Saved in:
| Main Authors: | Ali, Raja Farrukh, Milani, Stephanie, Woods, John, Adenij, Emmanuel, Farooq, Ayesha, Mansel, Clayton, Burns, Jeffrey, Hsu, William |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.07777 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Transformer Model for Alzheimer's Disease Progression Prediction Using Longitudinal Visit Sequences
by: Moghaddami, Mahdi, et al.
Published: (2025)
by: Moghaddami, Mahdi, et al.
Published: (2025)
Flexible Multimodal Neuroimaging Fusion for Alzheimer's Disease Progression Prediction
by: Burns, Benjamin, et al.
Published: (2025)
by: Burns, Benjamin, et al.
Published: (2025)
An Explainable Disease Surveillance System for Early Prediction of Multiple Chronic Diseases
by: Khan, Shaheer Ahmad, et al.
Published: (2025)
by: Khan, Shaheer Ahmad, et al.
Published: (2025)
Defining the Problem History Heterogeneity in Patients with Alzheimer’s Disease: A Data‐Driven Approach
by: Clayton Mansel, et al.
Published: (2024)
by: Clayton Mansel, et al.
Published: (2024)
LICORICE: Label-Efficient Concept-Based Interpretable Reinforcement Learning
by: Ye, Zhuorui, et al.
Published: (2024)
by: Ye, Zhuorui, et al.
Published: (2024)
Explainable Graph-theoretical Machine Learning: with Application to Alzheimer's Disease Prediction
by: Baghirova, Narmina, et al.
Published: (2025)
by: Baghirova, Narmina, et al.
Published: (2025)
Longitudinal Progression Prediction of Alzheimer's Disease with Tabular Foundation Model
by: Ding, Yilang, et al.
Published: (2025)
by: Ding, Yilang, et al.
Published: (2025)
An Adaptive Machine Learning Triage Framework for Predicting Alzheimer's Disease Progression
by: Hou, Richard, et al.
Published: (2025)
by: Hou, Richard, et al.
Published: (2025)
Graph-Based Biomarker Discovery and Interpretation for Alzheimer's Disease
by: Khalid, Maryam, et al.
Published: (2024)
by: Khalid, Maryam, et al.
Published: (2024)
R-GenIMA: Integrating Neuroimaging and Genetics with Interpretable Multimodal AI for Alzheimer's Disease Progression
by: Zhao, Kun, et al.
Published: (2025)
by: Zhao, Kun, et al.
Published: (2025)
How Will My Business Process Unfold? Predicting Case Suffixes With Start and End Timestamps
by: Ali, Muhammad Awais, et al.
Published: (2025)
by: Ali, Muhammad Awais, et al.
Published: (2025)
Flexible Multimodal Fusion of Neuroimaging Modalities for Alzheimer's Disease Progression Prediction
by: Benjamin Burns, et al.
Published: (2025)
by: Benjamin Burns, et al.
Published: (2025)
Pathways to the Present
by: Blackford, Mansel G.
Published: (2018)
by: Blackford, Mansel G.
Published: (2018)
The Role of ATP Bioluminescence in the Food Industry: New Light on Old Problems
by: Griffiths W. Mansel
Published: (1996)
by: Griffiths W. Mansel
Published: (1996)
Multi-Task Learning with Feature-Similarity Laplacian Graphs for Predicting Alzheimer's Disease Progression
by: Xu, Zixiang, et al.
Published: (2025)
by: Xu, Zixiang, et al.
Published: (2025)
Unified Binary and Multiclass Margin-Based Classification
by: Wang, Yutong, et al.
Published: (2023)
by: Wang, Yutong, et al.
Published: (2023)
Selecting Decision-Relevant Concepts in Reinforcement Learning
by: Raman, Naveen, et al.
Published: (2026)
by: Raman, Naveen, et al.
Published: (2026)
An Explainable Ensemble Framework for Alzheimer's Disease Prediction Using Structured Clinical and Cognitive Data
by: Mitra, Nishan
Published: (2026)
by: Mitra, Nishan
Published: (2026)
Interpretable-by-Design Transformers via Architectural Stream Independence
by: Kerce, Clayton, et al.
Published: (2026)
by: Kerce, Clayton, et al.
Published: (2026)
Tabular LLMs for Interpretable Few-Shot Alzheimer's Disease Prediction with Multimodal Biomedical Data
by: Kearney, Sophie, et al.
Published: (2026)
by: Kearney, Sophie, et al.
Published: (2026)
Transformers, parallel computation, and logarithmic depth
by: Sanford, Clayton, et al.
Published: (2024)
by: Sanford, Clayton, et al.
Published: (2024)
One-layer transformers fail to solve the induction heads task
by: Sanford, Clayton, et al.
Published: (2024)
by: Sanford, Clayton, et al.
