We address data-driven learning of the infinitesimal generator of stochastic diffusion processes, essential for understanding numerical simulations of natural and physical systems.

Our results are corroborated with experiments.

We introduce a novel score-based diffusion framework named Twigs that incorporates multiple co-evolving flows for enriching conditional generation tasks. Specifically, a central or trunk diffusion process is associated with a primary variable (e.g., graph structure), and additional offshoot or stem processes are dedicated

For example, hats and sunglasses are placed on incorrect objects. We introduce a novel method T oMeto address these challenges.

Attention-based transformers have been remarkably successful at modeling generative processes across various domains and modalities.

Convergence to a simplex ETF: Class means tend towards equinorm and equiangular vectors when centred about the global average.

Specifically, we summarize our contributions into the following points.

Our approach generalizes and improves all prior work on TDS learning: (1) we obtain universal learners that succeed simultaneously for large classes of test distributions, (2) achieve near-optimal error rates, and (3) give exponential improvements for constant depth circuits.