Exzellenzcluster Veranstaltungen
Hier finden Sie Informationen zu allen unseren aktuellen Cluster-Veranstaltungen im Überblick.
Alle zurückliegenden Cluster-Veranstaltungen finden Sie untenstehend im ARCHIV.
Cluster Kolloquium "Maschinelles Lernen" -- 1. Mittwoch im Monat
Seminarreihe des Exzellenzclusters "Maschinelles Lernen"
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Mittwoch, 14:00 - 15:00, mit anschliessendem Get Together
Hörsaal, AI Research Building, Maria von Linden-Str. 6 (Erdgeschoss), 72076 Tübingen
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PROGRAMM
Vortrag Reinhard Diestel - 24. Januar 2020
Tangles: from graph minors to identifying political mindsets
Vortrag von Reinhard Diestel, Universität Hamburg, Fachbereich Mathematik
WANN: Freitag, 24.01.2020 um 10:00
WO: Hörsaal am MPI for Intelligente Systeme, Erdgeschoss
ABSTRACT
Traditional clustering identifies groups of objects that share certain qualities. Tangles do the converse: they identify groups of qualities that often occur together. They can thereby discover, relate, and structure types of phenomena: of behaviour, political views, texts, or bacteria. Tangles can identify key phenomena that allow predictions of others. Tangles also offer a new paradigm for clustering in large data sets. Tangle clusters are, by necessity, fuzzy: they tell us where in a large structure a cluster lies, which key properties of data points identify it, and how the overall data set is structured with respect to these clusters. But they do this without needing, or attempting, to assign individual points to any cluster. Tangles of graphs are central to the theory of graph minors developed by Robertson and Seymour for their celebrated proof of the graph minor theorem. For many years, however, algorithmic applications of graph minor theory were largely confined to applications of tree-decompositions, an overall structure dual to the existence of large tangles. Very recently, tangles have been axiomatised in a way that makes them directly applicable to a much wider range of contexts than graphs, even outside mathematics. This talk will outline how this works, with an emphasis on the basic concepts of abstract tangle theory and how these are applicable in real-world scenarios. No knowledge of graph minor theory will be needed.
Workshop "Philosophy of Science Meets Machine Learning", 17. - 19. Juni 2020
Workshop "Philosophy of Science Meets Machine Learning"
Mittwoch 17. Juni bis Freitag 19. Juni 2020 | ca. 09:00 Uhr - 18:00 Uhr |
Alte Aula, Münzgasse 30, 72070 Tübingen
The workshop is organised by the ‘Ethics and Philosophy Lab’ of the Cluster of Excellence ‘Machine Learning: New Perspectives for Science’ at the University of Tübingen.
Workshop Convenors: Thomas Grote, Thilo Hagendorff, Eric Raidl
The CALL FOR ABSTRACTS is now open. We particularly welcome young researchers who have recently started to work on Machine Learning from a philosophical perspective. Please submit an anonymised extended abstract (750 words not including references) until 15 February 2020, 10 p.m. to the Registration Website
For young researchers, who do not have internal funding, we are able to subsidize the costs for travel and lodging (the details need to be discussed once the accepted papers have been selected).
For further questions, please send an email to Thomas Grote
Invited Speakers:
- Philipp Hennig (Tübingen)
- Anna Jobin (Zürich)
- Lena Kästner (Saarbrücken)
- Charlotte Stix (Eindhoven)
- Emily Sullivan (Eindhoven)
- Kate Vredenburg (Stanford/LSE)
- Gregory Wheeler (Frankfurt)
- Jon Williamson (Kent)
- Carlos Zednik (Magdeburg)
Machine learning does not only transform businesses and the social sphere, it also fundamentally transforms science and scientific practice. The workshop focuses on that latter issue. It aims to discuss whether and how exactly recent developments in the field of machine learning potentially transform the process of scientific inquiry. For this, it sets out to analyse the field of machine learning through the lenses of philosophy of science, epistemology, research ethics and cognate fields such as sociology of science. The workshop will bring together philosophers from different backgrounds (from formal epistemology to the study of the social dimensions of science) and machine learning researchers. The workshop`s central topics are:
- A critical reflection on key-concepts, such as ‘learning’, ‘inference’, ‘explanation’ or ‘understanding’.
- The implications of machine learning for the special sciences, e.g. cognitive science, social science or medicine.
- The ethics of machine learning-driven science, e.g. the moral responsibilities of researchers.
- Social aspects of machine learning-driven science, e.g. the impact of funding structures on research.
Das Programm folgt in Kürze.
