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Appendix

Neural Information Processing Systems

In this section, we provide further intuition about the proposed AdaQN method. In the next stage, with4m0 samples, we use the original Hessian inverse approximation 2Rm0(wm0) 1 and the new variablew2m0 for the BFGS updates. As Vn = O(1/n)(since n m0 = Ω(κ2logd)) and n = 2m, condition (38) is equivalent to (1/tn) tn (1/6.6). This parameter depends heavily on the variation/variance of the input features for linear models. Thus, we can focus on the diagonal components of these twomatrices only.