3 citations · 3 across the 2 of their papers we have counts for
6 papers
Relative Molecule Self-Attention Transformer
Łukasz Maziarka, Dawid Majchrowski, Tomasz Danel +5
Self-supervised learning holds promise to revolutionize molecule property prediction - a central task to drug discovery and many more industries - by enabling data efficient learni…
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…
One-element Batch Training by Moving Window
Przemysław Spurek, Szymon Knop, Jacek Tabor +2
Several deep models, esp. the generative, compare the samples from two distributions (e.g. WAE like AutoEncoder models, set-processing deep networks, etc) in their cost functions.…
Sliced generative models
Szymon Knop, Marcin Mazur, Jacek Tabor +2
In this paper we discuss a class of AutoEncoder based generative models based on one dimensional sliced approach. The idea is based on the reduction of the discrimination between s…
On Latent Distributions Without Finite Mean in Generative Models
Damian Leśniak, Igor Sieradzki, Igor Podolak
We investigate the properties of multidimensional probability distributions in the context of latent space prior distributions of implicit generative models. Our work revolves arou…
Cramer-Wold AutoEncoder
Szymon Knop, Jacek Tabor, Przemysław Spurek +3
We propose a new generative model, Cramer-Wold Autoencoder (CWAE). Following WAE, we directly encourage normality of the latent space. Our paper uses also the recent idea from Slic…