For a two-layer binary network with Gaussian inputs, O(n^2) samples guarantee ergodic convergence of STE training and O(n^4) guarantee that iterates revisit the optimal weights, even under label noise.
A simple proof of the restricted isometry property for random matrices.Constructive approximation, 28:253–263, 2008
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Beyond Discreteness: Sample Complexity Analysis of Straight-Through Estimator for 1-bit Quantization
For a two-layer binary network with Gaussian inputs, O(n^2) samples guarantee ergodic convergence of STE training and O(n^4) guarantee that iterates revisit the optimal weights, even under label noise.