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[D] Efficient GPU implementation of Empirical Fisher information matrix?

I have seen many implementations. It seems to be a limitation of autograd itself that we can compute the gradient of loglikelihood only one sample at a time.

The batch version has been used but in a WRONG way.

I have seen computing the gradient of a batch of a loglikelihood (essentially a mean of gradients), it doesn’t seem to be truthful to the real Empirical Fisher calculation at all (only a kind of approximation).

Is there a correct GPU efficient impementation of Empricial Fisher out there?

submitted by /u/phizaz
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.