Exzellenzcluster Veranstaltungen

Aufgrund der aktuellen Lage rund um die Corona-Pandemie finden einige unserer geplanten Präsenzveranstaltungen im Online Format statt oder sind auf unbestimmte Zeit ausgesetzt.

 

Alle zurückliegenden Cluster-Veranstaltungen sind untenstehend im ARCHIV zu finden.

Cluster Kolloquium "Maschinelles Lernen" -- 1. Mittwoch im Monat -- PAUSIERT

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
                      

Workshop "Philosophy of Medical AI", 08.-09. Okt 2020 -- ONLINE

Virtual Workshop on the Philosophy of Medical AI


► Donnerstag, 08. Oktober  | 09:30 - 17:30
► Freitag, 09. Oktober  | 10:00 - 16:00


Anmeldung
Der Workshop ist öffentlich zugänglich, eine Anmeldung ist nicht erforderlich.

Organisation
Thomas Grote; Ethics and Philosophy Lab; Cluster of Excellence “Machine Learning: New Perspectives for Science”

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  Der Workshop findet virtuell auf Zoom statt,  der Link wird demnächst hier veröffentlicht.
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Die jüngsten Fortschritte im Bereich des Deep Learning haben das Interesse an der Anwendung von KI-Systemen im Gesundheitswesen weiter gesteigert. Eine Vielzahl hochkarätiger Studien zeigt, wie vielfältig die Möglichkeiten sind, die KI für verschiedene Zweige der Medizin bietet: Sie reichen von der Verbesserung medizinischer Diagnosen über die rechtzeitige Vorhersage von Gesundheitsrisiken bis hin zur Entdeckung neuer Medikamente. Gleichzeitig gibt es Befürchtungen, dass die Unvollkommenheit der gegenwärtigen KI-Systeme strukturelle Missstände im Gesundheitssystem verstetigen könnten oder sogar neue ethische Probleme schaffen könnten. Ziel dieses Workshops ist es, über die Chancen und Herausforderungen des Einsatzes von KI in der Medizin nachzudenken. Dazu bringt der Workshop Wissenschaftsphilosoph*innen, Medizinethiker*innen sowie Forscher*innen aus den Bereichen Maschinelles Lernen oder Bioinformatik zusammen.


Donnerstag, 08. Oktober 2020

09:30 - 10:00  

Welcome address and brief introduction

10:00 - 10:50    

Sune Holm (University of Copenhagen)

Equality and Fair Algorithmic Decision Making

11:00 - 11:50     

Atoosa Kasirzadeh (Australian National University/University of Toronto)

The Use and Misuse of Counterfactuals in Machine Learning

    

MITTAGSPAUSE

13:00 - 13:50

Georg Starke (University of Basel)

Does Trust Constitute an Adequate Epistemic Stance Towards Medical AI?

14:00 - 14:50  

Geoff Keeling (Stanford University)

Decision-Support Systems and Clinical Reasoning – A Peircian Approach

    

PAUSE

15:30 -16:20    

Manfred Claassen (University of Tübingen)

Challenges in machine learning driven translation of single-cell biology studies

16:30 - 17:30           

Alex London (Carnegie Mellon University, Pittsburgh)

Keynote: Ethics in Medical AI: Explaining Models vs Explaining the Warrant for Their Use

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Freitag, 09. Oktober 2020 

10:00 - 10:50

Chris Burr (Alan Turing Institute)

Responsible Innovation and Digital Psychiatry

11:00 - 11:50

Rune Nyrup (University of Cambridge)

Value Transparency in Science and Machine Learning

 

MITTAGSPAUSE

13:00 - 13:50

Emily Sullivan (University of Eindhoven)

Opacity in Medical Explanations: Is AI Special?

14:00 - 14:50 

Zeynep Akata (University of Tübingen)

Explaining Neural Network Decisions Via Natural Language

15:00 - 16:00

Alex Broadbent (University of Johannesburg)

Keynote: Why Robots Cannot Do Epidemiology

Workshop "Philosophy of Science Meets ML", 28. - 30. April 2021 -- NEUER TERMIN

Workshop "Philosophy of Science Meets Machine Learning"

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  Der für Juni 2020 geplante Workshop wurde verschoben auf den

      28. - 30. April 2012
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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

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:

  1. A critical reflection on key-concepts, such as ‘learning’, ‘inference’, ‘explanation’ or ‘understanding’.
  2. The implications of machine learning for the special sciences, e.g. cognitive science, social science or medicine.
  3. The ethics of machine learning-driven science, e.g. the moral responsibilities of researchers.
  4. Social aspects of machine learning-driven science, e.g. the impact of funding structures on research.

Veranstaltungs-Archiv

Hier finden Sie alle vergangenen Cluster-Veranstaltungen im Überblick.

