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.
arXiv preprint arXiv:1903.06602 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
A CNN model trained with pseudo-label semi-supervised learning reports higher AUC than a supervised baseline on the PCam histopathology dataset.
citing papers explorer
-
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.
-
Semi-Supervised Learning for Cancer Detection of Lymph Node Metastases
A CNN model trained with pseudo-label semi-supervised learning reports higher AUC than a supervised baseline on the PCam histopathology dataset.