readme update

README.md

@@ -16,16 +16,16 @@ pinned: false
 
 ## Metric Description
 
-This metrics computes the expected calibration error (ECE).
-It directly calls the torchmetrics package:
-https://torchmetrics.readthedocs.io/en/stable/classification/calibration_error.html
+This metrics computes the expected calibration error (ECE). ECE evaluates how well a model is calibrated, i.e. how well its output probabilities match the actual ground truth distribution. It measures the $$L^p$$ norm difference between a model’s posterior and the true likelihood of being correct.
+This module directly calls the [torchmetrics package implementation](https://torchmetrics.readthedocs.io/en/stable/classification/calibration_error.html), allowing to use its flexible arguments.
 
 ## How to Use
 
 ### Inputs
 *List all input arguments in the format below*
-- **predictions** *(float32): predictions (after softmax). They must have a shape (N,C,...) if multiclass, or (N,...) if binary.*
-- **references** *(int64): reference for each prediction, with a shape (N,...).*
+- **predictions** *(float32): predictions (after softmax). They must have a shape (N,C) if multiclass, or (N,...) if binary;*
+- **references** *(int64): reference for each prediction, with a shape (N,...);*
+- **kwargs** *arguments to pass to the [ece](https://torchmetrics.readthedocs.io/en/stable/classification/calibration_error.html) methods.*
 
 ### Output Values
 
@@ -50,6 +50,24 @@ print(results)
 
 ## Citation
 
+```bibtex
+@InProceedings{pmlr-v70-guo17a,
+      title = 	 {On Calibration of Modern Neural Networks},
+      author =       {Chuan Guo and Geoff Pleiss and Yu Sun and Kilian Q. Weinberger},
+      booktitle = 	 {Proceedings of the 34th International Conference on Machine Learning},
+      pages = 	 {1321--1330},
+      year = 	 {2017},
+      editor = 	 {Precup, Doina and Teh, Yee Whye},
+      volume = 	 {70},
+      series = 	 {Proceedings of Machine Learning Research},
+      month = 	 {06--11 Aug},
+      publisher =    {PMLR},
+      pdf = 	 {http://proceedings.mlr.press/v70/guo17a/guo17a.pdf},
+      url = 	 {https://proceedings.mlr.press/v70/guo17a.html},
+}
+
+```
+
 ```bibtex
 @inproceedings{NEURIPS2019_f8c0c968,
      author = {Kumar, Ananya and Liang, Percy S and Ma, Tengyu},
@@ -62,3 +80,14 @@ print(results)
      year = {2019}
 }
 ```
+
+```bibtex
+@InProceedings{Nixon_2019_CVPR_Workshops,
+    author = {Nixon, Jeremy and Dusenberry, Michael W. and Zhang, Linchuan and Jerfel, Ghassen and Tran, Dustin},
+    title = {Measuring Calibration in Deep Learning},
+    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
+    month = {June},
+    year = {2019},
+    url = {https://openaccess.thecvf.com/content_CVPRW_2019/html/Uncertainty_and_Robustness_in_Deep_Visual_Learning/Nixon_Measuring_Calibration_in_Deep_Learning_CVPRW_2019_paper.html},
+}
+```