Schedule for the 2nd MLC Workshop

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2nd MLC Workshop Schedule

Registrations for the second workshop of the Machine Learning Community Dresden (MLC) are closed now. More than 50 registered participants will be attending the 21 scheduled talks. The final schedule is as follows:

10:00 – 11:45MLC-Orga-Team Welcome Address
 Graham ApplebyHZDRCenter of Advanced Systems Understanding (CASUS)
 Guido JuckelandHZDRHelmholtz Artificial Intelligence Cooperation Unit (HAICU)
 Nico HoffmannHZDRLearning partial differential equations via neural networks
 Stefan EcklebeTU DresdenModel identification for process control applications using machine learning
 Lena JurkschatScaDSNER on financial documents
 Andreas GochtTU DresdenA New Approach for Automated Feature Selection
11:45 – 12:30  Break
12:30 – 14:30Norman KochScaDSAutomatic highly-parallelized hyperparameter-optimization for Machine Learning Algorithms
 Andreas KnüpferTU DresdenThe HP-DLF Scalable Node-Parallel Deep Learning Framework
 Stefan ReitmannDLRUsage of machine learning in modern data-based air traffic management
 Bernhard VoggingerTU DresdenSpiNNaker2 - a Scalable Hardware Architecture for Real-time Neural Networks made in Dresden
 Simon WalzTU DresdenShow me that 3D model of an LSTM Autoencoder with many units on multivariate data
 Danell QuesadaTU DresdenStatistical Downscaling of CMIP5 projections for Costa Rica employing Artificial Neural Networks
 Lennart SchmidtUFZ LeipzigAutomated Quality-Control of Environmental Sensor Data
14:30 – 15:00  Break
15:00 – 17:15Frank FitzekTU DresdenTactile Internet
 Sebastian HahnTU DresdenDeep Structured Models for Semantic Segmentation of OCT-Scans of Oral Tissue
 Sarah SchmellBiotecAutomation of image-based routine diagnostics via deep learning approaches in pathology
 Felix KnorrTU DresdenHow well can an SVM deal with noise and small samples
 Peter SteinbachMPI-CBGAdversarial Attacks on Medical Imaging Revisited
 Ronald TetzlaffTU DresdenBio-inspired computing by Cellular Neuronal Networks
 Pavol MikolasTU DresdenDevelopment of a machine learning classifier to identify ADHD in real-world clinical data
 Sebastian StarkeHZDR/OncoRayDeep-learning based prediction of cancer recurrence risks
17:15 – 17:30  Wrap-up