Concept Flow Models use hierarchical concept-driven decision trees to mitigate information leakage in concept bottleneck models while matching their predictive performance.
Eliminating information leakage in hard concept bottleneck models with supervised, hierarchical concept learning.arXiv preprint arXiv:2402.05945,
6 Pith papers cite this work. Polarity classification is still indexing.
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Concept-based models can use controlled 'benign' information leakage to remain accurate and intervenable under real-world concept incompleteness by reframing their training objective.
TRACE is a RANO 2.0-aligned concept bottleneck model for 4-class longitudinal glioblastoma response classification on 3D MRI that reports 0.4769 macro F1 on the LUMIERE dataset via 5-fold patient-wise cross-validation.
Introduces synthetic benchmarks for concept bottleneck models that control data modality, concept choice, annotation quality, and completeness to evaluate performance in decision support and automation.
Post-hoc CBMs produce unfaithful concept projections due to covariate shifts and systematic label noise; new metrics are introduced to measure faithfulness separately from accuracy.
Formal concept lattices guide staged, hierarchical concept learning in deep networks to produce more interpretable and semantically structured representations.
citing papers explorer
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TRACE: A Concept Bottleneck Model for Longitudinal 3D Glioblastoma Response Assessment
TRACE is a RANO 2.0-aligned concept bottleneck model for 4-class longitudinal glioblastoma response classification on 3D MRI that reports 0.4769 macro F1 on the LUMIERE dataset via 5-fold patient-wise cross-validation.
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On the Faithfulness of Post-Hoc Concept Bottleneck Models
Post-hoc CBMs produce unfaithful concept projections due to covariate shifts and systematic label noise; new metrics are introduced to measure faithfulness separately from accuracy.
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Formal Concept Lattices are Good Semantic Scaffolds for Concept-Based Learning
Formal concept lattices guide staged, hierarchical concept learning in deep networks to produce more interpretable and semantically structured representations.