An uncertainty-aware test-time adaptation framework improves cross-region spatio-temporal fusion of land surface temperature by updating only the fusion module guided by epistemic uncertainty, land use consistency, and bias correction.
Dropout: a simple way to prevent neural networks from overfitting
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Machine learning methods including denoising autoencoders, unsupervised interference mitigation, blind source separation, and certifiable classification are developed and experimentally validated to improve multi-species laser spectroscopy under complex conditions.
A part-based vehicle classifier using spatial probability maps for parts and softmax regression achieves accuracy comparable to end-to-end CNNs with greater robustness and explainability.
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Uncertainty-Aware Test-Time Adaptation for Cross-Region Spatio-Temporal Fusion of Land Surface Temperature
An uncertainty-aware test-time adaptation framework improves cross-region spatio-temporal fusion of land surface temperature by updating only the fusion module guided by epistemic uncertainty, land use consistency, and bias correction.
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Machine Learning Enhanced Laser Spectroscopy for Multi-Species Gas Detection in Complex and Harsh Environments
Machine learning methods including denoising autoencoders, unsupervised interference mitigation, blind source separation, and certifiable classification are developed and experimentally validated to improve multi-species laser spectroscopy under complex conditions.
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Explainable Part-Based Vehicle Classifier with Spatial Awareness
A part-based vehicle classifier using spatial probability maps for parts and softmax regression achieves accuracy comparable to end-to-end CNNs with greater robustness and explainability.