# Benchmarking the Neural Linear Model for Regression

###### tags: `papers`

Paper main ideas:
- Three variations of neural linear regression
- MAP NL (first train the neural network using MAP estimation, then outputs of the last hidden layer of the network used as features for BLN. Use Bayesian opt for tuning)
- uncertainty is only added with the last layer, MAP training does not learn with the goal of uncertainty quantification in mind
- Regularized NL: learn the features by optimizing the tractable marginal likelihood with respect to the network weights (previous to the output layer),
- Bayesian noise NL
- Hyperparameter tuning is important for good performance