163 citations · 163 across the 5 of their papers we have counts for
5 papers
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…
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…
Learning based on neurovectors for tabular data: a new neural network approach
J. C. Husillos, A. Gallego, A. Roma +1
In this paper, we present a novel learning approach based on Neurovectors, an innovative paradigm that structures information through interconnected nodes and vector relationships…
Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on the COVID-19 propagation model
F. Martínez-Álvarez, G. Asencio-Cortés, J. F. Torres +6
A novel bioinspired metaheuristic is proposed in this work, simulating how the coronavirus spreads and infects healthy people. From an initial individual (the patient zero), the co…