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Challenges in real-world applications of neural networks

  • Michael Wand (Organiser)

Activity: Participating in or organising an eventOrganising a conference, workshop, ...

Description

Neural network based systems are now state-of-the-art for a large variety of tasks, often being orders of magnitude better than competing methods. However, many such results are obtained on large, well-cured standard data corpora, and may not always generalize well to novel types of data, or to novel tasks. In particular, tasks which relate on biosignals (i.e. physiological signals collected from the human body) come with their own set of challenges and constraints, including small corpus size, large data variability, recording artifacts, and missing annotations. In this discussion-style talk, I will present own work on some of these issues and outline possible remedies, as well as open questions and necessary steps to tackle them.
Period13 Sept 2019
Event typeGuest talk
LocationAustriaShow on map

Fields of science

  • 101031 Approximation theory
  • 102 Computer Sciences
  • 305901 Computer-aided diagnosis and therapy
  • 102033 Data mining
  • 102032 Computational intelligence
  • 101029 Mathematical statistics
  • 102013 Human-computer interaction
  • 305905 Medical informatics
  • 101028 Mathematical modelling
  • 101027 Dynamical systems
  • 101004 Biomathematics
  • 101026 Time series analysis
  • 202017 Embedded systems
  • 101024 Probability theory
  • 305907 Medical statistics
  • 102019 Machine learning
  • 202037 Signal processing
  • 102018 Artificial neural networks
  • 103029 Statistical physics
  • 202036 Sensor systems
  • 202035 Robotics
  • 106005 Bioinformatics
  • 106007 Biostatistics
  • 101019 Stochastics
  • 101018 Statistics
  • 101017 Game theory
  • 101016 Optimisation
  • 102001 Artificial intelligence
  • 101015 Operations research
  • 102004 Bioinformatics
  • 101014 Numerical mathematics
  • 102003 Image processing

JKU Focus areas

  • Digital Transformation