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Dylan16807

Many types of processing on small data sets are fine. Or lots of small data sets in parallel.

You could run statistics on data or do a grid-based simulation.

Running a neural net would be almost trivial.

General purpose code is extremely difficult to adapt, since it loves to branch based on data.

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Neural nets are a very convincing use case. Thanks!

No offense but just "running a neural net" is rarely trivial

What do you mean? I'm not talking about design or tuning or anything, just shoving raw data through.

All you have to do is multiply each weight and add up each node.

Pure bulk arithmetic is both simple and a perfect match for FHE.

“Trivial” is relative, and in the context of this forum post and the audience that is reading it, running a basic neural net is unlikely a large hurdle for most here.

Hell, I’m not an ML practitioner and I almost never use python, and yet it took me maybe a day or two learn enough about covnet training to detect my dog on a home security camera.

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