1 code implementation • 12 Mar 2024 • Łukasz Struski, Adam Pardyl, Jacek Tabor, Bartosz Zieliński

Partial Multi-label Learning (PML) is a type of weakly supervised learning where each training instance corresponds to a set of candidate labels, among which only some are true.

1 code implementation • 7 Nov 2023 • Łukasz Struski, Tomasz Urbańczyk, Krzysztof Bucki, Bartłomiej Cupiał, Aneta Kaczyńska, Przemysław Spurek, Jacek Tabor

However, video generation of high-resolution data is a very demanding task for generative models, due to the large need for memory.

no code implementations • 16 Aug 2023 • Mikołaj Sacha, Bartosz Jura, Dawid Rymarczyk, Łukasz Struski, Jacek Tabor, Bartosz Zieliński

Prototypical parts-based networks are becoming increasingly popular due to their faithful self-explanations.

no code implementations • 18 Jun 2023 • Łukasz Struski, Dawid Rymarczyk, Arkadiusz Lewicki, Robert Sabiniewicz, Jacek Tabor, Bartosz Zieliński

The most common MIL model is when we consider a bag as positive if at least one of its instances has a positive label.

1 code implementation • 11 Apr 2023 • Klaudia Bałazy, Łukasz Struski, Marek Śmieja, Jacek Tabor

Nowadays artificial neural network models achieve remarkable results in many disciplines.

1 code implementation • 28 Jan 2023 • Mikołaj Sacha, Dawid Rymarczyk, Łukasz Struski, Jacek Tabor, Bartosz Zieliński

We introduce ProtoSeg, a novel model for interpretable semantic image segmentation, which constructs its predictions using similar patches from the training set.

no code implementations • 21 Aug 2022 • Łukasz Struski, Jacek Tabor, Bartosz Zieliński

Partial label learning is a type of weakly supervised learning, where each training instance corresponds to a set of candidate labels, among which only one is true.

no code implementations • 19 Jun 2022 • Łukasz Struski, Marcin Mazur, Paweł Batorski, Przemysław Spurek, Jacek Tabor

Many crucial problems in deep learning and statistics are caused by a variational gap, i. e., a difference between evidence and evidence lower bound (ELBO).

1 code implementation • 6 Dec 2021 • Dawid Rymarczyk, Łukasz Struski, Michał Górszczak, Koryna Lewandowska, Jacek Tabor, Bartosz Zieliński

We introduce ProtoPool, an interpretable image classification model with a pool of prototypes shared by the classes.

1 code implementation • 26 Oct 2021 • Marcin Przewięźlikowski, Marek Śmieja, Łukasz Struski, Jacek Tabor

Processing of missing data by modern neural networks, such as CNNs, remains a fundamental, yet unsolved challenge, which naturally arises in many practical applications, like image inpainting or autonomous vehicles and robots.

no code implementations • 5 Oct 2021 • Łukasz Struski, Paweł Morkisz, Przemysław Spurek, Samuel Rodriguez Bernabeu, Tomasz Trzciński

In this work, we leverage efficient processing operations that can be run in parallel on modern Graphical Processing Units (GPUs), predominant computing architecture used e. g. in deep learning, to reduce the computational burden of computing matrix decompositions.

no code implementations • 4 Oct 2021 • Dawid Warszycki, Łukasz Struski, Marek Śmieja, Rafał Kafel, Rafał Kurczab

Structural fingerprints and pharmacophore modeling are methodologies that have been used for at least two decades in various fields of cheminformatics: from similarity searching to machine learning (ML).

no code implementations • 10 Aug 2021 • Marcin Sendera, Marek Śmieja, Łukasz Maziarka, Łukasz Struski, Przemysław Spurek, Jacek Tabor

We propose FlowSVDD -- a flow-based one-class classifier for anomaly/outliers detection that realizes a well-known SVDD principle using deep learning tools.

no code implementations • 28 Jul 2021 • Łukasz Struski, Tomasz Danel, Marek Śmieja, Jacek Tabor, Bartosz Zieliński

Recent years have seen a surge in research on deep interpretable neural networks with decision trees as one of the most commonly incorporated tools.

