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Special TAPIR Seminar

Wednesday, May 22, 2019
2:00pm to 3:00pm
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Cahill 312
Wasserstein Generative Adversarial Network for Time Series Data Augmentation in Astronomy
Pavlos Protopapas, Scientific Program Director and Lectur, Institute for Applied Computational Science, Harvard University,

Real-world datasets are often imbalance which is a problem for training conventional machine learning algorithms. To address the imbalance problem, many data augmentation techniques have been proposed for image recognition tasks, but only a few have been developed for time series. In this talk, I will describe a conditional Wasserstein GAN. Our model can learn the implicit probability distribution of a dataset conditioned on the irregular sampling times, amplitudes and class of the time series and generate a variety of realistic samples to complement the original dataset. We trained and evaluated our model using a pair of toy datasets and a real-world astronomical survey. We then generated realistic samples to augment the original datasets and compared the performance of a classifier trained on the GAN augmented datasets against oversampled datasets and noise-augmented datasets. The resulting generator can be used as any other generative model, allowing interpolations and extrapolations in the parameter space. [NOTE: Unusual venue]

For more information, please contact JoAnn Boyd by phone at x4280 or by email at [email protected].

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TAPIR Seminar Series