9.2 How are BDL methods being applied to solve real-world problems?

Just as deep learning is having an impact on a diverse variety of application domains, BDL is becoming an increasingly important tool, particularly where large amounts of data are being used within safety-critical or mission-critical systems. In these cases – as is the case for most real-world applications – being able to quantify when models ”know they don’t know” is crucial to developing reliable and robust systems.

One significant application area for BDL is in safety-critical systems. In their 2019 paper titled Safe Reinforcement Learning with Model Uncertainty Estimates, Björn Lütjens et al. demonstrate that the use of BDL methods can produce safer behavior in collision-avoidance scenarios (the inspiration for our reinforcement learning example in Chapter 8, Applying Bayesian Deep Learning).

Similarly, in the paper Uncertainty-Aware Deep Learning for Safe Landing...