2 citations · 4 across the 5 of their papers we have counts for
5 papers
Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal
Leah Chrestien, Tomás Pevný, Stefan Edelkamp +1
In imitation learning for planning, parameters of heuristic functions are optimized against a set of solved problem instances. This work revisits the necessary and sufficient condi…
Leveraging Data Geometry to Mitigate CSM in Steganalysis
Rony Abecidan, Vincent Itier, Jérémie Boulanger +2
In operational scenarios, steganographers use sets of covers from various sensors and processing pipelines that differ significantly from those used by researchers to train stegana…
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…
Explaining Classifiers Trained on Raw Hierarchical Multiple-Instance Data
Tomáš Pevný, Viliam Lisý, Branislav Bošanský +2
Learning from raw data input, thus limiting the need for feature engineering, is a component of many successful applications of machine learning methods in various domains. While m…
Heuristic Search Planning with Deep Neural Networks using Imitation, Attention and Curriculum Learning
Leah Chrestien, Tomas Pevny, Antonin Komenda +1
Learning a well-informed heuristic function for hard task planning domains is an elusive problem. Although there are known neural network architectures to represent such heuristic…