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What if we train a model to classify dogs and cats, but it is later tested with an image of a human? Generally, the model will output either dog or cat, and has no ability to signal that the image contains no class that it can recognize.
This is because classical neural networks do not contain ways to estimate their own uncertainty, and this has practical consequences for the use of these models, like safety when cooperating with humans, autonomous systems like robots, computer vision systems, and other uses that require reliable uncertainty quantification estimates.
In this short course, we will cover the basic concepts of how to train machine learning models with uncertainty, bayesian neural networks, uncertainty quantification, and related benchmarks and evaluation metrics, and have practical sessions on how to implement/use these techniques using either keras-uncertainty and/or pytorch’s pyro, depending on the students’ choice.