This week we presented two of our papers at ICML 2020, it was a great experience to talk with others about our research and to think of future directions and applications of our methods. I want to use this page to point out reference materials for these publications.
Set Functions for Time Series
In the core we propose to rephrase learning on irregularly sampled time series data as a set classification problem. This mitigates the necessity of imputing time series prior to the application of Deep Learning models and allows their direct application. For further details please see the links below:
In this work we propose to constrain the topology of the latent representation of an autoencoder using methods from topological data analysis. Michael wrote a wonderful blog post about the paper giving an intuitive introduction here. Below you can see our devised approach in action compared to a vanilla autoencoder.