Gradient matching empirically recovers implicit regularization effects such as l2 penalties from early stopping and dropout in neural networks.
Additionally, •(Sub-Gaussian correlated errors)E[ξ|Φ] = 0, and there existsσ≥0so that E[exp(tv⊤ξ)|Φ]≤exp(σ 2t2v⊤Σv/2)for allv∈R p
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Estimating Implicit Regularization in Deep Learning
Gradient matching empirically recovers implicit regularization effects such as l2 penalties from early stopping and dropout in neural networks.