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    <title>resource efficiency on DASYA</title>
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      <title>Data Attribution on Progressive Datasets for Deep Learning</title>
      <link>https://dasya.itu.dk/for-students/proposals/archive/pito-data-attribution/</link>
      <pubDate>Tue, 15 Jul 2025 00:00:00 +0000</pubDate>
      
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      <description>Deep convolutional networks are able to learn representation of images, scoring well in tasks such as image classification and object detection. During model training, these networks have the ability to process different input sizes without requiring changes to their architecture. In this project, we would like to investigate the effects that changing input sizes has on these kinds of models. We want to do this by investigating and using data attribution methods, as these techniques can help explain the effect of input data on the predictions of the model.</description>
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      <title>Efficient Data Selection Methods for Machine Learning</title>
      <link>https://dasya.itu.dk/for-students/proposals/archive/pito-data-selection/</link>
      <pubDate>Tue, 15 Jul 2025 00:00:00 +0000</pubDate>
      
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      <description>Today’s foundation models are trained on vast amounts of data. The quality and size of this data has a huge impact on the accuracy of these models. Selecting the right amount and variety of data for a given task, however, is a resource-intensive process. In this project, which is part of a larger collaboration, we would like to expand our investigation of state-of-the-art data selection mechanisms from a hardware requirements and resource-efficiency perspective in addition to their impact on model accuracy.</description>
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