Looks like you left the submissions instructions for AISTATS on the last page of the PDF. Don't know if that was intentional but I'm guessing it wasn't.

Ugh, LaTeX is such a pain sometimes. Thanks for catching that.

Reading these comments is starting to feel like an https://xkcd.com/2304/ situation.

How can a dataset be formatted for fine tuning llama?

Uh... You're asking the wrong guy :)

Well, you did say any questions. ;)

Speaking of questions, it seems to me that in light of the universal approximation theorem, the question isn't "can a transformer represent a Kalman filter," but can it do so simply and somewhat efficiently. That seems to be what you're getting at here, yes?

Yes. Also the universal approximation theorem won't directly yield the kind of "strong" approximation we prove, which is uniform in time and holds for an arbitrary (possibly adversarial) sequence of disturbances.

Nice paper! Just curious, do you reckon you could generalise the finding to a broader class of state-space models, e.g. particle filters?

Do you believe that neural nets could replace these kind of kalman like estimators for spatio-temporal processes? What do you gain? What do you lose? Do you think that implementing a neural net to perform a kalman like solution is easier on the user than implementing a statistical package (e.g., STAN, R-INLA, etc.)? Do you think that the claimed interpretability of these traditional models outweighs the impact of using a neural network instead? Will the neural network produce better estimates since it is theoretically capable of estimating any function? Are you looking for collaborators? Do you have a github repo?