Wouldn’t it take too long to train with such amount of data? I’m thinking the NN has to be constantly be in training because you never want to miss some data sets that contain special categories and not to miss them when in the inference stage
hi, Yashar here (one of the authors). These NNs were really fast to train. A day or so. We trained them on half a million simulated images. Then they're good to go for the analysis of any new data. We don't need to keep on training them as we get more data.
How long did it take you to get them to train in a day?
surprisingly not that long. We started this in Feb without any expectation that it would work at all, and after 2-3 weeks of playing with things they were working great.
I love how machine learning went to statistical learning and then back to machine learning. Playing is exactly the word to describe the discovery process.
I guess you train it on things you know and use it to tell if a new observation looks familiar or not. You can't use the NN for the final analysis because its just a pile of linear algebra shaped like a causal theory: there is no actual physics in it.
yes, in principle you're right. But in this case, the answer from the neural nets is so incredibly close to the true answer, that we think we can trust it for most purposes. If someone really wanted the most accurate answer, then they can start from the NN answer and do a proper model fitting procedure, which of course fits a simulated model with all the appropriate physics in it to the data.
Well, in the important case of finding something new and interesting you need proper MC to verify your understanding. In practice you are right that using the NN like it actually speaks about reality will be common and not too harmful, people do lots of dubious least squares fits and astronomy still survives!