Missing Features Reconstruction and Its Impact on Classification Accuracy. - Magda Friedjungová; Marcel Jirina; Daniel Vasata (ICCS (3), 2019)

Unsupervised Latent Space Translation Network. - Magda Friedjungová; Daniel Vasata; Tomás Chobola; Marcel Jirina (ESANN, 2020)

Missing Features Reconstruction Using a Wasserstein Generative Adversarial Imputation Network. - Magda Friedjungová; Daniel Vasata; Maksym Balatsko; Marcel Jirina (ICCS (4), 2020)

Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural network. - Tomás Chobola; Daniel Vasata; Pavel Kordík (MetaDL@AAAI, 2021)

Image Inpainting Using Wasserstein Generative Adversarial Imputation Network. - Daniel Vasata; Tomás Halama; Magda Friedjungová (ICANN (2), 2021)

Learning to Optimize with Dynamic Mode Decomposition. - Petr Simánek; Daniel Vasata; Pavel Kordík (IJCNN, 2022)

Evacuation trials from a double-deck electric train unit: Experimental data and sensitivity analysis. - Hana Najmanová; Veronika Pesková; Lukás Kuklík; Marek Bukácek; Pavel Hrabák; Daniel Vasata (CoRR, 2022)

Machine Learning Metrics for Network Datasets Evaluation. - Dominik Soukup; Daniel Uhrícek; Daniel Vasata; Tomás Cejka (SEC, 2023)

Predicting Blood Glucose Levels with LMU Recurrent Neural Networks: A Novel Computational Model. - Ladislav Floris; Daniel Vasata (AIME (1), 2024)

Investigation into the Training Dynamics of Learned Optimizers. - Jan Sobotka; Petr Simánek; Daniel Vasata (ICAART (2), 2024)

Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas. - Marek Miltner; Jakub Zíka; Daniel Vasata; Artem Bryksa; Magda Friedjungová; Ondrej Stogl; Ram Rajagopal; Oldrich Starý (CoRR, 2024)