An iERF-centric framework unifies local, global, and mechanistic interpretability in vision models via SRD for saliency, CAFE for concept anchoring, and ICAT for interlayer attribution.
Towards robust interpretability with self-explaining neural networks
3 Pith papers cite this work. Polarity classification is still indexing.
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PhiNet adds phonetic interpretability to speaker verification while matching the accuracy of standard black-box models on VoxCeleb, SITW, and LibriSpeech.
The paper proposes GESD, a procedural fairness metric for group disparities in explanation stability and robustness, and integrates it into the FEU multi-objective optimization framework.
citing papers explorer
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From Local to Global to Mechanistic: An iERF-Centered Unified Framework for Interpreting Vision Models
An iERF-centric framework unifies local, global, and mechanistic interpretability in vision models via SRD for saliency, CAFE for concept anchoring, and ICAT for interlayer attribution.
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PhiNet: Speaker Verification with Phonetic Interpretability
PhiNet adds phonetic interpretability to speaker verification while matching the accuracy of standard black-box models on VoxCeleb, SITW, and LibriSpeech.
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GESD: Beyond Outcome-Oriented Fairness
The paper proposes GESD, a procedural fairness metric for group disparities in explanation stability and robustness, and integrates it into the FEU multi-objective optimization framework.