Cover Image

LearnAD: Learning Interpretable Rules for Brain Networks in Alzheimer's Disease Classification

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Andrews, Thomas, Law, Mark, Ahmadi-Abhari, Sara, Russo, Alessandra
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

The Role of Foundation Models in Neuro-Symbolic Learning and Reasoning
by: Cunnington, Daniel, et al.
Published: (2024)

Learning and Enforcing Context-Sensitive Control for LLMs
by: Albinhassan, Mohammad, et al.
Published: (2026)

Deep Unlearn: Benchmarking Machine Unlearning for Image Classification
by: Cadet, Xavier F., et al.
Published: (2024)

A Unifying Framework for Learning Argumentation Semantics
by: Mileva, Zlatina, et al.
Published: (2023)

Failure Detection in Chemical Processes Using Symbolic Machine Learning: A Case Study on Ethylene Oxidation
by: Amblard, Julien, et al.
Published: (2026)

Learning Interpretable Rules for Scalable Data Representation and Classification
by: Wang, Zhuo, et al.
Published: (2023)

Interpretable Classification via a Rule Network with Selective Logical Operators
by: Wei, Bowen, et al.
Published: (2024)

Interpretable Representation Learning for Additive Rule Ensembles
by: Behzadimanesh, Shahrzad, et al.
Published: (2025)

Neural DNF-MT: A Neuro-symbolic Approach for Learning Interpretable and Editable Policies
by: Baugh, Kexin Gu, et al.
Published: (2025)

Refined Causal Graph Structure Learning via Curvature for Brain Disease Classification
by: Febrinanto, Falih Gozi, et al.
Published: (2025)

Brain Network Classification Based on Graph Contrastive Learning and Graph Transformer
by: Zhu, ZhiTeng, et al.
Published: (2025)

Visual Perceptual to Conceptual First-Order Rule Learning Networks
by: Gao, Kun, et al.
Published: (2026)

ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning
by: Sethi, Sahil, et al.
Published: (2025)

BrainHGT: A Hierarchical Graph Transformer for Interpretable Brain Network Analysis
by: Ma, Jiajun, et al.
Published: (2025)

Not Only Grey Matter: OmniBrain for Robust Multimodal Classification of Alzheimer's Disease
by: Sharshar, Ahmed, et al.
Published: (2025)

Learning Robust Reward Machines from Noisy Labels
by: Parac, Roko, et al.
Published: (2024)

Deep Convolutional Neural Networks on Multiclass Classification of Three-Dimensional Brain Images for Parkinson's Disease Stage Prediction
by: Huang, Guan-Hua, et al.
Published: (2024)

Inherently Interpretable Time Series Classification via Multiple Instance Learning
by: Early, Joseph, et al.
Published: (2023)

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

Incorporating Expert Rules into Neural Networks in the Framework of Concept-Based Learning
by: Konstantinov, Andrei V., et al.
Published: (2024)

Deep Reinforcement Learning for Inventory Networks: Toward Reliable Policy Optimization
by: Alvo, Matias, et al.
Published: (2023)

The Interpretability of Codebooks in Model-Based Reinforcement Learning is Limited
by: Eaton, Kenneth, et al.
Published: (2024)

DB-FGA-Net: Dual Backbone Frequency Gated Attention Network for Multi-Class Brain Tumor Classification with Grad-CAM Interpretability
by: Shreya, Saraf Anzum, et al.
Published: (2025)

Interpretable Hybrid-Rule Temporal Point Processes
by: Cao, Yunyang, et al.
Published: (2025)

AttnGen: Attention-Guided Saliency Learning for Interpretable Genomic Sequence Classification
by: Nia, Rayhaneh Shabani, et al.
Published: (2026)

Learning Covariance-Based Multi-Scale Representation of Neuroimaging Measures for Alzheimer Classification
by: Baek, Seunghun, et al.
Published: (2025)

Understanding Expressivity of GNN in Rule Learning
by: Qiu, Haiquan, et al.
Published: (2023)

R-GenIMA: Integrating Neuroimaging and Genetics with Interpretable Multimodal AI for Alzheimer's Disease Progression
by: Zhao, Kun, et al.
Published: (2025)

Prism: Policy Reuse via Interpretable Strategy Mapping in Reinforcement Learning
by: Pravetz, Thomas
Published: (2026)

Dynamic Interpretability for Model Comparison via Decision Rules
by: Rida, Adam, et al.
Published: (2023)

Stroke Disease Classification Using Machine Learning with Feature Selection Techniques
by: Hasan, Mahade, et al.
Published: (2025)

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression
by: Montazerin, Mansooreh, et al.
Published: (2025)

In-Context Learning for Pure Exploration
by: Russo, Alessio, et al.
Published: (2025)

Mechanistic Interpretability for Transformer-based Time Series Classification
by: Kalnāre, Matīss, et al.
Published: (2025)

Surrogate Fitness Metrics for Interpretable Reinforcement Learning
by: Altmann, Philipp, et al.
Published: (2025)

Multi-Task Learning with Feature-Similarity Laplacian Graphs for Predicting Alzheimer's Disease Progression
by: Xu, Zixiang, et al.
Published: (2025)

A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases
by: Wang, Zhepeng, et al.
Published: (2024)

Neuro-Symbolic Learning of Answer Set Programs from Raw Data
by: Cunnington, Daniel, et al.
Published: (2022)

Adversarial Diffusion for Robust Reinforcement Learning
by: Foffano, Daniele, et al.
Published: (2025)

Network-Aware Bilinear Tokenization for Brain Functional Connectivity Representation Learning
by: Milecki, Leo, et al.
Published: (2026)