We lifted 1 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 |
|---|---|---|
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| get_logits_labels | Ran | this paper's copy was not recorded; identical code first harvested from ondrejbohdal/meta-calibration pointer only · get_code("193b1a3f592079ed") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Calibration of neural networks is a topical problem that is becoming more and more important as neural networks increasingly underpin real-world applications. The problem is especially noticeable when using modern neural networks, for which there is a significant difference between the confidence of the model and the probability of correct prediction. Various strategies have been proposed to improve calibration, yet accurate calibration remains challenging. We propose a novel framework with two contributions: introducing a new differentiable surrogate for expected calibration error (DECE) that allows calibration quality to be directly optimised, and a meta-learning framework that uses DECE to optimise for validation set calibration with respect to model hyper-parameters. The results show that we achieve competitive performance with existing calibration approaches. Our framework opens up a new avenue and toolset for tackling calibration, which we believe will inspire further work on this important challenge.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2106.09613")
get_code_for_paper("2106.09613")
have("2106.09613")
Connect an agent — have() is free.