1. Great question! You're correct that standard NF isn't efficiently invertible. CNF is, and we wanted a fair comparison. So for this experiment, we reversed

1. Great question! You're correct that standard NF isn't efficiently invertible. CNF is, and we wanted a fair comparison. So for this experiment, we reversed the direction that NF transforms the data, so that it goes from the data to the latent space. Training this way means that you can't use the resulting model as a generator, but it at least let us compare likelihoods with CNF for this paper. 2. We had to set the error tolerance relatively small during training to keep the gradients stable. I don't think we used any fancy initialization tricks, but to be honest I have to ask Ricky Chen and Will Grathwohl, who ran all the FFJORD experiments.