SE(3) diffusion model for point cloud registration can be derived as below. By inserting Eq. 5 into the variational lower bound 4, we can further rewrite the variational lower As demonstrated in our main paper, we utilize the Lie algebra for randomly sampling the desired perturbation transformation to randomize our SE(3) diffusion process. This innovative registration framework exhibits promising registration performance. Learning 6d object pose estimation using 3d object coordinates.

'Scale the model, scale the data, scale the compute' is the reigning sentiment in the world of generative AI today.

Understanding treatment effect heterogeneity is vital for scientific and policy research.

In this work, we extend the above direction to a combinatorial semi-bandit setting and study avariant of stochastic MAB, where arms are subject to matroid constraints and each arm becomes unavailable (blocked) for afixed number of rounds after each play. A natural common generalization of the state-of-the-art for blocking bandits, and that for matroid bandits, only guarantees a1/2-approximation for general matroids.