Wu et al. use a feathered saturated mask to replace the light source.

They derived a parallel form of Newton's method to solve the fixed-point problem and achieved significant speedups over sequential evaluation.

Although importance sampling (IS) provides a basic principle for OPE, it is ill-posed for the deterministic target policy with continuous actions.

Addernet: Do we really need multiplications in deep learning?

DNNs arelargely composed ofmultiplication operations for both forward and backward propagation, which are much more computationally costly than addition [1].

Unlike commonly used iterative ODE solvers that necessitate time discretization, event-based simulations resolve neural network dynamics precisely between spike events.

Unlike commonly used iterative ODE solvers that necessitate time discretization, event-based simulations resolve neural network dynamics precisely between spike events.

Our lower bound analysis shows that the sample complexities ofBSGD cannot be improved for general convexobjectives and nonconvexobjectivesexcept for smooth nonconvexobjectiveswith Lipschitz continuous gradient estimator.