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Documents authored by Maass, Wolfgang

Document
DOI: 10.4230/LIPIcs.ITCS.2018.57
Long Term Memory and the Densest K-Subgraph Problem

Authors: Robert Legenstein, Wolfgang Maass, Christos H. Papadimitriou, and Santosh S. Vempala

Published in: LIPIcs, Volume 94, 9th Innovations in Theoretical Computer Science Conference (ITCS 2018)

Abstract
In a recent experiment, a cell in the human medial temporal lobe (MTL) encoding one sensory stimulus starts to also respond to a second stimulus following a combined experience associating the two. We develop a theoretical model predicting that an assembly of cells with exceptionally high synaptic intraconnectivity can emerge, in response to a particular sensory experience, to encode and abstract that experience. We also show that two such assemblies are modified to increase their intersection after a sensory event that associates the two corresponding stimuli. The main technical tools employed are random graph theory, and Bernoulli approximations. Assembly creation must overcome a computational challenge akin to the Densest K-Subgraph problem, namely selecting, from a large population of randomly and sparsely interconnected cells, a subset with exceptionally high density of interconnections. We identify three mechanisms that help achieve this feat in our model: (1) a simple two-stage randomized algorithm, and (2) the "triangle completion bias" in synaptic connectivity and a "birthday paradox", while (3) the strength of these connections is enhanced through Hebbian plasticity.
Cite as

Robert Legenstein, Wolfgang Maass, Christos H. Papadimitriou, and Santosh S. Vempala. Long Term Memory and the Densest K-Subgraph Problem. In 9th Innovations in Theoretical Computer Science Conference (ITCS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 94, pp. 57:1-57:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{legenstein_et_al:LIPIcs.ITCS.2018.57,
  author =	{Legenstein, Robert and Maass, Wolfgang and Papadimitriou, Christos H. and Vempala, Santosh S.},
  title =	{{Long Term Memory and the Densest K-Subgraph Problem}},
  booktitle =	{9th Innovations in Theoretical Computer Science Conference (ITCS 2018)},
  pages =	{57:1--57:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-060-6},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{94},
  editor =	{Karlin, Anna R.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2018.57},
  URN =		{urn:nbn:de:0030-drops-83593},
  doi =		{10.4230/LIPIcs.ITCS.2018.57},
  annote =	{Keywords: Brain computation, long term memory, assemblies, association}
}
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.
Cite as

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.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/DagSemRep.235
Unsupervised Learning (Dagstuhl Seminar 99121)

Authors: Joachim M. Buhmann, Wolfgang Maass, Helge Ritter, and Naftali Tishby

Published in: Dagstuhl Seminar Reports. Dagstuhl Seminar Reports, Volume 1 (2021)

Abstract
Cite as

Joachim M. Buhmann, Wolfgang Maass, Helge Ritter, and Naftali Tishby. Unsupervised Learning (Dagstuhl Seminar 99121). Dagstuhl Seminar Report 235, pp. 1-27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (1999)

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@TechReport{buhmann_et_al:DagSemRep.235,
  author =	{Buhmann, Joachim M. and Maass, Wolfgang and Ritter, Helge and Tishby, Naftali},
  title =	{{Unsupervised Learning (Dagstuhl Seminar 99121)}},
  pages =	{1--27},
  ISSN =	{1619-0203},
  year =	{1999},
  type = 	{Dagstuhl Seminar Report},
  number =	{235},
  institution =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemRep.235},
  URN =		{urn:nbn:de:0030-drops-151219},
  doi =		{10.4230/DagSemRep.235},
}
Document
DOI: 10.4230/DagSemRep.163
Theory and Practice of Machine Learning (Dagstuhl Seminar 9702)

Authors: Thomas G. Dietterich, Wolfgang Maass, Hans Ulrich Simon, and Robert S. Sutton

Published in: Dagstuhl Seminar Reports. Dagstuhl Seminar Reports, Volume 1 (2021)

Abstract
Cite as

Thomas G. Dietterich, Wolfgang Maass, Hans Ulrich Simon, and Robert S. Sutton. Theory and Practice of Machine Learning (Dagstuhl Seminar 9702). Dagstuhl Seminar Report 163, pp. 1-33, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (1997)

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@TechReport{dietterich_et_al:DagSemRep.163,
  author =	{Dietterich, Thomas G. and Maass, Wolfgang and Simon, Hans Ulrich and Sutton, Robert S.},
  title =	{{Theory and Practice of Machine Learning (Dagstuhl Seminar 9702)}},
  pages =	{1--33},
  ISSN =	{1619-0203},
  year =	{1997},
  type = 	{Dagstuhl Seminar Report},
  number =	{163},
  institution =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemRep.163},
  URN =		{urn:nbn:de:0030-drops-150503},
  doi =		{10.4230/DagSemRep.163},
}
Document
DOI: 10.4230/DagSemRep.103
Neural Computing (Dagstuhl Seminar 9445)

Authors: Wolfgang Maass, Christoph von der Malsburg, Eduardo Sontag, and Ingo Wegener

Published in: Dagstuhl Seminar Reports. Dagstuhl Seminar Reports, Volume 1 (2021)

Abstract
Cite as

Wolfgang Maass, Christoph von der Malsburg, Eduardo Sontag, and Ingo Wegener. Neural Computing (Dagstuhl Seminar 9445). Dagstuhl Seminar Report 103, pp. 1-27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (1995)

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@TechReport{maass_et_al:DagSemRep.103,
  author =	{Maass, Wolfgang and von der Malsburg, Christoph and Sontag, Eduardo and Wegener, Ingo},
  title =	{{Neural Computing (Dagstuhl Seminar 9445)}},
  pages =	{1--27},
  ISSN =	{1619-0203},
  year =	{1995},
  type = 	{Dagstuhl Seminar Report},
  number =	{103},
  institution =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemRep.103},
  URN =		{urn:nbn:de:0030-drops-149912},
  doi =		{10.4230/DagSemRep.103},
}
Document
DOI: 10.4230/DagSemRep.91
Theory and Praxis of Machine Learning (Dagstuhl Seminar 9426)

Authors: Thomas Dietterich, Wolfgang Maass, Hans-Ulrich Simon, and Manfred Warmuth

Published in: Dagstuhl Seminar Reports. Dagstuhl Seminar Reports, Volume 1 (2021)

Abstract
Cite as

Thomas Dietterich, Wolfgang Maass, Hans-Ulrich Simon, and Manfred Warmuth. Theory and Praxis of Machine Learning (Dagstuhl Seminar 9426). Dagstuhl Seminar Report 91, pp. 1-24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (1994)

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@TechReport{dietterich_et_al:DagSemRep.91,
  author =	{Dietterich, Thomas and Maass, Wolfgang and Simon, Hans-Ulrich and Warmuth, Manfred},
  title =	{{Theory and Praxis of Machine Learning (Dagstuhl Seminar 9426)}},
  pages =	{1--24},
  ISSN =	{1619-0203},
  year =	{1994},
  type = 	{Dagstuhl Seminar Report},
  number =	{91},
  institution =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemRep.91},
  URN =		{urn:nbn:de:0030-drops-149796},
  doi =		{10.4230/DagSemRep.91},
}
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