EnvCoLoc combines environment-conditioned diffusion meta-learning with 3D point cloud descriptors to reduce mean localization error by up to 20% in NLOS WiFi scenarios using 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.
GeoFormer achieves 3.19 m building-height RMSE with 0.32 M parameters by applying windowed local attention to Sentinel imagery, outperforming CNN baselines by 7.5 % while releasing all code and weights.
Controlled benchmarks of nine lightweight CNNs find that newer architectures deliver selective rather than universal improvements in accuracy and efficiency under fixed training protocols.
Fed-DLoRA combines low-rank adaptation with federated learning and an adaptive rank-bandwidth-vehicle selection algorithm to improve accuracy, convergence speed, and communication efficiency in wireless IoV environments.
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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.