arXiv:2606.08721v1 Announce Type: new Abstract: Modern neural classifiers commonly rely on linear readouts, yet predictive metrics alone do not characterize the class-wise geometry of the representations on which such readouts operate. We introduce the directional linear separability measure (LSM), a finite-sample diagnostic for one-sided affine separability. For a target class A and a competing set B, LSM searches over affine halfspaces that contain all samples in A and measures the smallest competing-sample intrusion that must remain on the target side, normalized by |A|. The resulting quant
The proliferation of advanced neural networks and the increasing demand for explainable AI push researchers to develop new metrics for understanding model internals, rather than just predictive performance.
A strategic reader should care as better metrics for neural representation geometry can lead to more robust, reliable, and potentially interpretable AI systems, impacting critical applications.
The introduction of the directional linear separability measure provides a novel diagnostic tool for evaluating the class-wise geometry of neural representations, moving beyond simple predictive accuracy.
- · AI researchers
- · ML model developers
- · AI ethics and safety organizations
- · Developers relying solely on black-box model performance
- · Opaquely designed neural networks
Improved understanding of how neural networks distinguish between different classes.
Development of more intrinsically interpretable and robust AI models due to better diagnostic tools.
Enhanced trust and deployment of AI in high-stakes domains, enabled by deeper insights into model decision-making.
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Read at arXiv cs.LG