MSTN introduces a lightweight multi-scale temporal network using convolutional encoding, recurrent or attention-based modeling, and gated fusion to achieve claimed state-of-the-art results on 21 of 27 time series benchmarks while using under 1.1M parameters and fast inference.
dataset on powered two wheelers fall and critical events detection
2 Pith papers cite this work. Polarity classification is still indexing.
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Hybrid predictive modeling of crash data identifies non-use of restraints as the primary risk factor for severe injury in collisions involving trees.
citing papers explorer
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MSTN: A Lightweight and Fast Model for General TimeSeries Analysis
MSTN introduces a lightweight multi-scale temporal network using convolutional encoding, recurrent or attention-based modeling, and gated fusion to achieve claimed state-of-the-art results on 21 of 27 time series benchmarks while using under 1.1M parameters and fast inference.
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From Canopy to Collision: A Hybrid Predictive Framework for Identifying Risk Factors in Tree-Involved Traffic Crashes
Hybrid predictive modeling of crash data identifies non-use of restraints as the primary risk factor for severe injury in collisions involving trees.