Interpretability Challenges in Deep Learning: A Focus on Symbolic Representation

As deep learning models continue to demonstrate unprecedented performance across various domains, the interpretability of these models becomes a critical concern. Deep learning models are often recognized for their proficiency in addressing statistical problems rather than excelling in calculations or processing symbolic data. This thesis explores the potential of incorporating symbolic representations, aiming to enhance the transparency and interpretability of deep learning models.