Teknologiseminar

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(Dates and subjects - Spring 2021 – Thursdays 12:00-12:45)
(Dates and subjects - Spring 2021 – Thursdays 12:00-12:45)
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|  28.01 || Casual meeting || └[∵┌]└[ ∵ ]┘[┐∵]┘  
|  28.01 || Casual meeting || └[∵┌]└[ ∵ ]┘[┐∵]┘  
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|  04.02 || The ROBIN API || Vegard
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|  04.02 || The ROBIN API (Slides can be found [https://slides.com/vegardds/robin here]) || Vegard
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|  11.02 ||  ||  
|  11.02 ||  ||  

Revision as of 12:20, 4 February 2021

Meeting structure

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

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

~12:20-12:35 Presentation on: Technology / Own Research / Journal review (see guidelines), time excludes discussion

Dates and subjects - Spring 2021 – Thursdays 12:00-12:45

5-20 min housekeeping/updates + 15 min talk (casual meetings = round-table)

When What Who
21.01 Who is Diana? What will she do here? Diana
28.01 Casual meeting └[∵┌]└[ ∵ ]┘[┐∵]┘
04.02 The ROBIN API (Slides can be found here) Vegard
11.02
18.02 Casual meeting (With short presentation from Andrija) └[∵┌]└[ ∵ ]┘[┐∵]┘
25.02 Do your dance moves seem too robotic? You might be one of Benedikte's trained networks. Benedikte
04.03 Julian
11.03 Casual meeting └[∵┌]└[ ∵ ]┘[┐∵]┘
18.03 TBA Farzan
25.03 Journal review. Journal: TBA Emma
01.04 Easter holiday ˭̡̞(◞⁎˃ᆺ˂)◞*✰
08.04
15.04
22.04 Casual meeting └[∵┌]└[ ∵ ]┘[┐∵]┘
29.04
06.05
13.05 National holiday ˭̡̞(◞⁎˃ᆺ˂)◞*✰
20.05
27.05 Not yet available
03.06 Casual meeting └[∵┌]└[ ∵ ]┘[┐∵]┘
10.06 Not yet available
17.06 Not yet available
24.06 Casual meeting └[∵┌]└[ ∵ ]┘[┐∵]┘
To be continued in August

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
    • SLURM
    • R
    • Python as alternative to R, incl. matplotlib
    • ROS
    • Deep learning frameworks and practice
    • Unity ML agents
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