is clear that any attempt to model brain function or development will
require access to data about the spatio-temporal distribution of
activity in the brain. Optical imaging of the brain provides a unique
opportunity to obtain such maps, and thus is essential for scientists
who are interested in theoretical approaches to neuroscience. In
addition, contact of biologists with theoretical approaches could help
them focus their studies on the essential theoretical questions, and on
new computation, mathematical, or theoretical tools and techniques.
We therefore organized a 6-hour workshop on optical imaging of the
cortex, to deal with both technical issues and physiological results.
The workshop will have the format of a mini-symposium, and will be
chaired by Ehud Kaplan (Mt. Sinai School of Medicine) and Gary Blasdel
(Harvard).
Technical issues to be discussed include:
1. What is the best way to extract faint images from the noisy data?
2. How does one compare/relate functional maps?
3. What is the best wavelength for reflectance measurements?
4. What is the needed (or possible) spatial resolution?
5. How do you deal with brain movement and other artifacts?
See also: http://camelot.mssm.edu/~udi
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OLFACTORY CODING: MYTHS, MODELS AND DATA
http://www.wjh.harvard.edu/~linster/nips98.html
Organizers: Christane Linster, Frank Grasso and Wayne Getz
Currently, two main models of olfactory coding are competing with each
other: (1) the selective receptor, labeled line model whish has been
popularized by recent results from molecular biology, and (2), the
non-selective receptor, distributive coding model, supported mainly by
data from electrophysiology and imaging in the olfactory bulbs. In this
workshop, we will discuss experimental evidence for each model.
Theorticians and experimentalists together will discuss the implications
for olfactory codoing and for neural porprocessing in the olfactory bulb
and cortex for each of the two predominant, and possibly, intermediate,
models.
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STATISTICAL THEORIES OF CORTICAL FUNCTION
http://www.cnl.salk.edu/~rao/workshop.html
Organizers: Rajesh P.N. Rao, Salk Institute (rao at salk.edu)
Bruno A. Olshausen, UC Davis (bruno at redwood.ucdavis.edu)
Michael S. Lewicki, Salk Institute (lewicki at salk.edu)
Participants are invited to attend a post-NIPS workshop on theories of
cortical function based on well-defined statistical principles such as
maximum likelihood and Bayesian estimation. Topics that are expected to
be addressed include: statistical interpretations of the function of
lateral and cortico-cortical feedback connections, theories of
perception and neural representations in the cortex, and development of
cortical receptive field properties from natural signals.
For further details, see: http://www.cnl.salk.edu/~rao/workshop.html
----------------------------------------------------------------------
LEARNING FROM AMBIGUOUS AND COMPLEX EXAMPLES
Organizers:
Oded Maron, PHZ Capital Partners
Thomas Dietterich, Oregon State University
Frameworks such as supervised learning, unsupervised learning, and
reinforcement learning have many established algorithms and theoretical
tools to analyze them. However, there are many learning problems that
do not fall into any of these established frameworks. Specifically,
situations where the examples are ambiguously labeled or cannot be
simply represented as a feature vector tend to be difficult for these
frameworks. This workshop will bring together researchers who are
interested in learning from ambiguous and complex examples. The
workshop will include, but not be limited to, discussions of
Multiple-Instance Learning, TDNN, bounded inconsistency, and other
frameworks for learning in unusual situations.
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TURNKEY ALGORITHMS FOR IMPROVING GENERALIZERS
http://ic.arc.nasa.gov/ic/people/kagan/nips98.html
Organizers: Kagan Tumer and David Wolpert
NASA Ames Research Center
Abstract: Methods for improving generalizers, such as stacking, bagging,
boosting and error correcting output codes (ECOCs) have recently been
receiving a lot of attention. We call such techniques "turnkey"
techniques. This reflects the fact that they were designed to improve
the generalization ability of generic learning algorithms, without
detailed knowledge about the inner workings of those learners. Whether
one particular turnkey technique is, in general, "better" than all
others, and if so under what circumstances, is a hotly debated issue.
Furthermore, it isn't clear whether it is meaningful to ask that
question without specific prior assumptions (e.g., specific domain
knowledge). This workshop aims at investigating these issues, building
a solid understanding of how and when turnkey techniques help
generalization ability, and lay out a road map to where the turnkey
methods should go.
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MINING MASSIVE DATABASES: SCALABLE ALGORITHMS FOR DATA MINING
http://research.microsoft.com/~fayyad/nips98/
Organizers: Usama Fayyad and Padhraic Smyth
With the explosive growth in the number of "data owners", interest
in scalable, integrated, data mining tools is reaching new heights.
