Imagen de Portada

Torch-Uncertainty: A Deep Learning Framework for Uncertainty Quantification

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Lafage, Adrien, Laurent, Olivier, Gabetni, Firas, Franchi, Gianni
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Packed-Ensembles for Efficient Uncertainty Estimation
por: Laurent, Olivier, et al.
Publicado: (2022)

Ensembling Pruned Attention Heads For Uncertainty-Aware Efficient Transformers
por: Gabetni, Firas, et al.
Publicado: (2025)

Uncertainty Quantification for Machine Learning in Healthcare: A Survey
por: López, L. Julián Lechuga, et al.
Publicado: (2025)

Uncertainty Quantification for Surface Ozone Emulators using Deep Learning
por: Doerksen, Kelsey, et al.
Publicado: (2025)

Uncertainty Quantification of Deep Learning for Spatiotemporal Data: Challenges and Opportunities
por: He, Wenchong, et al.
Publicado: (2023)

Tackling Incomplete Data in Air Quality Prediction: A Bayesian Deep Learning Framework for Uncertainty Quantification
por: Pian, Yuzhuang, et al.
Publicado: (2025)

From Local Geometry to Global Pseudo Labeling for Robust Positive Unlabeled Learning under Covariate Shift
por: Gabetni, Firas, et al.
Publicado: (2026)

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation
por: Franchi, Gianni, et al.
Publicado: (2024)

Towards Uncertainty Quantification in Generative Model Learning
por: Morales, Giorgio, et al.
Publicado: (2025)

Uncertainty Quantification in the Tsetlin Machine
por: Helin, Runar, et al.
Publicado: (2025)

Uncertainty Quantification in SVM prediction
por: Anand, Pritam
Publicado: (2025)

A Framework for Uncertainty Quantification Based on Nearest Neighbors Across Layers
por: Font, Miguel N., et al.
Publicado: (2025)

Towards Understanding Why Label Smoothing Degrades Selective Classification and How to Fix It
por: Xia, Guoxuan, et al.
Publicado: (2024)

A Rate-Distortion View of Uncertainty Quantification
por: Apostolopoulou, Ifigeneia, et al.
Publicado: (2024)

Credal Ensemble Distillation for Uncertainty Quantification
por: Wang, Kaizheng, et al.
Publicado: (2025)

Fair Uncertainty Quantification for Depression Prediction
por: Li, Yonghong, et al.
Publicado: (2025)

Uncertainty-Aware Deep Learning Framework for Remaining Useful Life Prediction in Turbofan Engines with Learned Aleatoric Uncertainty
por: Sharma, Krishang
Publicado: (2025)

Quantification of Credal Uncertainty: A Distance-Based Approach
por: Gonzalez-Garcia, Xabier, et al.
Publicado: (2026)

GNN's Uncertainty Quantification using Self-Distillation
por: Daneshvar, Hirad, et al.
Publicado: (2025)

Tackling Fake Forgetting through Uncertainty Quantification
por: Shi, Yingdan, et al.
Publicado: (2025)

Uncertainty Quantification via Stable Distribution Propagation
por: Petersen, Felix, et al.
Publicado: (2024)

Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial
por: Nemani, Venkat, et al.
Publicado: (2023)

Uncertainty Quantification in Probabilistic Machine Learning Models: Theory, Methods, and Insights
por: Ajirak, Marzieh, et al.
Publicado: (2025)

UncertaintyZoo: A Unified Toolkit for Quantifying Predictive Uncertainty in Deep Learning Systems
por: Wu, Xianzong, et al.
Publicado: (2025)

Shifting Uncertainty to Critical Moments: Towards Reliable Uncertainty Quantification for VLA Model
por: Tang, Yanchuan, et al.
Publicado: (2026)

A Simple and Effective Method for Uncertainty Quantification and OOD Detection
por: Ma, Yaxin, et al.
Publicado: (2025)

The Probabilistic Tsetlin Machine: A Novel Approach to Uncertainty Quantification
por: Abeyrathna, K. Darshana, et al.
Publicado: (2024)

Model-Agnostic Dynamic Feature Selection with Uncertainty Quantification
por: Fumanal-Idocin, Javier, et al.
Publicado: (2025)

Quantile-Free Uncertainty Quantification in Graph Neural Networks
por: park, Soyoung, et al.
Publicado: (2026)

Conf-Gen: Conformal Uncertainty Quantification for Generative Models
por: Loaiza-Ganem, Gabriel, et al.
Publicado: (2026)

Uncertainty Quantification for Gradient-based Explanations in Neural Networks
por: Mulye, Mihir, et al.
Publicado: (2024)

Improving Label Error Detection and Elimination with Uncertainty Quantification
por: Jakubik, Johannes, et al.
Publicado: (2024)

Streamflow Prediction with Uncertainty Quantification for Water Management: A Constrained Reasoning and Learning Approach
por: Gharsallaoui, Mohammed Amine, et al.
Publicado: (2024)

Complex-Valued Unitary Representations as Classification Heads for Improved Uncertainty Quantification in Deep Neural Networks
por: Jafari, Akbar Anbar, et al.
Publicado: (2026)

Calibrated Physics-Informed Uncertainty Quantification
por: Gopakumar, Vignesh, et al.
Publicado: (2025)

Constraint-Aware Neurosymbolic Uncertainty Quantification with Bayesian Deep Learning for Scientific Discovery
por: Alam, Shahnawaz, et al.
Publicado: (2026)

Robust Uncertainty Quantification Using Conformalised Monte Carlo Prediction
por: Bethell, Daniel, et al.
Publicado: (2023)

LLMs Uncertainty Quantification via Adaptive Conformal Semantic Entropy
por: Karimi, Hamed, et al.
Publicado: (2026)

FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification
por: Chen, Hui, et al.
Publicado: (2024)

Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Models
por: Neumeier, Marion, et al.
Publicado: (2024)