29.07.2020

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MCML - Virtual Workshop

Over 20 Presentations by Our PhD Students on Current Research Topics

Agenda

| | | | |—–|———–|—-| |14:00–
14:20|      |Prof. Dr. Thomas Seidl, Prof. Dr. Daniel Cremers
Welcome Greeting

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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

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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

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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

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| | | | |—–|———–|—-| |17:00–
17:30|      |Prof. Dr. Bernd Bischl
Closing Remarks

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29.07.2020