[D] Switching my VAE's latent distribution from Normal to Categorical, but I need a way to replace the Gaussian multiplication operation

So I have been reimplementing a VAE that has a recurrent latent distribution. After each new input, the latent distribution is updated using Gaussian multiplication of the encoder's output and the current latent distribution.

The model doesn't learn a good representation and I know that recent Dreamer models switched from continuous to categorical latent states which improved performance, so I want to give this a shot.

But, there's no Gaussian multiplication equivalent for categorical distributions. I have decided to take …

categorical current distribution encoder learn machinelearning normal vae