4 papers · 1 filter
Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learning
Alireza Moayedikia, Alicia Troncoso Lora
Sub-model federated learning lets resource-constrained clients train width-reduced versions of a global model, but existing methods allocate capacity by device resources alone. A n…
Quantifying Gate Contribution in Quantum Feature Maps for Scalable Circuit Optimization
F. RodrÃguez-DÃaz, D. Gutiérrez-Avilés, A. Troncoso +1
Quantum machine learning offers promising advantages for classification tasks, but noise, decoherence, and connectivity constraints in current devices continue to limit the efficie…
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…