Exploring layers in deep learning models: interview with Mara Graziani

Mara Graziani and colleagues Laura O' Mahony, An-Phi Nguyen, Henning Müller and Vincent Andrearczyk are researching deep learning models and the associated learned representations. In this interview, Mara tells us about the team's proposed framework for concept discovery. Our paper Uncovering Unique Concept Vectors through Latent Space Decomposition focuses on understanding how representations are organized by intermediate layers of complex deep learning models. The latent space of a layer can be interpreted as a vector space spanned by individual neuron directions. In our work, we identify a new basis that aligns with the variance of the training data.