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Percolation of Data Constraints and Reorganization of Boolean Function States in Parity Learning
disordered-systems-and-neural-networksquantum-physicsstatistical-mechanicsstrongly-correlated-electrons
We study how a neural network reorganizes its learned function as training proceeds and as previously unseen parity data accumulate. The trained model is itself a Boolean function on binary strings of finite length. We then evaluate it on all data, inside and outside the training set, and read off i
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