| 2024 | AIME | Predicting Blood Glucose Levels with LMU Recurrent Neural Networks: A Novel Computational Model. | Ladislav Floris, Daniel Vasata |
| 2024 | ICAART | Investigation into the Training Dynamics of Learned Optimizers. | Jan Sobotka, Petr Simnek, Daniel Vasata |
| 2023 | SEC | Machine Learning Metrics for Network Datasets Evaluation. | Dominik Soukup, Daniel Uhrcek, Daniel Vasata, Toms Cejka |
| 2022 | IJCNN | Learning to Optimize with Dynamic Mode Decomposition. | Petr Simnek, Daniel Vasata, Pavel Kordk |
| 2021 | AAAI | Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural network. | Toms Chobola, Daniel Vasata, Pavel Kordk |
| 2021 | ICANN | Image Inpainting Using Wasserstein Generative Adversarial Imputation Network. | Daniel Vasata, Toms Halama, Magda Friedjungov |
| 2020 | ESANN | Unsupervised Latent Space Translation Network. | Magda Friedjungov, Daniel Vasata, Toms Chobola, Marcel Jirina |
| 2020 | ICCS | Missing Features Reconstruction Using a Wasserstein Generative Adversarial Imputation Network. | Magda Friedjungov, Daniel Vasata, Maksym Balatsko, Marcel Jirina |
| 2019 | ICCS | Missing Features Reconstruction and Its Impact on Classification Accuracy. | Magda Friedjungov, Marcel Jirina, Daniel Vasata |