, a professor with UBC Computer Science and his co-authors, grad student and senior data scientist , have a paper accepted at the upcoming .
Generating multivariate time series with COmmon Source CoordInated GAN (COSCI-GAN), Ali Seyfi, Jean-Francois Rajotte, Raymond T. Ng
What is COSCI-GAN?
COSCI-GAN stands for "Common Source Coordinated GAN."
Rajotte explains, “In our collaboration at the Data Science Institute, we were exploring improved ways of generating matched synthetic data features.”
He said they realized there is a lack of methodology for ensuring matching data features. In the health domain for example, if one has multivariate data from a heartbeat feature and a respiratory feature, it’s not easy to tell if they come from the same source; that is, the same person.
When researchers are creating synthetic data (in order to protect private data sources), they wish to ensure similar characteristics between multiple features, to ensure the data from these two connected sources are correlated. In this health case, that would mean they would be able to create realistic heartrate and respiration data from the same person.
“We decided to generate signals individually, using a Generative Adversarial Network, or GAN for short,” said Rajotte.
In total, the department has 13 accepted papers by 9 professors at the NeurIPS conference. Read more about the .
More about Dr. Raymond Ng and his research
More about Jean Francois Rajotte
More about Ali Seyfi