45 citations · 95 across the 8 of their papers we have counts for
8 papers
Curriculum Design Helps Spiking Neural Networks to Classify Time Series
Chenxi Sun, Hongyan Li, Moxian Song +2
Spiking Neural Networks (SNNs) have a greater potential for modeling time series data than Artificial Neural Networks (ANNs), due to their inherent neuron dynamics and low energy c…
Curricular and Cyclical Loss for Time Series Learning Strategy
Chenxi Sun, Hongyan Li, Moxian Song +2
Time series widely exists in real-world applications and many deep learning models have performed well on it. Current research has shown the importance of learning strategy for mod…
Continuous Diagnosis and Prognosis by Controlling the Update Process of Deep Neural Networks
Chenxi Sun, Hongyan Li, Moxian Song +3
Continuous diagnosis and prognosis are essential for intensive care patients. It can provide more opportunities for timely treatment and rational resource allocation, especially fo…
Confidence-Guided Learning Process for Continuous Classification of Time Series
Chenxi Sun, Moxian Song, Derun Can +3
In the real world, the class of a time series is usually labeled at the final time, but many applications require to classify time series at every time point. e.g. the outcome of a…
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…