Generative adversarial model for clustering. This model is derived from a KL Divergence perspective between inference and generative distributions.
Many Generative models have an infertence counter part. In this work we explore how they are formulated. We also studied their representation learning and generative capabilities theoretically and empirically.
Many Generative models have an infertence counter part. In this work we explore how they are formulated. We also studied their representation learning and generative capabilities theoretically and empirically.
Amortized Gaussian Process is a model that amortizes the computation of a Gaussian Process. With this model we can use autoencoder for time series with variable length and irregular sampling.
We present LGAd, a variational autoencoder for anomaly detection in time series. Anomaly detection is fundamental task in astroinformatics that could allows us to find new astronomical objects not observed in the past.
