A new cross-modal learning method aligns WiFi fingerprint traces and inertial displacement traces in a shared latent space with additive structure to enable relative localization under weak supervision.
Gaussian processes for regression
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TSNN matches time series entries to a training-derived memory bank to forecast traffic without any trainable parameters and achieves competitive accuracy on four real-world datasets.
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Learning Displacement-Aware WiFi Representations for Weakly Supervised Relative Localization
A new cross-modal learning method aligns WiFi fingerprint traces and inertial displacement traces in a shared latent space with additive structure to enable relative localization under weak supervision.
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TSNN: A Non-parametric and Interpretable Framework for Traffic Time Series Forecasting
TSNN matches time series entries to a training-derived memory bank to forecast traffic without any trainable parameters and achieves competitive accuracy on four real-world datasets.