UTOPYA fuses eight modalities via FiLM-conditioned attention and physics-informed regularization to reach AUROC 0.874 for anomaly detection in batch distillation, outperforming baselines by 0.147.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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S2P-Net is a deep learning model that builds rotation invariance directly into its spectral-spatial polar design rather than learning it from augmented data.
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UTOPYA: A Multimodal Deep Learning Framework for Physics-Informed Anomaly Detection and Time-Series Prediction
UTOPYA fuses eight modalities via FiLM-conditioned attention and physics-informed regularization to reach AUROC 0.874 for anomaly detection in batch distillation, outperforming baselines by 0.147.
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S2P-Net: A Spectral-Spatial Polar Network for Rotation-Invariant Object Recognition in Low-Data Regimes
S2P-Net is a deep learning model that builds rotation invariance directly into its spectral-spatial polar design rather than learning it from augmented data.