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hackathon_2015 [2015/10/27 11:12] n.arakawa |
hackathon_2015 [2016/01/27 11:00] (current) n.arakawa |
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- | The first WBA hackathon was held in September 2015 organized by the Future Leaders of WBA and WBAI. | + | The first WBA hackathon was held in September 2015, organized by [[http://wbawakate.jp/|the Future Leaders of WBA]] and WBAI. |
* Date: 2015-09-19...23 | * Date: 2015-09-19...23 | ||
* Venue: Hiyoshi Campus, Keio University (Yokohama, Japan) | * Venue: Hiyoshi Campus, Keio University (Yokohama, Japan) | ||
- | * Participants: five teams (23 hackers) and 12 mentors | + | * Participants: seven teams (23 hackers) and 12 mentors |
- | * Purpose: Accumulating know-how and skills on using multiple machine learning modules | + | * Purpose: developing know-how and skills on using multiple machine learning modules |
- | * Result: to be announced (on GitHub) | + | * Result: visit [[https://github.com/wbap/Hackathon2015|the GitHub site]] ([[http://wbawakate.jp/posts/events/第1回wbaiハッカソン活動報告/|report in Japanese]]) |
- | * Financial aids: transportation fees, lodging fees | + | * Financial aids: domestic transportation & lodging |
- | * Prizes | + | * Prizes: |
* 1st: BICA2015 participation incl. transportation & lodging | * 1st: BICA2015 participation incl. transportation & lodging | ||
- | * Other prizes: from sponsoring companies | + | * other prizes donated by sponsoring companies |
---- | ---- | ||
**The Ideathon**\\ | **The Ideathon**\\ | ||
An ideathon was held in June to develop ideas on tasks for the hackathon.\\ | An ideathon was held in June to develop ideas on tasks for the hackathon.\\ | ||
- | * Ideas wanted: ideas to combine machine learning modules referring to the brain to attain advanced functionality | + | The hackathon teams were selected based on their ideathon proposals. |
+ | * Ideas wanted: to combine machine learning modules referring to the brain to attain advanced functionality | ||
+ | * Contents to be proposed | ||
+ | * Functionality to be realized | ||
+ | * The way to realize the functionality | ||
+ | * Combination of machine learning modules | ||
+ | * Relevance to brain architecture | ||
+ | * Required resources (human & machine) | ||
+ | * Criteria for adopting | ||
+ | * Importance of the functionality | ||
+ | * Feasibility in the hackathon | ||
+ | * Originality/Room for further development | ||
+ | * Biological reality | ||
+ | * Sample ideas | ||
+ | * Video game play combining deep learning (visual cortex) and reinforcement learning (basal ganglia) (as in Deep-Q Network) | ||
+ | * Caption generation combining deep learning (visual cortex) and RNN (prefrontal/language cortex) (as in NeuralTalk @ Stanford U.) |