Tanachat Nilanon

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We study the problem of learning domain invariant representations for time series data while transferring the complex temporal latent dependencies between domains. Our model termed as Variational Recurrent Adversarial Deep Domain Adaptation (VRADA) is built atop a variational recurrent neural network (VRNN) and trains adversarially to capture complex(More)
Data-driven machine learning, in particular deep learning, is improving state-ofthe-art in many healthcare prediction tasks. A current standard protocol is to collect patient data to build, evaluate, and deploy machine learning algorithms for specific age groups (say source domain), which, if not properly trained, can perform poorly on data from other age(More)
As part of the PhysioNet / Computing in Cardiology Challenge 2016, this work focuses on automatic classification of normal / abnormal phonocardiogram (PCG) recording, with the aim of quickly identifying subjects that need further expert diagnosis. To improve the robustness of the classifiers by increasing the number of training samples, the recordings were(More)
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