2nd MLC Workshop Schedule

Registrations for the second workshop of the Machine Learning Community Dresden (MLC) on the May 16, 2019 are closed now. More than 60 registered participants will be attending the 21 scheduled talks. The final schedule is as follows:

Time Presenter Affiliation Title
10:00 – 11:45 MLC-Orga-Team   Welcome Address
  Graham Appleby HZDR Center of Advanced Systems Understanding (CASUS)
  Guido Juckeland HZDR Helmholtz Artificial Intelligence Cooperation Unit (HAICU)
  Nico Hoffmann HZDR Learning partial differential equations via neural networks
  Stefan Ecklebe TU Dresden Model identification for process control applications using machine learning
  Lena Jurkschat ScaDS NER on financial documents
  Andreas Gocht TU Dresden A New Approach for Automated Feature Selection
11:45 – 12:30     Break
12:30 – 14:30 Norman Koch ScaDS Automatic highly-parallelized hyperparameter-optimization for Machine Learning Algorithms
  Andreas Knüpfer TU Dresden The HP-DLF Scalable Node-Parallel Deep Learning Framework
  Stefan Reitmann DLR Usage of machine learning in modern data-based air traffic management
  Bernhard Vogginger TU Dresden SpiNNaker2 - a Scalable Hardware Architecture for Real-time Neural Networks made in Dresden
  Simon Walz TU Dresden Show me that 3D model of an LSTM Autoencoder with many units on multivariate data
  Danell Quesada TU Dresden Statistical Downscaling of CMIP5 projections for Costa Rica employing Artificial Neural Networks
  Lennart Schmidt UFZ Leipzig Automated Quality-Control of Environmental Sensor Data
14:30 – 15:00     Break
15:00 – 17:15 Frank Fitzek TU Dresden Tactile Internet
  Sebastian Hahn TU Dresden Deep Structured Models for Semantic Segmentation of OCT-Scans of Oral Tissue
  Sarah Schmell Biotec Automation of image-based routine diagnostics via deep learning approaches in pathology
  Felix Knorr TU Dresden How well can an SVM deal with noise and small samples
  Peter Steinbach MPI-CBG Adversarial Attacks on Medical Imaging Revisited
  Ronald Tetzlaff TU Dresden Bio-inspired computing by Cellular Neuronal Networks
  Pavol Mikolas TU Dresden Development of a machine learning classifier to identify ADHD in real-world clinical data
  Sebastian Starke HZDR/OncoRay Deep-learning based prediction of cancer recurrence risks
17:15 – 17:30     Wrap-up

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