STEP embeds progressive time series into a manifold between orthogonal prototypes so that polar angle tracks irreversible state progression and radius tracks mode via self-supervised contrastive learning.
URL http://arxiv.org/ abs/2312.16424
2 Pith papers cite this work, alongside 4 external citations. Polarity classification is still indexing.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A new turbofan dataset with realistic maintenance patterns is used to benchmark Bayesian filters as strong baselines against self-supervised learning representations for component health estimation.
citing papers explorer
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STEP: Learning STructured Embeddings for Progressive Time Series
STEP embeds progressive time series into a manifold between orthogonal prototypes so that polar angle tracks irreversible state progression and radius tracks mode via self-supervised contrastive learning.
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A Machine Learning Framework for Turbofan Health Estimation via Inverse Problem Formulation
A new turbofan dataset with realistic maintenance patterns is used to benchmark Bayesian filters as strong baselines against self-supervised learning representations for component health estimation.