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The classical version of the universal approximation theorem states that, roughly, given a continuous function $f \colon [0, 1]^n \to [0, 1]^n$, there exists a single layer neural network and a set of weights and biases such that this network approximates the given function $f$ arbitrarily well. It doesn't say anything about how you obtain such weights: the ...


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The lower bound in MINE is as follows: $$\widehat{I(X;Z)}_n = \sup_{\theta\in\Theta} \mathbb{E}_{\mathbb{P}_{XZ}^{(n)}}[T_\theta] - \log{\mathbb{E}_{\mathbb{P}_X^{(n)} \otimes \hat{\mathbb{P}}_Z^{(n)}}[e^{T_\theta}]}$$ Here $\mathbb{\hat{P}^{(n)}}$ denotes the empirical distribution that we get from n i.i.d samples of $\mathbb{P}.$ Note that in the above ...


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