Variational Autoencoders are a type of generative model used in unsupervised learning. VAEs learn a low-dimensional representation of input data and can generate new data samples similar to the training data. They have applications in tasks such as image generation, anomaly detection, and data compression.
Constraint Satisfaction Problems are mathematical problems where a set of variables must satisfy a given set of constraints. CSPs are used in AI for…