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Umeå universitet
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Personalbild Saeideh Ghanbari Azar

Saeideh Ghanbari Azar

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saeideh.ghanbari@umu.se

Verksam som

Anknytning
Doktorand vid Institutionen för datavetenskap
Plats
MIT-huset, Umeå universitet, MIT.E.255 Umeå universitet, 901 87 Umeå
Rethinking the deepsmote penalty term and its role in imbalanced learning
2025 IEEE 37th International Conference on Tools with Artificial Intelligence (ICTAI): Proceedings, IEEE 2025 : 1110-1116
Ghanbari Azar, Saeideh; Nyholm, Tufve; Löfstedt, Tommy
From promise to practice: a study of common pitfalls behind the generalization gap in machine learning
Transactions on Machine Learning Research
Ghanbari Azar, Saeideh; Tronchin, Lorenzo; Simkó, Attila; et al.
Visa publikationer i DiVA

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