[itissid]

One common theme I see in the paper(e.g. in protein folding) is:

"Identify what properties are important (geometry, algebra, topo) and which one is an useful prior and then "use" the guide to select an initial struct. This is probably harder than it sounds(unlike bayesian priors which are more forgiving for one to select, but quite like them in that they both require special assumptions)."

I wonder: could one use it to bring together certain multimodal data and a proposed network for a task? Like could one bring in sensor, map topology, urban topology, pictures which have certain properties and that help me use this guide to make a statement like : "Street data could be embedded with Sensor data to do ABC kind of inference using XYZ NNetwork structure because this paper suggests that is a reasonable thing to do"?

[uoaei]

All machine learning is just embedding of various forms. If you have a way to translate disparate types of data into a common space, in ways that preserve inductive bias and information content, you can then combine them for downstream tasks.