Teknologiseminar

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(→Dates and subjects - Spring 2019 – Fridays 12:00-13:00)
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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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|  29.03     || Jørgen Nordmoen || [https://youtu.be/1RwJbhkCA58 New poster design (discussion)]
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|  30.08     || MEGA-seminar || Multiple!
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|  13.09     || TBD ||
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|  12.04     || Benedikte & Tønnes || Evo* extravaganza (Conference presentations)
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| 27.09     || TBD ||
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| <s>26.04</s> - 03.05     || Kai Olav Ellefsen || Self-Adapting Goals Allow Transfer of Predictive Models to New
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Tasks (Conference presentation)
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|  11.10    || TBD ||
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|  10.05     || Farzan Majeed Noori || Human Activity Recognition from Multiple Sensor Data Using Multi-Fusion Representation and CNNs (Conference presentation)
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|  25.10     || TBD ||
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|  24.05     || in5590 Students  || [http://www.robotikk.com/in5590/ Rapid Prototyping of Robotic Systems pitching session] (In Norwegian)
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|  07.06     || Stefano Nichele, Alex Alcocer and Hugo Lewi Hammer  || OsloMet AI activities
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|  08.11     || TBD ||
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|  21.06     || Luca Caltagirone || Deep Learning for road detection and classification of EEG
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|  22.11     || TBD ||
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| 06.12    || TBD ||  
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[[PreviousSeminars|Overview of earlier seminars]]
[[PreviousSeminars|Overview of earlier seminars]]

Revision as of 08:56, 23 August 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)
30.08 MEGA-seminar Multiple!
13.09 TBD
27.09 TBD
11.10 TBD
25.10 TBD
08.11 TBD
22.11 TBD
06.12 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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