Copertina

Heterogeneous Graph Neural Networks with Post-hoc Explanations for Multi-modal and Explainable Land Use Inference

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zhai, Xuehao, Jiang, Junqi, Dejl, Adam, Rago, Antonio, Guo, Fangce, Toni, Francesca, Sivakumar, Aruna
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Interval Abstractions for Robust Counterfactual Explanations
di: Jiang, Junqi, et al.
Pubblicazione: (2024)

Robust Counterfactual Explanations in Machine Learning: A Survey
di: Jiang, Junqi, et al.
Pubblicazione: (2024)

Argumentation for Explainable and Globally Contestable Decision Support with LLMs
di: Dejl, Adam, et al.
Pubblicazione: (2026)

Argumentative Large Language Models for Explainable and Contestable Claim Verification
di: Freedman, Gabriel, et al.
Pubblicazione: (2024)

Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation
di: Jiang, Junqi, et al.
Pubblicazione: (2023)

Synthesising Counterfactual Explanations via Label-Conditional Gaussian Mixture Variational Autoencoders
di: Jiang, Junqi, et al.
Pubblicazione: (2025)

Hidden Conflicts in Neural Networks and Their Implications for Explainability
di: Dejl, Adam, et al.
Pubblicazione: (2023)

Identifying Query-Relevant Neurons in Large Language Models for Long-Form Texts
di: Chen, Lihu, et al.
Pubblicazione: (2024)

ArgLLM-App: An Interactive System for Argumentative Reasoning with Large Language Models
di: Dejl, Adam, et al.
Pubblicazione: (2026)

Evaluating Uncertainty Quantification Methods in Argumentative Large Language Models
di: Zhou, Kevin, et al.
Pubblicazione: (2025)

Recourse under Model Multiplicity via Argumentative Ensembling (Technical Report)
di: Jiang, Junqi, et al.
Pubblicazione: (2023)

Argumentative Ensembling for Robust Recourse under Model Multiplicity
di: Jiang, Junqi, et al.
Pubblicazione: (2025)

Neurosymbolic Learning for Inference-Time Argumentation
di: Freedman, Gabriel, et al.
Pubblicazione: (2026)

NuGraph2 with Explainability: Post-hoc Explanations for Geometric Neural Network Predictions
di: Voetberg, Margaret, et al.
Pubblicazione: (2025)

MArgE: Meshing Argumentative Evidence from Multiple Large Language Models for Justifiable Claim Verification
di: Ng, Ming Pok, et al.
Pubblicazione: (2025)

RAW-Explainer: Post-hoc Explanations of Graph Neural Networks on Knowledge Graphs
di: Kubo, Ryoji, et al.
Pubblicazione: (2025)

Retrieval- and Argumentation-Enhanced Multi-Agent LLMs for Judgmental Forecasting (Extended Version with Supplementary Material)
di: Gorur, Deniz, et al.
Pubblicazione: (2025)

Explainable Prediction of the Mechanical Properties of Composites with CNNs
di: Raaghav, Varun, et al.
Pubblicazione: (2025)

Unifying Post-hoc Explanations of Knowledge Graph Completions
di: Lonardi, Alessandro, et al.
Pubblicazione: (2025)

Contestable AI needs Computational Argumentation
di: Leofante, Francesco, et al.
Pubblicazione: (2024)

Can Large Language Models perform Relation-based Argument Mining?
di: Gorur, Deniz, et al.
Pubblicazione: (2024)

Free Argumentative Exchanges for Explaining Image Classifiers
di: Kori, Avinash, et al.
Pubblicazione: (2025)

Argumentatively Coherent Judgmental Forecasting
di: Gorur, Deniz, et al.
Pubblicazione: (2025)

In Defence of Post-hoc Explainability
di: Oh, Nick
Pubblicazione: (2024)

Explanations Go Linear: Post-hoc Explainability for Tabular Data with Interpretable Meta-Encoding
di: Piaggesi, Simone, et al.
Pubblicazione: (2025)

Heterogeneous Graph Neural Networks for Assumption-Based Argumentation
di: Gehlot, Preesha, et al.
Pubblicazione: (2025)

Representation Consistency for Accurate and Coherent LLM Answer Aggregation
di: Jiang, Junqi, et al.
Pubblicazione: (2025)

XAI-Units: Benchmarking Explainability Methods with Unit Tests
di: Lee, Jun Rui, et al.
Pubblicazione: (2025)

Unlocking Post-hoc Dataset Inference with Synthetic Data
di: Zhao, Bihe, et al.
Pubblicazione: (2025)

EvalSense: A Framework for Domain-Specific LLM (Meta-)Evaluation
di: Dejl, Adam, et al.
Pubblicazione: (2026)

On Gradual Semantics for Assumption-Based Argumentation
di: Rapberger, Anna, et al.
Pubblicazione: (2025)

Exploring the Effect of Explanation Content and Format on User Comprehension and Trust in Healthcare
di: Rago, Antonio, et al.
Pubblicazione: (2024)

A Methodology for Incompleteness-Tolerant and Modular Gradual Semantics for Argumentative Statement Graphs
di: Rago, Antonio, et al.
Pubblicazione: (2024)

Towards a Framework for Evaluating Explanations in Automated Fact Verification
di: Kotonya, Neema, et al.
Pubblicazione: (2024)

Informative Perturbation Selection for Uncertainty-Aware Post-hoc Explanations
di: Chugh, Sumedha, et al.
Pubblicazione: (2026)

SAIL: Structure-Aware Interpretable Learning for Anatomy-Aligned Post-hoc Explanations in OCT
di: Chang, Tienyu, et al.
Pubblicazione: (2026)

Explainable Time Series Prediction of Tyre Energy in Formula One Race Strategy
di: Todd, Jamie, et al.
Pubblicazione: (2025)

Argumentative Human-AI Decision-Making: Toward AI Agents That Reason With Us, Not For Us
di: Vasileiou, Stylianos Loukas, et al.
Pubblicazione: (2026)

PXGen: A Post-hoc Explainable Method for Generative Models
di: Huang, Yen-Lung, et al.
Pubblicazione: (2025)

Fractal Graph Contrastive Learning
di: Li, Nero Z., et al.
Pubblicazione: (2025)