I haven't really absorbed this paper yet, but first thoughts were Hopfield Networks we used in the 1980s. For unsupervised learning algorithms like masked mode

I haven't really absorbed this paper yet, but first thoughts were Hopfield Networks we used in the 1980s. For unsupervised learning algorithms like masked models (BERT and some other Transformers), it makes sense to train in parallel with prediction. Why not? My imagination can't wrap around using this for supervised (labeled data) learning.