Walk on Heat Stars provides a boundary-integral Monte Carlo solver for parabolic PDEs with Neumann conditions via exact heat-ball sampling that yields unbiased estimators.
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Anchor-guided variance-aware reward modeling uses two response-level anchors to resolve non-identifiability in Gaussian models of pluralistic preferences, yielding provable identification, a joint training objective, and improved RLHF performance.
Symmetry-informed neural networks using SU(3)/SU(4) Casimir operators achieve lower RMSE on global nuclear masses than liquid-drop models, with WINN reaching 0.430 MeV validation error and showing dripline and superheavy patterns.
A probabilistic denoising model recovers spectral features from Poisson-noisy 3D ARPES data at 0.02 electrons per voxel and propagates uncertainties into superconducting gap fits for cuprate superconductors.
A relaxed Picard iteration plus heteroscedastic boundary denoising lets Monte Carlo PDE solvers solve heat equations with nonlinear radiation boundary conditions more accurately than linearization.
NeuroSymActive combines soft-unification symbolic modules, a neural path evaluator, and Monte-Carlo-style active exploration to reach strong answer accuracy on KGQA benchmarks while cutting graph lookups and model calls versus standard retrieval baselines.
Compares ensemble, Bayesian, and evidential regression approaches for uncertainty quantification in amplitude surrogates and shows they detect localized training data issues.
citing papers explorer
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Walking on Heat Stars for Parabolic Heat Equations with Neumann Boundary Conditions
Walk on Heat Stars provides a boundary-integral Monte Carlo solver for parabolic PDEs with Neumann conditions via exact heat-ball sampling that yields unbiased estimators.
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Variance-aware Reward Modeling with Anchor Guidance
Anchor-guided variance-aware reward modeling uses two response-level anchors to resolve non-identifiability in Gaussian models of pluralistic preferences, yielding provable identification, a joint training objective, and improved RLHF performance.
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Bridging Ab Initio Symmetries and Global Nuclear Masses with Interpretable Neural Networks
Symmetry-informed neural networks using SU(3)/SU(4) Casimir operators achieve lower RMSE on global nuclear masses than liquid-drop models, with WINN reaching 0.430 MeV validation error and showing dripline and superheavy patterns.
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Probabilistic denoising for reliable signal extraction in spectroscopy
A probabilistic denoising model recovers spectral features from Poisson-noisy 3D ARPES data at 0.02 electrons per voxel and propagates uncertainties into superconducting gap fits for cuprate superconductors.
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Monte Carlo PDE Solvers for Nonlinear Radiative Boundary Conditions
A relaxed Picard iteration plus heteroscedastic boundary denoising lets Monte Carlo PDE solvers solve heat equations with nonlinear radiation boundary conditions more accurately than linearization.
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NeuroSymActive: Differentiable Neural-Symbolic Reasoning with Active Exploration for Knowledge Graph Question Answering
NeuroSymActive combines soft-unification symbolic modules, a neural path evaluator, and Monte-Carlo-style active exploration to reach strong answer accuracy on KGQA benchmarks while cutting graph lookups and model calls versus standard retrieval baselines.
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Amplitude Uncertainties Everywhere All at Once
Compares ensemble, Bayesian, and evidential regression approaches for uncertainty quantification in amplitude surrogates and shows they detect localized training data issues.