Temporal Geometry of Deep Networks: Hyperbolic Representations of Training Dynamics for Intrinsic Explainability
This paper investigates how MLPs and their training dynamics can be represented and studied in non-Euclidean spaces and presents a geometric and temporal graph-based metalearning framework for obtaining dynamic hyperbolic representations of the underlying neural parameter graphs.
A. Moharil
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