Target-aligned data selection via normalized endpoint loss drop on a validation-induced reference path achieves competitive performance with reduced computational overhead.
Golub and Victor Pereyra
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
Dr-BA delivers a separable optimization approach for direct radar bundle adjustment and cross-session localization using full spinning-radar intensity images, achieving state-of-the-art performance on over 200 km of on-road data.
Orthogonal reparametrization via QR decomposition renders NSS linear parameters uncorrelated with diagonal conditional Fisher information, providing a scalar identifiability diagnostic and closed-form finite-horizon orthogonal basis.
citing papers explorer
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Let the Target Select for Itself: Data Selection via Target-Aligned Paths
Target-aligned data selection via normalized endpoint loss drop on a validation-induced reference path achieves competitive performance with reduced computational overhead.
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Dr-BA: Separable Optimization for Direct Radar Bundle Adjustment & Localization
Dr-BA delivers a separable optimization approach for direct radar bundle adjustment and cross-session localization using full spinning-radar intensity images, achieving state-of-the-art performance on over 200 km of on-road data.
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Orthogonal reparametrization of the Nelson-Siegel-Svensson interest rate curve model: conditioning, diagnostics, and identifiability
Orthogonal reparametrization via QR decomposition renders NSS linear parameters uncorrelated with diagonal conditional Fisher information, providing a scalar identifiability diagnostic and closed-form finite-horizon orthogonal basis.