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This paper proposes a transferable autologistic model for predicting rare equipment failures across heterogeneous sensor configurations, evaluating it on a synthetic refrigerator dataset.
This paper introduces a probabilistic ensemble model based on a conditional diffusion model for real-time tsunami inundation forecasting, offering uncertainty quantification in contrast to deterministic warnings. Validated with 2011 Tohoku-oki data, it demonstrates that generative AI can shift tsunami forecasting from deterministic to probabilistic approaches.
This paper proposes PTTSD, a probabilistic framework for depression severity detection from clinical interview transcripts that models uncertainty and provides temporal interpretability, achieving competitive performance on benchmark datasets.
This paper introduces Prob-BBDM, a probabilistic Brownian Bridge Diffusion Model for efficient and high-quality MRI sequence synthesis from 2D axial slices, achieving up to 88.46% SSIM and 26.09 dB PSNR with only 4 diffusion steps, and demonstrating clinical utility in tumor segmentation.
Mosaic is a probabilistic weather model that matches state-of-the-art skill while generating a 24-member, 10-day global forecast in under 12 seconds on a single H100.