Singularities — in the context of Information Geometry — refer to points where the usual smoothness assumptions about the space break down. These can be points where the model is not well-defined, or where there is a sudden change in the model’s structure (e.g., a phase transition).
Singularities in Information Theory
Singularities, in the context of Information Theory and deep learning, can refer to points in parameter space where the model’s behavior changes abruptly or where the information processing capacity of the model experiences a sudden transition. These points can be critical in understanding the Learning Dynamics of neural networks, as they may represent thresholds beyond which the network significantly alters its representation or processing of information.