26 Search Results for "Hammer, Barbara"

Document
DOI: 10.4230/DagRep.1.8.67
Learning in the context of very high dimensional data (Dagstuhl Seminar 11341)

Authors: Michael Biehl, Barbara Hammer, Erzsébet Merényi, Alessandro Sperduti, and Thomas Villman

Published in: Dagstuhl Reports, Volume 1, Issue 8 (2011)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 11341 "Learning in the context of very high dimensional data". The aim of the seminar was to bring together researchers who develop, investigate, or apply machine learning methods for very high dimensional data to advance this important field of research. The focus was be on broadly applicable methods and processing pipelines, which offer efficient solutions for high-dimensional data analysis appropriate for a wide range of application scenarios.
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Michael Biehl, Barbara Hammer, Erzsébet Merényi, Alessandro Sperduti, and Thomas Villman. Learning in the context of very high dimensional data (Dagstuhl Seminar 11341). In Dagstuhl Reports, Volume 1, Issue 8, pp. 67-95, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2011)

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@Article{biehl_et_al:DagRep.1.8.67,
  author =	{Biehl, Michael and Hammer, Barbara and Mer\'{e}nyi, Erzs\'{e}bet and Sperduti, Alessandro and Villman, Thomas},
  title =	{{Learning in the context of very high dimensional data (Dagstuhl Seminar 11341)}},
  pages =	{67--95},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2011},
  volume =	{1},
  number =	{8},
  editor =	{Biehl, Michael and Hammer, Barbara and Mer\'{e}nyi, Erzs\'{e}bet and Sperduti, Alessandro and Villman, Thomas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.1.8.67},
  URN =		{urn:nbn:de:0030-drops-33125},
  doi =		{10.4230/DagRep.1.8.67},
  annote =	{Keywords: Curse of dimensionality, Dimensionality reduction, Regularization Deep learning, Visualization}
}
Document
DOI: 10.4230/DagSemProc.10302.1
10302 Abstracts Collection – Learning paradigms in dynamic environments

Authors: Barbara Hammer, Pascal Hitzler, Wolfgang Maass, and Marc Toussaint

Published in: Dagstuhl Seminar Proceedings, Volume 10302, Learning paradigms in dynamic environments (2010)

Abstract
From 25.07. to 30.07.2010, the Dagstuhl Seminar 10302 ``Learning paradigms in dynamic environments '' was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.
Cite as

Barbara Hammer, Pascal Hitzler, Wolfgang Maass, and Marc Toussaint. 10302 Abstracts Collection – Learning paradigms in dynamic environments. In Learning paradigms in dynamic environments. Dagstuhl Seminar Proceedings, Volume 10302, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{hammer_et_al:DagSemProc.10302.1,
  author =	{Hammer, Barbara and Hitzler, Pascal and Maass, Wolfgang and Toussaint, Marc},
  title =	{{10302 Abstracts Collection – Learning paradigms in dynamic environments}},
  booktitle =	{Learning paradigms in dynamic environments},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10302},
  editor =	{Barbara Hammer and Pascal Hitzler and Wolfgang Maass and Marc Toussaint},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.10302.1},
  URN =		{urn:nbn:de:0030-drops-28048},
  doi =		{10.4230/DagSemProc.10302.1},
  annote =	{Keywords: Recurrent neural networks, Dynamic systems, Speech processing, Neurobiology, Neural-symbolic integration, Autonomous learning}
}
Document
DOI: 10.4230/DagSemProc.10302.2
10302 Summary – Learning paradigms in dynamic environments

Authors: Barbara Hammer, Pascal Hitzler, Wolfgang Maass, and Marc Toussaint

Published in: Dagstuhl Seminar Proceedings, Volume 10302, Learning paradigms in dynamic environments (2010)

