Before revision:  a second order stochastic optimization algorithm which dynamically incorporates the curvature of the loss function via ADAptive estimates of the Hessian . Second order algorithms are among the most powerful optimization algorithms with superior convergence properties as compared to first order methods such as SGD and ADAM . The main disadvantage of traditional second order methods is their heavier per-iteration computation and poor accuracy as compared to first order methods.
After revision:  a second order stochastic optimization algorithm which dynamically incorporates the curvature of the loss function via ADAptive estimates of the Hessian . Second order algorithms are among the most powerful optimization algorithms with superior convergence properties as compared to first order methods such as SGD and Adam . The main disadvantage of traditional second order methods is their heavier per-iteration computation and poor accuracy as compared to first order methods.