atomicarchitects/PriceofFreedom
[ICML'25] The Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Products
This project helps machine learning researchers explore the balance between how complex an AI model can be and how fast it runs. It takes as input various configurations of 'equivariant tensor products'—a specific type of mathematical operation in AI—and outputs measurements of their expressivity (power) and computational efficiency (speed). It's designed for researchers developing or evaluating advanced machine learning models, especially those in areas like physics-informed AI or geometric deep learning.
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Use this if you are a machine learning researcher or practitioner who needs to understand the performance tradeoffs of different equivariant neural network architectures.
Not ideal if you are looking for a general-purpose machine learning library for standard data analysis or an end-user application.
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Jul 16, 2025
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