29.07.2020
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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 |
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14:40– 15:00 |
Christopher Küster / Prof. Dr. Volker Schmid Bayesian image segmentation with hierarchical Potts models |
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15:00– 15:20 |
Prof. Dr. Matthias Schubert Resource Search in Data Driven Environments |
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15:20– 15:40 |
Guillem Brasó / Prof. Dr. Laura Leal-Taixé Learning a neural solver for multi-object tracking |
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15:40– 16:00 |
Yuesong Shen / Prof. Dr. Daniel Cremers Deep learning: a non-alchemical view |
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16:00– 16:20 |
Vladimir Golkov, / Prof. Dr. Daniel Cremers Equivariant Deep Learning |
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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 |
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14:40– 15:00 |
Cornelius Fritz, Marc Schneble, Sevag Kevork / Prof. Dr. Göran Kauermann Applied Network Science |
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15:00– 15:20 |
Max Berrendorf / Prof. Dr. Volker Tresp Knowledge Graph Matching |
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15:20– 15:40 |
Alex Markham / Prof. Dr. Moritz Grosse-Wentrup Measurement Dependence Inducing Latent Causal Models |
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15:40– 16:00 |
Nora Kassner / Prof. Dr. Hinrich Schütze Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly |
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16:00– 16:20 |
Mohammad Lotfollahi / Prof. Dr. Dr. Fabian Theis Query to reference single-cell integration with transfer learning |
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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 |
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14:40– 15:00 |
Julia Moosbauer, Martin Binder / Prof. Dr. Bernd Bischl Multi-Objective Hyperparameter Tuning and Feature Selection using Filter Ensembles |
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15:00– 15:20 |
Theresa Ullmann, Christina Nießl / Prof. Dr. Anne-Laure Boulesteix Cluster Analysis and Feature Rankings: Validation, benchmarking and over-optimism concerns |
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15:20– 15:40 |
Moritz Herrmann / PD Dr. Fabian Scheipl Finding and evaluating embeddings for functional data |
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15:40– 16:00 |
Li Qian / Prof. Dr. Christian Böhm Clustering Large-Scaled Datasets using Deep Learning |
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16:00– 16:20 |
Anna Beer / Prof. Dr. Peer Kröger Evaluation of Results from Unsupervised Learning Processes |
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16:20– 16:40 |
Daniyal Kazempour / Prof. Dr. Thomas Seidl Recent Advances in Correlation Clustering |
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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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