Abstract
The seminar centered around problems which arise in the context of machine learning in dynamic environments. Particular emphasis was put on a couple of specific questions in this context: how to represent and abstract knowledge appropriately to shape the problem of learning in a partially unknown and complex environment and how to combine statistical inference and abstract symbolic representations; how to infer from few data and how to deal with non i.i.d. data, model revision and life-long learning; how to come up with efficient strategies to control realistic environments for which exploration is costly, the dimensionality is high and data are sparse; how to deal with very large settings; and how to apply these models in challenging application areas such as robotics, computer vision, or the web.
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Barbara Hammer, Pascal Hitzler, Wolfgang Maass, and Marc Toussaint. 10302 Summary – Learning paradigms in dynamic environments. In Learning paradigms in dynamic environments. Dagstuhl Seminar Proceedings, Volume 10302, pp. 1-4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{hammer_et_al:DagSemProc.10302.2,
  author =	{Hammer, Barbara and Hitzler, Pascal and Maass, Wolfgang and Toussaint, Marc},
  title =	{{10302 Summary – Learning paradigms in dynamic environments}},
  booktitle =	{Learning paradigms in dynamic environments},
  pages =	{1--4},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10302},
  editor =	{Barbara Hammer and Pascal Hitzler and Wolfgang Maass and Marc Toussaint},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.10302.2},
  URN =		{urn:nbn:de:0030-drops-28027},
  doi =		{10.4230/DagSemProc.10302.2},
  annote =	{Keywords: Summary}
}
Document
DOI: 10.4230/DagSemProc.10302.3
Neurons and Symbols: A Manifesto

Authors: Artur S. d'Avila Garcez

Published in: Dagstuhl Seminar Proceedings, Volume 10302, Learning paradigms in dynamic environments (2010)

Abstract
We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty.
Cite as

Artur S. d'Avila Garcez. Neurons and Symbols: A Manifesto. In Learning paradigms in dynamic environments. Dagstuhl Seminar Proceedings, Volume 10302, pp. 1-16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{davilagarcez:DagSemProc.10302.3,
  author =	{d'Avila Garcez, Artur S.},
  title =	{{Neurons and Symbols: A Manifesto}},
  booktitle =	{Learning paradigms in dynamic environments},
  pages =	{1--16},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10302},
  editor =	{Barbara Hammer and Pascal Hitzler and Wolfgang Maass and Marc Toussaint},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.10302.3},
  URN =		{urn:nbn:de:0030-drops-28005},
  doi =		{10.4230/DagSemProc.10302.3},
  annote =	{Keywords: Neuro-symbolic systems, cognitive models, machine learning}
}
Document
DOI: 10.4230/DagSemProc.10302.4
One-shot Learning of Poisson Distributions in fast changing environments

Authors: Peter Tino

Published in: Dagstuhl Seminar Proceedings, Volume 10302, Learning paradigms in dynamic environments (2010)

Abstract
In Bioinformatics, Audic and Claverie were among the first to systematically study the influence of random fluctuations and sampling size on the reliability of digital expression profile data. For a transcript representing a small fraction of the library and a large number N of clones, the probability of observing x tags of the same gene will be well-approximated by the Poisson distribution parametrised by its mean (and variance) m>0, where the unknown parameter m signifies the number of transcripts of the given type (tag) per N clones in the cDNA library. On an abstract level, to determine whether a gene is differentially expressed or not, one has two numbers generated from two distinct Poisson distributions and based on this (extremely sparse) sample one has to decide whether the two Poisson distributions are identical or not. This can be used e.g. to determine equivalence of Poisson photon sources (up to time shift) in gravitational lensing. Each Poisson distribution is represented by a single measurement only, which is, of course, from a purely statistical standpoint very problematic. The key instrument of the Audic-Claverie approach is a distribution P over tag counts y in one library informed by the tag count x in the other library, under the null hypothesis that the tag counts are generated from the same but unknown Poisson distribution. P is obtained by Bayesian averaging (infinite mixture) of all possible Poisson distributions with mixing proportions equal to the posteriors (given x) under the flat prior over m. We ask: Given that the tag count samples from SAGE libraries are *extremely* limited, how useful actually is the Audic-Claverie methodology? We rigorously analyse the A-C statistic P that forms a backbone of the methodology and represents our knowledge of the underlying tag generating process based on one observation. We show will that the A-C statistic P and the underlying Poisson distribution of the tag counts share the same mode structure. Moreover, the K-L divergence from the true unknown Poisson distribution to the A-C statistic is minimised when the A-C statistic is conditioned on the mode of the Poisson distribution. Most importantly (and perhaps rather surprisingly), the expectation of this K-L divergence never exceeds 1/2 bit! This constitutes a rigorous quantitative argument, extending the previous empirical Monte Carlo studies, that supports the wide spread use of Audic-Claverie method, even though by their very nature, the SAGE libraries represent very sparse samples.
Cite as