This 1-day workshop aims at bringing together researchers and
practitioners from several communities to address topics of mutual
interest (and misunderstanding) such as: scaling clustering and
prediction to large databases, robust algorithms for high
dimensions, mathmatical approaches to mining massive datasets,
anytime algorithms, and dealing with discrete, mixed, and
multimedia (unstructured) data. The invited talks will be used to
drive discussion around the issues raised, common problems, and
definitions of research problems that need to be addressed.
Important questions include: why the need for integration with
databases? why deal with massive data stores? What are most
effective ways to scale algorithms? How do we help unsophisticated
users visualize the data/models extracted?
Contact information:
Usama Fayyad (Microsoft Research), Fayyad at microsoft.com,
http://research.microsoft.com/~fayyad
Padhraic Smyth (U.C. Irvine), Smyth at sifnos.ics.uci.edu,
http://www.ics.uci.edu/~smyth/
----------------------------------------------------------------------
INTEGRATING SUPERVISED AND UNSUPERVISED LEARNING
www.cs.cmu.edu/~mccallum/supunsup
Organizers:
Rich Caruana, Just Research
Virginia de Sa, UCSF
Andrew McCallum
This workshop will debate the relationship between supervised and
unsupervised learning. The discussion will run the gamut from
examining the view that supervised learning can be performed by
unsupervised learning of the joint distribution between the inputs and
targets, to discussion of how natural learning systems do supervised
learning without explicit labels, to the presentation of practical
methods of combining supervised and unsupervised learning by using
unsupervised clustering or unlabelled data to augment a labelled
corpus. The debate should be fun because some attendees believe
supervised learning has clear advantages, while others believe
unsupervised learning is the only game worth playing in the long run.
More information (including a call for abstracts) can be found at
www.cs.cmu.edu/~mccallum/supunsup.
----------------------------------------------------------------------
LEARNING ON RELATIONAL DATA REPRESENTATIONS
http://ni.cs.tu-berlin.de/nips98/
Organizers:
Thore Graepel, TU Berlin, Germany
Ralf Herbrich, TU Berlin, Germany
Klaus Obermayer, TU Berlin, Germany
Symbolic (structured) data representations such as strings, graphs or
logical expressions often provide a more natural basis for learning than
vector space representations which are the standard paradigm in
connectionism. Symbolic representations are currently subject to an
intensive discussion (cf. the recent postings on the connectionist
mailing list), which focuses on the question if connectionist models can
adequately process symbolic input data. One way of dealing with
structured data is to characterize them in relation to each other. To
this end a set of data items can be characterized by defining a
dissimilarity or distance measure on pairs of data items and to provide
learning algorithms with a dissimilarity matrix of a set of training
data. Prior knowledge about the data at hand can be incorporated
explicitly in the definition of the dissimilarity measure. One can even
go as far as trying to learn a distance measure appropriate for the task
at hand. This procedure may provide a bridge between the vector space
and the "structural" approaches to pattern recognition and should thus
be of interest to people from both communities. Additionally, pairwise
and other non-vectorial input data occur frequently in empirical
sciences and pose new problems for supervised and unsupervised learning
techniques.
More information can be found at http://ni.cs.tu-berlin.de/nips98/
------------------------------------------------------------------
SEQUENTIAL INFERENCE AND LEARNING
http://svr-www.eng.cam.ac.uk/~jfgf/workshop.html
Organizers:
Mahesan Niranjan, Cambridge University Engineering Department
Arnaud Doucet, Cambridge University Engineering Department
Nando de Freitas, Cambridge University Engineering Department
Sequential techniques are important in many applications of neural
networks involving real-time signal processing, where data arrival
is inherently sequential. Furthermore, one might wish to adopt a
sequential training strategy to deal with non-stationarity in
signals, so that information from the recent past is lent more
credence than information from the distant past. Sequential methods
also allow us to efficiently compute important model diagnostic
tools such as the one-step-ahead prediction densities. The advent
of cheap and massive computational power has stimulated many recent
advances in this field, including dynamic graphical models,
Expectation-Maximisation (EM) inference and learning for dynamical
models, dynamic Kalman mixture models and sequential Monte Carlo
sampling methods. More importantly, such methods are being applied
to a large number of interesting real problems such as computer
vision, econometrics, medical prognosis, tracking, communications,
blind deconvolution, statistical diagnosis, automatic control and
neural network training.