Anmeldung: Bitte melden Sie sich bis spätestens 01. Juni 2020 verbindlich zu derm Worskshop an, E-Mail an Thomas Grote. Bitte geben sie an, an welchem/n Tag/en Sie teilnehmen möchten.
2. Mini-Konferenz "Machine Learning in Science", 21. - 22. Juli 2020
Exzellenzcluster Konferenz "Machine Learning in Science"
Dienstag, 21.07.2020
Mittwoch, 22.07.2020
Nach dem Erfolg der 1. "Machine Learning in Science" Konferenz im Jui 2019, wollen wir auch in diesem Jahr unsere Cluster-Konferenz in ähnlichem Format (Vorträge, Posterbeträge,..) im Juli abhalten.
Veröffentlichung der Agenda und Bekanntgabe des Veranstaltungortes folgen demnächst.
Veranstaltungs-Archiv
Hier finden Sie alle vergangenen Cluster-Veranstaltungen im Überblick.
Cluster Kolloquium "Maschinelles Lernen" - 1. Mittwoch im Monat
Seminarreihe des Exzellenzclusters "Maschinelles Lernen"
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Mittwoch, 14:00 - 15:00, mit anschliessendem Get Together
Hörsaal, AI Research Building, Maria von Linden-Str. 6 (Erdgeschoss), 72076 Tübingen
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PROGRAMM
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08.01.2020 Mining observations and climate models for detecting and attributing anthropogenic
climate change in the world’s water cycle
Lukas Gudmundsson (Host: Fabian Sinz)
Institut für Atmosphäre und Klima, ETH Zürich, Webpage
Abstract -
04.12.2019 Explaining and Representing Novel Concepts With Minimal Supervision
Zeynep Akata
Exzellenzcluster Maschinelles Lernen, Erklärbares Machinelles Lernen, Universität Tübingen -
21.11.2019 Biomarker Discovery in Clinical Time Series
Karsten Borgwardt
Department für Biosystems Science and Engineering,
ETH Zuerich Ort: MPI Intelligente Systeme, Hörsaal Erdgeschoss -
06.11.2019 Gaussian Process emulation of tsunami and
climate models
Serge Guillas
University College London / The Alan Turing Institute, UK
Webpage (Host: )
Abstract
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03.07.2019 Machine Learning, Neuroscience, and Spiking Neural Networks
Robert Legenstein
Institut für Grundlagen der Informationsverarbeitung, TU Graz, Österreich
Webpage (Host: Harald Baayen)
Abstract
Machine Learning meets Social Science, 12. November 2019
Machine Learning meets Social Science
Max-Planck-Institut für Intelligente Systems, Seminarraum im Erdgeschoss
Max-Planck-Ring 4, 72076 Tübingen
Anmeldung: Wenn Sie an dem Meeting teilnehmen möchten, melden Sie sich bitte per E-Mail an, Sebastian Schwenk.
09:00 - 10:30 Session "ML Cluster"
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09:00 - 09:20 Begrüßung & Fairness in Machine Learning
Ulrike von Luxburg
09:20 - 09:40 On the Integration of Machine Learning into Healthcare
Thomas Grote
09:40 - 10:00 Explaining Neural Network Decisions with
Minimal Supervision
Zeynep Akata
10:00 - 10:20 Explainability & Explanation
Eric Raidl
10:30 - 11:00 Kaffeepause
11:00 - 12:30 Session "Sociology"
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11:00 - 11:20 What does ML do? Key questions from a sociology of
technology and science perspective
Renate Baumgartner
11:20 - 11:40 (How) does ML affect the shop floor and the
sociology of work?
Werner Schmidt
11:40 - 12:00 Three Worlds of AI – How political strategies differ
Daniel Buhr
12:00 - 12:20 Classifying scientific texts with supervised learning algorithms
Steffen Hillmert
12:30 Diskussion und Ende
Mini-Konferenz "Machine Learning in Science", 22. - 23. Juli 2019
Exzellenzcluster Konferenz "Machine Learning in Science"
Montag, 22.07.2019 | 09:00 Uhr - 19:00 Uhr | Alte Aula
Dienstag, 23.07.2019 | 09:00 Uhr - 16:00 Uhr | Pfleghofsaal
Anmeldung: Bitte melden Sie sich bis spätestens 15.07.2019 verbindlich zu der Tagung an, E-Mail an Sebastian Schwenk. Bitte geben sie an, an welchen Tagen Sie teilnehmen möchten.
