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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.

1 code implementation • ICLR Workshop GTRL 2021 • Piotr Tempczyk, Rafał Michaluk, Łukasz Garncarek, Przemysław Spurek, Jacek Tabor, Adam Goliński

We attempt to address that challenge by proposing a novel approach to the problem: Local Intrinsic Dimension estimation using approximate Likelihood (LIDL).

no code implementations • 28 Jun 2022 • Bartosz Wójcik, Jacek Grela, Marek Śmieja, Krzysztof Misztal, Jacek Tabor

The proposed classifier is confident only if a single class has a high probability and other probabilities are negligible.

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 • 16 Jun 2022 • Maciej Wołczyk, Karol J. Piczak, Bartosz Wójcik, Łukasz Pustelnik, Paweł Morawiecki, Jacek Tabor, Tomasz Trzciński, Przemysław Spurek

We introduce a new training paradigm that enforces interval constraints on neural network parameter space to control forgetting.

1 code implementation • 21 Mar 2022 • Marcin Sendera, Marcin Przewięźlikowski, Konrad Karanowski, Maciej Zięba, Jacek Tabor, Przemysław Spurek

Few-shot models aim at making predictions using a minimal number of labeled examples from a given task.

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.

1 code implementation • NeurIPS 2021 • Marcin Sendera, Jacek Tabor, Aleksandra Nowak, Andrzej Bedychaj, Massimiliano Patacchiola, Tomasz Trzciński, Przemysław Spurek, Maciej Zięba

This makes the GP posterior locally non-Gaussian, therefore we name our method Non-Gaussian Gaussian Processes (NGGPs).

no code implementations • 12 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.

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 • ACL (RepL4NLP) 2021 • Klaudia Bałazy, Mohammadreza Banaei, Rémi Lebret, Jacek Tabor, Karl Aberer

The adoption of Transformer-based models in natural language processing (NLP) has led to great success using a massive number of parameters.

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.

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.

no code implementations • 30 Nov 2020 • Maciej Zięba, Marcin Przewięźlikowski, Marek Śmieja, Jacek Tabor, Tomasz Trzcinski, Przemysław Spurek

Predicting future states or actions of a given system remains a fundamental, yet unsolved challenge of intelligence, especially in the scope of complex and non-deterministic scenarios, such as modeling behavior of humans.

no code implementations • 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 • 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 • 15 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.

no code implementations • 17 Jun 2020 • Bartosz Wójcik, Paweł Morawiecki, Marek Śmieja, Tomasz Krzyżek, Przemysław Spurek, Jacek Tabor

We present a mechanism for detecting adversarial examples based on data representations taken from the hidden layers of the target network.

1 code implementation • 15 Jun 2020 • Przemysław Spurek, Maciej Zięba, Jacek Tabor, Tomasz Trzciński

To that end, we devise a generative model that uses a hypernetwork to return the weights of a Continuous Normalizing Flows (CNF) target network.

no code implementations • 25 May 2020 • Dawid Rymarczyk, Adriana Borowa, Jacek Tabor, Bartosz Zieliński

There have been several attempts to create a model working with a bag of instances, however, they are assuming that there are no dependencies within the bag and the label is connected to at least one instance.

1 code implementation • 17 Apr 2020 • Bartosz Wójcik, Maciej Wołczyk, Klaudia Bałazy, Jacek Tabor

We develop a fast end-to-end method for training lightweight neural networks using multiple classifier heads.

no code implementations • ICLR 2020 • Stanislaw Jastrzebski, Maciej Szymczak, Stanislav Fort, Devansh Arpit, Jacek Tabor, Kyunghyun Cho, Krzysztof Geras

We argue for the existence of the "break-even" point on this trajectory, beyond which the curvature of the loss surface and noise in the gradient are implicitly regularized by SGD.

4 code implementations • 19 Feb 2020 • Łukasz Maziarka, Tomasz Danel, Sławomir Mucha, Krzysztof Rataj, Jacek Tabor, Stanisław Jastrzębski

Designing a single neural network architecture that performs competitively across a range of molecule property prediction tasks remains largely an open challenge, and its solution may unlock a widespread use of deep learning in the drug discovery industry.

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.

2 code implementations • ICML 2020 • Przemysław Spurek, Sebastian Winczowski, Jacek Tabor, Maciej Zamorski, Maciej Zięba, Tomasz Trzciński

The main idea of our HyperCloud method is to build a hyper network that returns weights of a particular neural network (target network) trained to map points from a uniform unit ball distribution into a 3D shape.

