476 citations · 492 across the 5 of their papers we have counts for
5 papers
Instructing the Architecture Search for Spatial-temporal Sequence Forecasting with LLM
Xin Xue, Haoyi Zhou, Tianyu Chen +3
Spatial-temporal sequence forecasting (STSF) is a long-standing research problem with widespread real-world applications. Neural architecture search (NAS), which automates the neur…
LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting
Md Kowsher, Md. Shohanur Islam Sobuj, Nusrat Jahan Prottasha +3
Time series forecasting remains a challenging task, particularly in the context of complex multiscale temporal patterns. This study presents LLM-Mixer, a framework that improves fo…
Learning Music Sequence Representation from Text Supervision
Tianyu Chen, Yuan Xie, Shuai Zhang +3
Music representation learning is notoriously difficult for its complex human-related concepts contained in the sequence of numerical signals. To excavate better MUsic SEquence Repr…
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Haoyi Zhou, Shanghang Zhang, Jieqi Peng +4
Many real-world applications require the prediction of long sequence time-series, such as electricity consumption planning. Long sequence time-series forecasting (LSTF) demands a h…
Stacked Kernel Network
Shuai Zhang, Jianxin Li, Pengtao Xie +4
Kernel methods are powerful tools to capture nonlinear patterns behind data. They implicitly learn high (even infinite) dimensional nonlinear features in the Reproducing Kernel Hil…