Imagen de Portada

Extreme-value forest fire prediction A study of the Loss Function in an Ordinality Scheme

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Caron, Nicolas, Guyeux, Christophe, Noura, Hassan, Aynes, Benjamin
Formato:	Preprint
Publicado:	2026
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

Localized Forest Fire Risk Prediction: A Department-Aware Approach for Operational Decision Support
por: Caron, Nicolas, et al.
Publicado: (2025)

Proof of Concept: Multi-Target Wildfire Risk Prediction and Large Language Model Synthesis
por: Caron, Nicolas, et al.
Publicado: (2026)

Unimodal Distributions for Ordinal Regression
por: Cardoso, Jaime S., et al.
Publicado: (2023)

Emotions as Ambiguity-aware Ordinal Representations
por: Wu, Jingyao, et al.
Publicado: (2025)

Ranking-aware Reinforcement Learning for Ordinal Ranking
por: Hao, Aiming, et al.
Publicado: (2026)

Treat Different Negatives Differently: Enriching Loss Functions with Domain and Range Constraints for Link Prediction
por: Hubert, Nicolas, et al.
Publicado: (2023)

Missing value imputation with adversarial random forests -- MissARF
por: Golchian, Pegah, et al.
Publicado: (2025)

ODRPO: Ordinal Decompositions of Discrete Rewards for Robust Policy Optimization
por: Patel, Nirmal, et al.
Publicado: (2026)

A comparative study of conformal prediction methods for valid uncertainty quantification in machine learning
por: Dewolf, Nicolas
Publicado: (2024)

Beyond Binary Preferences: A Principled Framework for Reward Modeling with Ordinal Feedback
por: Afsharrad, Amirhossein, et al.
Publicado: (2026)

BinConv: A Neural Architecture for Ordinal Encoding in Time-Series Forecasting
por: Chernov, Andrei, et al.
Publicado: (2025)

Meta-Learning Adaptive Loss Functions
por: Raymond, Christian, et al.
Publicado: (2023)

Extreme value forecasting using relevance-based data augmentation with deep learning models
por: Hua, Junru, et al.
Publicado: (2025)

Breaking Down Financial News Impact: A Novel AI Approach with Geometric Hypergraphs
por: Harit, Anoushka, et al.
Publicado: (2024)

A Note on Knowledge Distillation Loss Function for Object Classification
por: Chen, Defang
Publicado: (2021)

A Principled Loss Function for Direct Language Model Alignment
por: Tan, Yuandong
Publicado: (2025)

Visualizing Loss Functions as Topological Landscape Profiles
por: Geniesse, Caleb, et al.
Publicado: (2024)

Assessment of deep learning models integrated with weather and environmental variables for wildfire spread prediction and a case study of the 2023 Maui fires
por: Kim, Jiyeon, et al.
Publicado: (2025)

Reward Modeling with Ordinal Feedback: Wisdom of the Crowd
por: Liu, Shang, et al.
Publicado: (2024)

Dynamical Systems Analysis Reveals Functional Regimes in Large Language Models
por: Ugail, Hassan, et al.
Publicado: (2026)

Ordinal Adaptive Correction: A Data-Centric Approach to Ordinal Image Classification with Noisy Labels
por: Moghaddam, Alireza Sedighi, et al.
Publicado: (2025)

A Note on Loss Functions and Error Compounding in Model-based Reinforcement Learning
por: Jiang, Nan
Publicado: (2024)

Beyond the Mean: Distribution-Aware Loss Functions for Bimodal Regression
por: Mohammadi-Seif, Abolfazl, et al.
Publicado: (2026)

In Search for Architectures and Loss Functions in Multi-Objective Reinforcement Learning
por: Terekhov, Mikhail, et al.
Publicado: (2024)

UniExtreme: A Universal Foundation Model for Extreme Weather Forecasting
por: Ni, Hang, et al.
Publicado: (2025)

Ordinal Behavior Classification of Student Online Course Interactions
por: Trask, Thomas
Publicado: (2024)

Leaving the Nest: Going Beyond Local Loss Functions for Predict-Then-Optimize
por: Shah, Sanket, et al.
Publicado: (2023)

Continuity and Ordinality Matter: Constraining Time Series Tokens for Effective Time Series Analysis with Large Language Models
por: Li, Musheng, et al.
Publicado: (2026)

Geometry Preserving Loss Functions Promote Improved Adaptation of Blackbox Generative Model
por: Mitra, Sinjini, et al.
Publicado: (2026)

A new membership inference attack that spots memorization in generative and predictive models: Loss-Based with Reference Model algorithm (LBRM)
por: Taleb, Faiz, et al.
Publicado: (2025)

Loss Function Considering Dead Zone for Neural Networks
por: Inami, Koki, et al.
Publicado: (2024)

Energy Loss Functions for Physical Systems
por: Kaba, Sékou-Oumar, et al.
Publicado: (2025)

A study of Universal ODE approaches to predicting soil organic carbon
por: V. V, Satyanarayana Raju G., et al.
Publicado: (2025)

Foundation models for time series forecasting: Application in conformal prediction
por: Achour, Sami, et al.
Publicado: (2025)

Functional relevance based on the continuous Shapley value
por: Delicado, Pedro, et al.
Publicado: (2024)

ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast
por: Xu, Wanghan, et al.
Publicado: (2024)

Explainable Moral Values: a neuro-symbolic approach to value classification
por: Lazzari, Nicolas, et al.
Publicado: (2024)

AI to Identify Strain-sensitive Regions of the Optic Nerve Head Linked to Functional Loss in Glaucoma
por: Chuangsuwanich, Thanadet, et al.
Publicado: (2025)

Comparative study of clustering models for multivariate time series from connected medical devices
por: Courrier, Violaine, et al.
Publicado: (2023)

Barycentric Neural Networks and Length-Weighted Persistent Entropy Loss: A Green Geometric and Topological Framework for Function Approximation
por: Toscano-Duran, Victor, et al.
Publicado: (2025)