Cover Image

Multimodal normative modeling in Alzheimers Disease with introspective variational autoencoders

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kumar, Sayantan, Qiu, Peijie, Sotiras, Aristeidis
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Improving Normative Modeling for Multi-modal Neuroimaging Data using mixture-of-product-of-experts variational autoencoders
by: Kumar, Sayantan, et al.
Published: (2023)

Normative Modeling using Multimodal Variational Autoencoders to Identify Abnormal Brain Structural Patterns in Alzheimer Disease
by: Kumar, Sayantan, et al.
Published: (2021)

SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classification
by: Qiu, Peijie, et al.
Published: (2023)

How Effective Can Dropout Be in Multiple Instance Learning ?
by: Zhu, Wenhui, et al.
Published: (2025)

Analyzing heterogeneity in Alzheimer Disease using multimodal normative modeling on imaging-based ATN biomarkers
by: Kumar, Sayantan, et al.
Published: (2024)

Multimodal Variational Autoencoder: a Barycentric View
by: Qiu, Peijie, et al.
Published: (2024)

Physics-integrated generative modeling using attentive planar normalizing flow based variational autoencoder
by: Akhtar, Sheikh Waqas
Published: (2024)

Metabolic cost of information processing in Poisson variational autoencoders
by: Vafaii, Hadi, et al.
Published: (2026)

Inferring response times of perceptual decisions with Poisson variational autoencoders
by: Johnson, Hayden R., et al.
Published: (2025)

Variational autoencoder-based neural network model compression
by: Cheng, Liang, et al.
Published: (2024)

Applying sparse autoencoders to unlearn knowledge in language models
by: Farrell, Eoin, et al.
Published: (2024)

CAVACHON: a hierarchical variational autoencoder to integrate multi-modal single-cell data
by: Hsieh, Ping-Han, et al.
Published: (2024)

Scaling and evaluating sparse autoencoders
by: Gao, Leo, et al.
Published: (2024)

QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality Prediction
by: Qiu, Peijie, et al.
Published: (2024)

Text Knows What, Tables Know When: Clinical Timeline Reconstruction via Retrieval-Augmented Multimodal Alignment
by: Kumar, Sayantan, et al.
Published: (2026)

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

Denoising diffusion networks for normative modeling in neuroimaging
by: Whitbread, Luke, et al.
Published: (2026)

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

Reinforcing privacy reasoning in LLMs via normative simulacra from fiction
by: Franchi, Matt, et al.
Published: (2026)

An attempt to generate new bridge types from latent space of variational autoencoder
by: Zhang, Hongjun
Published: (2023)

Remote sensing framework for geological mapping via stacked autoencoders and clustering
by: Nagar, Sandeep, et al.
Published: (2024)

Towards efficient deep autoencoders for multivariate time series anomaly detection
by: Pietroń, Marcin, et al.
Published: (2024)

TimeMIL: Advancing Multivariate Time Series Classification via a Time-aware Multiple Instance Learning
by: Chen, Xiwen, et al.
Published: (2024)

Sequence Complementor: Complementing Transformers For Time Series Forecasting with Learnable Sequences
by: Chen, Xiwen, et al.
Published: (2025)

A practical existence theorem for reduced order models based on convolutional autoencoders
by: Franco, Nicola Rares, et al.
Published: (2024)

Can ChatGPT Diagnose Alzheimer's Disease?
by: Nguyen, Quoc-Toan, et al.
Published: (2025)

SincVAE: A new semi-supervised approach to improve anomaly detection on EEG data using SincNet and variational autoencoder
by: Pollastro, Andrea, et al.
Published: (2024)

AgileFormer: Spatially Agile Transformer UNet for Medical Image Segmentation
by: Qiu, Peijie, et al.
Published: (2024)

Multi-view autoencoders for Fake News Detection
by: Pereira, Ingryd V. S. T., et al.
Published: (2025)

Discretization of continuous input spaces in the hippocampal autoencoder
by: Amil, Adrian F., et al.
Published: (2024)

Transformer autoencoder with local attention for sparse and irregular time series with application on risk estimation
by: Rodis, Panteleimon
Published: (2026)

$Γ$-VAE: Curvature regularized variational autoencoders for uncovering emergent low dimensional geometric structure in high dimensional data
by: Kim, Jason Z., et al.
Published: (2024)

Temporal Subtyping of Alzheimer's Disease Using Medical Conditions Preceding Alzheimer's Disease Onset in Electronic Health Records
by: He, Zhe, et al.
Published: (2022)

Enhancing SHAP Explainability for Diagnostic and Prognostic ML Models in Alzheimer Disease
by: Guillén, Pablo, et al.
Published: (2026)

Can sparse autoencoders be used to decompose and interpret steering vectors?
by: Mayne, Harry, et al.
Published: (2024)

FIC-TSC: Learning Time Series Classification with Fisher Information Constraint
by: Chen, Xiwen, et al.
Published: (2025)

LearnAD: Learning Interpretable Rules for Brain Networks in Alzheimer's Disease Classification
by: Andrews, Thomas, et al.
Published: (2025)

PHGNN: A Novel Prompted Hypergraph Neural Network to Diagnose Alzheimer's Disease
by: Liu, Chenyu, et al.
Published: (2025)

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

MRC-GAT: A Meta-Relational Copula-Based Graph Attention Network for Interpretable Multimodal Alzheimer's Disease Diagnosis
by: Khalvandi, Fatemeh, et al.
Published: (2026)