3 papers
cs.AI2026
Meta-Continual Learning of Neural Fields
Seungyoon Woo, Junhyeog Yun, Gunhee Kim
Neural Fields (NF) have gained prominence as a versatile framework for complex data representation. This work unveils a new problem setting termed \emph{Meta-Continual Learning of…
cs.LG2025
Federated Learning via Meta-Variational Dropout
Insu Jeon, Minui Hong, Junhyeog Yun +1
Federated Learning (FL) aims to train a global inference model from remotely distributed clients, gaining popularity due to its benefit of improving data privacy. However, traditio…
cs.LG2025
FedMeNF: Privacy-Preserving Federated Meta-Learning for Neural Fields
Junhyeog Yun, Minui Hong, Gunhee Kim
Neural fields provide a memory-efficient representation of data, which can effectively handle diverse modalities and large-scale data. However, learning to map neural fields often…