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 - Spring | + | === Dates and subjects - Spring 2020 – Fridays 12:00-13:00 === |
''' Eat your own lunch while listening / discussing ''' | ''' Eat your own lunch while listening / discussing ''' | ||
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|''' When''' || '''Who''' || '''Content (title and type (tech/res/review)''' | |''' When''' || '''Who''' || '''Content (title and type (tech/res/review)''' | ||
|- | |- | ||
- | | | + | | 24.01 || Jendrik Seipp || Introduction and own research |
|- | |- | ||
- | | | + | | 07.02 || TBD || |
|- | |- | ||
- | | | + | | 21.02 || TBD || |
|- | |- | ||
- | | | + | | 20.03 || TBD || |
|- | |- | ||
- | | | + | | 03.04 || TBD || |
|- | |- | ||
- | | | + | | 24.04 || TBD || |
+ | |- | ||
+ | | 08.05 || TBD || | ||
+ | |- | ||
+ | | 29.05 || TBD || | ||
|- | |- | ||
|} | |} |
Revision as of 09:13, 20 January 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 - Spring 2020 – Fridays 12:00-13:00
Eat your own lunch while listening / discussing
When | Who | Content (title and type (tech/res/review) |
24.01 | Jendrik Seipp | Introduction and own research |
07.02 | TBD | |
21.02 | TBD | |
20.03 | TBD | |
03.04 | TBD | |
24.04 | TBD | |
08.05 | TBD | |
29.05 | TBD |
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