I will give you a list of reviews by humans followed by one generated by a model . Score the similarity between 1 and 10 . Keep the numbers integer .

Accept spotlight
The paper investigates the capacity for neural language models to perform fast-mapping word acquisition using a proposed multimodal external memory architecture. Much work exists that shows that neural models are capable of following instructions whose meaning persists across episodes (i.e., slow-learning), however much less attention has been paid to instruction-following in a one-shot learning context. Using a simulated 3D navigation/manipulation domain, the paper shows that the proposed multimodal memory network is capable of both slow and one-shot word learning when trained via standard RL.\n\nThe submission was reviewed by four knowledgable referees, who read the author feedback and engaged in discussion with the authors. The paper is topical---one-shot language learning for instruction-following using neural models is of significant interest of-late. The reviewers agree that the proposed multimodal memory architecture is both interesting and technically solid. The reviewers raise

Accept
This paper studies how to learn language from perception in a 3D world, and how to generalize to new objects. The paper is well-written, and the experiments are thorough. The reviewers agree that the paper has interesting results and is an important contribution to the community. The authors have done a good job addressing the concerns raised by the reviewers. 
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Reject
('This paper considers the problem of (biological) sequence design and optimization. The authors made an interesting yet important case that in certain sequence design tasks, a simple evolutionary greedy algorithm could be competitive with the increasingly complex contemporary black-box optimization models.\n\nMost reviewers appreciate the design of the open-source simulation environment in benchmarking AdaLead (and other competing algorithms) in a number of biological sequence design tasks (e.g. TF binding, RNA, and protein). However, there is a common concern that the experimental results are not fine-grained enough to explain the outperforming results of the proposed algorithm. There are also unresolved comments on missing important BO baselines in the empirical study. As a purely empirical work, these appear to be important concerns. While these results appear to be useful for the domain of biological sequence design, the reviewers are unconvinced that the proposed algorithm is significantly novel, or the results are sufficiently compelling to merit an acceptance to this venue.\n',

Reject
The paper proposes a simple evolutionary algorithm for sequence design. The algorithm is evaluated on several benchmarks, and is shown to outperform other evolutionary algorithms and Bayesian optimization. The paper is well written and the contributions are clearly stated.  The reviewers agreed that the paper is well written and that the problem is interesting, but they raised several concerns about the novelty of the method and the quality of the experimental evaluation.  In particular, the reviewers were not convinced that the algorithm is novel, and that the evaluation is not sufficiently rigorous.  The authors did not address the concerns of the reviewers, and did not provide any additional information about the novelty

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Accept
('This paper presents non-trivial and novel theoretical and computational modeling that accounts for experimentally observed phenomena: the theta phase procession and precession. These phenomena are implicated in the neural representation and learning of neuronal networks involving hippocampus. Although the current manuscript does not address the learning and the presentations are limited to a linear track environment, it represents a clear advance in linking spatial and temporal information representations by extending the standard continuous attractor models that do not exhibit phase procession/precession. Furthermore, the model elegantly explains the biologically observed unimodal and bimodal cells. The authors are encouraged to improve the clarity of some parts of the writing.\n',

Accept
The paper proposes a neural circuit model to elucidate the generation of both kinds of phase shift in place cells' firing. The reviewers and the authors had a discussion about the novelty of the paper, and the novelty was confirmed. The paper was recommended for acceptance by the reviewers. The paper will be accepted with minor revision.

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NA
("The proposed method introduces a method for unsupervised image-to-image mapping, using a new term into the objective function that enforces consistency in similarity between image patches across domains. Reviewers left constructive and detailed comments, which, the authors have made substantial efforts to address.\n\nReviewers have ranked paper as borderline, and in Area Chair's opinion, most major issued have been addressed:\n\n- R3&R2: Novelty compared to DistanceGAN/CRF limited: authors have clarified contributions in reference to DistanceGAN/CRF and demonstrated improved performance relative to several datasets.\n- R3&R1: Evaluation on additional datasets required: authors added evaluation on 4 more tasks\n- R3&R1: Details missing: authors added details. \n\n",

Reject
The paper proposes a new smoothness constraint in the original cycle-gan formulation. The cycle-gan formulation minimizes reconstruction error on the input, and there is no criterion other than the adversarial loss function to ensure that it produce a good output (this is in sync with the observations from Gokaslan et al. ECCV'18 and Bansal et al. ECCV'18). A smoothness constraint is defined across random patches in input image and corresponding patches in transformed image. This enables the translation network to preserve edge discontinuities and variation in the output, and leads to better outputs for medical imaging, image

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Accept
('The authors provide a benchmark for federated learning. \n\nThe commonly mentioned strengths:\n- there is a big need in the community for this type of work\n- easily accessible code\n- various levels of complexity with different granularity\n\nRegarding the weaknesses, I see no showstoppers\n- some feedback about the experiments done on the datasets -> this point comes back consistently among all reviewers, in terms of the amount, settings and reproducibility.. The authors are adviced to address this in future work, to keep momentum for the use of the community on this dataset \n- there was some discussion about tensorflow and pytorch, but based on the discussion both seem supported\n- some settings / image types could be better supported\n\nNone of these weaknesses seem serious, and quite frankly, we can not expect one dataset to cover all possible settings. Since the reviewers are in agreement about the quality of this work, I recommend them for an oral presentation. ',

Accept
This paper introduces a large-scale annotated image dataset for multi-label classification suitable for federated learning. The paper provides a detailed description of the dataset and its limitations, and includes a benchmark of different learning algorithms.  The reviewers found the paper to be of high quality and of high impact to the community.  The authors have addressed the main concerns raised by the reviewers.  In particular, they have added confidence intervals and added code to reproduce their benchmarks. 