Montag, 22.07.2019
Alte Aula, Münzgasse 30, 72070 Tübingen
9:00 | Opening Remarks Ulrike von Luxburg, Philipp Berens Speakers of the Cluster of Excellence “Machine Learning”, University of Tübingen |
09:15 | Towards Neural Networks Which Probably Know When They Don't Know Matthias Hein Department of Computer Science, University of Tübingen |
10:00 | Inception Loops - Using Deep Learning to Control Biological Neurons Fabian Sinz Department of Computer Science, University of Tübingen |
10:45 | Kaffeepause |
11:15 | Machine Learning for Heterogeneous and Partially Biased Data in Medicine Nico Pfeifer Department of Computer Science, University of Tübingen |
12:00 | The Art of Using t-SNE for Visualization of Very Large Data Sets Dmitry Kobak Institute for Ophthalmic Research, University of Tübingen |
12:45 | Mittagessen |
13:45
| Machine Learning inside Scientific Methods and Procedures Philipp Hennig Department of Computer Science, University of Tübingen |
14:30
| Dynamic Structural Equation Models in the Social and Behavioral Sciences Augustin Kelava Methods Center, University of Tübingen |
15:15
| Identifying Climate, Vegetation, and Plate Tectonic Controls on Earth’s Topography Todd Ehlers Department of Geosciences, University of Tübingen |
16:00 | Poster Session Posterbeiträge siehe unten * |
18:00 | Mitgliederversammlung Exzellenzcluster (nicht-öffentlich) |
19:00 | Speaker’s Dinner (nicht-öffentlich) |
Dienstag, 23.07.2019
Pfleghofsaal, Schulberg 2 (Pfleghof), 72070 Tübingen
09:00 | Language Change as a Random Walk in Vector Space Gerhard Jäger Institute of Linguistics, University of Tübingen |
09:45 | Ethics and Explainability Eric Raidl, Thomas Grote, Thilo Hagendorff Ethics & Philosophy Lab, Cluster of Excellence Machine Learning, University of Tübingen |
10:45 | Kaffeepause |
11:15
| Filter ranking for neural network compression Mijung Park Department of Computer Science, University of Tübingen |
12:00
| Fairness and Interpretability in ML for Consequential Decision Making Isabel Valera Max Planck Institute for Intelligent Systems, Tübingen |
12:45 | Mittagessen |
13:45 | Statistical Limits of Hypothesis Testing: Do We Expect Too Much from ML? Debarghya Ghoshdastibar Department of Computer Science, University of Tübingen |
14:30 | How to Learn Predictive Conceptual Structures, including Causal Relationships, and Generate Goal-Directed Control with them? Achievements and Challenges Martin Butz Department of Computer Science, University of Tübingen |
15:15 | Machine Learning Algorithms as Tools and Models in Vision Science Felix Wichmann Department of Computer Science, University of Tübingen |
16:00 | Closing Remarks Ulrike von Luxburg, Philipp Berens Speakers of the Cluster of Excellence “Machine Learning”, University of Tübingen |
* Poster Session, July 22, 16:00 – 18:00 *
Weber, D, Kasneci E., Zell A. (Cluster Innovation Fund Project) University of Tübingen, Department of Computer Science
Valera I.1, Utz S.² (Cluster Innovation Fund Project) 1Max Planck Institute for Intelligent Systems Tübingen, ² Leibniz-Institut für Wissensmedien
Luxburg U., Wichmann F. (Cluster Innovation Fund Project) Machine learning approaches for psychophysics with ordinal comparisons University of Tübingen, Department of Computer Science
Zabel S.1, Hennig P.2, Nieselt K.1 (Cluster Innovation Fund Project) Visualizing Uncertainty from Data, Model and Algorithm in Large-Scale Omics Data University of Tübingen, 1Center for Bioinformatics Tübingen, ²Department of Computer Science
