Recent research has shown that generative models with highly disentangled representations fail to generalise to unseen combination of generative factor values.

Inverse reinforcement learning (IRL) aims to recover the reward function and the associated optimal policy that best fits observed sequences of states and actions implemented byanexpert.

In this paper, we theoretically and empirically characterize obstructions to training encoders with geometric latent spaces. We show that local optima can arise due to singularities (e.g.

There exist a handful of techniques to tackle geometric transformations of 3D point cloud data in the context of 3D object recognition.

Additionally, for FVDcalculation, we generate 256 videosforevaluation.