Simulating large scenes with many rigid objects is crucial for a variety of applications, such as robotics, engineering, film and video games. Rigid interactions are notoriously hard to model: small changes to the initial state or the simulation parameters can lead to large changes in the final state.

Learning the intents of an agent, defined by its goals or motion style, is often extremely challenging from just a few examples.

These counts have very high global sensitivity.

Questions containing "What is the age gap between" appear 310 times.

We give a sufficient criterion to ensure that the problem is well-conditioned.

In practice, when training denoisers, we sample multiple noisy samples for each clean image and minimize the Mean Squared Error (MSE) loss for recovering the clean image.

In practice, when training denoisers, we sample multiple noisy samples for each clean image and minimize the Mean Squared Error (MSE) loss for recovering the clean image.