1 code implementation • 13 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.

no code implementations • NeurIPS Workshop Neuro_AI 2019 • Maciej Wołczyk, Jacek Tabor, Marek Śmieja, Szymon Maszke

We introduce bio-inspired artificial neural networks consisting of neurons that are additionally characterized by spatial positions.

1 code implementation • 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.

no code implementations • 21 Jun 2019 • Marek Śmieja, Maciej Wołczyk, Jacek Tabor, Bernhard C. Geiger

We propose a semi-supervised generative model, SeGMA, which learns a joint probability distribution of data and their classes and which is implemented in a typical Wasserstein auto-encoder framework.

1 code implementation • 3 Jun 2019 • Paweł Morawiecki, Przemysław Spurek, Marek Śmieja, Jacek Tabor

We present an efficient technique, which allows to train classification networks which are verifiably robust against norm-bounded adversarial attacks.

no code implementations • 31 May 2019 • Andrzej Bedychaj, Przemysław Spurek, Łukasz Struskim, Jacek Tabor

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

1 code implementation • 30 May 2019 • Przemysław Spurek, Szymon Knop, Jacek Tabor, Igor Podolak, Bartosz Wójcik

Several deep models, esp.

no code implementations • 6 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.

no code implementations • 1 Mar 2019 • Przemysław Spurek, Aleksandra Nowak, Jacek Tabor, Łukasz Maziarka, Stanisław Jastrzębski

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

no code implementations • 27 Feb 2019 • Sylwester Klocek, Łukasz Maziarka, Maciej Wołczyk, Jacek Tabor, Jakub Nowak, Marek Śmieja

Motivated by the human way of memorizing images we introduce their functional representation, where an image is represented by a neural network.

1 code implementation • 20 Feb 2019 • Bartosz Wójcik, Łukasz Maziarka, Jacek Tabor

In this paper, we propose a simple, fast and easy to implement algorithm LOSSGRAD (locally optimal step-size in gradient descent), which automatically modifies the step-size in gradient descent during neural networks training.

no code implementations • 29 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.

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).

no code implementations • 24 Sep 2018 • Wojciech Tarnowski, Piotr Warchoł, Stanisław Jastrzębski, Jacek Tabor, Maciej A. Nowak

We propose that in ResNets this can be resolved based on our results, by ensuring the same level of dynamical isometry at initialization.

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.

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.

1 code implementation • 11 Jul 2017 • Marek Śmieja, Krzysztof Hajto, Jacek Tabor

In this paper we propose a mixture model, SparseMix, for clustering of sparse high dimensional binary data, which connects model-based with centroid-based clustering.

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 • 4 May 2017 • Marek Śmieja, Jacek Tabor

In order to graphically represent and interpret the results the notion of Voronoi diagram was generalized to non Euclidean spaces and applied for introduced clustering method.

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.

no code implementations • 19 Aug 2015 • Jacek Tabor, Przemysław Spurek, Konrad Kamieniecki, Marek Śmieja, Krzysztof Misztal

The R Package CEC performs clustering based on the cross-entropy clustering (CEC) method, which was recently developed with the use of information theory.

no code implementations • 10 Apr 2015 • Wojciech Marian Czarnecki, Rafał Józefowicz, Jacek Tabor

Representation learning is currently a very hot topic in modern machine learning, mostly due to the great success of the deep learning methods.

no code implementations • 21 Jan 2015 • Wojciech Marian Czarnecki, Jacek Tabor

The main contribution of this paper is proposing a model based on the information theoretic concepts which on the one hand shows new, entropic perspective on known linear classifiers and on the other leads to a construction of very robust method competetitive with the state of the art non-information theoretic ones (including Support Vector Machines and Extreme Learning Machines).

no code implementations • 12 Aug 2014 • Wojciech Marian Czarnecki, Jacek Tabor

In the classical Gaussian SVM classification we use the feature space projection transforming points to normal distributions with fixed covariance matrices (identity in the standard RBF and the covariance of the whole dataset in Mahalanobis RBF).

no code implementations • 4 Aug 2014 • Wojciech Marian Czarnecki, Jacek Tabor

Then we prove that our method is a multithreshold large margin classifier, which shows the analogy to the SVM, while in the same time works with much broader class of hypotheses.

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