1 citations · 2 across the 5 of their papers we have counts for
5 papers
Iterated Population Based Training with Task-Agnostic Restarts
Alexander Chebykin, Tanja Alderliesten, Peter A. N. Bosman
Hyperparameter Optimization (HPO) can lift the burden of tuning hyperparameters (HPs) of neural networks. HPO algorithms from the Population Based Training (PBT) family are efficie…
Hyperparameter-Free Medical Image Synthesis for Sharing Data and Improving Site-Specific Segmentation
Alexander Chebykin, Peter A. N. Bosman, Tanja Alderliesten
Sharing synthetic medical images is a promising alternative to sharing real images that can improve patient privacy and data security. To get good results, existing methods for med…
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…
Evolutionary Neural Cascade Search across Supernetworks
Alexander Chebykin, Tanja Alderliesten, Peter A. N. Bosman
To achieve excellent performance with modern neural networks, having the right network architecture is important. Neural Architecture Search (NAS) concerns the automatic discovery…