Search Results for author: Igor Podolak

Found 8 papers, 4 papers with code

Cramer-Wold AutoEncoder

2 code implementations ICLR 2019 Szymon Knop, Jacek Tabor, Przemysław Spurek, Igor Podolak, Marcin Mazur, Stanisław Jastrzębski

The crucial new ingredient is the introduction of a new (Cramer-Wold) metric in the space of densities, which replaces the Wasserstein metric used in SWAE.

On Latent Distributions Without Finite Mean in Generative Models

no code implementations5 Jun 2018 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.

Sliced generative models

no code implementations29 Jan 2019 Szymon Knop, Marcin Mazur, Jacek Tabor, Igor Podolak, Przemysław Spurek

In this paper we discuss a class of AutoEncoder based generative models based on one dimensional sliced approach.

Distribution-Interpolation Trade off in Generative Models

no code implementations ICLR 2019 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.

Generative models with kernel distance in data space

1 code implementation15 Sep 2020 Szymon Knop, Marcin Mazur, Przemysław Spurek, Jacek Tabor, Igor Podolak

First, an autoencoder based architecture, using kernel measures, is built to model a manifold of data.

Zero Time Waste: Recycling Predictions in Early Exit Neural Networks

1 code implementation NeurIPS 2021 Maciej Wołczyk, Bartosz Wójcik, Klaudia Bałazy, Igor Podolak, Jacek Tabor, Marek Śmieja, Tomasz Trzciński

The problem of reducing processing time of large deep learning models is a fundamental challenge in many real-world applications.

Relative Molecule Self-Attention Transformer

no code implementations12 Oct 2021 Łukasz Maziarka, Dawid Majchrowski, Tomasz Danel, Piotr Gaiński, Jacek Tabor, Igor Podolak, Paweł Morkisz, Stanisław Jastrzębski

Self-supervised learning holds promise to revolutionize molecule property prediction - a central task to drug discovery and many more industries - by enabling data efficient learning from scarce experimental data.

Drug Discovery Property Prediction +1

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