text: This paper proposes a novel image/album geolocation algorithm. This is based on the recent PlaNet approach, with several extensions, including a better mesh representation for discretization and the time-aware prediction. However, contributions are limited to me. Pros: - Clearly written - Good results - Interesting empirical analysis between UTC time and geo-location Contribution: - Triangulation based meshify vs quad-tree meshification was claimed to be one of the major contributions. It is an incremental improvement towards Wayand et al. However I don’t think this is significant enough for an ICLR paper. - Incorporating the time-ordering into album based geo-localization is considered a contribution on the application side. But in computer vision this is not the first one to exploit this information (see [a]) while the technical approach to encode this information is quite standard (LSTM). Improvement:- Compared against Wayand et al. and Vo et al. the quantitative results are not very impressive. Minor: Some choices in feature engineering are not explained well: e.g. In section 3.2, why you only choose top 10 maximum entries, why l1-norm is appended as a feature? Are those choice made because of validation performance? Minor writing: Fig. 1 “01:00 than” Fig. 1 “to make a prediction” “Fig. 3.1.1” Table 5. Why not bold the best performance? Acknowledgements shouldn’t be put in double-blind reviewing process [A] Evangelos Kalogerakis, Olga Vesselova, James Hays, Alexei A. Efros, Aaron Hertzmann, "Image Sequence Geolocation with Human Travel Priors", Proceedings of the IEEE Internaltional Conference on Computer Vision Recognition (ICCV), 2009.