Teknologiseminar

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(Eat your own lunch while listening / discussing)
(Dates and subjects - Autumn 2018 – Fridays 12:00-13:00)
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12:15-13:00 Technology / Own Research / Journal review (see [http://robin.wiki.ifi.uio.no/Journal_Review guidelines]), including discussion
12:15-13:00 Technology / Own Research / Journal review (see [http://robin.wiki.ifi.uio.no/Journal_Review guidelines]), including discussion
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=== Dates and subjects - Autumn 2018 – Fridays 12:00-13:00 ===  
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=== Dates and subjects - Spring 2019 – Fridays 12:00-13:00 ===  
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===== Eat your own lunch while listening / discussing =====
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''' Eat your own lunch while listening / discussing '''
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'''DON'T EDIT THIS WITH THE RICH EDITOR! IT DESTROYS THE TEXT VERSION!'''
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{| class="wikitable"
{| class="wikitable"
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|''' When''' ||  '''Who''' || '''Content (title and type (tech/res/review)'''  
|''' When''' ||  '''Who''' || '''Content (title and type (tech/res/review)'''  
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19.10     || Mats || [http://www.robotikk.com/inf5207/softR/0.php Soft robotics og INF5207-V18]       
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29.03     || TBD ||
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02.11     || Weria || Mapless Navigation with Neuro-Fuzzy Intelligence (res)       
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12.04     || TBD ||
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16.11     || Eivind || Factorial Experiment design       
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26.04     || TBD ||
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30.11     || ||        
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10.05     || TBD ||
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14.12     || Arjun Chandra || Current challenges in deep RL
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24.05     || TBD ||
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Revision as of 10:25, 11 March 2019

Meeting structure

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

12:00-12:15 Brief exchange of recent news, plans etc.

12:15-13:00 Technology / Own Research / Journal review (see guidelines), including discussion

Dates and subjects - Spring 2019 – Fridays 12:00-13:00

Eat your own lunch while listening / discussing

When Who Content (title and type (tech/res/review)
29.03 TBD
12.04 TBD
26.04 TBD
10.05 TBD
24.05 TBD



Overview of earlier seminars

Examples of interesting topics for technology introduction

Feel free to add topics to the wishlist!

  • Robotics
    • Reinforcement learning for robotics
    • SLAM
    • embodied cognition
    • Swarm
  • Neural networks
    • Recursive NNs and LSTMs
    • NEAT / HyperNEAT
    • Deep NNs
  • Evolutionary algorithms and other types of optimization
    • Performance comparison of EAs (statistical tests)
    • Differential evolution
    • Diversity preservation methods
    • Novelty search, MAP-Elites and variants
    • Bayesian optimization
  • Tools
    • R
    • Python as alternative to R, incl. matplotlib
    • ROS
    • Deep learning frameworks and practice
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