Teknologiseminar
From Robin
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| 17.02 || Kai || Catastrophic Forgetting | | 17.02 || Kai || Catastrophic Forgetting | ||
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| - | | 24.02 || No presentation || | + | | 24.02 || No presentation/Casual meeting || |
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| 03.03 || Alex || Neural oscillator models | | 03.03 || Alex || Neural oscillator models | ||
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| - | | 17.03 || No presentation || | + | | 17.03 || No presentation/Casual meeting || |
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| 24.03 || || | | 24.03 || || | ||
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| - | | 31.03 || | + | | 31.03 || No presentation/Casual meeting || |
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| - | | 07.04 || | + | | 07.04 || Ulysse? || Adherence forecasting for G-ICBT |
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| - | | 14.04 || | + | | 14.04 || Easter break – no meeting || |
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| - | | 21.04 || | + | | 21.04 || || |
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| - | | 12.05 || || | + | | 12.05 || No presentation/Casual meeting || |
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| 19.05 || || | | 19.05 || || | ||
Revision as of 08:11, 10 February 2022
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 2022 – Thursdays 12:00-12:45
5-20 min housekeeping/updates + 15 min talk
| When | What | Who |
| 27.01 | Introductions, news | |
| 03.02 | Computational resources (Slides) | @Vegard |
| 10.02 | ||
| 17.02 | Kai | Catastrophic Forgetting |
| 24.02 | No presentation/Casual meeting | |
| 03.03 | Alex | Neural oscillator models |
| 10.03 | ||
| 17.03 | No presentation/Casual meeting | |
| 24.03 | ||
| 31.03 | No presentation/Casual meeting | |
| 07.04 | Ulysse? | Adherence forecasting for G-ICBT |
| 14.04 | Easter break – no meeting | |
| 21.04 | ||
| 28.04 | ||
| 05.05 | ||
| 12.05 | No presentation/Casual meeting | |
| 19.05 | ||
| 26.05 | (Yngve) |
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
