We lifted 3 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| danielracz/gradsim | canonical | 1 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| test_model | Ran | danielracz/gradsim/grad_utils.py pointer only (licence: NONE) · get_code("2ccdbf7196e90927") |
| get_data | Not yet run | danielracz/gradsim/grad_utils.py pointer only (licence: NONE) · get_code("c13c8a80a222bb96") |
| get_data_cifar100 | Not yet run | danielracz/gradsim/grad_utils.py pointer only (licence: NONE) · get_code("38ed6bea54548ead") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Feed-forward networks can be interpreted as mappings with linear decision surfaces at the level of the last layer. We investigate how the tangent space of the network can be exploited to refine the decision in case of ReLU (Rectified Linear Unit) activations. We show that a simple Riemannian metric parametrized on the parameters of the network forms a similarity function at least as good as the original network and we suggest a sparse metric to increase the similarity gap.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2110.13581")
get_code_for_paper("2110.13581")
have("2110.13581")
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