most citedOn the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity

2 citations · 2 across the 1 of their papers we have counts for

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stat.ML2022★ 2 cited

On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity

Vincent Szolnoky, Viktor Andersson, Balazs Kulcsar +1

Most complex machine learning and modelling techniques are prone to over-fitting and may subsequently generalise poorly to future data. Artificial neural networks are no different…