Universal Regular Conditional Distributions via Probabilistic Transformers

We introduce a deep learning model that can universally approximate regular conditional distributions (RCDs). The proposed model operates in three phases: first, it linearizes inputs from a given metric space X$$\mathcal {X}$$ to Rd$$\mathbb {R}^d$$ via a feature map, then a deep feedforward neural network processes these linearized features, and then the network’s outputs are then transformed to the 1-Wasserstein space P1(RD)$$\mathcal …