We lifted 5 functions out of this paper's own repositories and ran 5 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 |
|---|---|---|
| dtak/local-independence-public | canonical | 5 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| conv_initializer | Ran | dtak/local-independence-public/beta_vae.py code served (permissive licence) · get_code("0589d333d30064fb") |
| fc_initializer | Ran | dtak/local-independence-public/beta_vae.py code served (permissive licence) · get_code("23a8fceb6d37f5dd") |
| l1_loss | Ran | dtak/local-independence-public/neural_network.py code served (permissive licence) · get_code("071547946910b3c5") |
| l2_loss | Ran | dtak/local-independence-public/neural_network.py code served (permissive licence) · get_code("3a69a8465f2f99e5") |
| squared_cos_sim | Ran | dtak/local-independence-public/neural_network.py code served (permissive licence) · get_code("731cee44a8b1fc4c") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
There has been growing interest in developing accurate models that can also be explained to humans. Unfortunately, if there exist multiple distinct but accurate models for some dataset, current machine learning methods are unlikely to find them: standard techniques will likely recover a complex model that combines them. In this work, we introduce a way to identify a maximal set of distinct but accurate models for a dataset. We demonstrate empirically that, in situations where the data supports multiple accurate classifiers, we tend to recover simpler, more interpretable classifiers rather than more complex ones.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1806.08716")
get_code_for_paper("1806.08716")
have("1806.08716")
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