BNNVis: Towards Visual Analytics for Bayesian Neural Networks

Bayesian Neural Networks (BNNs) offer a principled approach to modeling uncertainty in addition to providing predictions, making them particularly valuable for high-stake domains where uncertainty quantification is required. However, their adoption remains low, partly due to the difficulty in tuning and interpreting these models and their results. To address this limitation, we introduce BNNVis, a visual analytics tool designed to visualize BNNs and their results. BNNVis allows the user to understand the architecture and learned posterior weight distributions of their BNN at a glance and how these distributions differ from their prior. Additionally, the system helps them understand the distribution and magnitude of the accompanying uncertainties of the model's predictions. BNNVis provides insight into the final predictions and the model, helping practitioners tune and interpret BNNs and their results. We describe a usage scenario to demonstrate how the features of BNNVis come together to support a practitioner in using a BNN.