29
Jul
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MCML Virtual workshop
Over 20 presentations by our PhD students on current research topics
29.07.2021
2:00 pm - 5:30 pm
Zoom
Our internal 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-Scale ML.
Agenda
Welcome
14:00–14:20
Prof. Thomas Seidl, Prof. Daniel Cremers
Welcome Greeting
Track 1: Spatial and Temporal Machine Learning & Computer Vision
14:20–14:40
Ashkan Khakzar, Azade Farshad / Prof. Nassir Navab
Machine Learning at CAMP: Interpretability and Spatio-temporal Learning for Medical Imaging
14:40–15:90
Christopher Küster / Prof. Volker Schmid
Bayesian image segmentation with hierarchical Potts models
15:00–15:20
Prof. Matthias Schubert
Resource Search in Data Driven Environments
15:20–15:40
Guillem Brasó / Prof. Laura Leal-Taixé
Learning a neural solver for multi-object tracking
15:40–16:00
Yuesong Shen / Prof. Daniel Cremers
Deep learning: a non-alchemical view
16:00–16:20
Vladimir Golkov, / Prof. Daniel Cremers
Equivariant Deep Learning
16:20–16:40
Andrei Burov / Prof. Matthias Niessner
Learning to Optimize for Human Reconstructions
Track 2: Learning on Graphs and Networks & Representation Learning
14:20–14:40
Daniel Zügner / Prof. Stephan Günnemann
Robust deep learning on graphs
14:40–15:00
Cornelius Fritz, Marc Schneble, Sevag Kevork / Prof. Göran Kauermann
Applied Network Science
15:00–15:20
Max Berrendorf / Prof. Volker Tresp
Knowledge Graph Matching
15:20–15:40
Alex Markham / Prof. Moritz Grosse-Wentrup
Measurement Dependence Inducing Latent Causal Models
15:40–16:00
Nora Kassner / Prof. Hinrich Schütze
Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly
16:00–16:20
Mohammad Lotfollahi / Prof. Fabian Theis
Query to reference single-cell integration with transfer learning
16:20–16:40
Marius Lange / Prof. 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. Bernd Bischl
Semi-Structured Deep Distributional Regression
14:40–15:00
Julia Moosbauer, Martin Binder / Prof. Bernd Bischl
Multi-Objective Hyperparameter Tuning and Feature Selection using Filter Ensembles
15:00–15:20
Theresa Ullmann, Christina Nießl / Prof. 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. Christian Böhm
Clustering Large-Scaled Datasets using Deep Learning
16:00–16:20
Anna Beer / Prof. Peer Kröger
Evaluation of Results from Unsupervised Learning Processes
16:20–16:40
Daniyal Kazempour / Prof. Thomas Seidl
Recent Advances in Correlation Clustering
16:40–17:00
Sandra Obermeier / Prof. Thomas Seidl
Active Learning - Diversity vs. Uncertainty Sampling
Closing
17:00–17:30
Prof. Bernd Bischl
Closing Remarks
Organized by:
Dr. Elke Achtert Munich Center for Machine Learning
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