Peter Tino. One-shot Learning of Poisson Distributions in fast changing environments. In Learning paradigms in dynamic environments. Dagstuhl Seminar Proceedings, Volume 10302, pp. 1-9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{tino:DagSemProc.10302.4,
  author =	{Tino, Peter},
  title =	{{One-shot Learning of Poisson Distributions in fast changing environments}},
  booktitle =	{Learning paradigms in dynamic environments},
  pages =	{1--9},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10302},
  editor =	{Barbara Hammer and Pascal Hitzler and Wolfgang Maass and Marc Toussaint},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.10302.4},
  URN =		{urn:nbn:de:0030-drops-27998},
  doi =		{10.4230/DagSemProc.10302.4},
  annote =	{Keywords: Audic-Claverie statistic, Bayesian averaging, information theory, one-shot learning, Poisson distribution}
}
Document
DOI: 10.4230/DagSemProc.10302.5
Some steps towards a general principle for dimensionality reduction mappings

Authors: Barbara Hammer, Kerstin Bunte, and Michael Biehl

Published in: Dagstuhl Seminar Proceedings, Volume 10302, Learning paradigms in dynamic environments (2010)

Abstract
In the past years, many dimensionality reduction methods have been established which allow to visualize high dimensional data sets. Recently, also formal evaluation schemes have been proposed for data visualization, which allow a quantitative evaluation along general principles. Most techniques provide a mapping of a priorly given finite set of points only, requiring additional steps for out-of-sample extensions. We propose a general view on dimensionality reduction based on the concept of cost functions, and, based on this general principle, extend dimensionality reduction to explicit mappings of the data manifold. This offers the possibility of simple out-of-sample extensions. Further, it opens a way towards a theory of data visualization taking the perspective of its generalization ability to new data points. We demonstrate the approach based in a simple example.
Cite as

Barbara Hammer, Kerstin Bunte, and Michael Biehl. Some steps towards a general principle for dimensionality reduction mappings. In Learning paradigms in dynamic environments. Dagstuhl Seminar Proceedings, Volume 10302, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{hammer_et_al:DagSemProc.10302.5,
  author =	{Hammer, Barbara and Bunte, Kerstin and Biehl, Michael},
  title =	{{Some steps towards a general principle for dimensionality reduction mappings}},
  booktitle =	{Learning paradigms in dynamic environments},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10302},
  editor =	{Barbara Hammer and Pascal Hitzler and Wolfgang Maass and Marc Toussaint},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.10302.5},
  URN =		{urn:nbn:de:0030-drops-28034},
  doi =		{10.4230/DagSemProc.10302.5},
  annote =	{Keywords: Visualization, dimensionality reduction}
}
Document
DOI: 10.4230/DagSemProc.10302.6
Why deterministic logic is hard to learn but Statistical Relational Learning works

Authors: Marc Toussaint

Published in: Dagstuhl Seminar Proceedings, Volume 10302, Learning paradigms in dynamic environments (2010)

Abstract
A brief note on why we think that the statistical relational learning framework is a great advancement over deterministic logic – in particular in the context of model-based Reinforcement Learning.
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Marc Toussaint. Why deterministic logic is hard to learn but Statistical Relational Learning works. In Learning paradigms in dynamic environments. Dagstuhl Seminar Proceedings, Volume 10302, pp. 1-2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{toussaint:DagSemProc.10302.6,
  author =	{Toussaint, Marc},
  title =	{{Why deterministic logic is hard to learn but Statistical Relational Learning works}},
  booktitle =	{Learning paradigms in dynamic environments},
  pages =	{1--2},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10302},
  editor =	{Barbara Hammer and Pascal Hitzler and Wolfgang Maass and Marc Toussaint},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.10302.6},
  URN =		{urn:nbn:de:0030-drops-28014},
  doi =		{10.4230/DagSemProc.10302.6},
  annote =	{Keywords: Statistical relational learning, relational model-based Reinforcement Learning}
}
Document
DOI: 10.4230/DagSemProc.09081.1
09081 Abstracts Collection – Similarity-based learning on structures

Authors: Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, and Thomas Villmann

Published in: Dagstuhl Seminar Proceedings, Volume 9081, Similarity-based learning on structures (2009)

Abstract
From 15.02. to 20.02.2009, the Dagstuhl Seminar 09081 ``Similarity-based learning on structures '' was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.
Cite as

Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, and Thomas Villmann. 09081 Abstracts Collection – Similarity-based learning on structures. In Similarity-based learning on structures. Dagstuhl Seminar Proceedings, Volume 9081, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{biehl_et_al:DagSemProc.09081.1,
  author =	{Biehl, Michael and Hammer, Barbara and Hochreiter, Sepp and Kremer, Stefan C. and Villmann, Thomas},
  title =	{{09081 Abstracts Collection – Similarity-based learning on structures}},
  booktitle =	{Similarity-based learning on structures},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9081},
  editor =	{Michael Biehl and Barbara Hammer and Sepp Hochreiter and Stefan C. Kremer and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.09081.1},
  URN =		{urn:nbn:de:0030-drops-20395},
  doi =		{10.4230/DagSemProc.09081.1},
  annote =	{Keywords: Similarity-based clustering and classification, metric adaptation and kernel design, learning on graphs, spatiotemporal data}
}
Document
DOI: 10.4230/DagSemProc.09081.2
09081 Summary – Similarity-based learning on structures

Authors: Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, and Thomas Villmann

Published in: Dagstuhl Seminar Proceedings, Volume 9081, Similarity-based learning on structures (2009)

Abstract
The seminar centered around different aspects of similarity-based clustering with the special focus on structures. This included theoretical foundations, new algorithms, innovative applications, and future challenges for the field.
Cite as

Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, and Thomas Villmann. 09081 Summary – Similarity-based learning on structures. In Similarity-based learning on structures. Dagstuhl Seminar Proceedings, Volume 9081, pp. 1-4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{biehl_et_al:DagSemProc.09081.2,
  author =	{Biehl, Michael and Hammer, Barbara and Hochreiter, Sepp and Kremer, Stefan C. and Villmann, Thomas},
  title =	{{09081 Summary – Similarity-based learning on structures}},
  booktitle =	{Similarity-based learning on structures},
  pages =	{1--4},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9081},
  editor =	{Michael Biehl and Barbara Hammer and Sepp Hochreiter and Stefan C. Kremer and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.09081.2},
  URN =		{urn:nbn:de:0030-drops-20382},
  doi =		{10.4230/DagSemProc.09081.2},
  annote =	{Keywords: Similarity-based clustering and classification, metric adaptation and kernel design, learning on graphs, spatiotemporal data}
}
Document
DOI: 10.4230/DagSemProc.09081.3
An adaptive model for learning molecular endpoints

Authors: Ian Walsh, Alessandro Vullo, and Gianluca Pollastri

Published in: Dagstuhl Seminar Proceedings, Volume 9081, Similarity-based learning on structures (2009)

Abstract
I will describe a recursive neural network that deals with undirected graphs, and its application to predicting property labels or activity values of small molecules. The model is entirely general, in that it can process any undirected graph with a finite number of nodes by factorising it into a number of directed graphs with the same skeleton. The model's only input in the applications I will present is the graph representing the chemical structure of the molecule. In spite of its simplicity, the model outperforms or matches the state of the art in three of the four tasks, and in the fourth is outperformed only by a method resorting to a very problem-specific feature.
Cite as

Ian Walsh, Alessandro Vullo, and Gianluca Pollastri. An adaptive model for learning molecular endpoints. In Similarity-based learning on structures. Dagstuhl Seminar Proceedings, Volume 9081, pp. 1-16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{walsh_et_al:DagSemProc.09081.3,
  author =	{Walsh, Ian and Vullo, Alessandro and Pollastri, Gianluca},
  title =	{{An adaptive model for learning molecular endpoints}},
  booktitle =	{Similarity-based learning on structures},
  pages =	{1--16},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9081},
  editor =	{Michael Biehl and Barbara Hammer and Sepp Hochreiter and Stefan C. Kremer and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.09081.3},
  URN =		{urn:nbn:de:0030-drops-20367},
  doi =		{10.4230/DagSemProc.09081.3},
  annote =	{Keywords: Recursive neural networks, qsar, qspr, small molecules}
}
Document
DOI: 10.4230/DagSemProc.09081.4
Analysis of Robust Soft Learning Vector Quantization and an application to Facial Expression Recognition

Authors: Gert-Jan de Vries and Michael Biehl

Published in: Dagstuhl Seminar Proceedings, Volume 9081, Similarity-based learning on structures (2009)

