Description
We summarize the recent machine learning efforts to tackle the computational and analytical challenges one faces in performing a lattice measurement. We start with direct methods which take in configurations and try to predict the final physical observable/correlator. Next, we briefly touch upon ML methods to perform inversions. Finally we elaborate on various noise reduction and computational effort reduction strategies using ML techniques.
