Zobrazeno 1 - 10
of 1 462
pro vyhledávání: '"Ertunc, A."'
Autor:
Cekmeceli, Kerem, Himmetoglu, Meva, Tombak, Guney I., Susmelj, Anna, Erdil, Ertunc, Konukoglu, Ender
Neural networks achieve state-of-the-art performance in many supervised learning tasks when the training data distribution matches the test data distribution. However, their performance drops significantly under domain (covariate) shift, a prevalent
Externí odkaz:
http://arxiv.org/abs/2409.07960
Autor:
Ertunc Erdil, Anton S. Becker, Moritz Schwyzer, Borja Martinez-Tellez, Jonatan R. Ruiz, Thomas Sartoretti, H. Alberto Vargas, A. Irene Burger, Alin Chirindel, Damian Wild, Nicola Zamboni, Bart Deplancke, Vincent Gardeux, Claudia Irene Maushart, Matthias Johannes Betz, Christian Wolfrum, Ender Konukoglu
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-14 (2024)
Abstract The standard method for identifying active Brown Adipose Tissue (BAT) is [18F]-Fluorodeoxyglucose ([18F]-FDG) PET/CT imaging, which is costly and exposes patients to radiation, making it impractical for population studies. These issues can b
Externí odkaz:
https://doaj.org/article/c805cb8195b94f0d8b4205323cae48a9
In human-AI collaboration systems for critical applications, in order to ensure minimal error, users should set an operating point based on model confidence to determine when the decision should be delegated to human experts. Samples for which model
Externí odkaz:
http://arxiv.org/abs/2308.05035
Variational autoencoders (VAEs) are powerful generative modelling methods, however they suffer from blurry generated samples and reconstructions compared to the images they have been trained on. Significant research effort has been spent to increase
Externí odkaz:
http://arxiv.org/abs/2304.05939
Publikováno v:
In Journal of Quantitative Spectroscopy and Radiative Transfer October 2024 325
Autor:
Karani, Neerav, Brunner, Georg, Erdil, Ertunc, Fei, Simin, Tezcan, Kerem, Chaitanya, Krishna, Konukoglu, Ender
Performance of convolutional neural networks (CNNs) in image analysis tasks is often marred in the presence of acquisition-related distribution shifts between training and test images. Recently, it has been proposed to tackle this problem by fine-tun
Externí odkaz:
http://arxiv.org/abs/2202.05271
Blind deconvolution is an ill-posed problem arising in various fields ranging from microscopy to astronomy. The ill-posed nature of the problem requires adequate priors to arrive to a desirable solution. Recently, it has been shown that deep learning
Externí odkaz:
http://arxiv.org/abs/2112.10271
Supervised deep learning-based methods yield accurate results for medical image segmentation. However, they require large labeled datasets for this, and obtaining them is a laborious task that requires clinical expertise. Semi/self-supervised learnin
Externí odkaz:
http://arxiv.org/abs/2112.09645
Publikováno v:
In Automation in Construction April 2024 160