Cluster Kolloquium "Maschinelles Lernen" - 1. Mittwoch im Monat

 
Seminarreihe des Exzellenzclusters "Maschinelles Lernen"

-------------------------
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

Machine Learning meets Environmental Science, 25. September 2020

Machine Learning meets Environmental Science

Friday, September 25, 2020
 

Meeting Venue
Neue Aula, Audimax, Geschwister-Scholl-Platz

Organizers
Prof. Martin Butz (ML Cluster, Dep. Of Computer Science)
Prof. Christiane Zarfl (Center for Applied Geoscience)

Registration
Registration is required by Email until Sept 22, 2020.
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PROGRAM

09:00    Welcome and Introduction: Cluster of Excellence – Machine Learning for Science
              Philipp Berens
09:10     ML Transfer Center and simulation based inference
              Álvaro Tejero-Cantero & Jakob Macke
09:40     Day-ahead optimization of production schedules for saving electrical energy costs
              Thomas Stüber & Michael Menth
10:00     Uncovering hidden structure in climate data
               Bedartha Goswami
10:20    Coffee Break
10:40    Learning spatiotemporal distributed generative graph neural networks
              Martin Butz
11:00    Modeling environmental processes in rivers
             Christiane Zarfl
11:20    Can plants learn? Coupling models and data in eco-evolutionary research
             Sara Tomiolo & Maximiliane Herberich (from Katja Tielbörger’s group)
11:40    Improving the spatial prediction of soil organic carbon content in two contrasting climatic regions by
             stacking machine learning models and rescanning covariate space

             Ruhollah Taghizadeh-Mehrjardi (from Thomas Scholten’s group)
12:00    The climate situation: Facts and Necessities
             Harald Baayen
12:05    Snack Break
12:30    ML and mobile robots in environmental science
             Andreas Zell
12:50    Turbulent transport of energy, momentum and matter by large data sets
              obtained from airborne probing of the lower atmosphere

             Jens Bange
13:20    Status of the CRC 1253 CAMPOS - Catchments as Reactors:
             Metabolism of Pollutants on the Landscape Scale

             Christiane Zarfl
13:40    Funding options in the ML Cluster of Excellence and beyond
             Tilman Gocht
14:00   End

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  Aim of the day

Environmental science studies spatio-temporal dynamics of various processes and on different topics, including climate and weather, geology, hydrology, vegetation and agriculture, various forms of pollution (e.g. of organic pollutants), to name just a few. In all these cases, multiple, often interrelated data sources are available at varying degrees of spatial and temporal granularity. Moreover, human activities, such as river dam building, CO2 release, plantations, industry etc., strongly influence the unfolding dynamics. Critical principles – such as basic laws in physics – apply universally in such systems. Environmental science has strong expertise in modeling the underlying processes – typically by systems of partial or ordinary differential equations.

As a result, besides the expertise about the underlying processes, environmental science offers two types of data – real-world data as well as data from the respectively available models of the considered environmental system. This offers essentially the perfect basis for a meaningful, science-driven application of ML algorithms. On the one hand, the parameters of the differential equations may be optimized more effectively by means of state-of-the-art gradient-based approximation approaches from ML. On the other hand, the available models may be augmented or fully substituted by distributed spatio-temporal, generative neural network approaches, such as convolutional networks, graph networks, autoencoders, recurrent neural networks, and combinations thereof.

Seeing that models are available to pre-train and analyze potentially applicable ML architectures, expertise is available to tune these models to the actual underlying processes, and that real world data is available to further train and test the generalizability of these ML architectures, it is time that ML meets Environmental Science! The aim is to foster collaboration with a focus on two main potential strands. First, available models of differential equations and involved prior situation assumptions may be optimized by means of state-of-the-art ML techniques. Second, ML techniques and particularly distributed, generative artificial neural networks may be designed to infer the processes and structures that generate particular data patterns, thus enabling (i) the fast, efficient, and accurate simulation of environmental processes and (ii) the consideration of impacts of human actions, including the potential to derive optimal actions for steering the environmental system towards a desired (stable / homeostatic) direction.

2. Jahres-Konferenz "Machine Learning in Science", 21. - 23. Juli 2020 -- ONLINE

2. Exzellenzcluster Konferenz
"Machine Learning in Science" 2020



► Dienstag 21. Juli  | 14:00 - 17:45
► Mittwoch 22. Juli  | 14:00 - 18:15
► Donnerstag 23. Juli  | 14:00 - 18:30

 

Programmänderung, Donnerstag 23. Juli: Der Vortrag von Manfred Claassen um 14:00 Uhr muss leider abgesagt werden. Phillip Berens übernimmt dankenswerterweise kurzfristig mit einem Vortrag zum Thema "Towards hybrid models of retinal circuits - integrating biophysical realism, anatomical constraints and predictive performance".