_______________________________________________________________________________
ABSTRACTION AND HIERARCHY IN REINFORCEMENT LEARNING
http://www-anw.cs.umass.edu/~dprecup/call_for_participation.html
Organizers:
Tom Dietterich, Oregon State University
Leslie Kaelbling, Brown University
Ron Parr, Stanford University
Doina Precup, University of Massachusetts, Amherst
When making everyday decisions, people are able to foresee the
consequences of their possible courses of action at multiple levels of
abstraction. Recent research in reinforcement learning (RL) has focused
on the way in which knowledge about abstract actions and abstract
representations can be incorporated into the framework of Markov
Decision Processes (MDPs). Several theoretical results and applications
suggest that these methods can improve significantly the scalability of
reinforcement learning systems by accelerating learning and by promoting
sharing and re-use of learned subtasks. This workshop aims to address
the following issues in this area:
- Task formulation and automated task creation
- The degree and complexity of action models
- The integration of different abstraction methods
- Hidden state issues
- Utility and computational efficiency considerations
- Multi-layer abstractions
- Temporally extended perception
- The design of autonomous agents based on hierarchical RL architectures
We are looking for volunteers to lead discussions and participate in
panels. We will also accept some technical papers for presentations.
For more details, please check out the workshop page:
http://www-anw.cs.umass.edu/~dprecup/call_for_participation.html
----------------------------------------------------------------------
MOVEMENT PRIMITIVES: BUILDING BLOCKS FOR LEARNING MOTOR CONTROL
http://www-slab.usc.edu/events/nips98
Organizers: Stefan Schaal (USC/ERATO(JST)) and Steve DeWeerth (GaTech)
Traditionally, learning control has been dominated by representations that
generate low level actions in response to some measured state information.
The learning of appropriate trajectory plans or control policies is usually
based on optimization approaches and reinforcement learning. It is well known
that these methods do not scale well to high dimensional control problems,
that they are computationally very expensive, that they are not particularly
robust to unforeseen perturbations in the environment, and that it is hard to
re-use these representations for related movement tasks. In order to make
progress towards a better understanding of biology and to create movement
systems that can automatically build new representations, it seems to be
necessary to develop a framework of how to control and to learn control with
movement primitives. This workshop will bring together neuroscientists,
roboticists, engineers, and mathematicians to explore how to approach the
topic of movement primitives in a principled way. Topics of the workshop
include the questions such as: what are appropriate movement primitives, how
are primitives learned, how can primitives be inserted into control loops, how
are primitives sequenced, how are primitives combined to form new primitives,
how is sensory information used to modulate primitives, how primitives primed
for a particular task, etc. These topics will be addressed from a hybrid
perspective combining biological and artificial movement systems.
----------------------------------------------------------------------
LARGE MARGIN CLASSIFIERS
http://svm.first.gmd.de/nips98/
Organizers: Alex J. Smola, Peter Bartlett,
Bernhard Schoelkopf, Dale Schuurmans
Many pattern classifiers are represented as thresholded real-valued
functions, eg: sigmoid neural networks, support vector machines,
voting classifiers, and Bayesian schemes. Recent theoretical and
experimental results show that such learning algorithms frequently
produce classifiers with large margins---where the margin is the
amount by which the classifier's prediction is to the correct side
of threshold. This has led to the important discovery that there
is a connection between large margins and good generalization
performance: classifiers that achieve large margins on given
training data also tend to perform well on future test data.
This workshop aims to provide an overview of recent developments
in large margin classifiers (ranging from theoretical results to
applications), to explore connections with other methods, and to
identify directions for future research. The workshop will
consist of four sessions over two days:
- Mathematical Programming
- Support Vector and Kernel Methods,
- Voting Methods (Boosting, Bagging, Arcing, etc), and
- Connections with Other Topics (including an organized panel
discussion)
Further details can be found at http://svm.first.gmd.de/nips98/
----------------------------------------------------------------------
DEVELOPMENT AND MATURATION IN NATURAL AND ARTIFICIAL STRUCTURES
http://www.cs.cmu.edu/Groups/NIPS/1998/Workshop-CFParticipation/haith.html
Organizers:
Gary Haith, Computational Sciences, NASA Ames Research Center
Jeff Elman, Cognitive Science, UCSD
Silvano Colombano, Computational Sciences, NASA Ames Research Center
Marshall Haith, Developmental Psychology, University of Denver
We believe that an ongoing collaboration between computational work and
developmental work could help unravel some of the most difficult issues
in each domain. Computational work can address dynamic, hierarchical
developmental processes that have been relatively intractable to
traditional developmental analysis, and developmental principles and
theory can generate insight into the process of building and modeling
complex and adaptive computational structures. In hopes of bringing
developmental processes and analysis into the neural modeling mainstream,
this session will focus developmental modelers and theorists on the task
of constructing a set of working questions, issues and approaches. The
session will hopefully include researchers studying developmental
phenomena across all levels of scale and analysis, with the aim of
highlighting both system-specific and general features of development.
