9 citations · 13 across the 3 of their papers we have counts for
4 papers · 1 filter
Learning to Optimize with Dynamic Mode Decomposition
Petr Šimánek, Daniel Vašata, Pavel Kordík
Designing faster optimization algorithms is of ever-growing interest. In recent years, learning to learn methods that learn how to optimize demonstrated very encouraging results. C…
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…
Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural network
Tomáš Chobola, Daniel Vašata, Pavel Kordík
MetaDL Challenge 2020 focused on image classification tasks in few-shot settings. This paper describes second best submission in the competition. Our meta learning approach modifie…
Deep Variational Autoencoder with Shallow Parallel Path for Top-N Recommendation (VASP)
Vojtěch Vančura, Pavel Kordík
Recently introduced EASE algorithm presents a simple and elegant way, how to solve the top-N recommendation task. In this paper, we introduce Neural EASE to further improve the per…