4 papers
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…
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…
QuantEvolve: Automating Quantitative Strategy Discovery through Multi-Agent Evolutionary Framework
Junhyeog Yun, Hyoun Jun Lee, Insu Jeon
Automating quantitative trading strategy development in dynamic markets is challenging, especially with increasing demand for personalized investment solutions. Existing methods of…
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…