Cluster Events

Cluster Colloquium "Machine Learning" - 1° Wednesday of the month

Seminar Series of the Cluster for Excellence "Machine Learning"

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Wednesday, 2:00 - 3:00
pm, followed by get-together

Lecture hall, AI Research Building, Maria von Linden-Str. 6 (ground floor), 72076 Tübingen
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PROGRAM

 

Machine Learning meets Social Science, November 12, 2019

Machine Learning meets Social Science

Max Planck Institute for Intelligent Systems , Tübingen
9:00 - 12:00

Program will be released soon

Mini-Conference "Machine Learning in Science", July 22-23, 2019

Cluster Conference "Machine Learning in Science"

Monday, July 22, 2019 | 09:00 am - 07:00 pm | Alte Aula
Tuesday, July 23, 2019 | 09:00 am - 04:00 pm | Pfleghofsaal


Registration: If you would like to attend the meeting, please register by July 15 latest by sending an email to Sebastian Schwenk. Please indicate on which day(s) you would like to participate.

Programm


Monady, July 22, 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

Coffee Break

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 

Lunch

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
and Some Estimation Problems

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 and Coffee

Poster contributions, see below *

18:00

General Assembly of the Cluster of Excellence (non-public)

19:00

Speaker’s Dinner (non-public)

   
Tuesday, July 23, 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

Coffee Break

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 

Lunch

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)
Human-robot interface with eye-tracking and augmented reality to teach mobile robots about the real-world.

             University of Tübingen, Department of Computer Science

Valera I.1, Utz S.² (Cluster Innovation Fund Project)
Extracting expertise from tweets: Exploring the boundary conditions of ambient awareness).

             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)
Feature engineering for spatial modelling.

             University of Tübingen, 1Department of Geosciences, ²Department of Computer Science

Baayen H.1, Lensch H.² (Cluster Innovation Fund Project)
Enhancing Machine Learning of Lexical Semantics with Image Mining

            University of Tübingen, 1Department of Linguistics, ²Department of Computer Science

 

Macke J.1, Hennig P.², Berens P.³, Oberlaender M.4
Automatic Data-driven Inference of Mechanistic Models

              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
Emotional cues and alcohol use: evidence from football.

               University of Tübingen, 1Department Institute of Sport Science, ²Institute for Ophthalmic Research, ³ Methods Center

Kilian P.
Predicting math student college dropout with sparse information using approaches from statistical learning

              University of Tübingen, Methods Center

Klopotek M., Oettel M.
Variational autoencoders put up to the test in learning a statistical-mechanical model system

             University of Tübingen, Institut für Angewandte Physik

Lin SC, Oettel M.
Classical density functionals from machine learning

             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,
Schreiber F.1
Fast Scattering Data Analysis Using Machine Learning.
                1University of Tübingen, Institut für Angewandte Physik, Uni Tübingen, ² Bundesanstalt für Materialforschung
                 und -prüfung (BAM), Berlin

Sümer Ö.1,2, Kasneci E.1
Attention Flow: End-to-End Joint Attention Estimation

             University of Tübingen, 1Department of Computer Science, ²Hector Research Institute of Education Sciences and 
           Psychology (HIB)

Fuhl W., Kasneci G., Rosenstiel W., Kasneci E.
Training decision trees as replacement for convolution layers

               University of Tübingen, Department of Computer Science

Zadaianchuk A., Martius G.
Equation Learning for Extrapolation and Control

             Max Planck Institute for Intelligent Systems Tübingen

Machine Learning meets Physics, July 10, 2019

Machine Learning meets Physics

AI Building, Lecture Hall (ground floor)

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: Coffee break

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: Discussion

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', May 22, 2019

Symposium 'Machine Learning in Science'

May 22, 2019

Max-Planck-Gästehaus – Lecture Hall (Hörsaal)
Max-Planck-Ring 6, 72076 Tübingen

Program

09:45

Tropical circulation: Current challenges and potential for machine learning algorithms
Bedartha Goswami
-- Potsdam Institute for Climate Impact Research (PIK), Germany

10:30

High-throughput behavioral analysis for neural circuit understanding
Alexander Mathis
-- Department of Molecular and Cellular Biology, Harvard University, USA

