A binning-based Bayesian ROPE equivalence testing method is introduced to quantitatively assess practical equivalence between synthetic and real pre-crash scenario datasets for driving automation safety impact evaluation.
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StaR-MoE adds sensitivity-aware routing alignment and asymmetric capacity regularization to expandable MoE architectures for class-incremental learning, reducing interference from routing drift and improving average and last-task accuracy on four benchmarks.
High-dimensional embeddings excel in few-shot regimes for some wireless tasks but carry high latency and parameter costs, whereas compressed autoencoder representations provide better noise robustness, stability, and efficiency.
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Practical validation of synthetic pre-crash scenarios
A binning-based Bayesian ROPE equivalence testing method is introduced to quantitatively assess practical equivalence between synthetic and real pre-crash scenario datasets for driving automation safety impact evaluation.
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Stable Routing for Mixture-of-Experts in Class-Incremental Learning
StaR-MoE adds sensitivity-aware routing alignment and asymmetric capacity regularization to expandable MoE architectures for class-incremental learning, reducing interference from routing drift and improving average and last-task accuracy on four benchmarks.
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Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness
High-dimensional embeddings excel in few-shot regimes for some wireless tasks but carry high latency and parameter costs, whereas compressed autoencoder representations provide better noise robustness, stability, and efficiency.