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How to model sequence data in various settings is an important machine learning problem across many domains, including prediction on time series data, natural language text, and event streams. Sequence data in different domains usually have different characteristics. For example, natural language text can be viewed as a sequence of discrete variables, while sensor network signals can be viewed as a multivariate sequence in a continuous vector space. To develop successful neural network models in so many real-world domains, we need to tailor architectures and algorithms to the nature of the data and problem. This paper designs a novel and efficient neural network solution for sequential modeling and its applications. Specifically, these contributions can be divided into four parts.
https://www.cs.cmu.edu/~glai1/
The first part mainly focuses on the correlation between variables in multivariate sequence data, such as time series of multiple sensors, and proposes a new algorithm to improve prediction accuracy using correlation patterns, namely Deep Separable Graph Convolutional Networks (DSGC) (Chapter 2 ) [60] and Factorization Recurrent Neural Networks (FRNN) (Chapter 3) [63].
The second part focuses on incorporating human prior knowledge in temporal modeling of dependent patterns of time series data. Specifically, we propose a novel method called Long Short-Term Time Series Network (LSTNet) (Chapter 4) [59], which has been shown to be particularly effective in capturing various periodic patterns in different applications.
Section III focuses on efficient algorithms for Transformers in sequence classification tasks. Specifically, by identifying computational redundancy in commonly used Transformer architectures and proposing a new alternative, Funnel Transformers (Chapter 5) [27], we achieve a better trade-off between computation and accuracy.
The fourth section focuses on modeling/predicting temporal relationships between events, where the main challenge is to learn efficiently from sparsely labeled data. We address this challenge by combining advanced data augmentation, semi-supervised learning, and introducing human priors (Chapter 6). Therefore, we greatly improve the state-of-the-art performance on this task.
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