Search Results for author: Aleksandra Nowak

Found 10 papers, 4 papers with code

Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery

no code implementations24 Nov 2022 Mateusz Olko, Michał Zając, Aleksandra Nowak, Nino Scherrer, Yashas Annadani, Stefan Bauer, Łukasz Kuciński, Piotr Miłoś

In this work, we propose a novel Gradient-based Intervention Targeting method, abbreviated GIT, that 'trusts' the gradient estimator of a gradient-based causal discovery framework to provide signals for the intervention acquisition function.

Causal Discovery Experimental Design

On the relationship between disentanglement and multi-task learning

no code implementations7 Oct 2021 Łukasz Maziarka, Aleksandra Nowak, Maciej Wołczyk, Andrzej Bedychaj

One of the main arguments behind studying disentangled representations is the assumption that they can be easily reused in different tasks.

Disentanglement Multi-Task Learning

Neural networks adapting to datasets: learning network size and topology

no code implementations22 Jun 2020 Romuald A. Janik, Aleksandra Nowak

We introduce a flexible setup allowing for a neural network to learn both its size and topology during the course of a standard gradient-based training.

Analyzing Neural Networks Based on Random Graphs

1 code implementation19 Feb 2020 Romuald A. Janik, Aleksandra Nowak

We perform a massive evaluation of neural networks with architectures corresponding to random graphs of various types.

WICA: nonlinear weighted ICA

1 code implementation13 Jan 2020 Andrzej Bedychaj, Przemysław Spurek, Aleksandra Nowak, Jacek Tabor

Independent Component Analysis (ICA) aims to find a coordinate system in which the components of the data are independent.

Realism Index: Interpolation in Generative Models With Arbitrary Prior

no code implementations6 Apr 2019 Łukasz Struski, Jacek Tabor, Igor Podolak, Aleksandra Nowak, Krzysztof Maziarz

In order to perform plausible interpolations in the latent space of a generative model, we need a measure that credibly reflects if a point in an interpolation is close to the data manifold being modelled, i. e. if it is convincing.

Non-linear ICA based on Cramer-Wold metric

no code implementations1 Mar 2019 Przemysław Spurek, Aleksandra Nowak, Jacek Tabor, Łukasz Maziarka, Stanisław Jastrzębski

Non-linear source separation is a challenging open problem with many applications.

Set Aggregation Network as a Trainable Pooling Layer

1 code implementation3 Oct 2018 Łukasz Maziarka, Marek Śmieja, Aleksandra Nowak, Jacek Tabor, Łukasz Struski, Przemysław Spurek

Global pooling, such as max- or sum-pooling, is one of the key ingredients in deep neural networks used for processing images, texts, graphs and other types of structured data.

Deep processing of structured data

no code implementations27 Sep 2018 Łukasz Maziarka, Marek Śmieja, Aleksandra Nowak, Jacek Tabor, Łukasz Struski, Przemysław Spurek

We construct a general unified framework for learning representation of structured data, i. e. data which cannot be represented as the fixed-length vectors (e. g. sets, graphs, texts or images of varying sizes).

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