AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A dual-stage deep learning framework combining Faster R-CNN for tooth localization with U-Net for pixel-wise caries segmentation in panoramic radiographs is presented, demonstrating superior accuracy compared to existing methods while significantly reducing false-positive rates.
LEXIC, a compact recurrent model that predicts response correctness from fixation sequences, word frequency, and character length, has 41.6K parameters and requires no language-model inference, supporting lightweight on-device inference.
A multi-source Masked Autoencoder (MAE) framework, named ProsMAE, for histopathology representation learning, which achieved a higher mean validation quadratic weighted kappa (QWK) than the vanilla MAE frozen linear-probe baseline under the evaluated disjoint PANDA split.
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