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The paper evaluates a configurable multi-agent system (nMAS) for extracting structured oncology data from fragmented clinical documents, achieving high performance compared to a baseline model.
This paper proposes LA-ReduNet, a lightweight adaptive architecture that uses hyperspherical manifold learning and adaptive step sizes to significantly reduce the number of layers needed for the MCR2 objective in neural networks, achieving comparable classification accuracy with far fewer parameters.
This paper introduces TS2TabPFN, a framework that combines explicit feature extraction with the TabPFN 2.5 tabular foundation model for time series classification and extrinsic regression. Experiments show it outperforms state-of-the-art models in TSER and achieves competitive results in TSC.
This study explores the feasibility of classifying ten hand gestures using a single-channel sEMG signal combined with lightweight machine learning models, achieving up to 90% accuracy. It demonstrates potential for cost-effective, low-power gesture recognition.
Proposes a quantum-inspired hybrid classical-quantum framework for image classification using a mixture of experts, demonstrating improved performance and reduced failure rate on MNIST and Fashion-MNIST datasets.
PARCEL introduces a novel vision-language model architecture that uses pool-anchored resampling and conditioned elastic queries to improve efficiency and performance across different visual-token budgets, outperforming existing matryoshka baselines.