Maciej Zieba
Maciej Zieba
Wroclaw University of Science and Technology, Tooploox
Verified email at - Homepage
Cited by
Cited by
Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction
M Zięba, SK Tomczak, JM Tomczak
Expert systems with applications 58, 93-101, 2016
Boosted SVM for extracting rules from imbalanced data in application to prediction of the post-operative life expectancy in the lung cancer patients
M Zięba, JM Tomczak, M Lubicz, J Świątek
Applied soft computing 14, 99-108, 2014
Classification restricted Boltzmann machine for comprehensible credit scoring model
JM Tomczak, M Zięba
Expert Systems with Applications 42 (4), 1789-1796, 2015
Adversarial autoencoders for compact representations of 3D point clouds
M Zamorski, M Zięba, P Klukowski, R Nowak, K Kurach, W Stokowiec, ...
Computer Vision and Image Understanding 193, 102921, 2020
Bingan: Learning compact binary descriptors with a regularized gan
M Zieba, P Semberecki, T El-Gaaly, T Trzcinski
NeurIPS 2018, 2018
UCSG-Net--Unsupervised Discovering of Constructive Solid Geometry Tree
K Kania, M Zięba, T Kajdanowicz
NeurIPS 2020, 2020
Diffused heads: Diffusion models beat gans on talking-face generation
M Stypułkowski, K Vougioukas, S He, M Zięba, S Petridis, M Pantic
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2024
NMRNet: a deep learning approach to automated peak picking of protein NMR spectra
P Klukowski, M Augoff, M Zięba, M Drwal, A Gonczarek, MJ Walczak
Bioinformatics 34 (15), 2590-2597, 2018
Boosted SVM with active learning strategy for imbalanced data
M Zięba, JM Tomczak
Soft Computing 19 (12), 3357-3368, 2015
Hypernetwork approach to generating point clouds
P Spurek, S Winczowski, J Tabor, M Zamorski, M Zięba, T Trzciński
ICML 2020, 2020
Service-oriented medical system for supporting decisions with missing and imbalanced data
M Zięba
IEEE journal of biomedical and health informatics 18 (5), 1533-1540, 2014
Probabilistic combination of classification rules and its application to medical diagnosis
JM Tomczak, M Zięba
Machine Learning 101, 105-135, 2015
Adversarial autoencoders for generating 3d point clouds
M Zamorski, M Zieba, R Nowak, W Stokowiec, T Trzcinski
arXiv preprint arXiv:1811.07605 2 (3), 2018
Training triplet networks with gan
M Zieba, L Wang
ICLR 2017 Workshop track, 2017
Hypershot: Few-shot learning by kernel hypernetworks
M Sendera, M Przewięźlikowski, K Karanowski, M Zięba, J Tabor, ...
WACV 2023, 2022
Generative adversarial networks: recent developments
M Zamorski, A Zdobylak, M Zięba, J Świątek
Artificial Intelligence and Soft Computing: 18th International Conference …, 2019
RBM-SMOTE: restricted boltzmann machines for synthetic minority oversampling technique
M Zięba, JM Tomczak, A Gonczarek
Intelligent Information and Database Systems: 7th Asian Conference, ACIIDS …, 2015
Speech driven video editing via an audio-conditioned diffusion model
D Bigioi, S Basak, M Stypułkowski, M Zieba, H Jordan, R McDonnell, ...
Image and Vision Computing 142, 104911, 2024
Non-Gaussian Gaussian Processes for Few-Shot Regression
M Sendera, J Tabor, A Nowak, A Bedychaj, M Patacchiola, T Trzciński, ...
NeurIPS 2021, 2021
The proposal of Service Oriented Data Mining System for solving real-life classification and regression problems
A Prusiewicz, M Zięba
Technological Innovation for Sustainability: Second IFIP WG 5.5/SOCOLNET …, 2011
The system can't perform the operation now. Try again later.
Articles 1–20