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Found 3 papers, 2 papers with code
Unsupervised Multi-modal Feature Alignment for Time Series Representation Learning
In contrast to conventional methods that fuse features from multiple modalities, our proposed approach simplifies the neural architecture by retaining a single time series encoder, consequently leading to preserved scalability.
Duet: efficient and scalable hybriD neUral rElation undersTanding
Although both data-driven and hybrid methods are proposed to avoid this problem, most of them suffer from high training and estimation costs, limited scalability, instability, and long-tail distribution problems on high-dimensional tables, which seriously affects the practical application of learned cardinality estimators.
TodyNet: Temporal Dynamic Graph Neural Network for Multivariate Time Series Classification
Unfortunately, the existing deep learning-based methods neglect the hidden dependencies in different dimensions and also rarely consider the unique dynamic features of time series, which lack sufficient feature extraction capability to obtain satisfactory classification accuracy.
