Marek Smieja

According to our database, Marek Smieja authored at least 66 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Multi-Label Conditional Generation From Pre-Trained Models.

Magdalena Proszewska, Maciej Wolczyk, Maciej Zieba, Patryk Wielopolski, Lukasz Maziarka, Marek Smieja

IEEE Trans. Pattern Anal. Mach. Intell., September, 2024

ADMET-PrInt: Evaluation of ADMET Properties: Prediction and Interpretation.

Ewelina Jamrozik, Marek Smieja, Sabina Podlewska

J. Chem. Inf. Model., March, 2024

Augmentation-aware self-supervised learning with conditioned projector.

Marcin Przewiezlikowski, Mateusz Pyla, Bartosz Zielinski, Bartlomiej Twardowski, Jacek Tabor, Marek Smieja

Knowl. Based Syst., 2024

RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNets.

Piotr Gainski, Michal Koziarski, Krzysztof Maziarz, Marwin H. S. Segler, Jacek Tabor, Marek Smieja

CoRR, 2024

Face Identity-Aware Disentanglement in StyleGAN.

Adrian Suwala, Bartosz Wójcik, Magdalena Proszewska, Jacek Tabor, Przemyslaw Spurek, Marek Smieja

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

A Deep Cut Into Split Federated Self-Supervised Learning.

Marcin Przewiezlikowski, Marcin Osial, Bartosz Zielinski, Marek Smieja

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2024

StyleAutoEncoder for Manipulating Image Attributes Using Pre-trained StyleGAN.

Andrzej Bedychaj, Jacek Tabor, Marek Smieja

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2024

RegFlow: Probabilistic Flow-Based Regression for Future Prediction.

Maciej Zieba, Marcin Przewiezlikowski, Marek Smieja, Jacek Tabor, Tomasz Trzcinski, Przemyslaw Spurek

Proceedings of the Recent Challenges in Intelligent Information and Database Systems, 2024

2023

Zero time waste in pre-trained early exit neural networks.

Bartosz Wójcik, Marcin Przewiezlikowski, Filip Szatkowski, Maciej Wolczyk, Klaudia Balazy, Bartlomiej Krzepkowski, Igor T. Podolak, Jacek Tabor, Marek Smieja, Tomasz Trzcinski

Neural Networks, November, 2023

Augmentation-aware Self-supervised Learning with Guided Projector.

Marcin Przewiezlikowski, Mateusz Pyla, Bartosz Zielinski, Bartlomiej Twardowski, Jacek Tabor, Marek Smieja

CoRR, 2023

SONGs: Self-Organizing Neural Graphs.

Lukasz Struski, Tomasz Danel, Marek Smieja, Jacek Tabor, Bartosz Zielinski

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023

Contrastive Hierarchical Clustering.

Michal Znalezniak, Przemyslaw Rola, Patryk Kaszuba, Jacek Tabor, Marek Smieja

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

ChiENN: Embracing Molecular Chirality with Graph Neural Networks.

Piotr Gainski, Michal Koziarski, Jacek Tabor, Marek Smieja

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

r-softmax: Generalized Softmax with Controllable Sparsity Rate.

Klaudia Balazy, Lukasz Struski, Marek Smieja, Jacek Tabor

Proceedings of the Computational Science - ICCS 2023, 2023

Hebbian Continual Representation Learning.

Pawel Morawiecki, Andrii Krutsylo, Maciej Wolczyk, Marek Smieja

Proceedings of the 56th Hawaii International Conference on System Sciences, 2023

HyperTab: Hypernetwork Approach for Deep Learning on Small Tabular Datasets.

Witold Wydmanski, Oleksii Bulenok, Marek Smieja

Proceedings of the 10th IEEE International Conference on Data Science and Advanced Analytics, 2023

2022

OneFlow: One-Class Flow for Anomaly Detection Based on a Minimal Volume Region.

Lukasz Maziarka, Marek Smieja, Marcin Sendera, Lukasz Struski, Jacek Tabor, Przemyslaw Spurek

IEEE Trans. Pattern Anal. Mach. Intell., 2022

SLOVA: Uncertainty estimation using single label one-vs-all classifier.

Bartosz Wójcik, Jacek Grela, Marek Smieja, Krzysztof Misztal, Jacek Tabor

Appl. Soft Comput., 2022

MisConv: Convolutional Neural Networks for Missing Data.

Marcin Przewiezlikowski, Marek Smieja, Lukasz Struski, Jacek Tabor

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022

Semi-supervised clustering via information-theoretic markov chain aggregation.

