Any explanation that faithfully explains this type of model needs to be in agreement with this invariance property.

Short-horizon tasks require few steps: e.g., go to a tree and chop it down, dig a hole.

Beyond estimating parameters of interest from data, one of the key goals of statistical inference is to properly quantify uncertainty in these estimates.

Contrastive learning has recently emerged as a promising approach for learning data representations that discover and disentangle the explanatory factors of the data.

Finally, PDE-Refiner's connection to diffusion models enables an accurate and efficient assessment of the model's predictive uncertainty, allowing us to estimate when the surrogate becomes inaccurate.

Generative diffusion models have recently emerged as a leading approach for generating high-dimensional data.