#geometric-ml News & Analysis

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

2 articles

AINeutralarXiv – CS AI · Jun 105/10

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Temporal Sheaf Neural Networks with Dynamic Orthogonal Transport

Researchers introduce Temporal Sheaf Neural Networks (TSNN), a novel framework for temporal link prediction that uses time-varying orthogonal coordinate frames to compare node states rather than operating in a shared global embedding space. The model demonstrates competitive performance on multiple benchmarks while offering theoretical guarantees on convergence and stability, with particular strength on heterogeneous graphs.

AINeutralarXiv – CS AI · Jun 16/10

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STEP: Learning STructured Embeddings for Progressive Time Series

Researchers introduce STEP, a self-supervised learning method that creates interpretable representations of time series data showing irreversible state transitions like equipment degradation or task completion. The approach encodes progression information in geometric coordinates (polar angles and radius) without requiring labeled data, matching or exceeding black-box models while providing transparency into underlying mechanisms.