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Christopher Hojny

2 accepted papers

2025

On the Expressiveness of Rational ReLU Neural Networks With Bounded Depth

ICLR 2025spotlight

To confirm that the expressive power of ReLU neural networks grows with their depth, the function $F_n = \max (0,x_1,\ldots,x_n )$ has been considered in the literature. A conjecture by Hertrich, Basu, Di Summa, and Skutella [NeurIPS 2021] states that any ReLU network that exactly represents $F_n$…

Cited by 1SourcePDFScholar
2024

Verifying message-passing neural networks via topology-based bounds tightening

ICML 2024poster

Since graph neural networks (GNNs) are often vulnerable to attack, we need to know when we can trust them. We develop a computationally effective approach towards providing robust certificates for message-passing neural networks (MPNNs) using a Rectified Linear Unit (ReLU) activation function. Becau…