Split-MoPE integrates split learning with predefined-expert routing to maximize usable data in vertical federated learning under sample misalignment, delivering state-of-the-art accuracy in one communication round plus built-in robustness and per-sample contribution scores.
arXiv preprint arXiv:1412.69801412(6) (2014) 25
3 Pith papers cite this work. Polarity classification is still indexing.
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URF-GS creates a single radiation field from visual and wireless observations via 3D Gaussian splatting to predict radio signals at any location and configuration with higher accuracy and fewer samples than prior NeRF approaches.
A deep learning method with an enhanced physical degradation model incorporating anisotropic light spread and hidden skyglow, trained via generative models and synthetic-real coupling, removes light pollution from night cityscape images more effectively than prior restoration techniques.
citing papers explorer
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Mixture of Predefined Experts: Maximizing Data Usage on Vertical Federated Learning
Split-MoPE integrates split learning with predefined-expert routing to maximize usable data in vertical federated learning under sample misalignment, delivering state-of-the-art accuracy in one communication round plus built-in robustness and per-sample contribution scores.
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Bridging Visual and Wireless Sensing via a Unified Radiation Field for 3D Radio Map Construction
URF-GS creates a single radiation field from visual and wireless observations via 3D Gaussian splatting to predict radio signals at any location and configuration with higher accuracy and fewer samples than prior NeRF approaches.
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Deep Light Pollution Removal in Night Cityscape Photographs
A deep learning method with an enhanced physical degradation model incorporating anisotropic light spread and hidden skyglow, trained via generative models and synthetic-real coupling, removes light pollution from night cityscape images more effectively than prior restoration techniques.