#extreme-events News & Analysis

2 articles tagged with #extreme-events. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AINeutralarXiv – CS AI · Jun 26/10

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E4GEN: Event-level Explainable Extreme-Enhanced Time-series Generation

Researchers introduce E4GEN, a diffusion-based framework that improves time-series generation by explicitly modeling extreme events alongside regular temporal patterns. The method uses adaptive control mechanisms to capture outliers and anomalies that existing generative models typically overlook, demonstrating superior performance across multiple evaluation metrics.

AINeutralarXiv – CS AI · Mar 34/103

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Phase-Type Variational Autoencoders for Heavy-Tailed Data

Researchers propose Phase-Type Variational Autoencoders (PH-VAE), a new deep learning model that uses Phase-Type distributions to better capture heavy-tailed data patterns where extreme events are critical. The approach outperforms standard VAE models with Gaussian decoders in modeling tail behavior and extreme quantiles, marking the first integration of Phase-Type distributions into deep generative modeling.