We present a differentiable formulation of rigid-body contact dynamics for objects and robots represented as compositions convex primitives. Classical physics engines rely on non-differentiable collision detection modules. More recent optimization-based approaches simulating between primitives bilevel that separates simulation. These latest are unreliable in realistic simulation scenarios becau...

Network architecture search achieves state-of-the-art results in various tasks such as classification and semantic segmentation. Recently, a reinforcement learning-based approach has been proposed for generative adversarial networks (GANs) search. In this work, we propose an alternative strategy GAN by using proxy task instead of common training. Our method is called differentiable efficient ge...

The AI community is increasingly putting its attention towards combining symbolic and neural approaches, as it often argued that the strengths weaknesses of these approaches are complementary. One recent trend in literature weakly supervised learning techniques employ operators from fuzzy logics. In particular, use prior background knowledge described such logics to help training a network unla...

Abstract I present general analytic expressions for distance calculations (comoving distance, time coordinate, and absorption distance) in the standard ΛCDM cosmology, allowing presence of radiation non-zero curvature. The solutions utilise symmetric Carlson basis elliptic integrals, which can be evaluated with fast numerical algorithms that allow trivial parallelisation on GPUs automatic diffe...