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# concept bottleneck models

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  1. arXiv

    ConceptTime turns forecast error into segment boundaries, keeping 97.91% epilepsy accuracy with 1% of labels

    ConceptTime uses the mismatch between a frozen probabilistic forecaster's next-step prediction and the realized signal as a single signal that both places time-series segment boundaries and describes how each segment behaved relative to expectation, yielding an inspectable concept vocabulary without fixed windows, expert concept annotations, or language-model calls. Across 17 public benchmarks it is competitive with black-box accuracy and beats every interpretable baseline; on Epilepsy it retains 97.91% accuracy with 1% of labels, and prototype distance doubles as a fidelity score for abstention.

    Sachith Abeywickrama（First author）
    ConceptTimePredictive Surpriseconcept bottleneck modelsTime Series ClassificationSelective Prediction
  2. researchsquare.com

    Hybrid quantum medical models get two-level explanations: exact attribution over 8 semantic dims, and CNOT pruning that raises accuracy under noise

    Hybrid quantum-classical medical models can predict but cannot say which semantic factors or which quantum gates drove a decision. FQXAI compresses inputs into an eight-dimensional proxy-supervised semantic bottleneck, turns it into IF-THEN rationales through Gaussian fuzzy membership states, and computes exact Shapley values over all 256 semantic coalitions without Monte Carlo sampling. On MIT-BIH Arrhythmia, PneumoniaMNIST and PathMNIST the models reached accuracies of 0.890, 0.768 and 0.726, and under 5% depolarizing noise SVQX-guided pruning cut CNOT counts from 14 to 11, 12 and 10 while raising accuracy by 2.20, 1.74 and 1.51 percentage points.

    Pondicherry University
    Ankit Raj（First author, Corresponding author）
    FQXAIQuantum Machine LearningExplainable AISHAPconcept bottleneck models

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