A new paper dropped from DeepMind: Deliberation in Latent Space via Differentiable Cache Augmentation The trouble is, it's not very readable. I tried making a thread here, but it got far too long, so it's a blog now: timkellogg.me/blog/2024/12...
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timkellogg.meExplainer: Latent Space Experts - Tim Kellogg
Definitely. For me, latent spaces are best interpreted by moving in them rather than looking statically. Seeing how the outputs change while moving one number at a time in the latent space shows what the dimensions "mean". But that doesn't help much at scale.
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