ShiFT uses deterministic temporal shifts to enforce shift invariance in contrastive learning, achieving state-of-the-art time series classification on six benchmarks plus UCR/UEA archives while cutting training time.
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2026 3verdicts
UNVERDICTED 3representative citing papers
A large-scale benchmark of 17 WHAR models across 30 datasets finds predictive performance has plateaued while efficiency favors compact neural models and random forests on the Pareto frontier.
A gradient-free framework adapts pretrained HAR classifiers to new users via Bayesian prototype updates in prototypical networks, improving F1 scores with 3s calibration data.
citing papers explorer
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Learning by Shifting: Temporal View Construction for Time Series Contrastive Learning
ShiFT uses deterministic temporal shifts to enforce shift invariance in contrastive learning, achieving state-of-the-art time series classification on six benchmarks plus UCR/UEA archives while cutting training time.
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WHAR Arena: Benchmarking the State of the Art in Efficient Wearable Human Activity Recognition
A large-scale benchmark of 17 WHAR models across 30 datasets finds predictive performance has plateaued while efficiency favors compact neural models and random forests on the Pareto frontier.
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Uncertainty-Aware (Un)Supervised Few-Shot User Adaptation for On-Device Personalized Human Activity Recognition
A gradient-free framework adapts pretrained HAR classifiers to new users via Bayesian prototype updates in prototypical networks, improving F1 scores with 3s calibration data.