With a suitably designed loss function, energy-based models can be trained without sampling, which the speaker considers a major advantage.

“But if you have if you set up your uh like your loss function in some clever way, you end up being able to”

“uh to to do the training at least without needing to do sampling. And that is a huge advantage because now you can um if you can directly fit to like an energy which you might have uh like literally measured, or you you do this for let's say samples of some density and you try to push up the energy for where the samples are rich and you push it down where the samples are not rich. Exactly the same way we see do with the density. The problem of course you run into with this is that now”