Spectral Integrated Gradients constructs SVD-based integration paths that activate singular components from largest to smallest, producing cleaner attribution maps and better quantitative scores than standard Integrated Gradients on image classification tasks.
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OCCAM discovers open-set visual concepts, estimates causal contributions via object-level interventions on black-box vision models, and induces a global concept ontology from aggregated dataset evidence.
A conjecture-then-validate method lets LLMs convert opaque lexical cues from deceptive-review classifiers into interpretable language phenomena that are empirically grounded and more predictive than direct LLM outputs.
citing papers explorer
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Spectral Integrated Gradients for Coarse-to-Fine Feature Attribution
Spectral Integrated Gradients constructs SVD-based integration paths that activate singular components from largest to smallest, producing cleaner attribution maps and better quantitative scores than standard Integrated Gradients on image classification tasks.
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OCCAM: Open-set Causal Concept explAnation and Ontology induction for black-box vision Models
OCCAM discovers open-set visual concepts, estimates causal contributions via object-level interventions on black-box vision models, and induces a global concept ontology from aggregated dataset evidence.
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Why is "Chicago" Predictive of Deceptive Reviews? Using LLMs to Discover Language Phenomena from Lexical Cues
A conjecture-then-validate method lets LLMs convert opaque lexical cues from deceptive-review classifiers into interpretable language phenomena that are empirically grounded and more predictive than direct LLM outputs.