model/ stores the configurations of training.
data_gen.py generates relabled MNIST synthetic dataset.
ran_gen.py generates Gaussian data.
ran_label_gen.py generates MNIST dataset with random label.
networks.py stores the model.
runner2.py runs the configs.
test.py calculates the effective Gram matrix for neural networks.
test_ker.pt calculates the effective Gram matrix for kernel machines.
hessian.py compares approximations of contraction factors.
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