Understanding Probabilistic Neural Networks: The Gaussian Output Layer (Theory and Implementation)
#ai #deeplearning #datascience
In this video, we discuss the concept of probabilistic neural networks for regression problems with the Gaussian head to predict a mean and variance for the normal or Gaussian distribution. This is important for understanding confidence in model predictions and decision making in machine learning, data science, and artificial intelligence.
We start by discussing the derivation of Gaussian Negative Log Likelihood (Gaussian NLL), and its implementation in PyTorch. We also discuss the role of sigma^2 in the scaled quadratic difference. We then show the training and testing loops and then apply the Energy Efficiency data set.
We then explain a pitfall or shortcoming of the Gaussian NLL loss function based on this paper: "ON THE PITFALLS OF HETEROSCEDASTIC UNCERTAINTY ESTIMATION WITH PROBABILISTIC NEURAL NETWORKS". Based on this paper, we discuss the beta-NLL loss function that aims to improve the accuracy and calibration of our probabilistic model.