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Time Series as Images: Vision Transformer for Irregularly Sampled Time Series
Zekun Li, Shiyang Li, Xifeng Yan
Irregularly sampled time series are increasingly prevalent, particularly in medical domains. While various specialized methods have been developed to handle these irregularities, e…
Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling
Xinlu Zhang, Shiyang Li, Zhiyu Chen +2
Health conditions among patients in intensive care units (ICUs) are monitored via electronic health records (EHRs), composed of numerical time series and lengthy clinical note sequ…
Domain Adaptation for Time Series Forecasting via Attention Sharing
Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix +2
Recently, deep neural networks have gained increasing popularity in the field of time series forecasting. A primary reason for their success is their ability to effectively capture…
Inter-Series Attention Model for COVID-19 Forecasting
Xiaoyong Jin, Yu-Xiang Wang, Xifeng Yan
COVID-19 pandemic has an unprecedented impact all over the world since early 2020. During this public health crisis, reliable forecasting of the disease becomes critical for resour…
Neural Assistant: Joint Action Prediction, Response Generation, and Latent Knowledge Reasoning
Arvind Neelakantan, Semih Yavuz, Sharan Narang +5
Task-oriented dialog presents a difficult challenge encompassing multiple problems including multi-turn language understanding and generation, knowledge retrieval and reasoning, an…
You May Not Need Order in Time Series Forecasting
Yunkai Zhang, Qiao Jiang, Shurui Li +3
Time series forecasting with limited data is a challenging yet critical task. While transformers have achieved outstanding performances in time series forecasting, they often requi…