👕 Rename tutorial

README.md

@@ -70,7 +70,7 @@ We provide the following tutorials in our documentation:
 - [From a Vanilla Classifier to a Packed-Ensemble](https://torch-uncertainty.github.io/auto_tutorials/tutorial_pe_cifar10.html)
 - [Training a Bayesian Neural Network in 3 minutes](https://torch-uncertainty.github.io/auto_tutorials/tutorial_bayesian.html)
 - [Improve Top-label Calibration with Temperature Scaling](https://torch-uncertainty.github.io/auto_tutorials/tutorial_scaler.html)
-- [Deep Evidential Regression Tutorial](https://torch-uncertainty.github.io/auto_tutorials/tutorial_der_cubic.html)
+- [Deep Evidential Regression on a Toy Example](https://torch-uncertainty.github.io/auto_tutorials/tutorial_der_cubic.html)
 
 ## Awesome Uncertainty repositories
 

auto_tutorials_source/tutorial_der_cubic.py

@@ -2,8 +2,8 @@
 # coding: utf-8
 
 """
-Deep Evidential Regression Tutorial
-===================================
+Deep Evidential Regression on a Toy Example
+===========================================
 
 This tutorial aims to provide an introductory overview of Deep Evidential Regression (DER) using a practical example. We demonstrate an application of DER by tackling the toy-problem of fitting :math:`y=x^3` using a Multi-Layer Perceptron (MLP) neural network model. The output layer of the MLP has four outputs, and is trained by minimizing the Normal Inverse-Gamma (NIG) loss function.