Dr. Jeff Beck

Good examples of this are like a variational autoencoder.

A variational autoencoder, I think, is a

is the best example of the most commonly used energy-based model out there.

Why?

Because you have an encoder network, you have a decoder network, right?

And your cost function is based on the difference between inputs and outputs, right?

So that's just like a, that's fine.

That's still a regular, but it also is how Gaussian, and it, well, it depends on what flavor of V8, but you also have some part of your cost function is a function of the actual internal representation, right?

In a traditional VAE, it's how Gaussian is.

You want that internal representation to be as Gaussian as possible.

If it's a VQVAE, then it's like mixture of Gaussians, but it's still like a cost function that is applied on the internal states as well as on the inputs and outputs.

Yeah.

Yeah, you're treating some of the weights of your model as if they're latent variables.

Because when you show a new input, you're allowed to change some of the weights without looking at the output.

And so what are you doing?

Well, you're treating the weights as latents.

Now, I think that, which makes it a great trick, in my opinion.

It's like, oh, great.

Yeah, they're moving in the direction of energy-based models.

I love it.