Search Results for author:
Found 4 papers, 1 papers with code
Bayesian Federated Neural Matching that Completes Full Information
Federated learning is a contemporary machine learning paradigm where locally trained models are distilled into a global model.
Capsule networks with non-iterative cluster routing
In the proposed cluster routing, capsules produce vote clusters instead of individual votes for next-layer capsules, and each vote cluster sends its centroid to a next-layer capsule.
Probabilistic Federated Learning of Neural Networks Incorporated with Global Posterior Information
In federated learning, models trained on local clients are distilled into a global model.
Neural Network for NILM Based on Operational State Change Classification
Energy disaggregation in a non-intrusive way estimates appliance level electricity consumption from a single meter that measures the whole house electricity demand.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
