1 citations · 2 across the 4 of their papers we have counts for
6 papers
Convolutional Signal Propagation: A Simple Scalable Algorithm for Hypergraphs
Pavel Procházka, Marek Dědič, Lukáš Bajer
Last decade has seen the emergence of numerous methods for learning on graphs, particularly Graph Neural Networks (GNNs). These methods, however, are often not directly applicable…
Malicious Internet Entity Detection Using Local Graph Inference
Simon Mandlik, Tomas Pevny, Vaclav Smidl +1
Detection of malicious behavior in a large network is a challenging problem for machine learning in computer security, since it requires a model with high expressive power and scal…
Learning Explainable Representations of Malware Behavior
Paul Prasse, Jan Brabec, Jan Kohout +3
We address the problems of identifying malware in network telemetry logs and providing \emph{indicators of compromise} -- comprehensible explanations of behavioral patterns that id…
Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models
Jakub Repicky, Lukas Bajer, Zbynek Pitra +1
The interest in accelerating black-box optimizers has resulted in several surrogate model-assisted version of the Covariance Matrix Adaptation Evolution Strategy, a state-of-the-ar…
Model Guided Sampling Optimization for Low-dimensional Problems
Lukas Bajer, Martin Holena
Optimization of very expensive black-box functions requires utilization of maximum information gathered by the process of optimization. Model Guided Sampling Optimization (MGSO) fo…
Two Gaussian Approaches to Black-Box Optomization
Lukáš Bajer, Martin Holeňa
Outline of several strategies for using Gaussian processes as surrogate models for the covariance matrix adaptation evolution strategy (CMA-ES).