10 citations · 32 across the 8 of their papers we have counts for
5 papers · 1 filter
Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas
Marek Miltner, Jakub Zíka, Daniel Vašata +5
This study addresses the challenge of predicting electric vehicle (EV) charging profiles in urban locations with limited data. Utilizing a neural network architecture, we aim to un…
Dynamic Neural Diversification: Path to Computationally Sustainable Neural Networks
Alexander Kovalenko, Pavel Kordík, Magda Friedjungová
Small neural networks with a constrained number of trainable parameters, can be suitable resource-efficient candidates for many simple tasks, where now excessively large models are…
Missing Features Reconstruction Using a Wasserstein Generative Adversarial Imputation Network
Magda Friedjungová, Daniel Vašata, Maksym Balatsko +1
Missing data is one of the most common preprocessing problems. In this paper, we experimentally research the use of generative and non-generative models for feature reconstruction.…
Unsupervised Latent Space Translation Network
Magda Friedjungová, Daniel Vašata, Tomáš Chobola +1
One task that is often discussed in a computer vision is the mapping of an image from one domain to a corresponding image in another domain known as image-to-image translation. Cur…
Missing Features Reconstruction and Its Impact on Classification Accuracy
Magda Friedjungová, Daniel Vašata, Marcel Jiřina
In real-world applications, we can encounter situations when a well-trained model has to be used to predict from a damaged dataset. The damage caused by missing or corrupted values…