Francesco Verdoja, Ville Kyrki
We lifted 5 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 |
|---|---|---|
| aalto-intelligent-robotics/mc-dropout-notebooks | canonical | 3 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| LineSample | Ran | aalto-intelligent-robotics/mc-dropout-notebooks/functions.py code served (permissive licence) · get_code("4242fbb59bb8ad5d") |
| to_cuda | Ran | aalto-intelligent-robotics/mc-dropout-notebooks/nets.py code served (permissive licence) · get_code("3748277200ac8e4c") |
| to_variable | Ran | aalto-intelligent-robotics/mc-dropout-notebooks/nets.py code served (permissive licence) · get_code("cf417f6499909dfa") |
| GMMsample | Not yet run | aalto-intelligent-robotics/mc-dropout-notebooks/functions.py code served (permissive licence) · get_code("9fb65a7ecff0a064") |
| ShapeSample | Not yet run | aalto-intelligent-robotics/mc-dropout-notebooks/functions.py code served (permissive licence) · get_code("d555811c9f1edccf") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Among the various options to estimate uncertainty in deep neural networks, Monte-Carlo dropout is widely popular for its simplicity and effectiveness. However the quality of the uncertainty estimated through this method varies and choices in architecture design and in training procedures have to be carefully considered and tested to obtain satisfactory results. In this paper we present study offering a different point of view on the behavior of Monte-Carlo dropout, which enable us to observe a few interesting properties of the technique to keep in mind when considering its use for uncertainty estimation.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2008.02627")
get_code_for_paper("2008.02627")
have("2008.02627")
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