Magda Gregorová

According to our database, Magda Gregorová authored at least 26 papers between 2015 and 2026.

Collaborative distances:

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

2026

Sugar-Beet Stress Detection Using Satellite Image Time Series.

Bhumika Laxman Sadbhave, Philipp Väth, Denise Dejon, Gunther Schorcht, Magda Gregorová

Proceedings of the 15th International Conference on Pattern Recognition Applications and Methods, 2026

2025

Diffusion Classifier Guidance for Non-robust Classifiers.

Philipp Väth, Dibyanshu Kumar, Benjamin Paassen, Magda Gregorová

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2025

Generative Example-Based Explanations: Bridging the Gap Between Generative Modeling and Explainability.

Philipp Väth, Alexander M. Frühwald, Benjamin Paassen, Magda Gregorová

Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2025

Loss Functions in Diffusion Models: A Comparative Study.

Dibyanshu Kumar, Philipp Väth, Magda Gregorová

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2025

2024

Variational autoencoder-based techniques for a streamlined cross-topology modeling and optimization workflow in electrical drives.

Marius Benkert, Michael Heroth, Rainer Herrler, Magda Gregorová, Helmut C. Schmid

Auton. Intell. Syst., December, 2024

Discrete Graph Auto-Encoder.

Yoann Boget, Magda Gregorova, Alexandros Kalousis

Trans. Mach. Learn. Res., 2024

GradCheck: Analyzing classifier guidance gradients for conditional diffusion sampling.

Philipp Väth, Alexander M. Frühwald, Benjamin Paassen, Magda Gregorova

CoRR, 2024

Variational Autoencoder-Based Multiobjective Topology Optimization of Electrical Machines Using Vector Graphics.

Michael Heroth, Helmut C. Schmid, Magda Gregorová, Rainer Herrler, Wilfried Hofmann

IEEE Access, 2024

An Investigative Study Exploring Machine Learning Approaches for Optimizing Deep Brain Stimulation Programming.

Esther Ademola, Martin Reich, Magda Gregorova

Proceedings of the Modelling and Development of Intelligent Systems, 2024

2023

Vector-Quantized Graph Auto-Encoder.

Yoann Boget, Magda Gregorova, Alexandros Kalousis

CoRR, 2023

Diffusion-Based Visual Counterfactual Explanations - Towards Systematic Quantitative Evaluation.

Philipp Väth, Alexander M. Frühwald, Benjamin Paassen, Magda Gregorova

Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2023

2022

GrannGAN: Graph annotation generative adversarial networks.

Yoann Boget, Magda Gregorova, Alexandros Kalousis

CoRR, 2022

Graph annotation generative adversarial networks.

Yoann Boget, Magda Gregorova, Alexandros Kalousis

Proceedings of the Asian Conference on Machine Learning, 2022

2021

Permutation Equivariant Generative Adversarial Networks for Graphs.

Yoann Boget, Magda Gregorova, Alexandros Kalousis

CoRR, 2021

Learned transform compression with optimized entropy encoding.

Magda Gregorová, Marc Desaules, Alexandros Kalousis

CoRR, 2021

2020

Lifelong generative modeling.

Jason Ramapuram, Magda Gregorova, Alexandros Kalousis

Neurocomputing, 2020

Data-Dependent Conditional Priors for Unsupervised Learning of Multimodal Data.

Frantzeska Lavda, Magda Gregorová, Alexandros Kalousis

Entropy, 2020

Improving VAE Generations of Multimodal Data Through Data-Dependent Conditional Priors.

Frantzeska Lavda, Magda Gregorová, Alexandros Kalousis

Proceedings of the ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020, 2020

2019

Sparse Learning for Variable Selection with Structures and Nonlinearities.

Magda Gregorova

CoRR, 2019

2018

Sparse learning for variable selection with structures and nonlinearities.

Magda Gregorova

PhD thesis, 2018

Continual Classification Learning Using Generative Models.

Frantzeska Lavda, Jason Ramapuram, Magda Gregorova, Alexandros Kalousis

CoRR, 2018

Structured nonlinear variable selection.

Magda Gregorova, Alexandros Kalousis, Stéphane Marchand-Maillet

Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Large-Scale Nonlinear Variable Selection via Kernel Random Features.

Magda Gregorová, Jason Ramapuram, Alexandros Kalousis, Stéphane Marchand-Maillet

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018

2017

Forecasting and Granger Modelling with Non-linear Dynamical Dependencies.

Magda Gregorová, Alexandros Kalousis, Stéphane Marchand-Maillet

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Learning Predictive Leading Indicators for Forecasting Time Series Systems with Unknown Clusters of Forecast Tasks.

Magda Gregorová, Alexandros Kalousis, Stéphane Marchand-Maillet

Proceedings of The 9th Asian Conference on Machine Learning, 2017

2015

Learning vector autoregressive models with focalised Granger-causality graphs.

Magda Gregorova, Alexandros Kalousis, Stéphane Marchand-Maillet, Jun Wang

CoRR, 2015