2 citations · 8 across the 11 of their papers we have counts for
14 papers
Bounding Evidence and Estimating Log-Likelihood in VAE
Łukasz Struski, Marcin Mazur, Paweł Batorski +2
Many crucial problems in deep learning and statistical inference are caused by a variational gap, i.e., a difference between model evidence (log-likelihood) and evidence lower boun…
Target Layer Regularization for Continual Learning Using Cramer-Wold Generator
Marcin Mazur, Łukasz Pustelnik, Szymon Knop +2
We propose an effective regularization strategy (CW-TaLaR) for solving continual learning problems. It uses a penalizing term expressed by the Cramer-Wold distance between two prob…
HyperCube: Implicit Field Representations of Voxelized 3D Models
Magdalena Proszewska, Marcin Mazur, Tomasz Trzciński +1
Recently introduced implicit field representations offer an effective way of generating 3D object shapes. They leverage implicit decoder trained to take a 3D point coordinate conca…
HyperPocket: Generative Point Cloud Completion
Przemysław Spurek, Artur Kasymov, Marcin Mazur +5
Scanning real-life scenes with modern registration devices typically give incomplete point cloud representations, mostly due to the limitations of the scanning process and 3D occlu…
Generative models with kernel distance in data space
Szymon Knop, Marcin Mazur, Przemysław Spurek +2
Generative models dealing with modeling a~joint data distribution are generally either autoencoder or GAN based. Both have their pros and cons, generating blurry images or being un…
Gravilon: Applications of a New Gradient Descent Method to Machine Learning
Chad Kelterborn, Marcin Mazur, Bogdan V. Petrenko
Gradient descent algorithms have been used in countless applications since the inception of Newton's method. The explosion in the number of applications of neural networks has re-e…