32 citations · 55 across the 13 of their papers we have counts for
7 papers · 1 filter
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…
DeepTopPush: Simple and Scalable Method for Accuracy at the Top
Václav Mácha, Lukáš Adam, Václav Šmídl
Accuracy at the top is a special class of binary classification problems where the performance is evaluated only on a small number of relevant (top) samples. Applications include i…
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…
Nonlinear classifiers for ranking problems based on kernelized SVM
Václav Mácha, Lukáš Adam, Václav Šmídl
Many classification problems focus on maximizing the performance only on the samples with the highest relevance instead of all samples. As an example, we can mention ranking proble…