Normalizing flows are a class of deep generative models that are especially interesting for
modeling probability distributions in physics, where the exact likelihood of flows allows …

We consider the task of traffic scene generation. A common approach in the self-driving
industry is to use manual creation to generate scenes with specific characteristics and …

Conventional machine learning algorithms have traditionally been designed under the
assumption that input data follows a vector-based format, with an emphasis on vector-centric …

Traditional methods for synthesizing 6-DoF grasp poses from 3D observations often rely on
geometric heuristics, resulting in poor generalizability, limited grasp options, and higher …

The training, testing, and deployment, of autonomous vehicles requires realistic and efficient
simulators. Moreover, because of the high variability between different problems presented …

Orbital-free density functional theory (OF-DFT) for real-space systems has historically
depended on Lagrange optimization techniques, primarily due to the inability of previously …

The maximum likelihood principle advocates parameter estimation via optimization of the
data likelihood function. Models estimated in this way can exhibit a variety of generalization …

When training object detection models on synthetic data, it is important to make the
distribution of synthetic data as close as possible to the distribution of real data. We …