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Uncertainty Quantification

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Showing 2001–2050 of 2366 papers

TitleStatusHype
Variational Inference for Uncertainty Quantification: an Analysis of Trade-offsCode0
High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled ApproachCode0
History-Based, Bayesian, Closure for Stochastic Parameterization: Application to Lorenz '96Code0
Toward Robust Uncertainty Estimation with Random Activation FunctionsCode0
Uncertainty Quantification using Variational Inference for Biomedical Image SegmentationCode0
Horseshoe Regularization for Feature Subset SelectionCode0
Uncertainty quantification in fine-tuned LLMs using LoRA ensemblesCode0
Velocity continuation with Fourier neural operators for accelerated uncertainty quantificationCode0
PDE-DKL: PDE-constrained deep kernel learning in high dimensionalityCode0
Conformal Risk Control for Pulmonary Nodule DetectionCode0
Bagging, optimized dynamic mode decomposition (BOP-DMD) for robust, stable forecasting with spatial and temporal uncertainty-quantificationCode0
Uncertainty quantification and out-of-distribution detection using surjective normalizing flowsCode0
Negative impact of heavy-tailed uncertainty and error distributions on the reliability of calibration statistics for machine learning regression tasksCode0
Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification ProceduresCode0
Accelerating Convergence in Bayesian Few-Shot ClassificationCode0
Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear ModelCode0
Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty QuantificationCode0
Conformal Prediction with Corrupted Labels: Uncertain Imputation and Robust Re-weightingCode0
An Online Bootstrap for Time SeriesCode0
Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detectionCode0
AMPL: A Data-Driven Modeling Pipeline for Drug DiscoveryCode0
Hyperparameter Ensembles for Robustness and Uncertainty QuantificationCode0
Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEsCode0
Semantic Consistency-Based Uncertainty Quantification for Factuality in Radiology Report GenerationCode0
Perturbation-Assisted Sample Synthesis: A Novel Approach for Uncertainty QuantificationCode0
Conformal Predictions Enhanced Expert-guided Meshing with Graph Neural NetworksCode0
Identifying Drivers of Predictive Aleatoric UncertaintyCode0
Benchmarking Probabilistic Deep Learning Methods for License Plate RecognitionCode0
Conformal Prediction for Multimodal RegressionCode0
IIFL: Implicit Interactive Fleet Learning from Heterogeneous Human SupervisorsCode0
PH-Dropout: Practical Epistemic Uncertainty Quantification for View SynthesisCode0
Empirical evaluation of Uncertainty Quantification in Retrieval-Augmented Language Models for ScienceCode0
Conformal Prediction: A Theoretical Note and Benchmarking Transductive Node Classification in GraphsCode0
Semantic uncertainty intervals for disentangled latent spacesCode0
Implementing measurement error models with mechanistic mathematical models in a likelihood-based framework for estimation, identifiability analysis, and prediction in the life sciencesCode0
SEMF: Supervised Expectation-Maximization Framework for Predicting IntervalsCode0
Implicit Generative Prior for Bayesian Neural NetworksCode0
Semi-automatic tuning of coupled climate models with multiple intrinsic timescales: lessons learned from the Lorenz96 modelCode0
Human-in-the-loop: Towards Label Embeddings for Measuring Classification DifficultyCode0
Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled DataCode0
Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?Code0
Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantificationCode0
Physics-Constrained Learning for PDE Systems with Uncertainty Quantified Port-Hamiltonian ModelsCode0
Improved uncertainty quantification for neural networks with Bayesian last layerCode0
Improved Uncertainty Quantification in Physics-Informed Neural Networks Using Error Bounds and Solution BundlesCode0
Improved User Identification through Calibrated Monte-Carlo DropoutCode0
Improvements on Uncertainty Quantification for Node Classification via Distance-Based RegularizationCode0
Integrating Physics of the Problem into Data-Driven Methods to Enhance Elastic Full-Waveform Inversion with Uncertainty QuantificationCode0
Efficient Variational Inference for Sparse Deep Learning with Theoretical GuaranteeCode0
On Calibration of Mixup Training for Deep Neural NetworksCode0
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