CORDIS Project
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This project focuses on improving the efficiency of deep learning by understanding and controlling algorithmic bias. It aims to identify how these biases impact model performance and generalization, ultimately facilitating the practical application of deep learning in various domains.
Deep Learning (DL) has reached unparalleled performance in many domains.
However, this impressive performance typically comes at the cost of gathering large datasets and training massive models, requiring extended time and prohibitive costs.
Significant research efforts are being invested in improving DL training efficiency, i.e., the amount of time, data, and resources required to train these models, by changing the model (e.g., architecture, numerical precision) or the training algorithm (e.g.…
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