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Found 3 papers, 1 papers with code
Structures of Neural Network Effective Theories
We develop a diagrammatic approach to effective field theories (EFTs) corresponding to deep neural networks at initialization, which dramatically simplifies computations of finite-width corrections to neuron statistics.
The Linearized Hellinger--Kantorovich Distance
Working with the local linearization and the corresponding embeddings allows for the advantages of the Euclidean setting, such as faster computations and a plethora of data analysis tools, whilst still enjoying approximately the descriptive power of the Hellinger--Kantorovich metric.
Optimization and Control
Linearized Optimal Transport for Collider Events
We introduce an efficient framework for computing the distance between collider events using the tools of Linearized Optimal Transport (LOT).
