A certificate-based regret analysis framework for guided-diffusion black-box optimization is introduced, with mass lift as the central quantity explaining convergence from pretrained generators.
Atomistic line graph neural network for improved materials property predictions.npj Computational Materials, 7(1):185
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
DualLGD reformulates molecular graph denoising as alternating atom and bond subproblems in separate streams, achieving 34.37% and 23.89% top-1 accuracy on NPLIB1 and MassSpecGym benchmarks, roughly 3x prior state of the art.
citing papers explorer
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Regret Analysis of Guided Diffusion for Black-Box Optimization over Structured Inputs
A certificate-based regret analysis framework for guided-diffusion black-box optimization is introduced, with mass lift as the central quantity explaining convergence from pretrained generators.
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Unlocking High-Fidelity Molecular Generation from Mass Spectra via Dual-Stream Line Graph Diffusion
DualLGD reformulates molecular graph denoising as alternating atom and bond subproblems in separate streams, achieving 34.37% and 23.89% top-1 accuracy on NPLIB1 and MassSpecGym benchmarks, roughly 3x prior state of the art.