NTK-UQ produces 31-37% sharper 90% prediction intervals than split conformal prediction for extreme weather forecasts, with adaptive scaling via architecture-dependent eigenvalue truncation and ICA decomposition of last-layer features.
Methods for comparing uncertainty quantifications for material property predictions.Machine Learning: Science and Technology, 1(2):025006, May 2020
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Uncertainty-aware RL for chemical language models raises true hit rate from 0.5 to 0.75 by favoring low-uncertainty regions during optimization.
Uncertainty-aware neural networks using Gaussian negative log-likelihood and dropout are applied to predict intrinsic magnetic properties and coercivity via graph neural networks in permanent magnet research.
citing papers explorer
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Scalable Uncertainty Quantification for Extreme Weather Forecasting via Empirical Neural Tangent Kernels
NTK-UQ produces 31-37% sharper 90% prediction intervals than split conformal prediction for extreme weather forecasts, with adaptive scaling via architecture-dependent eigenvalue truncation and ICA decomposition of last-layer features.
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Uncertainty-aware reinforcement learning for chemical language models
Uncertainty-aware RL for chemical language models raises true hit rate from 0.5 to 0.75 by favoring low-uncertainty regions during optimization.
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Modelling magnetic material properties with uncertainty-aware neural networks
Uncertainty-aware neural networks using Gaussian negative log-likelihood and dropout are applied to predict intrinsic magnetic properties and coercivity via graph neural networks in permanent magnet research.