From 9e8675ab9da7fecd5677d37a1c1359058686aa9a Mon Sep 17 00:00:00 2001
From: Luca Domeniconi <34924739+liuktc@users.noreply.github.com>
Date: Sun, 27 Oct 2024 17:14:34 +0100
Subject: [PATCH] Update _rnn.tex

---
 src/year2/natural-language-processing/sections/_rnn.tex | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/year2/natural-language-processing/sections/_rnn.tex b/src/year2/natural-language-processing/sections/_rnn.tex
index 50e5304..cc4f212 100644
--- a/src/year2/natural-language-processing/sections/_rnn.tex
+++ b/src/year2/natural-language-processing/sections/_rnn.tex
@@ -39,7 +39,7 @@
     \begin{description}
         \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:
-            \[ \mathcal{L}^{(t)}(\matr{\theta}) = - \sum_{v \in V} \vec{y}_v^{(t)} \log\left( \hat{\vec{y}}_w^{(t)} \right) \]
+            \[ \mathcal{L}^{(t)}(\matr{\theta}) = - \sum_{v \in V} \vec{y}_v^{(t)} \log\left( \hat{\vec{y}}_v^{(t)} \right) \]
 
             \begin{description}
                 \item[Teacher forcing] \marginnote{Teacher forcing}
@@ -68,4 +68,4 @@
             \item[Greedy] Select the token with the highest probability.
             \item[Sampling] Randomly sample the token following the probabilities of the output distribution.
         \end{descriptionlist}
-\end{description}
\ No newline at end of file
+\end{description}