Logo des Repositoriums

Auflistung nach Autor:in "Duong, Manh Khoi"

1 - 2 von 2
  • Textdokument
    Automated Architecture-Modeling for Convolutional Neural Networks
    (BTW 2019 – Workshopband, 2019) Duong, Manh Khoi
    Tuning hyperparameters can be very counterintuitive and misleading, yet it plays a big (or even the biggest) part in many machine learning algorithms. For instance, finding the right architecture for an artificial neural network (ANN) can also be seen as a hyperparameter e.g. number of convolutional layers, number of fully connected layers etc. Tuning these can be done manually or by techniques such as grid search or random search. Even then finding optimal hyperparameters seems to be impossible. This paper tries to counter this problem by using bayesian optimization, which finds optimal parameters, including the right architecture for ANNs. In our case, a histological image dataset was used to classify breast cancer into stages.
  • Konferenzbeitrag
    RAPP: A Responsible Academic Performance Prediction Tool for Decision-Making in Educational Institutes
    (BTW 2023, 2023) Duong, Manh Khoi; Dunkelau, Jannik; Cordova, José Andrés; Conrad, Stefan
    Due to the increasing importance of educational data mining for the early intervention of at-risk students and the growth of performance data collected in educational institutes, it becomes natural to employ machine learning models to predict student's performances based off prior data. Although machine learning pipelines are often similar, developing one for a specific target prediction of academic success can become a daunting task. In this work, we present a graphical user interface which implements a customisable machine learning pipeline which allows the training and evaluation of machine learning models for different definitions of academic success, \eg, collected credits, average grade, number of passed exams, etc. The evaluation is exported in PDF format after finishing training. As this tool serves as a decision support system for socially responsible AI systems, fairness notions were included in the evaluation to detect potential discrimination in the data and prediction space.