4 papers
Self-Supervised Siamese Autoencoders
Friederike Baier, Sebastian Mair, Samuel G. Fadel
In contrast to fully-supervised models, self-supervised representation learning only needs a fraction of data to be labeled and often achieves the same or even higher downstream pe…
Principled Interpolation in Normalizing Flows
Samuel G. Fadel, Sebastian Mair, Ricardo da S. Torres +1
Generative models based on normalizing flows are very successful in modeling complex data distributions using simpler ones. However, straightforward linear interpolations show unex…
Personalized Privacy Amplification via Importance Sampling
Dominik Fay, Sebastian Mair, Jens Sjölund
For scalable machine learning on large data sets, subsampling a representative subset is a common approach for efficient model training. This is often achieved through importance s…
Archetypal Analysis++: Rethinking the Initialization Strategy
Sebastian Mair, Jens Sjölund
Archetypal analysis is a matrix factorization method with convexity constraints. Due to local minima, a good initialization is essential, but frequently used initialization methods…