15 citations · 65 across the 23 of their papers we have counts for
27 papers
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…
RANSAC Scoring Functions: Analysis and Reality Check
A. Shekhovtsov
We revisit the problem of assigning a score (a quality of fit) to candidate geometric models -- one of the key components of RANSAC for robust geometric fitting. In a non-robust se…
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…
Generalized Differentiable RANSAC
Tong Wei, Yash Patel, Alexander Shekhovtsov +2
We propose -RANSAC, a generalized differentiable RANSAC that allows learning the entire randomized robust estimation pipeline. The proposed approach enables the use of rela…
Bias-Variance Tradeoffs in Single-Sample Binary Gradient Estimators
Alexander Shekhovtsov
Discrete and especially binary random variables occur in many machine learning models, notably in variational autoencoders with binary latent states and in stochastic binary networ…
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…