Teknologiseminar
From Robin
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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 | ||
| - | === Dates and subjects - Fall 2020 – Thursdays 12:00- | + | === Dates and subjects - Fall 2020 – Thursdays 12:00-13:00 === |
''' 5-10 min housekeeping updates + talks/roundtable ''' | ''' 5-10 min housekeeping updates + talks/roundtable ''' | ||
{| class="wikitable" | {| class="wikitable" | ||
|- | |- | ||
| - | |''' When''' || ''' | + | |''' When''' || '''Who1''' || '''Content1 (title and type (tech/res/review)''' '''Who2''' || '''Content2 (title and type (tech/res/review)''' |
|- | |- | ||
| - | | 17.09 || || | + | | 17.09 || || || || |
|- | |- | ||
| - | | 15.10 || || | + | | 15.10 || || || || |
|- | |- | ||
| - | | 05.11 || || | + | | 05.11 || || || || |
|- | |- | ||
| - | | 26.11 || || | + | | 26.11 || || || || |
|- | |- | ||
| - | | 10.12 || || | + | | 10.12 || || || || |
|} | |} | ||
Revision as of 09:57, 4 September 2020
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 - Fall 2020 – Thursdays 12:00-13:00
5-10 min housekeeping updates + talks/roundtable
| When | Who1 | Content1 (title and type (tech/res/review) Who2 | Content2 (title and type (tech/res/review) | |
| 17.09 | ||||
| 15.10 | ||||
| 05.11 | ||||
| 26.11 | ||||
| 10.12 |
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