Karlbauer, M.1, Lensch H.1, Scholten T.², Butz M.1 (Cluster Innovation Fund Project) Short-to-Mid Scale Weather Forecasting with a Distributed, Recurrent Convolutional ANN University of Tübingen, 1Department of Computer Science, ²Department of Geosciences
Behrens, T.1, Schmidt, K.1, Hennig, P.², Scholten, T.1 (Cluster Innovation Fund Project) University of Tübingen, 1Department of Geosciences, ²Department of Computer Science
University of Tübingen, 1Department of Linguistics, ²Department of Computer Science
Macke J.1, Hennig P.², Berens P.³, Oberlaender M.4 1Technische Universität München, Computational Neuroengineering Group 1 University of Tübingen, ²Department for Computer Science, ³Institute for Ophthalmic Research 4Center of advanced european studies and research
Pawlowski, T.1, Berens, P.², Kelava, A.³ University of Tübingen, 1Department Institute of Sport Science, ²Institute for Ophthalmic Research, ³ Methods Center Kilian P. University of Tübingen, Methods Center
Klopotek M., Oettel M. University of Tübingen, Institut für Angewandte Physik
Lin SC, Oettel M. University of Tübingen, Institut für Angewandte Physik
Greco A.1, Starostin V.1, Hinderhofer A.1, Gerlach A.1, Karapanagiotis C.², Liehr S.², Kowarik S.²,
Sümer Ö.1,2, Kasneci E.1 University of Tübingen, 1Department of Computer Science, ²Hector Research Institute of Education Sciences and
Fuhl W., Kasneci G., Rosenstiel W., Kasneci E. University of Tübingen, Department of Computer Science Zadaianchuk A., Martius G. | |
Machine Learning meets Physics, 10. Juli 2019
Machine Learning meets Physics
AI Gebäude, Hörsaal (EG)
Maria von Linden Str. 6, 72076 Tübingen
9:00 – 9:25: Frank Schreiber et al: “Analysis of X-ray Scattering Data Using Artificial Neural Networks”
9:25 – 9:50: Hendrik Lensch: “Deep Learning on Unstructured Point Clouds”
9:50 – 10:15: Martin Oettel: “Density functionals from machine learning”
10:15 – 10:45: Kaffeepause
10:45 – 11:10: Miriam Klopotek: “Variational autoencoders put up to the test in learning a statistical-mechanical
model system”
11:10 – 11:35: Georg Martius: “Machine Learning for Equation Identification”
11:35 – 12:00: Andreas Zell: “ML, Physics and Robotics”
12:00 – 13:00: Diskussion
TÜFFF - Tübinger Fenster für Forschung, 24. Mai 2019
TÜFFF - Tübinger Fenster für Forschung
Spitzenforschung zum Anfassen für alle Altersgruppen
WANN: Freitag, 24. Mai 2019, 15 – 22 Uhr
WO: Hörsaalzentrum der Naturwissenschaften, Auf der Morgenstelle 16
Eintritt frei
Das „Tübinger Fenster für Forschung“ (TÜFFF) bietet allgemein verständliche und interaktive Einblicke in die Tübinger Spitzenforschung. Mitmach-aktionen, Demonstrationen, Laborführungen, Vorträge, eine Informations-messe sowie ein Science Slam erwarten die interessierte Öffentlichkeit beim 4. TÜFFF an der Universität Tübingen. Durch die Aufbereitung und Präsentation aktueller Forschungsthemen für ein fachfremdes Publikum richtet sich die Veranstaltung an alle Altersgruppen.
Das Exzellenzcluster „Maschinelles Lernen“ beteiligt sich mit 8 Ständen am „Markt der Möglichkeiten“:
- Deep Capturing - Computer Vision, Prof. Hendrik Lensch
- Deep Deblurring - Computer Vision, Prof. Hendrik Lensch
- Interaktive Karte zur Bodenqualität im Raum Tübingen - Geowissenschaften, Prof. Thomas Scholten
- Was ist ein neuronales Netzwerk? – Bioinformatik, Prof. Dr. Kay Nieselt
- Antizipatives Verhalten in künstlichen neuronalen Netzen - Kognitionswissenschaften, Prof. Martin Butz
- Wie kann ein Computer lernen, Wörter in Latein, Russisch, Estnisch und Hebräisch zu beugen?
