Search Results for author: Marek Śmieja

Found 36 papers, 13 papers with code

Face Identity-Aware Disentanglement in StyleGAN

no code implementations21 Sep 2023 Adrian Suwała, Bartosz Wójcik, Magdalena Proszewska, Jacek Tabor, Przemysław Spurek, Marek Śmieja

Conditional GANs are frequently used for manipulating the attributes of face images, such as expression, hairstyle, pose, or age.


ChiENN: Embracing Molecular Chirality with Graph Neural Networks

1 code implementation5 Jul 2023 Piotr Gaiński, Michał Koziarski, Jacek Tabor, Marek Śmieja

Graph Neural Networks (GNNs) play a fundamental role in many deep learning problems, in particular in cheminformatics.

Drug Discovery Molecular Property Prediction +1

Augmentation-aware Self-supervised Learning with Guided Projector

1 code implementation31 May 2023 Marcin Przewięźlikowski, Mateusz Pyla, Bartosz Zieliński, Bartłomiej Twardowski, Jacek Tabor, Marek Śmieja

By learning to remain invariant to applied data augmentations, methods such as SimCLR and MoCo are able to reach quality on par with supervised approaches.

Self-Supervised Learning

r-softmax: Generalized Softmax with Controllable Sparsity Rate

1 code implementation11 Apr 2023 Klaudia Bałazy, Łukasz Struski, Marek Śmieja, Jacek Tabor

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

Language Modelling

HyperTab: Hypernetwork Approach for Deep Learning on Small Tabular Datasets

1 code implementation7 Apr 2023 Witold Wydmański, Oleksii Bulenok, Marek Śmieja

We evaluated HyperTab on more than 40 tabular datasets of a varying number of samples and domains of origin, and compared its performance with shallow and deep learning models representing the current state-of-the-art.

Data Augmentation

Contrastive Hierarchical Clustering

1 code implementation3 Mar 2023 Michał Znaleźniak, Przemysław Rola, Patryk Kaszuba, Jacek Tabor, Marek Śmieja

Deep clustering has been dominated by flat models, which split a dataset into a predefined number of groups.

Clustering Deep Clustering +2

SLOVA: Uncertainty Estimation Using Single Label One-Vs-All Classifier

no code implementations28 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.

Hebbian Continual Representation Learning

no code implementations28 Jun 2022 Paweł Morawiecki, Andrii Krutsylo, Maciej Wołczyk, Marek Śmieja

Although this setting is natural for biological systems, it proves very difficult for machine learning models such as artificial neural networks.

BIG-bench Machine Learning class-incremental learning +3

MisConv: Convolutional Neural Networks for Missing Data

1 code implementation26 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.

Image Inpainting Imputation

Pharmacoprint -- a combination of pharmacophore fingerprint and artificial intelligence as a tool for computer-aided drug design

no code implementations4 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).

Dimensionality Reduction

PluGeN: Multi-Label Conditional Generation From Pre-Trained Models

1 code implementation18 Sep 2021 Maciej Wołczyk, Magdalena Proszewska, Łukasz Maziarka, Maciej Zięba, Patryk Wielopolski, Rafał Kurczab, Marek Śmieja

Modern generative models achieve excellent quality in a variety of tasks including image or text generation and chemical molecule modeling.

Text Generation

Flow-based SVDD for anomaly detection

no code implementations10 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.

Anomaly Detection One-class classifier

SONG: Self-Organizing Neural Graphs

no code implementations28 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.

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.

RegFlow: Probabilistic Flow-based Regression for Future Prediction

no code implementations30 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.

Future prediction regression

Processing of incomplete images by (graph) convolutional neural networks

no code implementations26 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.


Estimating conditional density of missing values using deep Gaussian mixture model

1 code implementation5 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.


Adversarial Examples Detection and Analysis with Layer-wise Autoencoders

no code implementations17 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.

Can auto-encoders help with filling missing data?

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.

A Classification-Based Approach to Semi-Supervised Clustering with Pairwise Constraints

no code implementations18 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.

Binary Classification Classification +2

Biologically-Inspired Spatial Neural Networks

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.

Continual Learning

Spatial Graph Convolutional Networks

2 code implementations11 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.

Image Classification

SeGMA: Semi-Supervised Gaussian Mixture Auto-Encoder

no code implementations21 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.

Style Transfer

Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models

1 code implementation3 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.

Hypernetwork functional image representation

no code implementations27 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.

Image Super-Resolution

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).

Cascade context encoder for improved inpainting

no code implementations11 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.

Efficient mixture model for clustering of sparse high dimensional binary data

1 code implementation11 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.

Clustering Vocal Bursts Intensity Prediction

Spherical Wards clustering and generalized Voronoi diagrams

no code implementations4 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.


Semi-supervised cross-entropy clustering with information bottleneck constraint

no code implementations3 May 2017 Marek Śmieja, Bernhard C. Geiger

By combining the ideas from cross-entropy clustering (CEC) with those from the information bottleneck method (IB), our method trades between three conflicting goals: the accuracy with which the data set is modeled, the simplicity of the model, and the consistency of the clustering with side information.


Pointed subspace approach to incomplete data

no code implementations2 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.

Dimensionality Reduction General Classification

Generalized RBF kernel for incomplete data

no code implementations5 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.

Introduction to Cross-Entropy Clustering The R Package CEC

no code implementations19 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.


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