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hackathon_2015 [2015/10/27 11:01]
n.arakawa created
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 2015organized 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 
-  * Resultto be announced (on GitHub)+  * Purposedeveloping know-how and skills ​on using multiple machine learning modules 
 +  * 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: domestic transportation & lodging 
 +  * Prizes: 
 +     * 1st: BICA2015 participation incl. transportation & lodging 
 +     * 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.\\
-The criteria ​for ideas are as follows:+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.)