Guido Mont, Benjamin Bowman
We lifted 4 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| bbowman223/deepspec | canonical | 3 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| coor_loss | Ran | bbowman223/deepspec/deep/hercules/ntk_utils.py code served (permissive licence) · get_code("043d8579b7468851") |
| grad_size | Ran | bbowman223/deepspec/deep/hercules/ntk_utils.py code served (permissive licence) · get_code("3a1adab214500167") |
| is_supported | Ran | bbowman223/deepspec/deep/hercules/autograd_hacks.py code served (permissive licence) · get_code("d5f71e934c65e691") |
| symsqrt | Not yet run | bbowman223/deepspec/deep/hercules/autograd_hacks.py code served (permissive licence) · get_code("56afcb3496c8fd91") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We provide quantitative bounds measuring the L 2 difference in function space between the trajectory of a finite-width network trained on finitely many samples from the idealized kernel dynamics of infinite width and infinite data. An implication of the bounds is that the network is biased to learn the top eigenfunctions of the Neural Tangent Kernel not just on the training set but over the entire input space. This bias depends on the model architecture and input distribution alone and thus does not depend on the target function which does not need to be in the RKHS of the kernel. The result is valid for deep architectures with fully connected, convolutional, and residual layers. Furthermore the width does not need to grow polynomially with the number of samples in order to obtain high probability bounds up to a stopping time. The proof exploits the low-effective-rank property of the Fisher Information Matrix at initialization, which implies a low effective dimension of the model (far smaller than the number of parameters). We conclude that local capacity control from the low effective rank of the Fisher Information Matrix is still underexplored theoretically.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2206.02927")
get_code_for_paper("2206.02927")
have("2206.02927")
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