32 citations · 106 across the 33 of their papers we have counts for
5 papers · 1 filter
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…
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…
When Should You Defend Your Classifier -- A Game-theoretical Analysis of Countermeasures against Adversarial Examples
Maximilian Samsinger, Florian Merkle, Pascal Schöttle +1
Adversarial machine learning, i.e., increasing the robustness of machine learning algorithms against so-called adversarial examples, is now an established field. Yet, newly propose…
Mill.jl and JsonGrinder.jl: automated differentiable feature extraction for learning from raw JSON data
Simon Mandlik, Matej Racinsky, Viliam Lisy +1
Learning from raw data input, thus limiting the need for manual feature engineering, is one of the key components of many successful applications of machine learning methods. While…
Mapping the Internet: Modelling Entity Interactions in Complex Heterogeneous Networks
Simon Mandlik, Tomas Pevny
Even though machine learning algorithms already play a significant role in data science, many current methods pose unrealistic assumptions on input data. The application of such me…