Wenjie Pei

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We present a new model for multivariate time-series classification, called the hidden-unit logistic model (HULM), that uses binary stochastic hidden units to model latent structure in the data. The hidden units are connected in a chain structure that models temporal dependencies in the data. Compared with the prior models for time-series classification such(More)
Traditional techniques for measuring similarities between time series are based on hand-crafted similarity measures, whereas more recent learning-based approaches cannot exploit external supervision. We combine ideas from time-series modeling and metric learning, and study siamese recurrent networks (SRNs) that minimize a classification loss to learn a good(More)
Typical techniques for sequence classification are designed for well-segmented sequences which has been edited to remove noisy or irrelevant parts. Therefore, such methods cannot be easily applied on noisy sequences which are expected in real-world applications. We present the Temporal Attention-Gated Model (TAGM) which is able to deal with noisy sequences.(More)
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