5 citations · 11 across the 5 of their papers we have counts for
9 papers
Fitting large mixture models using stochastic component selection
Milan Papež, Tomáš Pevný, Václav Šmídl
Traditional methods for unsupervised learning of finite mixture models require to evaluate the likelihood of all components of the mixture. This becomes computationally prohibitive…
Comparison of Anomaly Detectors: Context Matters
Vít Škvára, Jan Franců, Matěj Zorek +2
Deep generative models are challenging the classical methods in the field of anomaly detection nowadays. Every new method provides evidence of outperforming its predecessors, often…
Solvability of the Power Flow Problem in DC Overhead Wire Circuit
Jakub Ševčík, Lukáš Adam, Jan Přikryl +1
Proper traffic simulation of electric vehicles, which draw energy from overhead wires, requires adequate modeling of traction infrastructure. Such vehicles include trains, trams or…
Neural Power Units
Niklas Heim, Tomáš Pevný, Václav Šmídl
Conventional Neural Networks can approximate simple arithmetic operations, but fail to generalize beyond the range of numbers that were seen during training. Neural Arithmetic Unit…
Sum-Product-Transform Networks: Exploiting Symmetries using Invertible Transformations
Tomas Pevny, Vasek Smidl, Martin Trapp +2
In this work, we propose Sum-Product-Transform Networks (SPTN), an extension of sum-product networks that uses invertible transformations as additional internal nodes. The type and…
General Framework for Binary Classification on Top Samples
Lukáš Adam, Václav Mácha, Václav Šmídl +1
Many binary classification problems minimize misclassification above (or below) a threshold. We show that instances of ranking problems, accuracy at the top or hypothesis testing m…