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For example, if we set the random seed to be 0, will we run into any problems? For example, maybe for seed 0, we can only reach a certain training error, but other seeds will converge to a much lower error

I'm specifically concerned about supervised learning on point cloud data, but curious about whether it matters in general whenever you use a neural network.

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  • $\begingroup$ Do you know what the seed is used for? (I did not downvote, by the way). $\endgroup$
    – nbro
    Nov 14 '20 at 14:04
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When you use a particular seed, it actually ceases to become a random initialization and is instead fixed. I believe the only reason to actually do this would be for reliable reproduction in research and not as a method of training production models.

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  • $\begingroup$ But why doesn't it work? If the "random" initialization is important, then we would expect different convergence results for different initializations - for supervised learning this is rarely the case. $\endgroup$
    – user3180
    Nov 16 '20 at 7:13
  • $\begingroup$ different random initializations will give different convergence results (slightly different). If you read research papers, they will often cite a $\pm$ after the results which are the different values on different random initializations. They use the seed so that they can reproduce the EXACT same results again. Different random initializations without a seed will give slightly different results. If you use a fixed seed and flip a coin 100 times, you will get the exact same pattern of H and T every time with that seed. While a different random seed would give a random pattern $\endgroup$
    – Joff
    Nov 16 '20 at 8:53
  • $\begingroup$ Why would we ever care about having slightly different convergence results? $\endgroup$
    – user3180
    Nov 23 '20 at 11:38
  • $\begingroup$ You just gave me a good reasoning for why we can set and forget seed=0 in production as well. +- .001% is not going to make a difference (plus, you have to maximize over the seed to get the best result, not just randomly pick one) $\endgroup$
    – user3180
    Nov 23 '20 at 11:39

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