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| Feedforward neural networks as statistical models: Improving interpretability through uncertainty quantification | Nov 14, 2023 | PredictionUncertainty Quantification | —Unverified | 0 |
| Frequentist Guarantees of Distributed (Non)-Bayesian Inference | Nov 14, 2023 | Bayesian InferenceElectrical Engineering | —Unverified | 0 |
| Uncertainty Quantification in Neural-Network Based Pain Intensity Estimation | Nov 14, 2023 | Decision MakingManagement | —Unverified | 0 |
| Probabilistic Physics-integrated Neural Differentiable Modeling for Isothermal Chemical Vapor Infiltration Process | Nov 13, 2023 | Uncertainty Quantification | —Unverified | 0 |
| Data-Efficient Task Generalization via Probabilistic Model-based Meta Reinforcement Learning | Nov 13, 2023 | Meta-LearningMeta Reinforcement Learning | —Unverified | 0 |
| Uncertainty estimation of machine learning spatial precipitation predictions from satellite data | Nov 13, 2023 | BenchmarkingFeature Importance | —Unverified | 0 |
| Improvements on Uncertainty Quantification for Node Classification via Distance-Based Regularization | Nov 10, 2023 | Node ClassificationOut of Distribution (OOD) Detection | CodeCode Available | 0 |
| Early-Exit Neural Networks with Nested Prediction Sets | Nov 10, 2023 | Conformal PredictionPrediction | —Unverified | 0 |
| Perfecting Liquid-State Theories with Machine Intelligence | Nov 9, 2023 | Computational EfficiencyDimensionality Reduction | —Unverified | 0 |
