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.
ISBN 978-1-5090-5710-8
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A hybrid framework bifurcates RUL prediction for turbofan engines into healthy and degraded regimes via LSTM autoencoder state classification, using Weibull survival analysis and probabilistic neural networks with MC dropout for uncertainty-aware estimates on the C-MAPSS dataset.
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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Bifurcated Remaining Useful Life Prediction: A Hybrid Approach for Realistic Uncertainty Characterization
A hybrid framework bifurcates RUL prediction for turbofan engines into healthy and degraded regimes via LSTM autoencoder state classification, using Weibull survival analysis and probabilistic neural networks with MC dropout for uncertainty-aware estimates on the C-MAPSS dataset.