MulViT-TF uses distributed multi-view vision and Transformer fusion to estimate RSSI, cutting RMSE by up to 26.3% versus single-view baselines in two indoor scenes while using fewer resources.
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Scaling experiments on structured medical claims data show task-dependent saturation: disease incidence prediction benefits from models up to 101M parameters while medication prediction saturates at 11M, with all models outperforming a LightGBM baseline.
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Distributed Multi-View Vision-Only RSSI Estimation
MulViT-TF uses distributed multi-view vision and Transformer fusion to estimate RSSI, cutting RMSE by up to 26.3% versus single-view baselines in two indoor scenes while using fewer resources.
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A Nationwide Japanese Medical Claims Foundation Model: Balancing Model Scaling and Task-Specific Computational Efficiency
Scaling experiments on structured medical claims data show task-dependent saturation: disease incidence prediction benefits from models up to 101M parameters while medication prediction saturates at 11M, with all models outperforming a LightGBM baseline.