Teknologiseminar
From Robin
(Difference between revisions)
Line 27: | Line 27: | ||
| 14.09 || Travel portal and reimbursement tips || Jim | | 14.09 || Travel portal and reimbursement tips || Jim | ||
|- | |- | ||
- | | 21.09 || ?? || | + | | 21.09 || ?? || Ole Jakobs' PhD students |
|- | |- | ||
| 28.09 || || Frank? | | 28.09 || || Frank? |
Revision as of 06:54, 19 September 2023
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.
~12:20-12:40 Presentation on: Technology / Own Research / Journal review (see guidelines), time excludes discussion
Whatever content you present, remember that some of those attending have no advance knowledge about your project or work so introduce it in the beginning and stay focused on the main issues/challenges etc. Thus, make your presentation simple and understandable for as many as possible.
Dates and subjects - Autumn 2023 – Thursdays 12:00-12:45
5-20 min housekeeping/updates + 20 min talk
When | What | Who |
10.08 | Robotics at NTNU, Ålesund | Assoc. Prof. Di Wu [1] |
17.08 | Introuction to Ege [2] | Ege de Bruin |
24.08 | UiO Growth House [3] | Ivar Bergland |
31.08 | Modeling centipede gaits at Tohoku university [4] | Emma Stensby Norstein |
07.09 | Introduction to visiting PhD student Elias [5] | Elias Najarro |
14.09 | Travel portal and reimbursement tips | Jim |
21.09 | ?? | Ole Jakobs' PhD students |
28.09 | Frank? | |
05.10 | Casual meeting due to autumn school break | Updates from attendees |
12.10 | Casual/no meeting due to IFI conference (for permanent staff/postdocs) | |
19.10 | TBA | Visitors from Japan? (Kyrre) |
26.10 | TBA | Mateusz Wasiluk |
02.11 | Marieke? | |
09.11 | Mojtaba? | |
16.11 | Tom Frode? | |
23.11 | Vegard/Adrian? | |
30.11 | Kyrre? | |
07.12 | Katrine? | |
14.12 | Shin Watanabe |
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