4 citations · 9 across the 10 of their papers we have counts for
10 papers
Solving the Tree Containment Problem Using Graph Neural Networks
Arkadiy Dushatskiy, Esther Julien, Leen Stougie +1
Tree Containment is a fundamental problem in phylogenetics useful for verifying a proposed phylogenetic network, representing the evolutionary history of certain species. Tree Cont…
Shrink-Perturb Improves Architecture Mixing during Population Based Training for Neural Architecture Search
Alexander Chebykin, Arkadiy Dushatskiy, Tanja Alderliesten +1
In this work, we show that simultaneously training and mixing neural networks is a promising way to conduct Neural Architecture Search (NAS). For hyperparameter optimization, reusi…
Multi-Objective Population Based Training
Arkadiy Dushatskiy, Alexander Chebykin, Tanja Alderliesten +1
Population Based Training (PBT) is an efficient hyperparameter optimization algorithm. PBT is a single-objective algorithm, but many real-world hyperparameter optimization problems…
Data variation-aware medical image segmentation
Arkadiy Dushatskiy, Gerry Lowe, Peter A. N. Bosman +1
Deep learning algorithms have become the golden standard for segmentation of medical imaging data. In most works, the variability and heterogeneity of real clinical data is acknowl…
Mixed-Block Neural Architecture Search for Medical Image Segmentation
Martijn M. A. Bosma, Arkadiy Dushatskiy, Monika Grewal +2
Deep Neural Networks (DNNs) have the potential for making various clinical procedures more time-efficient by automating medical image segmentation. Due to their strong, in some cas…
Heed the Noise in Performance Evaluations in Neural Architecture Search
Arkadiy Dushatskiy, Tanja Alderliesten, Peter A. N. Bosman
Neural Architecture Search (NAS) has recently become a topic of great interest. However, there is a potentially impactful issue within NAS that remains largely unrecognized: noise.…
Parameterless Gene-pool Optimal Mixing Evolutionary Algorithms
Arkadiy Dushatskiy, Marco Virgolin, Anton Bouter +2
When it comes to solving optimization problems with evolutionary algorithms (EAs) in a reliable and scalable manner, detecting and exploiting linkage information, i.e., dependencie…
A Novel Surrogate-assisted Evolutionary Algorithm Applied to Partition-based Ensemble Learning
Arkadiy Dushatskiy, Tanja Alderliesten, Peter A. N. Bosman
We propose a novel surrogate-assisted Evolutionary Algorithm for solving expensive combinatorial optimization problems. We integrate a surrogate model, which is used for fitness va…
Local Search is a Remarkably Strong Baseline for Neural Architecture Search
T. Den Ottelander, A. Dushatskiy, M. Virgolin +1
Neural Architecture Search (NAS), i.e., the automation of neural network design, has gained much popularity in recent years with increasingly complex search algorithms being propos…
Observer variation-aware medical image segmentation by combining deep learning and surrogate-assisted genetic algorithms
Arkadiy Dushatskiy, Adriënne M. Mendrik, Peter A. N. Bosman +1
There has recently been great progress in automatic segmentation of medical images with deep learning algorithms. In most works observer variation is acknowledged to be a problem a…