This approach is based on the estimates of the Laplace-Beltrami operator proposed in the diffusion-map theory. Analytical convergence results of the Riemannian gradient expansion are proved. The ...

For instance, Latent Space Diffusion Evolution reduced the computational steps significantly in high-dimensional spaces, handling tasks with up to 17,410 parameters effectively. In reinforcement ...

That’s more or less how we ended up with both modern diffusion models and ChatGPT. This is a self-limiting process because practically you can only dedicate so much computation to a given task.

Furthermore, based on the equivalence of this diffusion model to genetic algorithms, the paper proposed an evolutionary algorithm called the 'Diffusion Evolution method' that seeks solutions in ...

Stable Diffusion is a machine learning algorithm capable of generating weirdly complex and (somewhat) believable images just from interpreting natural language descriptions. The text-to-image AI ...

The following new files have been added to implement D4ORM: multi_car.py Defines a multi-robot 2D environment where multiple robots move and avoid collisions.. run_multicar.py Runs the MBD diffusion ...

This approach is based on the estimates of the Laplace-Beltrami operator proposed in the diffusion-map theory. Analytical convergence results of the Riemannian gradient expansion are proved. The ...

The goal with consistency models was to make something that got decent results in a single computation step, or at most two. Consistency models aren't particularly easy to explain, but make more ...