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Code for feature learning with Average Gradient Outer Product (AGOP)

In ansatz_verification, we present code for verifying that AGOP is highly correlated with Neural Feature Matrices (NFMs) for transformers, CNNs, RNNs, and MLPs. In each directory, we provide a README outlining the requirements needed to run corresponding code.

In feature_visualizations, we provide code for visualizing features captured by AGOP of CNNs (an example is provided for VGG-19) and GPT2-architecture language models (an example is provided for a model trained on the TinyStories dataset).

In kernel_machine_agop, we provide code for computing AGOP of trained kernel machines using Laplace kernel and Convolutional Neural Tangent Kernel (CNTK).

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  • Jupyter Notebook 80.5%
  • Python 19.5%