minimal random code learning which is a coding scheme for latent variable models which achieves the bits that code link without needing bits back so the way that works is that you know the encoder samples a lot of weights the number of leads and samples is 2 to the KL divergence between the encoder and D and and prior and then picks a random one I did just a uniform random one and the decoder can do the same thing if they share the same random number generator and so it turns out that this is a way to basically get a sort of a low bias