ReshapeOT improves optimal transport reliability for distribution shifts by replacing the Euclidean ground metric with a Mahalanobis distance derived from observed displacement second moments.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
HELIX uses learnable feature identities and hybrid temporal-feature attention to achieve state-of-the-art time series imputation across multiple datasets and settings.
A pipeline uses community detection plus attention to create dynamic hypergraphs from raw multivariate time series and feeds them to DHACN for forecasting without prior structural knowledge
SPaRSe-TIME introduces a decomposition of time series into saliency-projected low-rank components that delivers competitive accuracy with lower computation and explicit interpretability.
citing papers explorer
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Reliable Modeling of Distribution Shifts via Displacement-Reshaped Optimal Transport
ReshapeOT improves optimal transport reliability for distribution shifts by replacing the Euclidean ground metric with a Mahalanobis distance derived from observed displacement second moments.
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HELIX: Hybrid Encoding with Learnable Identity and Cross-dimensional Synthesis for Time Series Imputation
HELIX uses learnable feature identities and hybrid temporal-feature attention to achieve state-of-the-art time series imputation across multiple datasets and settings.
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Dynamic Hypergraph Representation Learning for Multivariate Time Series without Prior Knowledge
A pipeline uses community detection plus attention to create dynamic hypergraphs from raw multivariate time series and feeds them to DHACN for forecasting without prior structural knowledge
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SPaRSe-TIME: Saliency-Projected Low-Rank Temporal Modeling for Efficient and Interpretable Time Series Prediction
SPaRSe-TIME introduces a decomposition of time series into saliency-projected low-rank components that delivers competitive accuracy with lower computation and explicit interpretability.