Machine learning assists with image denoising and soliton detection in cold-atom quantum simulators while addressing the need for model interpretability.
This does not gen- eralize to the optimal linear method, which is not built from a simple sum over orthogonal components
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Can machine learning for quantum-gas experiments be explainable?
Machine learning assists with image denoising and soliton detection in cold-atom quantum simulators while addressing the need for model interpretability.