Editing Small Group Subproblems

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**Soon to be posted data on crcns.org of auditory brain cells.
**Soon to be posted data on crcns.org of auditory brain cells.
**Figure out how to predict brain cell output.
**Figure out how to predict brain cell output.
*Deep Belief Networks for Images/Sounds (Mike S)
**Train deep belief networks on images, project output to monitor to see what network is "thinking"
**Do same for sounds
**Use auto-encoders to recall memories based on new input
*Kinects (Jared, Praveen)
*Kinects (Jared, Praveen)
**Get another kinect and do awesome shit with it.
**Get another kinect and do awesome shit with it.
**Would be useful to prototype some Noisebrain stuff below.
**Would be useful to prototype some Noisebrain stuff below.
*Noisebrain(Jared, Mike)
*Noisebrain(Jared, Mike)
**Deep Belief Networks for Images/Sounds (Mike S)
***Train deep belief networks on images, project output to monitor to see what network is "thinking"
***Do same for sounds
***Use auto-encoders to recall memories based on new input
**Install sensors all over Noisebridge.** (need to deal with some privacy concerns first)
**Install sensors all over Noisebridge.** (need to deal with some privacy concerns first)
***IR? PIR? Visual spectrum? Sonar? Decibel level?
***IR? PIR? Visual spectrum? Sonar? Decibel level?
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***What parts of the space are being used? Main Hall? Church? Turing? Kitchen?
***What parts of the space are being used? Main Hall? Church? Turing? Kitchen?
***Where is there motion? Where are things out of place?
***Where is there motion? Where are things out of place?
**Perhaps Mike's interests in Deep Belief Networks and Auditory Brain Cells above can be applied here?
**Application ideas for the Noisebrain once working:
**Application ideas for the Noisebrain once working:
***Feed this information into [[Noise-Bot]] in addition to it's on-board information.
***Feed this information into [[Noise-Bot]] in addition to it's on-board information.
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**I would like to take a different approach to the recommendation algorithm from my undergrad approach, which was based around domain specific knowledge about relationships and influences about various musicians.  Instead I'd like to train models using features like time of date, location, weather, etc tied to track plays by individual users.  I do still want past play history to also influence the weightings in some way.
**I would like to take a different approach to the recommendation algorithm from my undergrad approach, which was based around domain specific knowledge about relationships and influences about various musicians.  Instead I'd like to train models using features like time of date, location, weather, etc tied to track plays by individual users.  I do still want past play history to also influence the weightings in some way.
**Capturing music play events isn't hard: We are just use last.fm [http://www.last.fm/download#content_ipod scrobbler] & [http://ws.audioscrobbler.com/1.0/user/jareddunne/recenttracks.rss rss feeds].
**Capturing music play events isn't hard: We are just use last.fm [http://www.last.fm/download#content_ipod scrobbler] & [http://ws.audioscrobbler.com/1.0/user/jareddunne/recenttracks.rss rss feeds].
**Weather and Location feature collection might be some what time consuming but not unmanageable.  See 4square spying idea above.
**Weather and Location feature collection might be some what time consuming but not unmanageable.
**When a music play event (a 'scrobble') happens, we'd collect and log all the contextual information possible.
**When a music play event (a 'scrobble') happens, we'd collect and log all the contextual information possible.
***When a song is scrobbled on iPhone or iTunes, we'd know about the event almost instantly allowing more contextual information.
***When a song is scrobbled on iPhone or iTunes, we'd know about the event almost instantly allowing more contextual information.
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