PADAM runs K differently averaged Adam trajectories in parallel, selects the one with the smallest test error, and achieves the best optimization error in nearly all of 13 tested scientific machine learning problems without extra gradient evaluations.
Solving the Kolmogorov PDE by means of deep learning
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PADAM: Parallel averaged Adam reduces the error for stochastic optimization in scientific machine learning
PADAM runs K differently averaged Adam trajectories in parallel, selects the one with the smallest test error, and achieves the best optimization error in nearly all of 13 tested scientific machine learning problems without extra gradient evaluations.