Pandora's Regret is a closed-form pairwise scoring rule derived from expected optimal search costs that elicits true probabilities and outperforms log loss, accuracy, and F1 at predicting diagnostic costs on MedMNIST models.
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MMM-Bench supplies 5,990 multi-modal documents from 12 commercial domains annotated along a 5-level taxonomy to test document classification under realistic business conditions.
EnCoDe enables design-time prediction of block-level energy consumption in Python code via static features and ML models trained on a dataset from 18,000 programs, achieving R²=0.75 and 80.6% hotspot classification accuracy.
Re-evaluation corrects prior migraine classification baselines to macro-F1 0.71; class-dependent hybrid augmentation plus subtype aggregation reaches 0.914 with FT-Transformer, but aggregation drives most gains while the framework mainly improves average robustness across eight classifiers.
A neurosymbolic pipeline extracts predicates from offer texts with an LLM and validates them via Logic Tensor Networks, delivering performance comparable to standard models plus built-in interpretability on a real corpus.
Cascaded neural networks classify 10 eye-movement classes from single-cycle EOG signals at 99% accuracy with sub-83 ms latency below human reaction time.
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EnCoDe: Energy Estimation of Source Code At Design-Time
EnCoDe enables design-time prediction of block-level energy consumption in Python code via static features and ML models trained on a dataset from 18,000 programs, achieving R²=0.75 and 80.6% hotspot classification accuracy.