Imagen de Portada

Interpreting Inflammation Prediction Model via Tag-based Cohort Explanation

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Meng, Fanyu, Larke, Jules, Liu, Xin, Kong, Zhaodan, Chen, Xin, Lemay, Danielle, Tagkopoulos, Ilias
Formato:	Preprint
Publicado:	2024
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

CohEx: A Generalized Framework for Cohort Explanation
por: Meng, Fanyu, et al.
Publicado: (2024)

SkillFlow: Scalable and Efficient Agent Skill Retrieval System
por: Li, Fangzhou, et al.
Publicado: (2025)

Translating Dietary Standards into Healthy Meals with Minimal Substitutions
por: Chan, Trevor, et al.
Publicado: (2026)

Implet: A Post-hoc Subsequence Explainer for Time Series Models
por: Meng, Fanyu, et al.
Publicado: (2025)

Explanation Space: A New Perspective into Time Series Interpretability
por: Rezaei, Shahbaz, et al.
Publicado: (2024)

Revitalizing Black-Box Interpretability: Actionable Interpretability for LLMs via Proxy Models
por: Liu, Junhao, et al.
Publicado: (2025)

Explanation-Guided Adversarial Training for Robust and Interpretable Models
por: Chen, Chao, et al.
Publicado: (2026)

Interpreting Language Reward Models via Contrastive Explanations
por: Jiang, Junqi, et al.
Publicado: (2024)

The Future of Food: How Artificial Intelligence is Transforming Food Manufacturing
por: Zhou, Xu, et al.
Publicado: (2025)

Benchmarking Counterfactual Interpretability in Deep Learning Models for Time Series Classification
por: Kan, Ziwen, et al.
Publicado: (2024)

Causality-Aware Local Interpretable Model-Agnostic Explanations
por: Cinquini, Martina, et al.
Publicado: (2022)

SHapley Estimated Explanation (SHEP): A Fast Post-Hoc Attribution Method for Interpreting Intelligent Fault Diagnosis
por: Chen, Qian, et al.
Publicado: (2025)

Black Box Model Explanations and the Human Interpretability Expectations -- An Analysis in the Context of Homicide Prediction
por: Ribeiro, José, et al.
Publicado: (2022)

From Explainability to Interpretability: Interpretable Policies in Reinforcement Learning Via Model Explanation
por: Li, Peilang, et al.
Publicado: (2025)

LLMCheckup: Conversational Examination of Large Language Models via Interpretability Tools and Self-Explanations
por: Wang, Qianli, et al.
Publicado: (2024)

An Interpretable Alternative to Neural Representation Learning for Rating Prediction -- Transparent Latent Class Modeling of User Reviews
por: Serra, Giuseppe, et al.
Publicado: (2024)

Faithful and Stable Neuron Explanations for Trustworthy Mechanistic Interpretability
por: Yan, Ge, et al.
Publicado: (2025)

Seismic Acoustic Impedance Inversion Framework Based on Conditional Latent Generative Diffusion Model
por: Chen, Jie, et al.
Publicado: (2025)

SUNY: A Visual Interpretation Framework for Convolutional Neural Networks from a Necessary and Sufficient Perspective
por: Xuan, Xiwei, et al.
Publicado: (2023)

Are We Merely Justifying Results ex Post Facto? Quantifying Explanatory Inversion in Post-Hoc Model Explanations
por: Tan, Zhen, et al.
Publicado: (2025)

Towards Interpretable Drug-Drug Interaction Prediction: A Graph-Based Approach with Molecular and Network-Level Explanations
por: Chen, Mengjie, et al.
Publicado: (2025)

DEM: A Distilled Explanation Model for Interpretable Anomaly Detection in Physiological Sensor Networks
por: Singh, Jyotirmoy, et al.
Publicado: (2026)

Provable Robust Saliency-based Explanations
por: Chen, Chao, et al.
Publicado: (2022)

Counterfactual Explanations for Deep Learning-Based Traffic Forecasting
por: Wang, Rushan, et al.
Publicado: (2024)

Interpretable Diffusion via Information Decomposition
por: Kong, Xianghao, et al.
Publicado: (2023)

Towards Consistent Natural-Language Explanations via Explanation-Consistency Finetuning
por: Chen, Yanda, et al.
Publicado: (2024)

Hyperparameter Optimisation with Practical Interpretability and Explanation Methods in Probabilistic Curriculum Learning
por: Salt, Llewyn, et al.
Publicado: (2025)

A New Approach to Backtracking Counterfactual Explanations: A Unified Causal Framework for Efficient Model Interpretability
por: Fatemi, Pouria, et al.
Publicado: (2025)

Learning Model Agnostic Explanations via Constraint Programming
por: Koriche, Frederic, et al.
Publicado: (2024)

DPCformer: An Interpretable Deep Learning Model for Genomic Prediction in Crops
por: Deng, Pengcheng, et al.
Publicado: (2025)

Ensured: Explanations for Decreasing the Epistemic Uncertainty in Predictions
por: Löfström, Helena, et al.
Publicado: (2024)

Beyond One-Size-Fits-All: Adaptive Subgraph Denoising for Zero-Shot Graph Learning with Large Language Models
por: Li, Fengzhi, et al.
Publicado: (2026)

ST-TGExplainer: Disentangling Stability and Transition Patterns for Temporal GNN Interpretability
por: Chen, Hongjiang, et al.
Publicado: (2026)

EVO-LRP: Evolutionary Optimization of LRP for Interpretable Model Explanations
por: Zhang, Emerald, et al.
Publicado: (2025)

A Positive Case for Faithfulness: LLM Self-Explanations Help Predict Model Behavior
por: Mayne, Harry, et al.
Publicado: (2026)

GCFX: Generative Counterfactual Explanations for Deep Graph Models at the Model Level
por: Hu, Jinlong, et al.
Publicado: (2026)

Hierarchical Concept-based Interpretable Models
por: Hill, Oscar, et al.
Publicado: (2026)

Graph Neural Network Causal Explanation via Neural Causal Models
por: Behnam, Arman, et al.
Publicado: (2024)

DLM-Scope: Mechanistic Interpretability of Diffusion Language Models via Sparse Autoencoders
por: Wang, Xu, et al.
Publicado: (2026)

Learning Safety Constraints for Large Language Models
por: Chen, Xin, et al.
Publicado: (2025)