:: Library Catalog

Image de couverture de livre

Enregistré dans:

Détails bibliographiques
Auteur principal:	Breeden, Joseph L.
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning
Accès en ligne:	https://arxiv.org/abs/2602.00179
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Matching Problems to Solutions: An Explainable Way of Solving Machine Learning Problems
par: Saleh, Lokman, et autres
Publié: (2024)

How to Evaluate Uncertainty Estimates in Machine Learning for Regression?
par: Sluijterman, Laurens, et autres
Publié: (2021)

The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
par: Krishna, Satyapriya, et autres
Publié: (2022)

Explainability of Machine Learning Models under Missing Data
par: Vo, Tuan L., et autres
Publié: (2024)

Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models
par: Meng, Shuchen, et autres
Publié: (2024)

The Distributional Uncertainty of the SHAP score in Explainable Machine Learning
par: Cifuentes, Santiago, et autres
Publié: (2024)

Site-specific Deterministic Temperature and Humidity Forecasts with Explainable and Reliable Machine Learning
par: Han, MengMeng, et autres
Publié: (2024)

Utilizing Large Language Models for Machine Learning Explainability
par: Vassiliades, Alexandros, et autres
Publié: (2025)

A Critical Assessment of Interpretable and Explainable Machine Learning for Intrusion Detection
par: Subasi, Omer, et autres
Publié: (2024)

Uncertainty in Machine Learning
par: Weytjens, Hans, et autres
Publié: (2025)

An Ensemble Framework for Explainable Geospatial Machine Learning Models
par: Liu, Lingbo
Publié: (2024)

Hierarchical Industrial Demand Forecasting with Temporal and Uncertainty Explanations
par: Kamarthi, Harshavardhan, et autres
Publié: (2026)

Evaluating Local Explainability Metrics for Machine Learning Models on Tabular Data
par: Pereira, Tomás, et autres
Publié: (2026)

Data Model Design for Explainable Machine Learning-based Electricity Applications
par: Fortuna, Carolina, et autres
Publié: (2025)

Understanding Uncertainty-based Active Learning Under Model Mismatch
par: Rahmati, Amir Hossein, et autres
Publié: (2024)

Rethinking Explainable Machine Learning as Applied Statistics
par: Bordt, Sebastian, et autres
Publié: (2024)

An Explainable Pipeline for Machine Learning with Functional Data
par: Goode, Katherine, et autres
Publié: (2025)

REPEAT: Improving Uncertainty Estimation in Representation Learning Explainability
par: Wickstrøm, Kristoffer K., et autres
Publié: (2024)

Unemployment Dynamics Forecasting with Machine Learning Regression Models
par: Kim, Kyungsu
Publié: (2025)

Uncertainty-Aware Explainable Federated Learning
par: Zhang, Yanci, et autres
Publié: (2025)

ProtoTS: Learning Hierarchical Prototypes for Explainable Time Series Forecasting
par: Peng, Ziheng, et autres
Publié: (2025)

Macroeconomic Forecasting and Machine Learning
par: Chi, Ta-Chung, et autres
Publié: (2025)

Enhancing Retail Sales Forecasting with Optimized Machine Learning Models
par: Ganguly, Priyam, et autres
Publié: (2024)

Comparative Evaluation of Weather Forecasting using Machine Learning Models
par: Rahman, Md Saydur, et autres
Publié: (2024)

Machine Learning Models for Dengue Forecasting in Singapore
par: Lai, Zi Iun, et autres
Publié: (2024)

Efficient Milling Quality Prediction with Explainable Machine Learning
par: Gross, Dennis, et autres
Publié: (2024)

How does the Performance of the Data-driven Traffic Flow Forecasting Models deteriorate with Increasing Forecasting Horizon? An Extensive Approach Considering Statistical, Machine Learning and Deep Learning Models
par: Sherfenaz, Amanta, et autres
Publié: (2025)

A Multimodal and Explainable Machine Learning Approach to Diagnosing Multi-Class Ejection Fraction from Electrocardiograms
par: Ning, Catherine, et autres
Publié: (2026)

Explainable Load Forecasting with Covariate-Informed Time Series Foundation Models
par: Hertel, Matthias, et autres
Publié: (2026)

Feature Importance and Explainability in Quantum Machine Learning
par: Power, Luke, et autres
Publié: (2024)

On the Relationship Between Interpretability and Explainability in Machine Learning
par: Leblanc, Benjamin, et autres
Publié: (2023)

Investigating the Duality of Interpretability and Explainability in Machine Learning
par: Garouani, Moncef, et autres
Publié: (2025)

Explainable Machine Learning for ICU Readmission Prediction
par: de Sá, Alex G. C., et autres
Publié: (2023)

Exploring Quantum Machine Learning for Weather Forecasting
par: da Silva, Maria Heloísa F., et autres
Publié: (2025)

Explainable Machine Learning: An Illustration of Kolmogorov-Arnold Network Model for Airfoil Lift Prediction
par: Kulkarni, Sudhanva
Publié: (2025)

Inherently Interpretable and Uncertainty-Aware Models for Online Learning in Cyber-Security Problems
par: Kolicic, Benjamin, et autres
Publié: (2024)

Dual Interpretation of Machine Learning Forecasts
par: Coulombe, Philippe Goulet, et autres
Publié: (2024)

No More Maybe-Arrows: Resolving Causal Uncertainty by Breaking Symmetries
par: Huang, Tingrui, et autres
Publié: (2026)

Explainable Machine-Learning based Detection of Knee Injuries in Runners
par: Fuentes-Jiménez, David, et autres
Publié: (2026)

An Explainable Machine Learning Approach to Traffic Accident Fatality Prediction
par: Rifat, Md. Asif Khan, et autres
Publié: (2024)