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bumbo-pa t1_iuwt4vz wrote

I think you mostly answered your question. How would you reverse engineer a mean to deduce the data distribution?

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ojiber OP t1_iuwy5xs wrote

I'm sorry I don't understand, are you saying that I answered the question in that you cannot do it? That you can't predict a likely input parameter for a network using a set of known outputs? It seems like you very much can do this, but that my approach to solving the problem is wrong. Do you have any suggestions as to how this could be done?

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bumbo-pa t1_iuxguex wrote

I'm saying that neural networks are fundamentally lossy dimension reductions. From a high theoretic level that makes it irreversible (at least 100% or without any prior assumptions on the input data). Any estimation of inputs need some additional knowledge or presupposition. That makes the "inversion" problem very sensible to the specifics of your situation.

That being said, interesting posts in the thread.

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