Abstract
Learning Vector Quantization (LVQ) is a popular method for multiclass classification. Several variants of LVQ have been developed recently, of which Robust Soft Learning Vector Quantization (RSLVQ) is a promising one. Although LVQ methods have an intuitive design with clear updating rules, their dynamics are not yet well understood. In simulations within a controlled environment RSLVQ performed very close to optimal. This controlled environment enabled us to perform a mathematical analysis as a first step in obtaining a better theoretical understanding of the learning dynamics. In this talk I will discuss the theoretical analysis and its results. Moreover, I will focus on the practical application of RSLVQ to a real world dataset containing extracted features from facial expression data.
Cite as

Gert-Jan de Vries and Michael Biehl. Analysis of Robust Soft Learning Vector Quantization and an application to Facial Expression Recognition. In Similarity-based learning on structures. Dagstuhl Seminar Proceedings, Volume 9081, pp. 1-5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{devries_et_al:DagSemProc.09081.4,
  author =	{de Vries, Gert-Jan and Biehl, Michael},
  title =	{{Analysis of Robust Soft Learning Vector Quantization and an application to Facial Expression Recognition}},
  booktitle =	{Similarity-based learning on structures},
  pages =	{1--5},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9081},
  editor =	{Michael Biehl and Barbara Hammer and Sepp Hochreiter and Stefan C. Kremer and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.09081.4},
  URN =		{urn:nbn:de:0030-drops-20356},
  doi =		{10.4230/DagSemProc.09081.4},
  annote =	{Keywords: Learning Vector Quantization, Analysis, Facial Expression Recognition}
}
Document
DOI: 10.4230/DagSemProc.09081.5
Estimating Time Delay in Gravitationally Lensed Fluxes

Authors: Peter Tino, Juan C. Cuevas-Tello, and Somak Raychaudhury

Published in: Dagstuhl Seminar Proceedings, Volume 9081, Similarity-based learning on structures (2009)

Abstract
We study the problem of estimating the time delay between two signals representing delayed, irregularly sampled and noisy versions of the same underlying pattern. We propose a kernel-based technique in the context of an astronomical problem, namely estimating the time delay between two gravitationally lensed signals from a distant quasar. We test the algorithm on several artificial data sets, and also on real astronomical observations. By carrying out a statistical analysis of the results we present a detailed comparison of our method with the most popular methods for time delay estimation in astrophysics. Our method yields more accurate and more stable time delay estimates. Our methodology can be readily applied to current state-of-the-art optical monitoring data in astronomy, but can also be applied in other disciplines involving similar time series data.
Cite as

Peter Tino, Juan C. Cuevas-Tello, and Somak Raychaudhury. Estimating Time Delay in Gravitationally Lensed Fluxes. In Similarity-based learning on structures. Dagstuhl Seminar Proceedings, Volume 9081, pp. 1-3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{tino_et_al:DagSemProc.09081.5,
  author =	{Tino, Peter and Cuevas-Tello, Juan C. and Raychaudhury, Somak},
  title =	{{Estimating Time Delay in Gravitationally Lensed Fluxes}},
  booktitle =	{Similarity-based learning on structures},
  pages =	{1--3},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9081},
  editor =	{Michael Biehl and Barbara Hammer and Sepp Hochreiter and Stefan C. Kremer and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.09081.5},
  URN =		{urn:nbn:de:0030-drops-20378},
  doi =		{10.4230/DagSemProc.09081.5},
  annote =	{Keywords: Time series, kernel regression, statistical analysis, evolutionary algorithms, mixed representation}
}
Document
DOI: 10.4230/DagSemProc.08041.1
08041 Abstracts Collection – Recurrent Neural Networks - Models, Capacities, and Applications

Authors: Luc De Raedt, Barbara Hammer, Pascal Hitzler, and Wolfgang Maass

Published in: Dagstuhl Seminar Proceedings, Volume 8041, Recurrent Neural Networks- Models, Capacities, and Applications (2008)

Abstract
From January 20 to 25 2008, the Dagstuhl Seminar 08041 ``Recurrent Neural Networks- Models, Capacities, and Applications'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.
Cite as

