108 citations · 110 across the 13 of their papers we have counts for
7 papers · 1 filter
Long-Term EEG Partitioning for Seizure Onset Detection
Zheng Chen, Yasuko Matsubara, Yasushi Sakurai +1
Deep learning models have recently shown great success in classifying epileptic patients using EEG recordings. Unfortunately, classification-based methods lack a sound mechanism to…
FredNormer: Frequency Domain Normalization for Non-stationary Time Series Forecasting
Xihao Piao, Zheng Chen, Yushun Dong +2
Recent normalization-based methods have shown great success in tackling the distribution shift issue, facilitating non-stationary time series forecasting. Since these methods opera…
SplitSEE: A Splittable Self-supervised Framework for Single-Channel EEG Representation Learning
Rikuto Kotoge, Zheng Chen, Tasuku Kimura +4
While end-to-end multi-channel electroencephalography (EEG) learning approaches have shown significant promise, their applicability is often constrained in neurological diagnostics…
GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation
Ziwei Yang, Zheng Chen, Xin Liu +5
Retrieving gene functional networks from knowledge databases presents a challenge due to the mismatch between disease networks and subtype-specific variations. Current solutions, i…
BernGraph: Probabilistic Graph Neural Networks for EHR-based Medication Recommendations
Xihao Piao, Pei Gao, Zheng Chen +4
The medical community believes binary medical event outcomes in EHR data contain sufficient information for making a sensible recommendation. However, there are two challenges to e…
MLOmics: Cancer Multi-Omics Database for Machine Learning
Ziwei Yang, Rikuto Kotoge, Xihao Piao +6
Framing the investigation of diverse cancers as a machine learning problem has recently shown significant potential in multi-omics analysis and cancer research. Empowering these su…
Fredformer: Frequency Debiased Transformer for Time Series Forecasting
Xihao Piao, Zheng Chen, Taichi Murayama +2
The Transformer model has shown leading performance in time series forecasting. Nevertheless, in some complex scenarios, it tends to learn low-frequency features in the data and ov…