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Update _rnn.tex

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Luca Domeniconi
2024-10-27 17:14:34 +01:00
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src/year2/natural-language-processing/sections/_rnn.tex
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@ -39,7 +39,7 @@
\begin{description} \begin{description}
\item[Training] \item[Training]
Given the predicted distribution $\hat{\vec{y}}^{(t)}$ and ground-truth $\vec{y}^{(t)}$ at step $t$, the loss is computed as the cross-entropy: Given the predicted distribution $\hat{\vec{y}}^{(t)}$ and ground-truth $\vec{y}^{(t)}$ at step $t$, the loss is computed as the cross-entropy:
\[ \mathcal{L}^{(t)}(\matr{\theta}) = - \sum_{v \in V} \vec{y}_v^{(t)} \log\left( \hat{\vec{y}}_w^{(t)} \right) \] \[ \mathcal{L}^{(t)}(\matr{\theta}) = - \sum_{v \in V} \vec{y}_v^{(t)} \log\left( \hat{\vec{y}}_v^{(t)} \right) \]
\begin{description} \begin{description}
\item[Teacher forcing] \marginnote{Teacher forcing} \item[Teacher forcing] \marginnote{Teacher forcing}