[D] Trying to understand inference with JEPA

Inspired by the Lex Fridmans [Podcast episode](https://www.youtube.com/watch?v=5t1vTLU7s40) with Yann LeCun I tried to improve my understand of JEPA and energy based models in general by reading the [I-JEPA paper](https://arxiv.org/pdf/2301.08243.pdf) and these [lecture notes.](https://arxiv.org/pdf/2306.02572.pdf)

I understand the appeal of JEPA from the perspective of learning highly semantic features/representations of continuous image data in a semi supervised procedure. But what really puzzles me is the statement by Yann LeCun that once a model like this is trained you could do optimization based …

continuous data energy features image image data inference jepa lecun machinelearning optimization perspective semantic yann yann lecun