Small Group Subproblems: Difference between revisions
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**Figure out how difficult questions are, what kind of knowledge they test | **Figure out how difficult questions are, what kind of knowledge they test | ||
**Determine how good are people at certain types of knowledge. | **Determine how good are people at certain types of knowledge. | ||
*Auditory Brain Cells | *Auditory Brain Cells (Mike S) | ||
**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 | *Deep Belief Networks for Images/Sounds (Mike S) | ||
**Train deep belief networks on images, project output to monitor to see what network is "thinking" | **Train deep belief networks on images, project output to monitor to see what network is "thinking" | ||
**Do same for sounds | **Do same for sounds | ||
**Use auto-encoders to recall memories based on new input | **Use auto-encoders to recall memories based on new input |
Revision as of 21:34, 16 February 2011
Joe H proposed that we come up with some smaller problems that allow people to interactively solve them in short periods of time in ML meetups. We can construct a git hub project for them, create a skeleton project, and use issue tracking to list the problems and work on them. Here we list these problems and plan to build them out.
- Time Series Data (Erin)
- Rock Climbing (Joe H)
- Rock climbing data analysis
- Generate predictive system for how much someone should climb based on how they're doing that week.
- Noted that amount of deep sleep can be correlated to mood, which can be correlated to climbing.
- Suggestion: we all track one aspect and combine it into one big dataset.
- 4square Checkins (Joe H)
- Use 4square checkins to find out what would be the best time to visit a place in a region.
- Education Learning Problem (Tom)
- Determine how people learn over time.
- Figure out how difficult questions are, what kind of knowledge they test
- Determine how good are people at certain types of knowledge.
- Auditory Brain Cells (Mike S)
- Soon to be posted data on crcns.org of auditory brain cells.
- 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