Image de couverture de livre

DiffHybrid-UQ: Uncertainty Quantification for Differentiable Hybrid Neural Modeling

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Akhare, Deepak, Luo, Tengfei, Wang, Jian-Xun
Format:	Preprint
Publié:	2023
Sujets:	Machine Learning Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Implicit Neural Differential Model for Spatiotemporal Dynamics
par: Akhare, Deepak, et autres
Publié: (2025)

Data-Augmented Few-Shot Neural Emulator for Computer-Model System Identification
par: Jantre, Sanket, et autres
Publié: (2025)

HybridFlow: Quantification of Aleatoric and Epistemic Uncertainty with a Single Hybrid Model
par: Van Katwyk, Peter, et autres
Publié: (2025)

DiffLoad: Uncertainty Quantification in Electrical Load Forecasting with the Diffusion Model
par: Wang, Zhixian, et autres
Publié: (2023)

RewardUQ: A Unified Framework for Uncertainty-Aware Reward Models
par: Yang, Daniel, et autres
Publié: (2026)

DeepUQ: Assessing the Aleatoric Uncertainties from two Deep Learning Methods
par: Nevin, Rebecca, et autres
Publié: (2024)

Physics-Based Hybrid Machine Learning for Critical Heat Flux Prediction with Uncertainty Quantification
par: Furlong, Aidan, et autres
Publié: (2025)

High-Fidelity Industrial Crash Dynamics Prediction via Geometry-Aware Operator Learning with Memory-Efficient Low-Rank Attention
par: Akhare, Deepak, et autres
Publié: (2026)

UQ: Assessing Language Models on Unsolved Questions
par: Nie, Fan, et autres
Publié: (2025)

Quantile-Free Uncertainty Quantification in Graph Neural Networks
par: park, Soyoung, et autres
Publié: (2026)

Uncertainty Quantification for Gradient-based Explanations in Neural Networks
par: Mulye, Mihir, et autres
Publié: (2024)

UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction
par: Yu, Dahai, et autres
Publié: (2025)

Conformalized Neural Networks for Federated Uncertainty Quantification under Dual Heterogeneity
par: Nguyen, Quang-Huy, et autres
Publié: (2026)

Structure-Aware Epistemic Uncertainty Quantification for Neural Operator PDE Surrogates
par: Song, Haoze, et autres
Publié: (2026)

Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial
par: Nemani, Venkat, et autres
Publié: (2023)

HybridNorm: Towards Stable and Efficient Transformer Training via Hybrid Normalization
par: Zhuo, Zhijian, et autres
Publié: (2025)

Towards Uncertainty Quantification in Generative Model Learning
par: Morales, Giorgio, et autres
Publié: (2025)

Credal Ensemble Distillation for Uncertainty Quantification
par: Wang, Kaizheng, et autres
Publié: (2025)

NASH: Neural Architecture and Accelerator Search for Multiplication-Reduced Hybrid Models
par: Xu, Yang, et autres
Publié: (2024)

Conf-Gen: Conformal Uncertainty Quantification for Generative Models
par: Loaiza-Ganem, Gabriel, et autres
Publié: (2026)

Model-Agnostic Dynamic Feature Selection with Uncertainty Quantification
par: Fumanal-Idocin, Javier, et autres
Publié: (2025)

Uncertainty Quantification in the Tsetlin Machine
par: Helin, Runar, et autres
Publié: (2025)

Uncertainty Quantification in SVM prediction
par: Anand, Pritam
Publié: (2025)

Accurate Differential Operators for Hybrid Neural Fields
par: Chetan, Aditya, et autres
Publié: (2023)

Shifting Uncertainty to Critical Moments: Towards Reliable Uncertainty Quantification for VLA Model
par: Tang, Yanchuan, et autres
Publié: (2026)

Two Birds with One Stone: Enhancing Uncertainty Quantification and Interpretability with Graph Functional Neural Process
par: Kong, Lingkai, et autres
Publié: (2025)

Tackling Fake Forgetting through Uncertainty Quantification
par: Shi, Yingdan, et autres
Publié: (2025)

Automotive Crash Dynamics Modeling Accelerated with Machine Learning
par: Nabian, Mohammad Amin, et autres
Publié: (2025)

Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Models
par: Neumeier, Marion, et autres
Publié: (2024)

Fair Uncertainty Quantification for Depression Prediction
par: Li, Yonghong, et autres
Publié: (2025)

Torch-Uncertainty: A Deep Learning Framework for Uncertainty Quantification
par: Lafage, Adrien, et autres
Publié: (2025)

Complex-Valued Unitary Representations as Classification Heads for Improved Uncertainty Quantification in Deep Neural Networks
par: Jafari, Akbar Anbar, et autres
Publié: (2026)

Uncertainty Quantification in Probabilistic Machine Learning Models: Theory, Methods, and Insights
par: Ajirak, Marzieh, et autres
Publié: (2025)

Uncertainty Quantification of Large Language Models using Approximate Bayesian Computation
par: Sharma, Mridul, et autres
Publié: (2025)

A Rate-Distortion View of Uncertainty Quantification
par: Apostolopoulou, Ifigeneia, et autres
Publié: (2024)

Uncertainty Quantification via Stable Distribution Propagation
par: Petersen, Felix, et autres
Publié: (2024)

GNN's Uncertainty Quantification using Self-Distillation
par: Daneshvar, Hirad, et autres
Publié: (2025)

Efficient Non-Parametric Uncertainty Quantification for Black-Box Large Language Models and Decision Planning
par: Tsai, Yao-Hung Hubert, et autres
Publié: (2024)

HyCARD-Net: A Synergistic Hybrid Intelligence Framework for Cardiovascular Disease Diagnosis
par: Gupta, Rajan Das, et autres
Publié: (2026)

Quantum-Classical Hybrid Quantized Neural Network
par: Li, Wenxin, et autres
Publié: (2025)