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
Welcome
2:00 pm – 2:20 pm
Welcome Greeting
Thomas Seidl, Daniel Cremers
Track 1: Spatial and Temporal Machine Learning & Computer Vision
2:20 pm – 2:40 pm
Machine Learning at CAMP: Interpretability and Spatio-temporal Learning for Medical Imaging
Ashkan Khakzar, Azade Farshad (Nassir Navab)
2:40 pm – 3:00 pm
Bayesian image segmentation with hierarchical Potts models
Christopher Küster (Volker Schmid)
3:00 pm – 3:20 pm
Resource Search in Data Driven Environments
Matthias Schubert
3:20 pm – 3:40 pm
Learning a neural solver for multi-object tracking
Guillem Brasó (Laura Leal-Taixé)
3:20 pm – 3:40 pm
Learning a neural solver for multi-object tracking
Guillem Brasó (Laura Leal-Taixé)
3:40 pm – 4:00 pm
Deep learning: a non-alchemical view
Yuesong Shen (Daniel Cremers)
4:00 pm – 4:20 pm
Equivariant Deep Learning
Vladimir Golkov (Daniel Cremers)
4:20 pm – 4:40 pm
Learning to Optimize for Human Reconstructions
Andrei Burov (Matthias Niessner)
Track 2: Learning on Graphs and Networks & Representation Learning
2:20 pm – 2:40 pm
Robust deep learning on graphs
Daniel Zügner (Stephan Günnemann)
2:40 pm – 3:00 pm
Applied Network Science
Cornelius Fritz, Marc Schneble, Sevag Kevork (Göran Kauermann)
3:00 pm – 3:20 pm
Knowledge Graph Matching
Max Berrendorf (Volker Tresp)
3:20 pm – 3:40 pm
Measurement Dependence Inducing Latent Causal Models
Alex Markham (Moritz Grosse-Wentrup)
3:40 pm – 4:00 pm
Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly
Nora Kassner (Hinrich Schütze)
4:00 pm – 4:20 pm
Query to reference single-cell integration with transfer learning
Mohammad Lotfollahi (Fabian Theis)
4:20 pm – 4:40 pm
Mapping the fate of single cells with RNA velocity using CellRank
Marius Lange (Fabian Theis)
Track 3: Automatic and Explainable Modeling & Computational Models for Large-Scale ML
2:20 pm – 2:40 pm
Semi-Structured Deep Distributional Regression
David Rügamer (Bernd Bischl)
2:40 pm – 3:00 pm
Multi-Objective Hyperparameter Tuning and Feature Selection using Filter Ensembles
Julia Moosbauer, Martin Binder (Bernd Bischl)
3:00 pm – 3:20 pm
Cluster Analysis and Feature Rankings: Validation, benchmarking and over-optimism concerns
Theresa Ullmann, Christina Nießl (Anne-Laure Boulesteix)
3:20 pm – 3:40 pm
Finding and evaluating embeddings for functional data
Moritz Herrmann (Fabian Scheipl)
3:40 pm – 4:00 pm
Clustering Large-Scaled Datasets using Deep Learning
Li Qian (Christian Böhm)
4:00 pm – 4:20 pm
Evaluation of Results from Unsupervised Learning Processes
Anna Beer (Peer Kröger)
4:20 pm – 4:40 pm
Recent Advances in Correlation Clustering
Daniyal Kazempour (Thomas Seidl)
4:40 pm – 5:00 pm
Active Learning - Diversity vs. Uncertainty Sampling
Sandra Obermeier (Thomas Seidl)
Closing
5:00 pm – 5:30 pm
Closing Remarks
Bernd Bischl
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