From Independent to Correlated Diffusion: Generalized Generative Modeling with Probabilistic Computers

ArXi:2603.27996v1 Announce Type: new Diffusion models have emerged as a powerful framework for generative tasks in deep learning. They decompose generative modeling into two computational primitives: deterministic neural-network evaluation and stochastic sampling. Current implementations usually place most computation in the neural network, but diffusion as a framework allows a broader range of choices for the stochastic transition kernel.