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pro vyhledávání: '"Mihael Cudic"'
While Generative Adversarial Networks (GANs) can now reliably produce realistic images in a multitude of imaging domains, they are ill-equipped to model thin, stochastic textures present in many large 3D fluorescent microscopy (FM) images acquired in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::03f43b634e6607f21b204104ec007426
https://doi.org/10.1016/j.media.2023.102768
https://doi.org/10.1016/j.media.2023.102768
Publikováno v:
Neurocomputing. 291:128-135
In order to move toward efficient autonomous learning, we must have control over our datasets to test and adaptively train systems for complex problems such as Visual Question Answering (VQA). Thus, we created a testing environment around MNIST image
Autor:
Mare Cudic, Enbo Liu, Tatyana G. Karabencheva-Christova, Austin B. Yongye, Karina Martinez Mayorga, Mihael Cudic, Jon Ainsley, Predrag Cudic, Maria C. Rodriguez, Barbara M. Mueller, Christo Z. Christov
Publikováno v:
Amino Acids. 49:1867-1883
The transformation from normal to malignant phenotype in human cancers is associated with aberrant cell-surface glycosylation. Thus, targeting glycosylation changes in cancer is likely to provide not only better insight into the roles of carbohydrate
Autor:
Mihael Cudic, Jose C. Principe
Publikováno v:
IJCNN
Many machine learning algorithms, like Convolutional Neural Networks (CNNs), have excelled in image processing tasks; however, they have many practical limitations. For one, these systems require large datasets that accurately represent the sample di
Publikováno v:
ICMLA
Extensive genetic and phenotypic research is necessary for any effective plant breeding program. Such studies, however, require an immense amount of time and resources. In order to expedite the breeding process, we provide a novel method for rapid ge
Publikováno v:
IJCNN
Visual Question Answering is a complex problem that fuses natural language and image processing to answer a question based on information from the image. The basic architecture for accomplishing this is using a CNN to extract features from the image