1 citations · 3 across the 5 of their papers we have counts for
5 papers
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…
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…
Deep ReLU Programming
Peter Hinz, Sara van de Geer
Feed-forward ReLU neural networks partition their input domain into finitely many "affine regions" of constant neuron activation pattern and affine behaviour. We analyze their math…
The Oracle of DLphi
Dominik Alfke, Weston Baines, Jan Blechschmidt +24
We present a novel technique based on deep learning and set theory which yields exceptional classification and prediction results. Having access to a sufficiently large amount of l…
A Framework for the construction of upper bounds on the number of affine linear regions of ReLU feed-forward neural networks
Peter Hinz, Sara van de Geer
We present a framework to derive upper bounds on the number of regions that feed-forward neural networks with ReLU activation functions are affine linear on. It is based on an indu…