Published: (2024)
Model Interpretation and Explainability: Towards Creating Transparency in Prediction Models
by: Kridel, Donald, et al.
Published: (2024)
by: Kridel, Donald, et al.
Published: (2024)
Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability
by: Zhang, Shichang, et al.
Published: (2025)
by: Zhang, Shichang, et al.
Published: (2025)
A Deep Learning Architectures for Kidney Disease Classification
by: Farooq, Muhammad Shoaib, et al.
Published: (2024)
by: Farooq, Muhammad Shoaib, et al.
Published: (2024)
Unified Multimodal Vessel Trajectory Prediction with Explainable Navigation Intention
by: Zhang, Rui, et al.
Published: (2025)
by: Zhang, Rui, et al.
Published: (2025)
Enhancing SHAP Explainability for Diagnostic and Prognostic ML Models in Alzheimer Disease
by: Guillén, Pablo, et al.
Published: (2026)
by: Guillén, Pablo, et al.
Published: (2026)
Flexible and Explainable Graph Analysis for EEG-based Alzheimer's Disease Classification
by: Wang, Jing, et al.
Published: (2025)
by: Wang, Jing, et al.
Published: (2025)
ByteStack-ID: Integrated Stacked Model Leveraging Payload Byte Frequency for Grayscale Image-based Network Intrusion Detection
by: Khan, Irfan, et al.
Published: (2023)
by: Khan, Irfan, et al.
Published: (2023)
Towards Interpretable End-Stage Renal Disease (ESRD) Prediction: Utilizing Administrative Claims Data with Explainable AI Techniques
by: Li, Yubo, et al.
Published: (2024)
by: Li, Yubo, et al.
Published: (2024)
An Explainable Transformer Model for Alzheimer's Disease Detection Using Retinal Imaging
by: Jamshidiha, Saeed, et al.
Published: (2025)
by: Jamshidiha, Saeed, et al.
Published: (2025)
MABL: Bi-Level Latent-Variable World Model for Sample-Efficient Multi-Agent Reinforcement Learning
by: Venugopal, Aravind, et al.
Published: (2023)
by: Venugopal, Aravind, et al.
Published: (2023)
The Dual-Stream Transformer: Channelized Architecture for Interpretable Language Modeling
by: Kerce, J. Clayton, et al.
Published: (2026)
by: Kerce, J. Clayton, et al.
Published: (2026)
Efficient and Interpretable Traffic Destination Prediction using Explainable Boosting Machines
by: Yousif, Yasin, et al.
Published: (2024)
by: Yousif, Yasin, et al.
Published: (2024)
Adaptive Norm-Based Regularization for Neural Networks
by: Qasim, Muhammad, et al.
Published: (2026)
by: Qasim, Muhammad, et al.
Published: (2026)
Artificial Intelligence for Personalized Prediction of Alzheimer's Disease Progression: A Survey of Methods, Data Challenges, and Future Directions
by: Koksalmis, Gulsah Hancerliogullari, et al.
Published: (2025)
by: Koksalmis, Gulsah Hancerliogullari, et al.
Published: (2025)
Explainable AI-Guided Efficient Approximate DNN Generation for Multi-Pod Systolic Arrays
by: Siddique, Ayesha, et al.
Published: (2025)
by: Siddique, Ayesha, et al.
Published: (2025)
Distinct medical and substance use histories associate with cognitive decline in Alzheimer's disease
by: Clayton Mansel, et al.
Published: (2025)
by: Clayton Mansel, et al.
Published: (2025)
Survival Meets Classification: A Novel Framework for Early Risk Prediction Models of Chronic Diseases
by: Khan, Shaheer Ahmad, et al.
Published: (2026)
by: Khan, Shaheer Ahmad, et al.
Published: (2026)
LearnAD: Learning Interpretable Rules for Brain Networks in Alzheimer's Disease Classification
by: Andrews, Thomas, et al.
Published: (2025)
by: Andrews, Thomas, et al.
Published: (2025)
Similar Items
-
Transformer Model for Alzheimer's Disease Progression Prediction Using Longitudinal Visit Sequences
by: Moghaddami, Mahdi, et al.
Published: (2025) -
Flexible Multimodal Neuroimaging Fusion for Alzheimer's Disease Progression Prediction
by: Burns, Benjamin, et al.
Published: (2025) -
An Explainable Disease Surveillance System for Early Prediction of Multiple Chronic Diseases
by: Khan, Shaheer Ahmad, et al.
Published: (2025) -
Defining the Problem History Heterogeneity in Patients with Alzheimer’s Disease: A Data‐Driven Approach
by: Clayton Mansel, et al.
Published: (2024) -
LICORICE: Label-Efficient Concept-Based Interpretable Reinforcement Learning
by: Ye, Zhuorui, et al.
Published: (2024)