Soft-MSM is a smooth, gradient-enabled version of the context-aware MSM distance for time series alignment that outperforms Soft-DTW alternatives in clustering and nearest-centroid classification.
Multilevelk-way partitioning scheme for irregular graphs,
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
MALOQ introduces a scalable SO(2)-equivariant ML framework with custom kernels and edge-wise graph distribution for predicting large-scale quantum transport operators.
citing papers explorer
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Soft-MSM: Differentiable Context-Aware Elastic Alignment for Time Series
Soft-MSM is a smooth, gradient-enabled version of the context-aware MSM distance for time series alignment that outperforms Soft-DTW alternatives in clustering and nearest-centroid classification.
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MALOQ: Massively Accelerated Learning of Operators for Quantum Transport
MALOQ introduces a scalable SO(2)-equivariant ML framework with custom kernels and edge-wise graph distribution for predicting large-scale quantum transport operators.