3 citations · 6 across the 9 of their papers we have counts for
5 papers · 1 filter
Deep Multivariate Models with Parametric Conditionals
Dmitrij Schlesinger, Boris Flach, Alexander Shekhovtsov
We consider deep multivariate models for heterogeneous collections of random variables. In the context of computer vision, such collections may e.g. consist of images, segmentation…
Game-theoretic distributed learning of generative models for heterogeneous data collections
Dmitrij Schlesinger, Boris Flach
One of the main challenges in distributed learning arises from the difficulty of handling heterogeneous local models and data. In light of the recent success of generative models,…
Symmetric Equilibrium Learning of VAEs
Boris Flach, Dmitrij Schlesinger, Alexander Shekhovtsov
We view variational autoencoders (VAE) as decoder-encoder pairs, which map distributions in the data space to distributions in the latent space and vice versa. The standard learnin…
VAE Approximation Error: ELBO and Exponential Families
Alexander Shekhovtsov, Dmitrij Schlesinger, Boris Flach
The importance of Variational Autoencoders reaches far beyond standalone generative models -- the approach is also used for learning latent representations and can be generalized t…
Implicit Modeling -- A Generalization of Discriminative and Generative Approaches
Dmitrij Schlesinger, Carsten Rother
We propose a new modeling approach that is a generalization of generative and discriminative models. The core idea is to use an implicit parameterization of a joint probability dis…