:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Abulawi, Zaid, Hu, Rui, Balaprakash, Prasanna, Liu, Yang
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2412.08776
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Surrogate modelling and uncertainty quantification based on multi-fidelity deep neural network
by: Li, Zhihui, et al.
Published: (2023)

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials
by: Farris, Riccardo, et al.
Published: (2025)

Scalable Bayesian Monte Carlo: fast uncertainty estimation beyond deep ensembles
by: Liang, Xinzhu, et al.
Published: (2025)

Comparative study of ensemble-based uncertainty quantification methods for neural network interatomic potentials
by: Kurniawan, Yonatan, et al.
Published: (2025)

AutoSAM: an Agentic Framework for Automating Input File Generation for the SAM Code with Multi-Modal Retrieval-Augmented Generation
by: Abulawi, Zaid, et al.
Published: (2026)

Fast and reliable uncertainty quantification with neural network ensembles for industrial image classification
by: Thuy, Arthur, et al.
Published: (2024)

Automating Data-Driven Modeling and Analysis for Engineering Applications using Large Language Model Agents
by: Liu, Yang, et al.
Published: (2025)

Improved uncertainty quantification for neural networks with Bayesian last layer
by: Fiedler, Felix, et al.
Published: (2023)

Bayesian sparsification for deep neural networks with Bayesian model reduction
by: Marković, Dimitrije, et al.
Published: (2023)

Bayesian neural network correction of RANS turbulence models with uncertainty quantification in separated flows
by: Buchanan, Tyler, et al.
Published: (2026)

DADEE: Well-calibrated uncertainty quantification in neural networks for barriers-based robot safety
by: Ataei, Masoud, et al.
Published: (2024)

Structure and asymptotic preserving deep neural surrogates for uncertainty quantification in multiscale kinetic equations
by: Chen, Wei, et al.
Published: (2025)

Quantum parameter estimation with uncertainty quantification from continuous measurement data using neural network ensembles
by: Anteneh, Amanuel
Published: (2025)

Duqtools: Dynamic uncertainty quantification for Tokamak reactor simulations modelling
by: Azizi, Victor, et al.
Published: (2024)

A systematic evaluation of uncertainty quantification techniques in deep learning: a case study in photoplethysmography signal analysis
by: Bench, Ciaran, et al.
Published: (2025)

Physics-guided Bayesian neural networks for zonal corrections and uncertainty quantification in separated flows
by: Eidi, Ali, et al.
Published: (2025)

Predictive uncertainty in deep learning–based MR image reconstruction using deep ensembles: Evaluation on the fastMRI data set
by: Thomas Küstner, et al.
Published: (2024)

Partially stochastic deep learning with uncertainty quantification for model predictive heating control
by: Hannula, Emma, et al.
Published: (2025)

NOVAK: Unified adaptive optimizer for deep neural networks
by: Kavun, Sergii
Published: (2026)

Distribution free uncertainty quantification in neuroscience-inspired deep operators
by: Garg, Shailesh, et al.
Published: (2024)

BO4IO: A Bayesian optimization approach to inverse optimization with uncertainty quantification
by: Lu, Yen-An, et al.
Published: (2024)

Hierarchical Bayesian inference for uncertainty quantification of thermal grease rheology
by: Nagrani, Pranay P., et al.
Published: (2025)

Bayesian implementation of Targeted Maximum Likelihood Estimation for uncertainty quantification in causal effect estimation
by: Nannapaneni, Saideep, et al.
Published: (2025)

Flow field tomography with uncertainty quantification using a Bayesian physics-informed neural network
by: Molnar, Joseph P., et al.
Published: (2021)

Incorporating uncertainty quantification into travel mode choice modeling: a Bayesian neural network (BNN) approach and an uncertainty-guided active survey framework
by: Zheng, Shuwen, et al.
Published: (2024)

An algorithmic framework for the optimization of deep neural networks architectures and hyperparameters
by: Keisler, Julie, et al.
Published: (2023)

Predicting small molecules solubilities on endpoint devices using deep ensemble neural networks
by: Ramos, Mayk Caldas, et al.
Published: (2023)

An axiomatized PDE model of deep neural networks
by: Wang, Tangjun, et al.
Published: (2023)

Stochastic full waveform inversion with deep generative prior for uncertainty quantification
by: Xie, Yuke, et al.
Published: (2024)

Efficient model predictive control for nonlinear systems modelled by deep neural networks
by: Lan, Jianglin
Published: (2024)

Simulation-based inference with deep ensembles: Evaluating calibration uncertainty and detecting model misspecification
by: Alvey, James, et al.
Published: (2025)

Bayesian estimation and uncertainty quantification of a temperature-dependent thermal conductivity
by: Silva, Rodrigo L. S., et al.
Published: (2024)

Software bug localization based on optimized and ensembled deep learning models
by: Waqas Ali, et al.
Published: (2024)

Automated HER2 scoring with uncertainty quantification using lensfree holography and deep learning
by: Shen, Che-Yung, et al.
Published: (2026)

On the optimal approximation of Sobolev and Besov functions using deep ReLU neural networks
by: Yang, Yunfei
Published: (2024)

Prediction uncertainty-aware planning using deep ensembles and trajectory optimisation
by: Nayak, Anshul, et al.
Published: (2025)

Inverse modeling of porous flow through deep neural networks: the case of coffee percolation
by: Barletta, Antoniorenee, et al.
Published: (2025)

Generative modelling for mass-mapping with fast uncertainty quantification
by: Whitney, Jessica J., et al.
Published: (2024)

Accelerating shape optimization by deep neural networks with on-the-fly determined architecture
by: Kubíčková, Lucie, et al.
Published: (2025)

Towards Scaling Law Analysis For Spatiotemporal Weather Data
by: Kiefer, Alexander, et al.
Published: (2026)