Searched for: author%3A%22Rikalo%2C+N.%22
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Balayn, A.M.A. (author), Rikalo, N. (author), Yang, J. (author), Bozzon, A. (author)
Handling failures in computer vision systems that rely on deep learning models remains a challenge. While an increasing number of methods for bug identification and correction are proposed, little is known about how practitioners actually search for failures in these models. We perform an empirical study to understand the goals and needs of...
conference paper 2023
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Balayn, A.M.A. (author), Rikalo, N. (author), Lofi, C. (author), Yang, J. (author), Bozzon, A. (author)
Deep learning models for image classification suffer from dangerous issues often discovered after deployment. The process of identifying bugs that cause these issues remains limited and understudied. Especially, explainability methods are often presented as obvious tools for bug identification. Yet, the current practice lacks an understanding...
conference paper 2022
document
Rikalo, N. (author)
Human induced pluripotent stem cells (hiPSCs) offer the possibility to model human disease and study their behavior. They help scientists discover early disease-causing events in cells and are therefore used in discoveries about premature aging, congenital heart disease, cancer, and disorders connected to fetal development. Because of their...
master thesis 2020