Lightning Talk: Bayesian Neural Networks With Variational Inference in PyTorch - Lars Heyen, Karlsruhe Instute of Technology, Scientific Computing Center
Uncertainty quantification is becoming more and more important as neural networks are used for increasingly critical tasks. Bayesian neural networks (BNNs) inherently provide a measure of their own uncertainty, but can be either hard to implement or inflexible if one uses common frameworks. In this session I discuss how to efficiently implement BNNs using Variational Inference within PyTorch and present torch_blue, a light-weight open source library that implements these methods with the goal of being easy to pick up, yet flexible enough for research on BNNs.