17 citations · 19 across the 8 of their papers we have counts for
19 papers
Towards Learned Simulators for Cell Migration
Koen Minartz, Yoeri Poels, Vlado Menkovski
Simulators driven by deep learning are gaining popularity as a tool for efficiently emulating accurate but expensive numerical simulators. Successful applications of such neural si…
Calibrated Adversarial Training
Tianjin Huang, Vlado Menkovski, Yulong Pei +1
Adversarial training is an approach of increasing the robustness of models to adversarial attacks by including adversarial examples in the training set. One major challenge of prod…
Process Discovery Using Graph Neural Networks
Dominique Sommers, Vlado Menkovski, Dirk Fahland
Automatically discovering a process model from an event log is the prime problem in process mining. This task is so far approached as an unsupervised learning problem through graph…
VAE-CE: Visual Contrastive Explanation using Disentangled VAEs
Yoeri Poels, Vlado Menkovski
The goal of a classification model is to assign the correct labels to data. In most cases, this data is not fully described by the given set of labels. Often a rich set of meaningf…
On Generalization of Graph Autoencoders with Adversarial Training
Tianjin Huang, Yulong Pei, Vlado Menkovski +1
Adversarial training is an approach for increasing model's resilience against adversarial perturbations. Such approaches have been demonstrated to result in models with feature rep…
A Metric for Linear Symmetry-Based Disentanglement
Luis A. Pérez Rey, Loek Tonnaer, Vlado Menkovski +2
The definition of Linear Symmetry-Based Disentanglement (LSBD) proposed by (Higgins et al., 2018) outlines the properties that should characterize a disentangled representation tha…