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I am working on the calibration of low-cost air sensor data (a time series regression problem). My primary focus is to use some meta/ few-shot learning approach to solve this problem with fewer data. I have tried using MAML on top of LSTM/vanilla NN but the results are not satisfactory.

Is there a different approach/paper for meta-regression? Anything that I should be doing differently? Things to avoid?

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  • $\begingroup$ Hello. When you say that the results are not satisfactory, maybe you should provide more details. Maybe you should describe more in detail how you're using MAML with LSTMs for this problem. $\endgroup$
    – nbro
    May 10 at 11:15

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