EnvCoLoc uses 3D point cloud-conditioned diffusion meta-learning to reduce mean WiFi localization error by up to 20% in NLOS scenarios with only 10 support samples.
Deep residual learning for image recognition
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Light-ResKAN reaches 99.09% accuracy on MSTAR SAR images with 82.9 times fewer FLOPs and 163.78 times fewer parameters than VGG16 by combining KAN convolutions, Gram polynomials, and channel-wise parameter sharing.
citing papers explorer
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Environment-Conditioned Diffusion Meta-Learning for Data-Efficient WiFi Localization
EnvCoLoc uses 3D point cloud-conditioned diffusion meta-learning to reduce mean WiFi localization error by up to 20% in NLOS scenarios with only 10 support samples.
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Light-ResKAN: A Parameter-Sharing Lightweight KAN with Gram Polynomials for Efficient SAR Image Recognition
Light-ResKAN reaches 99.09% accuracy on MSTAR SAR images with 82.9 times fewer FLOPs and 163.78 times fewer parameters than VGG16 by combining KAN convolutions, Gram polynomials, and channel-wise parameter sharing.