R1: We agree that defining a canonical orientation for local patches is mainly aimed at descriptor matching. Moreover, as recently shown in Bai et al. in "D3Feat: Joint Learning of Dense Detection We will add this information to the revised version. In the evaluation in Table 2, we follow the standard protocol used in [39] to perform a fair comparison. R2: We used the more general term "pose" to refer to canonical orientation as achieving translation invariance is usually As suggested, we will use only the term orientation. We will modify it in the final version of the paper.

Exp. Psychology, Oxford ELSC, HebrewU Department of Computing Brain Mind Institute, EPFL Gatsby Unit, UCL Imperial College London Andrew M. Saxe Christopher Summerfield Ali Hummos

The key to our analysis of Langevin MCMC is a bound on the discretization error of the geometric Euler-Murayama scheme, assuming h is Lipschitz and M has bounded sectional curvature. Our error bound matches the error of Euclidean Euler-Murayama in terms of its stepsize dependence.

Generalized self-concordance is a key property present in the objective function of many important learning problems.