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.
Communications in Statistics 3(1), 1-27 (1974)
5 Pith papers cite this work. Polarity classification is still indexing.
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AFGNN detects API misuses in Java code more effectively than prior methods by representing usage as graphs and clustering learned embeddings from self-supervised training.
New hardware-usage-based similarity metrics can identify matching computational kernels between proxy applications and performance suites on both CPU and GPU systems.
Inpainting auxiliary task improves clustering of embeddings for individual zebrafish identification based on skin patterns.
An unsupervised-to-supervised ML pipeline on UK NDNS data discovers four dietary patterns, reproduces them with macro-F1 0.963 using a surrogate classifier, and interprets them via SHAP for potential clinical use.
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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AFGNN: API Misuse Detection using Graph Neural Networks and Clustering
AFGNN detects API misuses in Java code more effectively than prior methods by representing usage as graphs and clustering learned embeddings from self-supervised training.
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On Similarity of Computational Kernels in our Codes and Proxies
New hardware-usage-based similarity metrics can identify matching computational kernels between proxy applications and performance suites on both CPU and GPU systems.
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Exploring Clustering Capability of Inpainting Model Embeddings for Pattern-based Individual Identification
Inpainting auxiliary task improves clustering of embeddings for individual zebrafish identification based on skin patterns.
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An Explainable Unsupervised-to-Supervised Machine Learning Framework for Dietary Pattern Discovery Using UK National Dietary Survey Data
An unsupervised-to-supervised ML pipeline on UK NDNS data discovers four dietary patterns, reproduces them with macro-F1 0.963 using a surrogate classifier, and interprets them via SHAP for potential clinical use.