On the Effects of Quantisation on Model Uncertainty in Bayesian Neural Networks

Understand

Bayesian neural networks (BNNs) are making significant progress in many research areas where decision-making needs to be accompanied by uncertainty estimation.

• Being able to quantify uncertainty while making decisions is essential for understanding when the model is over-/under-confident, and hence BNNs are attracting interest in safety-critical applications, such as autonomous driving, healthcare, and robotics.
• Nevertheless, BNNs have not been as widely used in industrial practice, mainly because of their increased memory and compute costs.
• In this work, we investigate quantisation of BNNs by compressing 32-bit floating-point weights and activations to their integer counterparts, that has already been successful in reducing the compute demand in standard pointwise neural networks.