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A new DBMI estimator uses discrete diffusion bridges to accurately estimate mutual information for discrete data, performing well on low-dimensional and image tasks.

The work unifies significant parts of the generative modeling landscape. It shows that CFM and (forward-only) IFM are dual perspectives of the same approach, while positioning general IFM as a broader, more expressive paradigm that encompasses and extends beyond CFM. This bridge deepens theoretical understanding and opens avenues for practical innovation in both lines of research.

We propose Electric Current Discrete Data Generation (ECD2G), a novel method for discrete data generation that uses a neural network to learn electric currents in a circuit, which represent a provable probability flow for transferring a source distribution to a target.

We reveal several limitations of existing solvers for the continuous Gromov-Wasserstein problem and pave the way to get rid of them by proposing a novel solver.