FIX: Removes duplicate explicit target name 'here'

RST was complaining about using 'here' more thanonce for referencing a link.

posts/2024/2024_07_26_Inigo_week_9.rst

@@ -44,7 +44,7 @@ After discussing with my mentors, we decided to take two steps:
 
     - Visual checking fiber generation for specific bundles. Knowing that different bundles have different fiber lengths, we try to generate fibers of specific length, and see whether the generated fibers belong to the desired bundle (no matter if they are plausible or implausible). Having length as the conditioning variable allows us to perform this trick, what would not be so intuitive to check if we had used Fractional Anisotropy or other DTI-derived metrics, as these are not visually as intuitive as length.
 
-2. To try out an adversarial framework, which is 1) easier to implement 2) easier to understand, and 3) likely to also work (we'll see if better or not). The idea is to have a discriminator that tries to predict the conditioning variable from the latent space, and the encoder tries to fool the discriminator. This way, the encoder learns to encode the conditioning variable in the latent space, and the discriminator learns to predict it. This is a very common approach in GANs, and it is called "Conditional GAN" (cGAN). As a result, we would have what I would call a conditional adversarial VAE (CA-VAE). You can read more about adversarial VAEs `here <https://arxiv.org/pdf/2012.11551>`_ or `here <https://arxiv.org/pdf/1702.08423>`_
+2. To try out an adversarial framework, which is 1) easier to implement 2) easier to understand, and 3) likely to also work (we'll see if better or not). The idea is to have a discriminator that tries to predict the conditioning variable from the latent space, and the encoder tries to fool the discriminator. This way, the encoder learns to encode the conditioning variable in the latent space, and the discriminator learns to predict it. This is a very common approach in GANs, and it is called "Conditional GAN" (cGAN). As a result, we would have what I would call a conditional adversarial VAE (CA-VAE). You can read more about adversarial VAEs `in this work <https://arxiv.org/pdf/2012.11551>`_ or `in this one <https://arxiv.org/pdf/1702.08423>`_
 
 Did I get stuck anywhere
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