11:15

Coffee Break

11:30

Reverse Engineering the Early Visual System with Artificial Neural Networks
Stéphane Deny
-- Department of Applied Physics at Stanford University, USA

12:15

Visualization of georeferenced open government data: benefits, issues, opportunities for machine learning research
Auriol Degbelo
-- Institute of Geography, University of Osnabrück, Deutschland

Symposium 'Ethics and Philosophy of Machine Learning in Science', May 15, 2019

Symposium 'Ethics and Philosophy of Machine Learning in Science'

May 15, 2019

Max-Planck-Gästehaus – Lecture Hall (Hörsaal)
Max-Planck-Ring 6, 72076 Tübingen

Program

08:30

Simplicity and Scientific Progress: A Topological Perspective
Konstantin Genin -- Department of Philosophy, University of Toronto, Canada

09:15

Learning Through Creativity
Caterina Moruzzi
-- Department of Philosophy, University of Nottingham, UK

10:00

Coffee break

10:20

Black-Boxes, Understanding, and Machine Learning
Emily Sullivan-Mumm
-- Ethics and Philosophy of Technology, Delft Data Science, The Netherlands

11:05

Working at the margins of machine learning – the ethics of labeling
Thilo Hagendorff
-- International Center for Ethics in the Sciences and Humanities,
University of Tübingen, Germany

11:50

Inductive Bias and Adversarial Data
Tom Sterkenburg -- LMU München, Munich Center for Mathematical Philosophy, Germany

12:35

Lunch break

13:30 - 14:30

Invited Talk

Co-Opt AI! Charting the emerging field of AI, ethics and social justice

Mona Sloane, Institute for Public Knowledge, New York University, USA
Abstract

17:10 - 17:55

Conference Room

ML from a DiscO viewpoint: Compressed Sensing, Dictionary Learning and beyond
(machine learning)

Andreas M. Tillmann -- Operations Research & Visual Computing Institute,
RWTH Aachen, Deutschland

Symposium 'Machine Learning in Science', March 18 and 25 - 27, 2019

Symposium „Machine Learning in Science“

March 18 and 25-26, 2018

Max-Planck-Gästehaus – Lecture hall (Hörsaal)
Max-Planck-Ring 6, 72076 Tübingen

Program

Monday, March 18, 2019

09:30 – 10:30

Neutrino Cosmology - Weighing the Ghost Particle with the Universe

   Dr. Elena Giusarma -- Simons Foundation, Flatiron Institute Center for Computational
   Astrophysics, New York, USA

 

Monday March 25, 2019

08:30

Information Field Theory
   PD Dr. Torsten Enßlin -- MPI für Astrophysik, Garching

09:30

Active machine learning for automating scientific discovery
   Prof. Dr. Roman Garnett -- Washington University in St. Louis, USA

10:30

Coffee break

11:00

Bayesian optimisation: nano-machine-learning
   Assoc. Prof. Dr. Michael Osborne -- University of Oxford, UK

12:00

Robust and Scalable Learning with Graphs
   Prof. Dr. Stephan Günnemann  -- TU München

13:00

Lunch

15:00

Representing and Explaining Novel Concepts with Minimal Supervision

   Asst. Prof. Dr. Zeynep Akata -- University of Amsterdam

16:00

Coffee Break

17:45

Cluster Member Meeting and General Assembly (non-public)

19:00

Joint Dinner (by invitation)

 

Tuesday, March 26, 2019

08:30

Expressive, Robust and Accountable Machine Learning for Real-world Data
   Dr. Isabel Valera -- MPI for Intelligent Systems, Tübingen

09:30

Algorithms of Vision: From Brains to Machines and Back
   Dr. Alexander Ecker -- Universität Tübingen

10:30

Coffee break

11:00

From Paired to Unpaired Image-to-Image Translation and Beyond
   Dr. Radu Timofte -- ETH Zürich, Schweiz

12:00

Face processing: Bridging Natural and Artificial Intelligence

   Assoc. Prof. Dr. Angela J. Yu -- University of California San Diego, USA

13:00

Lunch

14:00 From statistics to mechanisms, and back
   Prof. Dr. Jakob Macke -- TU München

 

Machine Learning meets Law, March 19, 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

Meeting of the Cluster 'Machine Learning in Science', November 12-13, 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
=========
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
========
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

Download program