45 citations · 153 across the 9 of their papers we have counts for
9 papers
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…
K-margin-based Residual-Convolution-Recurrent Neural Network for Atrial Fibrillation Detection
Yuxi Zhou, Shenda Hong, Junyuan Shang +4
Atrial Fibrillation (AF) is an abnormal heart rhythm which can trigger cardiac arrest and sudden death. Nevertheless, its interpretation is mostly done by medical experts due to hi…
MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals
Shenda Hong, Cao Xiao, Tengfei Ma +2
Electrocardiography (ECG) signals are commonly used to diagnose various cardiac abnormalities. Recently, deep learning models showed initial success on modeling ECG data, however t…
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…
GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination
Junyuan Shang, Cao Xiao, Tengfei Ma +2
Recent progress in deep learning is revolutionizing the healthcare domain including providing solutions to medication recommendations, especially recommending medication combinatio…
Generative Adversarial Network for Abstractive Text Summarization
Linqing Liu, Yao Lu, Min Yang +3
In this paper, we propose an adversarial process for abstractive text summarization, in which we simultaneously train a generative model G and a discriminative model D. In particul…