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Denoising Diffusion Probabilistic Generative Models

Generative models that learn to reverse a gradual noising (forward diffusion) process, sampling new data by iteratively denoising from noise. Adapted to graphs, they can synthesize new samples/environments, which is how diffIRM generates augmented spatiotemporal graph data.

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Denoising Diffusion Probabilistic Models (DDPM) in Machine Learning

Denoising Diffusion Probabilistic Models (DDPM) represent a framework in machine learning that models data generation as a reversible Markov chain composed of two stochastic processes: a forward diff…