We thank the reviewers for their insightful feedback, and we appreciate the opportunity to improve our paper. We would like to emphasize that Theorem 1 is the most important contribution of our paper due to its generality. In the Gaussian case, our sample complexity result follows directly from the expression for the optimal loss. Finally, while Dohmatob's bounds become non-trivial only when the adversarial We will also add a clearer description of the "translate and pair in place" coupling. Comparisons with Sinha et al. are in Section 7 and we compare to Dohmatob above.

We thank all reviewers for their careful reviews and many positive comments. We feel that the typos and minor issues are easily addressable and will be corrected. We will incorporate this analysis into a revision of the paper. We thank R1 for bringing this highly related work to our attention. That work focuses on environments for which mice have previously developed spatial maps.

We thank the reviewers for their insightful feedback, and we appreciate the opportunity to improve our paper. We would like to emphasize that Theorem 1 is the most important contribution of our paper due to its generality. In the Gaussian case, our sample complexity result follows directly from the expression for the optimal loss. Finally, while Dohmatob's bounds become non-trivial only when the adversarial We will also add a clearer description of the "translate and pair in place" coupling. Comparisons with Sinha et al. are in Section 7 and we compare to Dohmatob above.

Because the initial reviews were mixed, I obtained an additional review from an expert in the area of this paper. This 4th review came back clearly positive, but in the mean time one of the positive reviewers changed to negative (and later one of the negatives turned to positive). Then we had a lot of discussion, but the reviewers never did agree on how best to view this paper. In fact, they seemed to talk past each other, and in the end we had two positive and two negative reviews. As the area chair, reading the reviews and listening to the discussion, I found the 4th, very-positive review to be the most compelling.

Reading the rebuttal and the promised improvements to the writing have increased my score to a 7. ---------------------- The paper presents a spatially-structured memory model capable of registering observations onto a globally consistent map, localizing incoming data and hallucinating areas of the map not yet or partially visited. Although borrowing architectural details from previous work especially with respect to MapNet, the paper proposes a way to incorporate a generative process directly into a spatially structured memory. Previous generative models for scenes have omitted any spatial inductive bias, and present the model directly with the sequence of observations. Additionally, previous spatial architectures often assume the setting where an oracle localizer is available. The proposed architecture provides the generative model with strong geometric priors, which enable it to perform localization without needing an oracle and accurate view generation.

Originality: The main originality of the paper is a data transformation process applied to tabular data so a GAN can learn from them. This is definitely higher novel and can be potentially useful in similar situations involving such distributions. Apart from this, however, I feel that the authors are overclaiming a bit regarding several challenge/contributions: -C2 (L86): The choice of activation function certainly depends on the data format, listing that as a "challenge" seems a bit too much to me, unless the authors can point out non-trivial adaptations they made to address the problem (and apologize if I missed that...) -C4 (L98): again, hardly something new -C5 (L105): mode collapse is certainly well studied in literature (speaking of which, the authors should add references on newer approaches such as BourGAN), using an off-the-shelf solution (PacGAN), again, does not seem to me as an important contribution. Rephrasing the section and focus on the important contributions (C3, and perhaps C1) will make the contributions of the paper more clear, in my opinion. Quality: The paper is of high quality and the description of techniques is sound.

Based on the rebuttal letter, in the final version I'd suggest emphasizing the provable defense is guaranteed in probabilistic sense. Even though I agree in test time the geometric estimator is not necessary, what you indeed certified are training data, instead of test data. This is a nice piece of work and I enjoy reading it. In my opinion, this work has made important contributions in norm-bounded robustness verification by proposing a scalable and more generic toolkit for robustness certification. The autodual framework is both theoretically grounded and algorithmically efficient. However, I also have two major concerns about this work: (I) the proposed nonlinear random projection leads to an estimated (i.e., probabilistic) lower bound of the minimum distortion towards misclassification, which is a soft robustness certification and does not follow the mainstream definition of deterministic lower bound; (II) Since this method yields an estimated lower bound, it then lacks performance comparison to existing bound estimation methods.

International Women's Day is a time to celebrate the achievements of women and raise awareness about the ongoing struggle for gender equality. This year, we want to take a moment to highlight the contributions of women in the rapidly growing field of artificial intelligence. Artificial intelligence is a rapidly evolving field that has the potential to transform the world in many ways. It is also a field that is traditionally male-dominated, with women often underrepresented in leadership positions and technical roles. However, despite these challenges, there are many women making important contributions to the field of artificial intelligence. One of the biggest areas where women are making an impact in AI is in the development of ethical AI.

Sentiment analysis became a hot topic, specially with the amount of opinions available in social media data. With the increasing interest in this theme, several methods have been proposed in the literature. Recent efforts have showed that there is no single method that always achieves the best prediction performance for different datasets. Additionally, novel methods have not being extensively compared with other methods and across different datasets, specially methods that are not designed to the English language. Consequently, researchers tend to accept any popular method as a valid methodology to measure sentiments, a practice that is usual in science. In this context, we propose iFeel 2.0, an online web system that implements 19 sentence-level sentiment analysis methods and allows users to easily label a dataset with all of them.