Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Dushatskiy, Arkadiy"'
Tree Containment is a fundamental problem in phylogenetics useful for verifying a proposed phylogenetic network, representing the evolutionary history of certain species. Tree Containment asks whether the given phylogenetic tree (for instance, constr
Externí odkaz:
http://arxiv.org/abs/2404.09812
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, reusing the partially trained weights allows for efficient search, as was p
Externí odkaz:
http://arxiv.org/abs/2307.15621
Population Based Training (PBT) is an efficient hyperparameter optimization algorithm. PBT is a single-objective algorithm, but many real-world hyperparameter optimization problems involve two or more conflicting objectives. In this work, we therefor
Externí odkaz:
http://arxiv.org/abs/2306.01436
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 acknowledged to still be a problem. One way to automatically overcome this is
Externí odkaz:
http://arxiv.org/abs/2202.12099
Autor:
Bosma, Martijn M. A., Dushatskiy, Arkadiy, Grewal, Monika, Alderliesten, Tanja, Bosman, Peter A. N.
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 cases human-level, performance, they have become the standard approach in
Externí odkaz:
http://arxiv.org/abs/2202.11401
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. Due to stochastic factors in neural network initialization, training,
Externí odkaz:
http://arxiv.org/abs/2202.02078
When it comes to solving optimization problems with evolutionary algorithms (EAs) in a reliable and scalable manner, detecting and exploiting linkage information, i.e., dependencies between variables, can be key. In this article, we present the lates
Externí odkaz:
http://arxiv.org/abs/2109.05259
We propose a novel surrogate-assisted Evolutionary Algorithm for solving expensive combinatorial optimization problems. We integrate a surrogate model, which is used for fitness value estimation, into a state-of-the-art P3-like variant of the Gene-Po
Externí odkaz:
http://arxiv.org/abs/2104.08048
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 as it makes training data heterogeneous but so far no attempts have bee
Externí odkaz:
http://arxiv.org/abs/2001.08552
Autor:
den Ottelander, Tom, Dushatskiy, Arkadiy, Virgolin, Marco, Bosman, Peter A. N., Ishibuchi, Hisao, Zhang, Qingfu, Cheng, Ran, Li, Ke, Li, Hui, Wang, Handing, Zhou, Aimin
Publikováno v:
Evolutionary Multi-Criterion Optimization-11th International Conference, EMO 2021, Proceedings, 12654 LNCS, 465-479
Lecture Notes in Computer Science ISBN: 9783030720612
EMO
Lecture Notes in Computer Science ISBN: 9783030720612
EMO
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 proposed. Yet, solid comparisons with simple baselines are often missing. At