Wenjie Pei

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We present a new model for time series classification, called the hidden-unit logistic model, 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 to the prior models for time series classification such as the hidden(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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