SAIL reduces prediction error by up to 28.8% on the hardest 1% of long-tail trajectory samples in AV datasets through attribute-guided augmentation and adaptive contrastive learning with cosine momentum, hard-negative mining, and dynamic pseudo-labeling.
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Numerical study comparing feedforward NN and DeepONet with data-driven and physics-informed losses on stochastic heat equation, highlighting larger errors at distribution tails due to extrapolation.
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SAIL: Scene-aware Adaptive Iterative Learning for Long-Tail Trajectory Prediction in Autonomous Vehicles
SAIL reduces prediction error by up to 28.8% on the hardest 1% of long-tail trajectory samples in AV datasets through attribute-guided augmentation and adaptive contrastive learning with cosine momentum, hard-negative mining, and dynamic pseudo-labeling.
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A numerical study into neural network surrogate model performance for uncertainty propagation
Numerical study comparing feedforward NN and DeepONet with data-driven and physics-informed losses on stochastic heat equation, highlighting larger errors at distribution tails due to extrapolation.