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

©MCML

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©MCML

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