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Die Konferenz findet virtuell auf Crowdcast mit einem Live-Stream auf Youtube statt.

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Anmeldung
Die Konferenz ist zugänglich für die Öffentlichkeit. Um per Chat an den Diskussionen teilnehmen zu können, muss man sich über Crowdcast anmelden - Kamera und Mikrofon der Teilnehmer bleiben dabei ausgeschaltet. Einfach an jedem Konferenztag auf den entsprechenden Link auf unserem Crowdcast-Profil klicken. Zuerst die E-Mail-Adresse angeben und dann den vollständigen Namen (Vor- und Nachname). 
Wer die Vorträge nur verfolgen möchten, kann dies über Youtube tun, eine Registrierung ist nicht erforderlich. Hier sind die Livestreams aller Vorträge zu finden. 

Jeder Vortrag dauert 30 Minuten plus 15 Minuten Diskussion, jede Spotlight-Präsentation 5 Minuten plus 5 Minuten Diskussion.

Wichtig: Alle Zeiten sind in MESZ, Mitteleuropäische Sommerzeit, angegeben.

Programm [PDF]

Dienstag, 21. Juli 2020

14:00 - 14:15     

Ulrike von Luxburg, Philipp Berens
(Sprecher Exzellenzcluster “Maschinelles Lernen”, Universität Tübingen)

► Eröffnung
Dr. Simone Schwanitz
(Ministerialdirigentin, Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg)

► Grußworte

14:15 - 15:00      

Kyle Cranmer (Center for Cosmology and Particle Physics, New York University)

Keynote Lecture: How Machine Learning Can Help us Get the Most out of our Highest Fidelity Physical Models

15:00 - 15:45       

Zeynep Akata
(Fachbereich Informatik und Exzellenzcluster “Maschinelles Lernen”, Universität Tübingen)

► Learning Decision Tress Recurrently through Communication

15:45 - 16:00      

PAUSE

 

Spotlight Präsentationen
Innovation Fund Projekte des Exzellenclusters “Maschinelles Lernen”

16:00 - 16:10      

David Künstle

Machine Learning Approaches for Psychophysics with Ordinal Comparisons

16:10 - 16:20      

Zohreh Ghaderi / Hassan Shahmohammadi

Enhancing Machine Learning of Lexical Semantics with Image Mining

16:20 - 16:30      

Matthias Karlbauer

Causal Inference with a Spatio-Temporal Generative Model

16:30 - 16:40      

Thomas Gläßle / Kerstin Rau

Interpretable Spatial Machine Learning for Environmental Modelling

16:40 - 17:00      

PAUSE

17:00 - 17:45      

Jakob Macke
(Fachbereich Informatik und Exzellenzcluster “Maschinelles Lernen”, Universität Tübingen)

► Training Neural Networks to Identify Mechanistic Models of Neural Networks

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Mittwoch, 22. Juli 2020 

14:00 - 14:45             

Peter Dayan (Max Planck Institute for Biological Cybernetics, Tübingen)

Modelling and Manipulating Behaviour Using Recurrent Networks

14:45 - 15:30      

Dominik Papies (Fachbereich Wirtschaftswissenschaft, Universität Tübingen)

Machine Learning in Business and Economics - Can it Help us Understand the Relevance of Visual Product Characteristics? 

15:30 - 15:45     

PAUSE

 

Spotlight Präsentationen
Innovation Fund Projekte des Exzellenclusters “Maschinelles Lernen”

15:45 - 15:55         

Eric Raidl / Thomas Grote

Artificial Intelligence, Trustworthiness and Explainability

15:55 - 16:05      

Thilo Hagendorff

The Big Picture: Ethical Considerations and Statistical Analysis of Industry Involvement in Machine Learning Research

16:05 - 16:15      

Daniel Weber

Human-robot Interface with Eye-tracking

16:15 - 16:25      

Pablo Sanchez Martin

Exploring Ambient Awareness in Twitter

16:25 - 16:30      

PAUSE

16:30 - 17:15     

Ingo Steinwart (Institut für Stochastik und Anwendungen, Universität Stuttgart)

Some Thoughts towards a Statistical Understanding of Deep Neural Networks

17:15 - 17:30 PAUSE
17:30 - 18:15

Claire Monteleoni (Department of Computer Science, University of Colorado Boulder)

Deep Unsupervised Learning for Climate Informatics

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Donnerstag, 23. Juli 2020 

14:00 - 14:45             

Philipp Berens (Exzellencluster “Maschinelles Lernen”, Universität Tübingen)

Towards hybrid models of retinal circuits - integrating biophysical realism, anatomical constraints and predictive performance

14:45 - 14:50      

PAUSE

 

Spotlight Präsentationen
Innovation Fund Projekte des Exzellenclusters “Maschinelles Lernen”

