7 papers
Conformal Fusion Under Missing Modalities
Alireza Moayedikia
Multimodal fusion architectures typically assume all modalities are available at inference, yet sensor failures, acquisition variability, and cost constraints routinely produce inc…
Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learning
Alireza Moayedikia, Alicia Troncoso Lora, Alicia Trocoso Lora
Sub-model federated learning allows clients with limited memory and compute to join training by optimising width-reduced slices of a shared global network. Established schemes such…
A Two-Scan Deep Learning Model for Predicting Dementia in Mild Cognitive Impairment
Alireza Moayedikia, Sara Fin, Alicia Troncoso Lora +3
Predicting which people with mild cognitive impairment (MCI) will develop dementia matters for starting treatment early, yet computational work on structural brain imaging has reli…
Dual Model Deep Learning for Alzheimer Prognostication
Alireza Moayedikia, Sara Fin, Uffe Kock Wiil
Disease modifying therapies for Alzheimer's disease demand precise timing decisions, yet current predictive models require longitudinal observations and provide no uncertainty quan…
Attention Fusion for Bridge Deck Delamination Detection
Alireza Moayedikia, Amirhossein Moayedikia
Subsurface delaminations in reinforced concrete bridge decks escape conventional visual inspection, and the two principal sensing techniques used to find them are individually inco…
Alzheimer's Disease Brain Network Mining
Alireza Moayedikia, Sara Fin
Machine learning approaches for Alzheimer's disease (AD) diagnosis face a fundamental challenges. Clinical assessments are expensive and invasive, leaving ground truth labels avail…
Bridging Training and Merging Through Momentum-Aware Optimization
Alireza Moayedikia, Alicia Troncoso
Training large neural networks and merging task-specific models both exploit low-rank structure and require parameter importance estimation, yet these challenges have been pursued…