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stat.ML2021★ 1 cited
The layer-wise L1 Loss Landscape of Neural Nets is more complex around local minima
Peter Hinz
For fixed training data and network parameters in the other layers the L1 loss of a ReLU neural network as a function of the first layer's parameters is a piece-wise affine functio…
stat.ML2021★ 1 cited
Using activation histograms to bound the number of affine regions in ReLU feed-forward neural networks
Peter Hinz
Several current bounds on the maximal number of affine regions of a ReLU feed-forward neural network are special cases of the framework [1] which relies on layer-wise activation hi…