1 code implementation • 11 Feb 2021 • Przemysław Spurek, Artur Kasymov, Marcin Mazur, Diana Janik, Sławomir Tadeja, Łukasz Struski, Jacek Tabor, Tomasz Trzciński

In this work, we reformulate the problem of point cloud completion into an object hallucination task.

1 code implementation • 29 Nov 2020 • Dawid Rymarczyk, Łukasz Struski, Jacek Tabor, Bartosz Zieliński

In this paper, we introduce ProtoPShare, a self-explained method that incorporates the paradigm of prototypical parts to explain its predictions.

no code implementations • 26 Oct 2020 • Tomasz Danel, Marek Śmieja, Łukasz Struski, Przemysław Spurek, Łukasz Maziarka

We investigate the problem of training neural networks from incomplete images without replacing missing values.

no code implementations • 6 Oct 2020 • Łukasz Maziarka, Marek Śmieja, Marcin Sendera, Łukasz Struski, Jacek Tabor, Przemysław Spurek

We propose OneFlow - a flow-based one-class classifier for anomaly (outlier) detection that finds a minimal volume bounding region.

1 code implementation • 5 Oct 2020 • Marcin Przewięźlikowski, Marek Śmieja, Łukasz Struski

We consider the problem of estimating the conditional probability distribution of missing values given the observed ones.

no code implementations • ICLR Workshop DeepDiffEq 2019 • Marek Śmieja, Maciej Kołomycki, Łukasz Struski, Mateusz Juda, Mário A. T. Figueiredo

This paper introduces an approach to filling in missing data based on deep auto-encoder models, adequate to high-dimensional data exhibiting complex dependencies, such as images.

1 code implementation • 18 Feb 2020 • Łukasz Struski, Szymon Knop, Jacek Tabor, Wiktor Daniec, Przemysław Spurek

In the paper we construct a fully convolutional GAN model: LocoGAN, which latent space is given by noise-like images of possibly different resolutions.

no code implementations • 18 Jan 2020 • Marek Śmieja, Łukasz Struski, Mário A. T. Figueiredo

In this paper, we introduce a neural network framework for semi-supervised clustering (SSC) with pairwise (must-link or cannot-link) constraints.

2 code implementations • 11 Sep 2019 • Tomasz Danel, Przemysław Spurek, Jacek Tabor, Marek Śmieja, Łukasz Struski, Agnieszka Słowik, Łukasz Maziarka

Graph Convolutional Networks (GCNs) have recently become the primary choice for learning from graph-structured data, superseding hash fingerprints in representing chemical compounds.

1 code implementation • 6 Apr 2019 • Łukasz Struski, Michał Sadowski, Tomasz Danel, Jacek Tabor, Igor T. Podolak

In the case of geodesics, we search for the curves with the shortest length, while in the case of generative models we typically apply linear interpolation in the latent space.

1 code implementation • 3 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.

no code implementations • 27 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).

1 code implementation • NeurIPS 2018 • Marek Smieja, Łukasz Struski, Jacek Tabor, Bartosz Zieliński, Przemysław Spurek

We propose a general, theoretically justified mechanism for processing missing data by neural networks.

no code implementations • 11 Mar 2018 • Bartosz Zieliński, Łukasz Struski, Marek Śmieja, Jacek Tabor

For this purpose, we train context encoder for 64x64 pixels images in a standard way and use its resized output to fill in the missing input region of the 128x128 context encoder, both in training and evaluation phase.

no code implementations • 4 May 2017 • Marek Śmieja, Łukasz Struski, Jacek Tabor

In this paper, we focus on finding clusters in partially categorized data sets.

no code implementations • 2 May 2017 • Łukasz Struski, Marek Śmieja, Jacek Tabor

Incomplete data are often represented as vectors with filled missing attributes joined with flag vectors indicating missing components.

no code implementations • 5 Dec 2016 • Łukasz Struski, Marek Śmieja, Jacek Tabor

We construct $\bf genRBF$ kernel, which generalizes the classical Gaussian RBF kernel to the case of incomplete data.

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