DVAC uses denoising variance as an intrinsic signal to adaptively chunk actions in flow-based robot policies, improving success rates and cutting replans on LIBERO, RoboTwin, CALVIN, and real-world tasks.
Applying tabular deep learning models to estimate crash injury types of young motorcyclists
2 Pith papers cite this work. Polarity classification is still indexing.
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Benchmarks TabPFN, MambaNet and MambaAttention on imbalanced EV crash severity classification with SMOTEENN resampling on Texas data, identifying intersection relation and speed limit as top features and MambaAttention as strongest on severe cases.
citing papers explorer
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Denoising Tells When to Replan: Denoising-Variance Adaptive Chunking for Flow-Based Robot Policies
DVAC uses denoising variance as an intrinsic signal to adaptively chunk actions in flow-based robot policies, improving success rates and cutting replans on LIBERO, RoboTwin, CALVIN, and real-world tasks.
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Tabular Data with Class Imbalance: Predicting Electric Vehicle Crash Severity with Pretrained Transformers (TabPFN) and Mamba-Based Models
Benchmarks TabPFN, MambaNet and MambaAttention on imbalanced EV crash severity classification with SMOTEENN resampling on Texas data, identifying intersection relation and speed limit as top features and MambaAttention as strongest on severe cases.