Teknologiseminar

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(Dates and subjects - Autumn 2016)
(Dates and subjects - Autumn 2016)
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|  04.11    || Eivind - Statistical performance evaluation ||  Tønnes - Real world robot evolution  ||   
|  04.11    || Eivind - Statistical performance evaluation ||  Tønnes - Real world robot evolution  ||   
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|  02.12    || Sondre - ROS/New sensors/(Hyper-)NEAT?      ||  Mats - Optimization of plant growth  ||  Jim - AI for new health projects
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|  02.12    || Sondre - ROS current and future possibilities  ||  Mats - Optimization of plant growth  ||  Jim - AI for new health projects
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Revision as of 09:38, 1 December 2016

Meeting structure

Place: ROBIN pause area, 4th floor Ole-Johan Dahls hus

13:00-13:15 Brief exchange of recent news, plans etc

13:15-14:00 Technology introduction or conference / journal review, see guidelines

14:00-14:30 Research presentation by two people (presentation of and discussion around group member's current research (15 mins each including discussion))

Please upload pdf of your presentation here: http://robinternal.wiki.ifi.uio.no and link to it from the table below.

Dates and subjects - Autumn 2016

DON'T EDIT THIS WITH THE RICH EDITOR! IT DESTROYS THE TEXT VERSION!

When Technology / journal review Own research 1 Own research 2
30.09 Jørgen -Fil:Quality_Diversity.pdf Charles - Ensemble Director Agents for mediating ensemble music Fil:EDA-tech-talk.pdf Kai Olav - Neural networks that learn without forgetting Fil:Research_presentation.pdf
04.11 Eivind - Statistical performance evaluation Tønnes - Real world robot evolution
02.12 Sondre - ROS current and future possibilities Mats - Optimization of plant growth Jim - AI for new health projects


Overview of earlier seminars

Examples of interesting topics for technology introduction

  • Robotics
    • SLAM
    • embodied cognition
    • New sensors
    • Flying robotics
  • ANN
    • Recursive NNs
    • learning: supervised/unsupervised/reinforcement
    • spiking networks
    • NEAT / HyperNEAT
    • Deep NNs
  • Evolutionary algorithms
    • Performance comparison of EAs (statistical tests)
    • Differential evolution
    • Diversity preservation methods
    • Novelty search, MAP-Elites and variants
  • Tools
    • R
    • ROS
    • Deep learning tools
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