FK-eABF replaces histogram accumulation in eABF with Gaussian force kernels and Nadaraya-Watson regression to achieve faster free-energy landscape coverage while retaining quantitative accuracy across simulation timescales.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
A semi-supervised kernel two-sample test integrates unlabeled covariate data to achieve asymptotic normality under the null, higher power than standard kernel tests, and consistency against fixed and local alternatives.
PPM injects parametric structural priors into generative models via a learnable mapping to improve probabilistic forecasts on non-stationary MTS data.
citing papers explorer
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A Force-Kernel Reformulation of the Extended-System Adaptive Biasing Force for Free-Energy Calculations
FK-eABF replaces histogram accumulation in eABF with Gaussian force kernels and Nadaraya-Watson regression to achieve faster free-energy landscape coverage while retaining quantitative accuracy across simulation timescales.
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A Semi-Supervised Kernel Two-Sample Test
A semi-supervised kernel two-sample test integrates unlabeled covariate data to achieve asymptotic normality under the null, higher power than standard kernel tests, and consistency against fixed and local alternatives.
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Parametric Prior Mapping Framework for Non-stationary Probabilistic Time Series Forecasting
PPM injects parametric structural priors into generative models via a learnable mapping to improve probabilistic forecasts on non-stationary MTS data.