Linguistik, Prof. Harald Baayen - Vorhersage von Blickrichtungen - Neurowissenschaften,
Prof. Matthias Bethge - Briefumschlag-Computer – Theorie des maschinellen Lernens,
Prof. Ulrike Luxburg
Weitere Informationen im Programmheft und auf der Veranstaltungsseite
Symposium "Machine Learning in Science", 22. Mai 2019
Symposium "Machine Learning in Science"
22. Mai 2019
Max-Planck-Gästehaus – Hörsaal
Max-Planck-Ring 6, 72076 Tübingen
09:45 | Tropical circulation: Current challenges and potential for machine learning algorithms |
| 10:30 | High-throughput behavioral analysis for neural circuit understanding Alexander Mathis -- Dept. of Molecular and Cellular Biology, Harvard University, USA |
11:15 | Kaffeepause |
11:30 | Reverse Engineering the Early Visual System with Artificial Neural Networks |
12:15 | Visualization of georeferenced open government data: benefits, issues, opportunities for machine learning research |
Symposium "Ethik und Philosophie des Maschinellen Lernens in der Wissenschaft", 15. Mai 2019
„Ethik und Philosophie des Maschinellen Lernens in der Wissenschaft“
15. Mai 2019
08:30 | Simplicity and Scientific Progress: A Topological Perspective |
09:15 | Learning Through Creativity |
10:00 | Kaffeepause |
10:20 | Black-Boxes, Understanding, and Machine Learning |
11:05 | Working at the margins of machine learning – the ethics of labeling |
11:50 | Inductive Bias and Adversarial Data |
12:35 | Mittagspause |
13:30 - 14:30 | Invited Talk Co-Opt AI! Charting the emerging field of AI, ethics and social justiceMona Sloane, Institute for Public Knowledge, New York University, USA |
17:10 - 17:55 | KONFERENZRAUM |
Symposium "Machine Learning in Science", 18. und 25. - 27. März 2019
Symposium „Machine Learning in Science“
18. und 25. - 26. März 2019
Max-Planck-Gästehaus – Hörsaal
Max-Planck-Ring 6, 72076 Tübingen
Montag, 18. März 2019
09:30 – 10:30 | Neutrino Cosmology - Weighing the Ghost Particle with the Universe Dr. Elena Giusarma -- Simons Foundation, Flatiron Institute Center for Computational |
Montag, 25. März 2019
08:30 | Information Field Theory |
09:30 | Active machine learning for automating scientific discovery |
10:30 | Kaffeepause |
11:00 | Bayesian optimisation: nano-machine-learning |
12:00 | Robust and Scalable Learning with Graphs Prof. Dr. Stephan Günnemann -- TU München |
13:00 | Mittagessen |
15:00 | Representing and Explaining Novel Concepts with Minimal Supervision Asst. Prof. Dr. Zeynep Akata -- University of Amsterdam |
16:00 | Kaffeepause |
17:45 | Konstituierende Clustersitzung und Mitgliederversammlung (nicht-öffentlich) |
19:00 | Gemeinsames Abendessen (nicht öffentlich) |
Dienstag, 26. März 2019
08:30 | Expressive, Robust and Accountable Machine Learning for Real-world Data |
09:30 | Algorithms of Vision: From Brains to Machines and Back |
10:30 | Kaffeepause |
11:00 | From Paired to Unpaired Image-to-Image Translation and Beyond |
12:00 | Face processing: Bridging Natural and Artificial Intelligence Assoc. Prof. Dr. Angela J. Yu -- University of California San Diego, USA |
13:00 | Mittagessen |
| 14:00 | From statistics to mechanisms, and back Prof. Dr. Jakob Macke - TU München |
Machine Learning meets Law, 19. März 2019
Machine Learning meets Law, Neue Aula
9:00 Stefan Thomas: Algorithms and Antitrust: How can the law make sure that machine learning does not impede competitive freedom?
9:15 Thilo Hagendorff: Regularory Needs in the Field of AI - From Ethics to Policies
9:30 Thomas Grote: The ethics of (expert-level) algorithmic decision-making
9:45 Isabel Valera: Fairness in Machine Learning
10:00 Oliver Kohlbacher: Legal issues related to AI in medicine
10:15 Discussion as long as we want
Erstes Treffen des Cluster "Machine Learning in Science", 12.-13. November, 2018
Internal Meeting of the Cluster "Machine Learning in Science": November 12-13, 2018
Meeting location: Ground floor lecture hall at the Max-Planck Institute for Intelligent Systems (directions)
Preliminary schedule:
Nov 12th
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9:00-10:00 Welcome, information & organisation
Ulrike von Luxburg and Philipp Berens
10:00-12:15 Short introductory talks of new group leaders
10:00-10:15 Jörg Stückler
10:15-10:30 Falk Lieder
10:30-11:00 Coffee break
11:00-11:15 Georg Martius
11:15-11:30 Britta Dorn
11:30-11:45 Fabian Sinz
11:45-12:00 Zhaoping Li
12:00-12:15 Gabriele Schweikert
12:15-12:30 Augustin Kelava
12:30-12:45 Michael Krone
12.45 -14:00 Lunch
14:00-15:00 Spotlights for open questions
(all PIs: please prepare exactly 1 slide (3 minutes) and send it to Alla at latest Nov 11)
15:00-15:30 Coffee break
15:30-18:00 Work phase for project teams
18:30 Dinner at Hofgut Rosenau
Nov 13th
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9:00-10:30 Discussion of open questions, directions, ideas for how
the Excellence Cluster should start and work
10:30-11:00 Coffee break
11:00-12:00 Discussion and work phase
12:00-14:00 Lunch
14:00-15:00 Presentations of project ideas and discussion
15:00-15:30 Coffee break