Luc De Raedt, Barbara Hammer, Pascal Hitzler, and Wolfgang Maass. 08041 Abstracts Collection – Recurrent Neural Networks - Models, Capacities, and Applications. In Recurrent Neural Networks- Models, Capacities, and Applications. Dagstuhl Seminar Proceedings, Volume 8041, pp. 1-16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{deraedt_et_al:DagSemProc.08041.1,
  author =	{De Raedt, Luc and Hammer, Barbara and Hitzler, Pascal and Maass, Wolfgang},
  title =	{{08041 Abstracts Collection – Recurrent Neural Networks - Models, Capacities, and Applications}},
  booktitle =	{Recurrent Neural Networks- Models, Capacities, and Applications},
  pages =	{1--16},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8041},
  editor =	{Luc De Raedt and Barbara Hammer and Pascal Hitzler and Wolfgang Maass},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.08041.1},
  URN =		{urn:nbn:de:0030-drops-14250},
  doi =		{10.4230/DagSemProc.08041.1},
  annote =	{Keywords: Recurrent Neural Networks, Neural-Symbolic Integration, Biological Models, Hybrid Models, Relational Learning Echo State Networks, Spike Prediction, Unsupervised Recurrent Networks}
}
Document
DOI: 10.4230/DagSemProc.08041.2
08041 Summary – Recurrent Neural Networks - Models, Capacities, and Applications

Authors: Luc De Raedt, Barbara Hammer, Pascal Hitzler, and Wolfgang Maass

Published in: Dagstuhl Seminar Proceedings, Volume 8041, Recurrent Neural Networks- Models, Capacities, and Applications (2008)

Abstract
The seminar centered around recurrent information processing in neural systems and its connections to brain sciences, on the one hand, and higher symbolic reasoning, on the other side. The goal was to explore connections across the disciplines and to tackle important questions which arise in all sub-disciplines such as representation of temporal information, generalization ability, inference, and learning.
Cite as

Luc De Raedt, Barbara Hammer, Pascal Hitzler, and Wolfgang Maass. 08041 Summary – Recurrent Neural Networks - Models, Capacities, and Applications. In Recurrent Neural Networks- Models, Capacities, and Applications. Dagstuhl Seminar Proceedings, Volume 8041, pp. 1-4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{deraedt_et_al:DagSemProc.08041.2,
  author =	{De Raedt, Luc and Hammer, Barbara and Hitzler, Pascal and Maass, Wolfgang},
  title =	{{08041 Summary – Recurrent Neural Networks - Models, Capacities, and Applications}},
  booktitle =	{Recurrent Neural Networks- Models, Capacities, and Applications},
  pages =	{1--4},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8041},
  editor =	{Luc De Raedt and Barbara Hammer and Pascal Hitzler and Wolfgang Maass},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.08041.2},
  URN =		{urn:nbn:de:0030-drops-14243},
  doi =		{10.4230/DagSemProc.08041.2},
  annote =	{Keywords: Recurrent networks}
}
Document
DOI: 10.4230/DagSemProc.08041.3
Equilibria of Iterative Softmax and Critical Temperatures for Intermittent Search in Self-Organizing Neural Networks

Authors: Peter Tino

Published in: Dagstuhl Seminar Proceedings, Volume 8041, Recurrent Neural Networks- Models, Capacities, and Applications (2008)

Abstract
Optimization dynamics using self-organizing neural networks (SONN) driven by softmax weight renormalization has been shown to be capable of intermittent search for high-quality solutions in assignment optimization problems. However, the search is sensitive to temperature setting in the softmax renormalization step. The powerful search occurs only at the critical temperature that depends on the problem size. So far the critical temperatures have been determined only by tedious trial-and-error numerical simulations. We offer a rigorous analysis of the search performed by SONN and derive analytical approximations to the critical temperatures. We demonstrate on a set of N-queens problems for a wide range of problem sizes N that the analytically determined critical temperatures predict the optimal working temperatures for SONN intermittent search very well.
Cite as

Peter Tino. Equilibria of Iterative Softmax and Critical Temperatures for Intermittent Search in Self-Organizing Neural Networks. In Recurrent Neural Networks- Models, Capacities, and Applications. Dagstuhl Seminar Proceedings, Volume 8041, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{tino:DagSemProc.08041.3,
  author =	{Tino, Peter},
  title =	{{Equilibria of Iterative Softmax and Critical Temperatures for Intermittent Search in Self-Organizing Neural Networks}},
  booktitle =	{Recurrent Neural Networks- Models, Capacities, and Applications},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8041},
  editor =	{Luc De Raedt and Barbara Hammer and Pascal Hitzler and Wolfgang Maass},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.08041.3},
  URN =		{urn:nbn:de:0030-drops-14202},
  doi =		{10.4230/DagSemProc.08041.3},
  annote =	{Keywords: Recurrent self-organizing maps, symmetry breaking bifurcation, N-queens}
}
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