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.
After correcting prior flaws, a class-dependent hybrid augmentation strategy plus clinical subtype aggregation raises average macro-F1 robustness across eight classifiers on a 400-patient seven-subtype migraine dataset, with peak 0.914 under proportional growth.
LLM-scored offer predicates aggregated by a Logic Tensor Network classify procurement documents about as accurately as BERT or LLM baselines while exposing auditable predicate and rule truth values.
Coarsening smart meter load profile granularity produces two performance plateaus in socio-demographic inference (15 min–1 h and 1–7 days), enabling data minimization strategies that preserve some predictive utility.
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.
EfficientNetB0 achieves the highest accuracy (95%) among five CNNs tested on multi-class brain tumor MRI classification, with notably better meningioma recall than shallower or custom models.
citing papers explorer
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Pandora's Regret: A Proper Scoring Rule for Evaluating Sequential Search
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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Multi-domain Multi-modal Document Classification Benchmark with a Multi-level Taxonomy
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.
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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.
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Class-Dependent Hybrid Data Augmentation for Multiclass Migraine Classification under Severe Class Imbalance
After correcting prior flaws, a class-dependent hybrid augmentation strategy plus clinical subtype aggregation raises average macro-F1 robustness across eight classifiers on a 400-patient seven-subtype migraine dataset, with peak 0.914 under proportional growth.
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From Large Language Model Predicates to Logic Tensor Networks: Neurosymbolic Offer Validation in Regulated Procurement
LLM-scored offer predicates aggregated by a Logic Tensor Network classify procurement documents about as accurately as BERT or LLM baselines while exposing auditable predicate and rule truth values.
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The Impact of Temporal Granularity on Socio-Demographic Inference from Household Load Profiles
Coarsening smart meter load profile granularity produces two performance plateaus in socio-demographic inference (15 min–1 h and 1–7 days), enabling data minimization strategies that preserve some predictive utility.
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Single-Cycle Multidirectional EOG Classification Faster than Human Reaction Time for Wearable Human-Computer Interactions
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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Multi-Class Brain Tumor Classification Using Advanced Deep Learning Models: A Comparative Study
EfficientNetB0 achieves the highest accuracy (95%) among five CNNs tested on multi-class brain tumor MRI classification, with notably better meningioma recall than shallower or custom models.