32 citations · 55 across the 13 of their papers we have counts for
14 papers
Is AUC the best measure for practical comparison of anomaly detectors?
Vít Škvára, Tomáš Pevný, Václav Šmídl
The area under receiver operating characteristics (AUC) is the standard measure for comparison of anomaly detectors. Its advantage is in providing a scalar number that allows a nat…
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…
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…
Anomaly scores for generative models
Václav Šmídl, Jan Bím, Tomáš Pevný
Reconstruction error is a prevalent score used to identify anomalous samples when data are modeled by generative models, such as (variational) auto-encoders or generative adversari…
Are generative deep models for novelty detection truly better?
Vít Škvára, Tomáš Pevný, Václav Šmídl
Many deep models have been recently proposed for anomaly detection. This paper presents comparison of selected generative deep models and classical anomaly detection methods on an…
Sparse and Smooth Prior for Bayesian Linear Regression with Application to ETEX Data
Lukas Ulrych, Vaclav Smidl
Sparsity of the solution of a linear regression model is a common requirement, and many prior distributions have been designed for this purpose. A combination of the sparsity requi…
Adaptive Multiple Importance Sampling for Gaussian Processes
Xiaoyu Xiong, Václav Šmídl, Maurizio Filippone
In applications of Gaussian processes where quantification of uncertainty is a strict requirement, it is necessary to accurately characterize the posterior distribution over Gaussi…
Non-parametric Bayesian Models of Response Function in Dynamic Image Sequences
Ondřej Tichý, Václav Šmídl
Estimation of response functions is an important task in dynamic medical imaging. This task arises for example in dynamic renal scintigraphy, where impulse response or retention fu…