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Why Adam divides by 1 - beta^t

Adam uses m_hat = m / (1 - beta1^t) and v_hat = v / (1 - beta2^t). Why?

A.It applies weight decay to the moment estimatesB.The moments start at zero, so early estimates are biased lowC.It clips any gradient that is too largeD.It anneals the learning rate to zero by the end

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Why Adam divides by 1 - beta^t

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