Review for NeurIPS paper: Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE

Additional Feedback: On Reproducibility: - I think the basic methodology could be replicated, but it would have been nice to include code as a supplementary material. I hope the authors can assure me that the code will be documented and made available upon publication. On The Motor Cortex Dataset: - The VAE and pi-VAE seem to perform similarity in panel i and panel m - The better performance of pi-VAE in panel h vs l is likely due to the input variable "u" which forces different latent represenations (update: after writing this, I noticed that this is indeed the case based on supplementary figure S1; though pi-VAE is still slightly better). This is fine, but perhaps makes the result unsurprising -- wouldn't other supervised methods (e.g. On The Hippocampal Dataset: - For fig 4B, I think that linear discriminant analysis (LDA) would be sufficient to get you separation between the two running directions --- i.e. this would recover "latent 1".