Sophie Steger, Bernhard C. Geiger, Marek Smieja

Proceedings of the SAC '22: The 37th ACM/SIGAPP Symposium on Applied Computing, Virtual Event, April 25, 2022

PluGeN: Multi-Label Conditional Generation from Pre-trained Models.

Maciej Wolczyk, Magdalena Proszewska, Lukasz Maziarka, Maciej Zieba, Patryk Wielopolski, Rafal Kurczab, Marek Smieja

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

SeGMA: Semi-Supervised Gaussian Mixture Autoencoder.

Marek Smieja, Maciej Wolczyk, Jacek Tabor, Bernhard C. Geiger

IEEE Trans. Neural Networks Learn. Syst., 2021

Pharmacoprint: A Combination of a Pharmacophore Fingerprint and Artificial Intelligence as a Tool for Computer-Aided Drug Design.

Dawid Warszycki, Lukasz Struski, Marek Smieja, Rafal Kafel, Rafal Kurczab

J. Chem. Inf. Model., 2021

Semi-Supervised Clustering via Markov Chain Aggregation.

Sophie Steger, Bernhard C. Geiger, Marek Smieja

CoRR, 2021

Flow-based SVDD for anomaly detection.

Marcin Sendera, Marek Smieja, Lukasz Maziarka, Lukasz Struski, Przemyslaw Spurek, Jacek Tabor

CoRR, 2021

SONG: Self-Organizing Neural Graphs.

Lukasz Struski, Tomasz Danel, Marek Smieja, Jacek Tabor, Bartosz Zielinski

CoRR, 2021

Zero Time Waste: Recycling Predictions in Early Exit Neural Networks.

Maciej Wolczyk, Bartosz Wójcik, Klaudia Balazy, Igor T. Podolak, Jacek Tabor, Marek Smieja, Tomasz Trzcinski

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Adversarial Examples Detection and Analysis with Layer-wise Autoencoders.

Bartosz Wójcik, Pawel Morawiecki, Marek Smieja, Tomasz Krzyzek, Przemyslaw Spurek, Jacek Tabor

Proceedings of the 33rd IEEE International Conference on Tools with Artificial Intelligence, 2021

2020

A classification-based approach to semi-supervised clustering with pairwise constraints.

Marek Smieja, Lukasz Struski, Mário A. T. Figueiredo

Neural Networks, 2020

Flow-based anomaly detection.

Lukasz Maziarka, Marek Smieja, Marcin Sendera, Lukasz Struski, Jacek Tabor, Przemyslaw Spurek

CoRR, 2020

Pointed Subspace Approach to Incomplete Data.

Lukasz Struski, Marek Smieja, Jacek Tabor

J. Classif., 2020

Iterative Imputation of Missing Data Using Auto-Encoder Dynamics.

Marek Smieja, Maciej Kolomycki, Lukasz Struski, Mateusz Juda, Mário A. T. Figueiredo

Proceedings of the Neural Information Processing - 27th International Conference, 2020

Estimating Conditional Density of Missing Values Using Deep Gaussian Mixture Model.

Marcin Przewiezlikowski, Marek Smieja, Lukasz Struski

Proceedings of the Neural Information Processing - 27th International Conference, 2020

Spatial Graph Convolutional Networks.

Tomasz Danel, Przemyslaw Spurek, Jacek Tabor, Marek Smieja, Lukasz Struski, Agnieszka Slowik, Lukasz Maziarka

Proceedings of the Neural Information Processing - 27th International Conference, 2020

Processing of Incomplete Images by (Graph) Convolutional Neural Networks.

Tomasz Danel, Marek Smieja, Lukasz Struski, Przemyslaw Spurek, Lukasz Maziarka

Proceedings of the Neural Information Processing - 27th International Conference, 2020

Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models.

Pawel Morawiecki, Przemyslaw Spurek, Marek Smieja, Jacek Tabor

Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020

2019

Projected memory clustering.

Lukasz Struski, Przemyslaw Spurek, Jacek Tabor, Marek Smieja

Pattern Recognit. Lett., 2019

SVM with a neutral class.

Marek Smieja, Jacek Tabor, Przemyslaw Spurek

Pattern Anal. Appl., 2019

Generalized RBF kernel for incomplete data.

Marek Smieja, Lukasz Struski, Jacek Tabor, Mateusz Marzec

Knowl. Based Syst., 2019

Efficient mixture model for clustering of sparse high dimensional binary data.

Marek Smieja, Krzysztof Hajto, Jacek Tabor

Data Min. Knowl. Discov., 2019

Biologically-Inspired Spatial Neural Networks.

