19 citations · 67 across the 17 of their papers we have counts for
3 papers · 1 filter
Towards Lower Bounds on the Depth of ReLU Neural Networks
Christoph Hertrich, Amitabh Basu, Marco Di Summa +1
We contribute to a better understanding of the class of functions that can be represented by a neural network with ReLU activations and a given architecture. Using techniques from…
The Computational Complexity of ReLU Network Training Parameterized by Data Dimensionality
Vincent Froese, Christoph Hertrich, Rolf Niedermeier
Understanding the computational complexity of training simple neural networks with rectified linear units (ReLUs) has recently been a subject of intensive research. Closing gaps an…
ReLU Neural Networks of Polynomial Size for Exact Maximum Flow Computation
Christoph Hertrich, Leon Sering
This paper studies the expressive power of artificial neural networks with rectified linear units. In order to study them as a model of real-valued computation, we introduce the co…