14:50 - 15:00      

Jonas Ditz

Extending Deep Kernel Approaches for Better Prediction and Understanding of ADME Phenotypes and Related Drug Response

15:00 - 15:10      

Susanne Zabel

Visualizing Uncertainty from Data, Model and Algorithm in Large-Scale Omics Data

15:10 - 15:20      

Paolo Mazza

Understanding Quantum Effects in Neural Network Models through Machine Learning

15:20 - 15:30      

Jonathan Fuhr

Applied Causal Inference in Social Sciences and Medicine

15:30 - 15:45      

PAUSE

15:45 - 16:30      

Stefanie Jegelka

Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology

► Keynote Lecture: Representation and Learning in Graph Neural Networks

16:30 - 17:15      

Bedartha Goswami (Exzellenzcluster “Maschinelles Lernen”, Universität Tübingen)

 Inferring Climate Variability from Patterns Hidden in Modern and Paleo Time Series Data

17:15 - 17:30      

PAUSE

17:30 - 18:15      

Igor Lesanovsky

Fachbereich Physik, Universität Tübingen

Neural Network Dynamics in Quantum Many-Body Systems

18:15 - 18:30       

Ulrike von Luxburg, Philipp Berens

Sprecher Exzellenzcluster “Maschinelles Lernen”, Universität Tübingen

Closing Remarks

Symposium "Machine Learning in Science", 7. - 8. Juli 2020 -- ONLINE

Symposium "Machine Learning in Science"

am 7. und 8 Juli 2020
Zoom Videokonferenz


Jeder Vortrag dauert 30 Minuten, gefolgt von einer Diskussion von 15 Minuten.

An die Diskussion schließt sich eine 45-minütige nicht-öffentliche Sitzung an, die als Break-out-Gruppe organisiert ist, so dass ALLE Teilnehmer während der gesamten Dauer des Symposiums im Online-Konferenzraum bleiben können.

Anmeldung:
Die Anmeldung ist nur für Nicht-Cluster-Mitglieder erforderlich. Bitte senden Sie eine E-Mail an Sebastian Schwenk (sebastian.schwenkspam prevention@uni-tuebingen.de), der Link zur Zoom-Konferenz wird dann zur Verfügung gestellt.

Wichtig: Teilnehmer, die nicht ihren vollständigen Namen angeben, schließt der Moderator von der Videokonferenz aus.

Programm [PDF]

09:00 – 09:45

Nicole Ludwig (Karlsruhe Institute of Technology)

How Machine Learning Changes Research in Energy

09:45 – 10:15

nicht-öffentliche Sitzung

10:30 – 11:15

Michal Rolínek (Max Planck Institute for Intelligent Systems, Tübingen)

Machine Learning and Combinatorial Optimization

11:15 – 11:45

nicht-öffentliche Sitzung

11:45 – 13:00
PAUSE

13:00 –  13:45

Thilo Wrona (GFZ Helmholtz-Zentrum, Potsdam)

How can Machine Learning Help Us Advance Solid Earth Science?

13:45 –  14:15

nicht-öffentliche Sitzung

14:30 – 15:15

Niklas Wahl (German Cancer Research Center – DKFZ, Heidelberg)

How will Machine Learning change Radiotherapy?

15:15 – 15:45

nicht-öffentliche Sitzung

16:00 – 16:45

Charley Wu (Harvard University, Cambridge, USA)

Bridging the Gap Between Human and Machine Learning

16:45 – 17:15

nicht-öffentliche Sitzung

17:15
Ende Tag 1


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09:00 – 09:45              

Christin Beck (University of Konstanz)

Learning the Language of the Past: Historical Linguistics, Natural Language Processing and Machine Learning

09:45 – 10:15        

nicht-öffentliche Sitzung

 

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.

1. 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.

Programm


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
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

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)
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, 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 "Ethik und Philosophie des Maschinellen Lernens in der Wissenschaft", 15. Mai 2019

„Ethik und Philosophie des Maschinellen Lernens in der Wissenschaft“

15. Mai 2019

Max-Planck-Gästehaus – Hörsaal
Max-Planck-Ring 6, 72076 Tübingen

Programm

 

08:30

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

09:15

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

10:00

Kaffeepause

10:20

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

11:05

Working at the margins of machine learning – the ethics of labeling
Thilo Hagendorff
-- Internationales Zentrum für Ethik in den Wissenschaften,
Universität Tübingen, Deutschland

11:50

Inductive Bias and Adversarial Data
Tom Sterkenburg
-- LMU München, Philosophie,
Wissenschaftstheorie und Religionswissenschaft, Deutschland

12:35

Mittagspause

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

KONFERENZRAUM
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", 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

Programme

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
    Astrophysics, New York, USA

 

Montag, 25. März 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

Kaffeepause

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

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
   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

Kaffeepause

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

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
=========
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