We lifted 3 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 |
|---|---|---|
| uncertainty-toolbox/uncertainty-toolbox | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| plot_intervals | Ran | uncertainty-toolbox/uncertainty-toolbox/uncertainty_toolbox/viz.py code served (permissive licence) · get_code("fc0d6fe304a9300e") |
| plot_intervals_ordered | Ran | uncertainty-toolbox/uncertainty-toolbox/uncertainty_toolbox/viz.py code served (permissive licence) · get_code("c91b9d1ff6fd6336") |
| plot_xy | Ran | uncertainty-toolbox/uncertainty-toolbox/uncertainty_toolbox/viz.py code served (permissive licence) · get_code("cb5c16f07da4476c") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
With increasing deployment of machine learning systems in various real-world tasks, there is a greater need for accurate quantification of predictive uncertainty. While the common goal in uncertainty quantification (UQ) in machine learning is to approximate the true distribution of the target data, many works in UQ tend to be disjoint in the evaluation metrics utilized, and disparate implementations for each metric lead to numerical results that are not directly comparable across different works. To address this, we introduce Uncertainty Toolbox, an open-source python library that helps to assess, visualize, and improve UQ. Uncertainty Toolbox additionally provides pedagogical resources, such as a glossary of key terms and an organized collection of key paper references. We hope that this toolbox is useful for accelerating and uniting research efforts in uncertainty in machine learning.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2109.10254")
get_code_for_paper("2109.10254")
have("2109.10254")
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