add accuracy versus uncertainty calibration (AvUC) loss in util,

which can be used as an additional penalty loss to train the model
to yield well-calibrated uncertainty estimates.

Signed-off-by: Ranganath Krishnan <[email protected]>

Author: ranganathkrishnan

README.md

@@ -38,8 +38,8 @@ The repository has implementations for the following Bayesian layers:
 Please refer to [documentation](doc/bayesian_torch.layers.md#layers) of Bayesian layers for details.
 
 Other features include:
+- [x] AvUC: Accuracy versus Uncertainty Calibration loss [[Krishnan et al. 2020](https://proceedings.neurips.cc/paper/2020/file/d3d9446802a44259755d38e6d163e820-Paper.pdf)]
 - [x] MOPED: specifying weight priors and variational posteriors with Empirical Bayes [[Krishnan et al. 2019](https://arxiv.org/abs/1906.05323)]
-- [ ] AvUC: Accuracy versus Uncertainty Calibration [[Krishnan et al. 2020](https://proceedings.neurips.cc/paper/2020/file/d3d9446802a44259755d38e6d163e820-Paper.pdf)]
 - [ ] dnn_to_bnn: An API to convert deterministic deep neural network (dnn) model of any architecture to Bayesian deep neural network (bnn) model, simplifying the model definition by replacing neural network layers with corresponding Bayesian layers (`updating soon...`)
 
 
@@ -52,7 +52,7 @@ git clone https://github.com/IntelLabs/bayesian-torch
 cd bayesian-torch
 pip install .
 ```
-This code has been tested on PyTorch v1.6.0 and torchvision v0.7.0 with python 3.7.7.
+This code has been tested on PyTorch v1.8.1 LTS.
 
 Dependencies: