29

Jul

Teaser image to MCML Virtual workshop

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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