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Uncertainty Quantification

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Showing 1451–1500 of 2366 papers

TitleStatusHype
Privacy Preserving Bayesian Federated Learning in Heterogeneous Settings—0
A Brief Review of Hypernetworks in Deep LearningCode0
Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic SmoothingCode0
Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural NetworksCode0
Real-time whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations—0
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift—0
Using Imperfect Surrogates for Downstream Inference: Design-based Supervised Learning for Social Science Applications of Large Language Models—0
Estimating Uncertainty in PET Image Reconstruction via Deep Posterior Sampling—0
Uncertainty in Natural Language Processing: Sources, Quantification, and Applications—0
Global universal approximation of functional input maps on weighted spacesCode0
Distributionally robust uncertainty quantification via data-driven stochastic optimal control—0
Deep Operator Learning-based Surrogate Models with Uncertainty Quantification for Optimizing Internal Cooling Channel Rib Profiles—0
A General Framework for Uncertainty Quantification via Neural SDE-RNN—0
A New PHO-rmula for Improved Performance of Semi-Structured Networks—0
Quantifying Deep Learning Model Uncertainty in Conformal Prediction—0
Quantifying Representation Reliability in Self-Supervised Learning ModelsCode0
Offline Meta Reinforcement Learning with In-Distribution Online AdaptationCode0
Perturbation-Assisted Sample Synthesis: A Novel Approach for Uncertainty QuantificationCode0
Probabilistic computation and uncertainty quantification with emerging covarianceCode0
Non-convex Bayesian Learning via Stochastic Gradient Markov Chain Monte Carlo—0
Acceleration of stochastic gradient descent with momentum by averaging: finite-sample rates and asymptotic normality—0
USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution—0
Bayesian Spike Train Inference via Non-Local Priors—0
Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks—0
Better Batch for Deep Probabilistic Time Series ForecastingCode0
Bayesian Analysis for Over-parameterized Linear Model via Effective Spectra—0
Bi-fidelity Variational Auto-encoder for Uncertainty QuantificationCode0
Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior InferenceCode0
Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning—0
Probabilistic Exponential IntegratorsCode0
A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods—0
UPNet: Uncertainty-based Picking Deep Learning Network for Robust First Break Picking—0
Equivariant Neural Simulators for Stochastic Spatiotemporal DynamicsCode0
Cycle Consistency-based Uncertainty Quantification of Neural Networks in Inverse Imaging Problems—0
Uncertainty and Structure in Neural Ordinary Differential Equations—0
Posterior Inference on Shallow Infinitely Wide Bayesian Neural Networks under Weights with Unbounded VarianceCode0
Uncertainty Quantification in Deep Neural Networks through Statistical Inference on Latent Space—0
Synthetic data, real errors: how (not) to publish and use synthetic dataCode0
Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior—0
Learning Switching Port-Hamiltonian Systems with Uncertainty Quantification—0
Online machine-learning forecast uncertainty estimation for sequential data assimilation—0
ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-temporal Graph Attention and Bidirectional Recurrent United Neural Networks—0
Fully Bayesian VIB-DeepSSMCode0
On the Limitations of Model Stealing with Uncertainty Quantification Models—0
Neural Likelihood Surfaces for Spatial Processes with Computationally Intensive or Intractable LikelihoodsCode0
Bayesian Over-the-Air FedAvg via Channel Driven Stochastic Gradient Langevin Dynamics—0
Self-Supervised Learning for Organs At Risk and Tumor Segmentation with Uncertainty Quantification—0
Correcting for Interference in Experiments: A Case Study at Douyin—0
Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensemblesCode0
Proper Scoring Rules for Survival AnalysisCode0
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