Geoff Pleiss, Kilian Weinberger, Yu Sun, Chuan Guo
We lifted 4 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| saurabhgarg1996/calibration | reimplementation | 3 of 3 |
| sirius8050/Expected-Calibration-Error | reimplementation | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| add_softmax | Ran | saurabhgarg1996/calibration/calibration/calibrators.py code served (permissive licence) · get_code("5ac25fc20a9cb76f") |
| cross_entropy_loss | Ran | saurabhgarg1996/calibration/calibration/calibrators.py code served (permissive licence) · get_code("155f63d9cf89a950") |
| ece_loss | Ran | saurabhgarg1996/calibration/calibration/calibrators.py code served (permissive licence) · get_code("2bc928c2a34c7e13") |
| ece_score | Ran | sirius8050/Expected-Calibration-Error/ECE.py pointer only (licence: NONE) · get_code("aaabd8e8f8f13cc9") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Confidence calibration -the problem of predicting probability estimates representative of the true correctness likelihood -is important for classification models in many applications. We discover that modern neural networks, unlike those from a decade ago, are poorly calibrated. Through extensive experiments, we observe that depth, width, weight decay, and Batch Normalization are important factors influencing calibration. We evaluate the performance of various post-processing calibration methods on state-ofthe-art architectures with image and document classification datasets. Our analysis and experiments not only offer insights into neural network learning, but also provide a simple and straightforward recipe for practical settings: on most datasets, temperature scaling -a singleparameter variant of Platt Scaling -is surprisingly effective at calibrating predictions.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1706.04599")
get_code_for_paper("1706.04599")
have("1706.04599")
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