2 citations · 3 across the 3 of their papers we have counts for
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stat.ML2018
MaxGain: Regularisation of Neural Networks by Constraining Activation Magnitudes
Henry Gouk, Bernhard Pfahringer, Eibe Frank +1
Effective regularisation of neural networks is essential to combat overfitting due to the large number of parameters involved. We present an empirical analogue to the Lipschitz con…
stat.ML2018
Regularisation of Neural Networks by Enforcing Lipschitz Continuity
Henry Gouk, Eibe Frank, Bernhard Pfahringer +1
We investigate the effect of explicitly enforcing the Lipschitz continuity of neural networks with respect to their inputs. To this end, we provide a simple technique for computing…