Uncertainty-DTW models pairwise correspondences with Normal distributions and uses an MLE objective with precision-weighted matching plus log-variance regularization for robust alignment of sequences and visual tokens.
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2 Pith papers cite this work. Polarity classification is still indexing.
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FMMVCC combines Mamba-based encoders with multi-view contrastive learning and fuzzy clustering to achieve state-of-the-art univariate time series clustering with linear computational complexity.
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Uncertainty-DTW for Sequences and Visual Tokens
Uncertainty-DTW models pairwise correspondences with Normal distributions and uses an MLE objective with precision-weighted matching plus log-variance regularization for robust alignment of sequences and visual tokens.
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FMMVCC: Fuzzy Mamba-based Multi-View Contrastive Clustering for Univariate Time Series
FMMVCC combines Mamba-based encoders with multi-view contrastive learning and fuzzy clustering to achieve state-of-the-art univariate time series clustering with linear computational complexity.