45 citations · 153 across the 9 of their papers we have counts for
5 papers · 1 filter
GRP-FED: Addressing Client Imbalance in Federated Learning via Global-Regularized Personalization
Yen-Hsiu Chou, Shenda Hong, Chenxi Sun +3
Since data is presented long-tailed in reality, it is challenging for Federated Learning (FL) to train across decentralized clients as practical applications. We present Global-Reg…
TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Data
Chenxi Sun, Shenda Hong, Moxian Song +4
Prediction based on Irregularly Sampled Time Series (ISTS) is of wide concern in the real-world applications. For more accurate prediction, the methods had better grasp more data c…
A Review of Designs and Applications of Echo State Networks
Chenxi Sun, Moxian Song, Shenda Hong +1
Recurrent Neural Networks (RNNs) have demonstrated their outstanding ability in sequence tasks and have achieved state-of-the-art in wide range of applications, such as industrial,…
A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data
Chenxi Sun, Shenda Hong, Moxian Song +1
Irregularly sampled time series (ISTS) data has irregular temporal intervals between observations and different sampling rates between sequences. ISTS commonly appears in healthcar…
RDPD: Rich Data Helps Poor Data via Imitation
Shenda Hong, Cao Xiao, Trong Nghia Hoang +3
In many situations, we need to build and deploy separate models in related environments with different data qualities. For example, an environment with strong observation equipment…