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How does MLC verify results without running multiple tasks per work unit (redundancy)?
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Send message Joined: 30 Aug 20 Posts: 25 Credit: 47,025,926 RAC: 0 |
Asking just out of curiosity. Some older tasks did have other canonical results such as this one: https://www.mlcathome.org/mlcathome/workunit.php?wuid=1120016 Newer tasks are validated without redundancy: https://www.mlcathome.org/mlcathome/workunit.php?wuid=2602952 Thanks! |
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Send message Joined: 3 Aug 20 Posts: 8 Credit: 7,650,164 RAC: 0 |
hmm, i hadn't noticed this, but this is a very good question, i would like to know this as well. |
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Send message Joined: 9 Jul 20 Posts: 142 Credit: 11,536,204 RAC: 3 |
Isn't each network learning task a stochastic process, in the sense that no 2 tasks will ever (very unlikely) produce the same result? |
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Send message Joined: 30 Aug 20 Posts: 25 Credit: 47,025,926 RAC: 0 |
Very interesting. If that is the case, does that also mean that MLC would need to process far more results to reach statistical significance versus a project that uses redundancy and can prove that the calculated results are valid? |
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Send message Joined: 30 Jun 20 Posts: 462 Credit: 21,406,548 RAC: 0 |
Each network is compared against a test dataset that is held back from being sent to you users. If the network performs well on that test set (and is a valid network, and is not identical to a previously submitted result) then it is considered valid. |
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Send message Joined: 30 Jun 20 Posts: 462 Credit: 21,406,548 RAC: 0 |
I'm working on adding a few more criteria that I'll keep vague to stop potential cheating, but in general that's why we don't do multiple results per WU, because they're stochastic and won't be identical at all. In fact, this stochastic nature is what we're trying to capture so it helps. |
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