Compares foundation models for probabilistic low-voltage load forecasting on 200 real feeders and introduces a grid-planning metric that scores peak prediction by its effect on asset cost-risk decisions.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 47, 4, (Apr
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Audio language models are benchmarked on five semantic and paralinguistic reasoning tasks to reveal limitations in handling spoken audio evidence, accent variation, and domain shifts.
Systematic tests of 27 ultrasound tasks show that unified training is more consistent than clinically-grouped training, with performance hinging on data availability and task characteristics.
Zero-shot VLMs reach at most 62% accuracy on agricultural classification tasks while supervised models like YOLO11 perform markedly higher, indicating they are not ready to replace task-specific systems.
A DenseNet201 base model trained on a constructed plant leaf disease dataset outperforms baselines and enables faster, more robust transfer learning with less data than general models.
citing papers explorer
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Probabilistic Low-Voltage Peak Load Forecasting with Time Series Foundation Models Evaluated on Application-Oriented Metrics
Compares foundation models for probabilistic low-voltage load forecasting on 200 real feeders and introduces a grid-planning metric that scores peak prediction by its effect on asset cost-risk decisions.
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Afrispeech Semantics: Evaluating Audio Semantic Reasoning in Spoken Language Models Across Domains and Accents
Audio language models are benchmarked on five semantic and paralinguistic reasoning tasks to reveal limitations in handling spoken audio evidence, accent variation, and domain shifts.
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Understanding Task Aggregation for Generalizable Ultrasound Foundation Models
Systematic tests of 27 ultrasound tasks show that unified training is more consistent than clinically-grouped training, with performance hinging on data availability and task characteristics.
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Developing a Strong Pre-Trained Base Model for Plant Leaf Disease Classification
A DenseNet201 base model trained on a constructed plant leaf disease dataset outperforms baselines and enables faster, more robust transfer learning with less data than general models.