For more information, contact:
Gary Haith, Computational Sciences, NASA Ames Research Center
phone #: (650) 604-3049
FAX #: (650) 604-3594
E-mail: haith at ptolemy.arc.nasa.gov
Mail: NASA Ames Research Center
Mail Stop 269-3
Mountain View, CA 94035-1000
----------------------------------------------------------------------
HYBRID NEURAL SYMBOLIC INTEGRATION
http://osiris.sunderland.ac.uk/~cs0stw/wermter/workshops/nips-workshop.html
Organizers:
Stefan Wermter, University of Sunderland, UK
Ron Sun, University of Alabama, USA
In the past it was very controversial whether neural or symbolic
approaches alone will be sufficient to provide a general framework for
intelligent processing. The motivation for the integration of symbolic
and neural models of cognition and intelligent behavior comes from many
different sources. From the perspective of cognitive neuroscience, a
symbolic interpretation of an artificial neural network architecture is
desirable, since the brain has a neuronal structure and the capability to
perform symbolic processing. From the perspective of knowledge-based
processing, hybrid neural/symbolic representations are advantageous,
since different mutually complementary properties can be
integrated. However, neural representations show advantages for gradual
analog plausibility, learning, robust fault-tolerant processing, and
generalization to similar input. Areas of interest include: Integration
of symbolic and neural techniques for language and speech processing,
reasoning and inferencing, data mining, integration for vision, language,
multimedia; combining fuzzy/neuro techniques in engineering; exploratory
research in emergent symbolic behavior based on neural networks,
interpretation and explanation of neural networks, knowledge extraction
from neural networks, interacting knowledge representations, dynamic
systems and recurrent networks, evolutionary techniques for cognitive
tasks (language, reasoning, etc), autonomous learning systems for
cognitive agents that utilize both neural and symbolic learning
techniques.
For more information please see
http://osiris.sunderland.ac.uk/~cs0stw/wermter/workshops/nips-workshop.html
Workshop contact person:
Professor Stefan Wermter
Research Chair in Intelligent Systems
University of Sunderland
School of Computing & Information Systems
St Peters Way
Sunderland SR6 0DD
United Kingdom
phone: +44 191 515 3279
fax: +44 191 515 2781
email: stefan.wermter at sunderland.ac.uk
http://osiris.sunderland.ac.uk/~cs0stw/
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SIMPLE INFERENCE HEURISTICS VS. COMPLEX DECISION MACHINES
http://www.cs.cmu.edu/Groups/NIPS/1998/Workshop-CFParticipation/todd.html
Organizers: Peter M. Todd, Laura Martignon, Kathryn Blackmond Laskey
Participants and presentations are invited for this post-NIPS
workshop on the contrast in both psychology and machine learning
between a probabilistically- defined view of rational decision
making with its apparent demand for complex Bayesian models, and a
more performance-based view of rationality built on the use of
simple, fast and frugal decision heuristics.
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CONTINUOUS LEARNING
http://www.forwiss.uni-erlangen.de/aknn/cont-learn/
Organizers:
Peter Protzel, Lars Kindermann, Achim Lewandowski, and Michael Tagscherer
FORWISS and Chemnitz University of Technology, Germany
By continuous learning we mean that learning takes place all the time and
is not interrupted, that there is no difference between periods of training
and operation, and that learning AND operation start with the first
pattern. In this workshop, we will especially focus on the approximation of
non-linear, time-varying functions. The goal is modeling and adapting the
model to follow the changes of the underlying process, not merely
forecasting the next output. In order to facilitate the comparison of the
various methods, we provide different benchmark data sets and participants
are encouraged to discuss their results on these benchmarks during the
workshop.
Further information: http://www.forwiss.uni-erlangen.de/aknn/cont-learn/
----------------------------------------------------------------------
LEARNING CHIPS AND NEUROBOTS
http://bach.ece.jhu.edu/nips98
Organizers:
Gert Cauwenberghs, Johns Hopkins University
Ralph Etienne-Cummings, Johns Hopkins University
Marwan Jabri, Sydney University
This workshop aims at a better understanding of how different
approaches to learning and sensorimotor control, including
algorithms and hardware, from backgrounds in neuromorphic VLSI,
robotics, neural nets, AI, genetic programming etc. can be combined
to create more intelligent systems interacting with their
environment.
We encourage active participation, and welcome live demonstrations
of systems. The panel has a representation over a wide range of
disciplines. Machine learning approaches include: reinforcement
learning, TD-lambda (or predictive hebbian learning), Q-learning,
and classical as well as operand conditioning. VLSI implementations
cover some of these, integrated on-chip, plus the sensory and motor
interfaces. Evolutionary approaches cover genetic techniques,
applied to populations of robots. Finally, we have designers of
microrobots and walking robots on the panel. This list is by no
means exhaustive!
More information can be found at URL: http://bach.ece.jhu.edu/nips98
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