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cs.LG2025
Revisiting the Relation Between Robustness and Universality
M. Klabunde, L. Caspari, F. Lemmerich
The modified universality hypothesis proposed by Jones et al. (2022) suggests that adversarially robust models trained for a given task are highly similar. We revisit the hypothesi…
cs.LG2025
Similarity of Neural Network Models: A Survey of Functional and Representational Measures
Max Klabunde, Tobias Schumacher, Markus Strohmaier +1
Measuring similarity of neural networks to understand and improve their behavior has become an issue of great importance and research interest. In this survey, we provide a compreh…
cs.LG2025
ReSi: A Comprehensive Benchmark for Representational Similarity Measures
Max Klabunde, Tassilo Wald, Tobias Schumacher +3
Measuring the similarity of different representations of neural architectures is a fundamental task and an open research challenge for the machine learning community. This paper pr…