To do so, we first form a family of unnormalized densities (or a "path") parameterized by 2 [0, 1] between the two distributions of interest

Modularity is a critical design principle for both software systems and artificial intelligence (AI).

Riemannian diffusion models draw inspiration from standard Euclidean space diffusion models to learn distributions on general manifolds.

In this paper, we are particularly interested in understanding whether, and how, the choice of distributional robustness bears statistical implications in learning the desired policy, by studying the sample complexity in the widely-used generative model (Kearns and Singh, 1999).

Sampling in discrete spaces, with critical applications in simulation and optimization, has recently been boosted by significant advances in gradient-based approaches that exploit modern accelerators like GPUs. However, two key challenges are hindering further advancement in research on discrete sampling.

Bayesian brain theory suggests that the brain employs generative models to understand the external world.

With the proposed novel sampling strategy, our model achieves instance-aware colorization in diverse and complex scenarios.