Maciej Wolczyk, Jacek Tabor, Marek Smieja, Szymon Maszke

CoRR, 2019

Geometric Graph Convolutional Neural Networks.

Przemyslaw Spurek, Tomasz Danel, Jacek Tabor, Marek Smieja, Lukasz Struski, Agnieszka Slowik, Lukasz Maziarka

CoRR, 2019

SeGMA: Semi-Supervised Gaussian Mixture Auto-Encoder.

Marek Smieja, Maciej Wolczyk, Jacek Tabor, Bernhard C. Geiger

CoRR, 2019

Multi-task hypernetworks.

Sylwester Klocek, Lukasz Maziarka, Maciej Wolczyk, Jacek Tabor, Marek Smieja, Jakub Nowak

CoRR, 2019

Set Aggregation Network as a Trainable Pooling Layer.

Lukasz Maziarka, Marek Smieja, Aleksandra Nowak, Jacek Tabor, Lukasz Struski, Przemyslaw Spurek

Proceedings of the Neural Information Processing - 26th International Conference, 2019

Hypernetwork Functional Image Representation.

Sylwester Klocek, Lukasz Maziarka, Maciej Wolczyk, Jacek Tabor, Jakub Nowak, Marek Smieja

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2019 - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, 2019

2018

Fast independent component analysis algorithm with a simple closed-form solution.

Przemyslaw Spurek, Jacek Tabor, Lukasz Struski, Marek Smieja

Knowl. Based Syst., 2018

Semi-supervised discriminative clustering with graph regularization.

Marek Smieja, Oleksandr Myronov, Jacek Tabor

Knowl. Based Syst., 2018

Deep processing of structured data.

Lukasz Maziarka, Marek Smieja, Aleksandra Nowak, Jacek Tabor, Lukasz Struski, Przemyslaw Spurek

CoRR, 2018

Cascade context encoder for improved inpainting.

Bartosz Zielinski, Lukasz Struski, Marek Smieja, Jacek Tabor

CoRR, 2018

Processing of missing data by neural networks.

Marek Smieja, Lukasz Struski, Jacek Tabor, Bartosz Zielinski, Przemyslaw Spurek

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Semi-supervised cross-entropy clustering with information bottleneck constraint.

Marek Smieja, Bernhard C. Geiger

Inf. Sci., 2017

R Package CEC.

Przemyslaw Spurek, Konrad Kamieniecki, Jacek Tabor, Krzysztof Misztal, Marek Smieja

Neurocomputing, 2017

Semi-supervised model-based clustering with controlled clusters leakage.

Marek Smieja, Lukasz Struski, Jacek Tabor

Expert Syst. Appl., 2017

Constrained clustering with a complex cluster structure.

Marek Smieja, Magdalena Wiercioch

Adv. Data Anal. Classif., 2017

2016

Incomplete data representation for SVM classification.

Lukasz Struski, Marek Smieja, Jacek Tabor

CoRR, 2016

Fast Entropy Clustering of sparse high dimensional binary data.

Marek Smieja, Szymon Nakoneczny, Jacek Tabor

Proceedings of the 2016 International Joint Conference on Neural Networks, 2016

2015

Weighted approach to general entropy function.

Marek Smieja

IMA J. Math. Control. Inf., 2015

Entropy Approximation in Lossy Source Coding Problem.

Marek Smieja, Jacek Tabor

Entropy, 2015

Introduction to Cross-Entropy Clustering The R Package CEC.

Jacek Tabor, Przemyslaw Spurek, Konrad Kamieniecki, Marek Smieja, Krzysztof Misztal

CoRR, 2015

Spherical wards clustering and generalized Voronoi diagrams.

Marek Smieja, Jacek Tabor

Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, 2015

2014

Subspaces Clustering Approach to Lossy Image Compression.

Przemyslaw Spurek, Marek Smieja, Krzysztof Misztal

Proceedings of the Computer Information Systems and Industrial Management, 2014

2013

Image Segmentation with Use of Cross-Entropy Clustering.

Marek Smieja, Jacek Tabor

Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013, 2013

2012

Entropy of the Mixture of Sources and Entropy Dimension.

Marek Smieja, Jacek Tabor

IEEE Trans. Inf. Theory, 2012

Partition Reduction for Lossy Data Compression Problem

Marek Smieja, Jacek Tabor

CoRR, 2012

Weighted Approach to Rényi Entropy

Marek Smieja, Jacek Tabor

CoRR, 2012