33 citations · 62 across the 20 of their papers we have counts for
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Shallower ReLU Network Representations via Exact Linear Algebra
Kilian Rueß, Gennadiy Averkov, Florestan Brunck +7
We study the depth required by ReLU networks to exactly represent piecewise linear functions, focusing specifically on the maximum function. This problem has recently received sign…
Depth-Bounds for Neural Networks via the Braid Arrangement
Moritz Grillo, Christoph Hertrich, Georg Loho
We contribute towards resolving the open question of how many hidden layers are required in ReLU networks for exactly representing all continuous and piecewise linear functions on…
Lower Bounds on the Depth of Integral ReLU Neural Networks via Lattice Polytopes
Christian Haase, Christoph Hertrich, Georg Loho
We prove that the set of functions representable by ReLU neural networks with integer weights strictly increases with the network depth while allowing arbitrary width. More precise…