Deep generative models for scientific applications - Web EventOf all machine learning methods, generative models are particularly interesting for scientific applications because of their probabilistic nature and ability to fit complex data and probability distributions. However, in their vanilla forms, generative models have a number of shortcomings and failure modes which can be a hindrance to their application: ...
