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cs.LG2026
SPICE: Simple Polysemantic Feature Interpretation via Clustering-based Explanation
Sehyun Lee, Dahee Kwon, Damin Lee +1
One of the pivotal recent challenges in neural network interpretability is polysemanticity, where a single neuron is activated by multiple, often unrelated concepts, hindering clea…
cs.LG2025
Pathwise Explanation of ReLU Neural Networks
Seongwoo Lim, Won Jo, Joohyung Lee +1
Neural networks have demonstrated a wide range of successes, but their ``black box" nature raises concerns about transparency and reliability. Previous research on ReLU networks ha…
cs.LG2025★ 1 cited
Probing Network Decisions: Capturing Uncertainties and Unveiling Vulnerabilities Without Label Information
Youngju Joung, Sehyun Lee, Jaesik Choi
To improve trust and transparency, it is crucial to be able to interpret the decisions of Deep Neural classifiers (DNNs). Instance-level examinations, such as attribution technique…