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pro vyhledávání: '"Piao, Xihao"'
Recent normalization-based methods have shown great success in tackling the distribution shift issue, facilitating non-stationary time series forecasting. Since these methods operate in the time domain, they may fail to fully capture the dynamic patt
Externí odkaz:
http://arxiv.org/abs/2410.01860
Machine learning has shown great potential in the field of cancer multi-omics studies, offering incredible opportunities for advancing precision medicine. However, the challenges associated with dataset curation and task formulation pose significant
Externí odkaz:
http://arxiv.org/abs/2409.02143
Autor:
Piao, Xihao, Gao, Pei, Chen, Zheng, Zhu, Lingwei, Matsubara, Yasuko, Sakurai, Yasushi, Sun, Jimeng
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 effectively utilizing such data: (1) modeling the relationship between
Externí odkaz:
http://arxiv.org/abs/2408.09410
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 overlook high-frequency features, showing a frequency bias. This bias pr
Externí odkaz:
http://arxiv.org/abs/2406.09009