29.07.2020

Teaser image to MCML - Virtual workshop

MCML - Virtual workshop

Over 20 presentations by our PhD students on current research topics

The workshop includes presentations on Spatial and Temporal Machine Learning & Computer Vision, Learning on Graphs and Networks & Representation Learning, and Automatic and Explainable Modeling & Computational Models for Large-Sclae ML.

Agenda

14:00–
14:20
       Prof. Dr. Thomas Seidl, Prof. Dr. Daniel Cremers
Welcome Greeting


Track 1: Spatial and Temporal Machine Learning & Computer Vision

14:20–
14:40
       Ashkan Khakzar, Azade Farshad / Prof. Dr. Nassir Navab
Machine Learning at CAMP: Interpretability and Spatio-temporal Learning for Medical Imaging

14:40–
15:00
Christopher Küster / Prof. Dr. Volker Schmid
Bayesian image segmentation with hierarchical Potts models

15:00–
15:20
Prof. Dr. Matthias Schubert
Resource Search in Data Driven Environments

15:20–
15:40
Guillem Brasó / Prof. Dr. Laura Leal-Taixé
Learning a neural solver for multi-object tracking

15:40–
16:00
Yuesong Shen / Prof. Dr. Daniel Cremers
Deep learning: a non-alchemical view

16:00–
16:20
Vladimir Golkov, / Prof. Dr. Daniel Cremers
Equivariant Deep Learning

16:20–
16:40
Andrei Burov / Prof. Dr. Matthias Niessner
Learning to Optimize for Human Reconstructions


Track 2: Learning on Graphs and Networks & Representation Learning

14:20–
14:40
       Daniel Zügner / Prof. Dr. Stephan Günnemann
Robust deep learning on graphs

14:40–
15:00
Cornelius Fritz, Marc Schneble, Sevag Kevork / Prof. Dr. Göran Kauermann
Applied Network Science

15:00–
15:20
Max Berrendorf / Prof. Dr. Volker Tresp
Knowledge Graph Matching

15:20–
15:40
Alex Markham / Prof. Dr. Moritz Grosse-Wentrup
Measurement Dependence Inducing Latent Causal Models

15:40–
16:00
Nora Kassner / Prof. Dr. Hinrich Schütze
Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly

16:00–
16:20
Mohammad Lotfollahi / Prof. Dr. Dr. Fabian Theis
Query to reference single-cell integration with transfer learning

16:20–
16:40
Marius Lange / Prof. Dr. Dr. Fabian Theis
Mapping the fate of single cells with RNA velocity using CellRank


Track 3: Automatic and Explainable Modeling & Computational Models for Large-Sclae ML

14:20–
14:40
       Dr. David Ruegamer / Prof. Dr. Bernd Bischl
Semi-Structured Deep Distributional Regression

14:40–
15:00
Julia Moosbauer, Martin Binder / Prof. Dr. Bernd Bischl
Multi-Objective Hyperparameter Tuning and Feature Selection using Filter Ensembles

15:00–
15:20
Theresa Ullmann, Christina Nießl / Prof. Dr. Anne-Laure Boulesteix
Cluster Analysis and Feature Rankings: Validation, benchmarking and over-optimism concerns

15:20–
15:40
Moritz Herrmann / PD Dr. Fabian Scheipl
Finding and evaluating embeddings for functional data

15:40–
16:00
Li Qian / Prof. Dr. Christian Böhm
Clustering Large-Scaled Datasets using Deep Learning

16:00–
16:20
Anna Beer / Prof. Dr. Peer Kröger
Evaluation of Results from Unsupervised Learning Processes

16:20–
16:40
Daniyal Kazempour / Prof. Dr. Thomas Seidl
Recent Advances in Correlation Clustering

16:40–
17:00
Sandra Obermeier / Prof. Dr. Thomas Seidl
Active Learning - Diversity vs. Uncertainty Sampling


17:00–
17:30
       Prof. Dr. Bernd Bischl
Closing Remarks

29.07.2020


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