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Reject
("This paper studies a timely problem and consider an interesting approach, but overall there were many concerns about technical details and the validity of the framework. The positive reviewer also mentioned concerns about the experiments, which others also found to be an insular comparison with weak baselines. Following the response period, in discussion there are additional concerns arising related to the lack of details, for instance related to possible unidentifiability of the model. As one reviewer discusses,  the authors are attempting to use RNNs to impute missing infection status labels when the missingness mechanism is assumed to be (i) not at random, (ii) playing out over time (as it is unclear whether Y^t is assumed (conditionally) independent of Y^t' with t' << t), and (iii) subject to interference (whether someone is tested is the 'treatment' here since it's a missingess problem and one person's propensity to be tested could causally affect another person's downstream infection status since apparently no Markov independence is assumed. There is also consensus that the writing quality can be greatly improved. Overall this work contains some ideas with potential in a thorough revision ",

Accept
The paper proposes a method to predict infection in a population, with the goal of improving control of the spread of an infectious disease. The proposed method is a recurrent neural network that incorporates information on both the individuals (features of the individual) as well as a contact network that defines which individuals are related to each other. The paper presents a simulation framework to compare the proposed method to baselines.  The paper is well written, but there are some concerns. The reviewers raised some questions about the notation and wording that should be addressed by the authors.  The authors should provide a more comprehensive set of baselines that compare their method to


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Reject
('The paper addresses the problem of domain generalization for learning spatio-temporal dynamics. It proposes a solution where an encoder captures some characteristics of a given environment, and a forecaster autoregressively predicts future dynamics conditioned on the characteristics learned by the encoder. Said otherwise, the forecaster learns the general form of dynamics parameterized by an environment representation extracted by the encoder. The conditioning is implemented via an adaptive instance normalization mechanism. A form of padding is also introduced in order to take into account boundary conditions. The two components encoder and forecaster are trained sequentially. This approach is casted in a meta-learning framework. Theoretical results inspired by multi-task learning and domain adaptation are also demonstrated. The model is evaluated and compared to different baselines on three problems, and for two different settings: varying initial conditions with a given dynamics, and dynamics with varying parameters.\n\nThis is a borderline paper. It targets a timely and important problem of domain generalization for dynamic environments. The proposed solution is original and compares well experimentally to several baselines. It allows for better generalization performance for the two test settings considered. In the current version, the paper however suffers from different weaknesses. First there is the imprecision of the arguments and the description of the experiments. Some of the arguments and claims are vague and sometimes abusive, not backed up by evidence. For example, a central claim is that the encoder learns time invariant quantities characterizing the environment when the learned representations indeed change with a time shift in the input for any environment. The same goes for the argument developed for the padding construction. It is claimed to model boundary conditions, but this is not supported by any theoretical or empirical evidence.\nAs noted by the reviewers, the theoretical analysis is disconnected from the algorithmic and experimental developments and does not bring much additional value to the paper. What is more embarrassing is that some of the claims in this section are overstated and induce incorrect conclusions.  From Theorem 3.1 and proposition 3.3, the authors suggest that multitask learning leads to better generalization than learning independently, while this is not formally guaranteed by the results (this is acknowledged by the authors in a later comment). Besides, the conditions of validity are not discussed while they seem to only cover situations for which the train and the test distributions are the same. The same holds for the second theoretical results (theorem 3.4). It is claimed that this result supports the authors’ idea of training encoder and forecaster sequentially, while it does not. Besides, the bounds in this result cannot be controlled as noted by the reviewers and are not useful in practice.\n\nOverall, the paper addresses an important topic and proposes new solutions. The results are promising and it is indeed an interesting contribution. However, inaccuracies and incorrect or exaggerated claims make it difficult to accept the current version of the article. The article would make a strong and innovative contribution if it were written as a purely experimental article with a detailed description of the experiments and comparisons.',

Reject
The paper tackles the problem of forecasting dynamics in different domains simultaneously. The authors propose a meta-learning method which consists of two parts: an encoder which infers the time-invariant hidden features of the task with weak supervision, and a forecaster which learns the shared dynamics of the entire domain. The encoder adapts and controls the forecaster during inference using adaptive instance normalization and adaptive padding. The paper provides some theoretical analysis, and qualitative analysis of what is learned.  The paper was well received by the reviewers, with two reviewers recommending acceptance and one recommending rejection. The main concern with the paper is the theoretical analysis, which has some

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 'Accept')
 ('After careful review, I believe that this paper is a useful contribution to the study of animal pose estimation and tracking. The paper received positive reviews from all the reviewers and the authors have successfully addressed the concerns regarding the lack of sufficient novelty and insufficient benchmarking. The authors also added additional low shot experiments to demonstrate the usefulness of the dataset. Based on this, I think the paper meets the bar for the track and should be accepted.',
 
Accept (Poster)
The paper presents a large-scale dataset for animal pose estimation and tracking. The dataset contains 2,400 video clips with 30 animal species. The paper also presents three tasks for the dataset: (1) single-frame animal pose estimation, (2) inter-species domain generalization, and (3) animal pose estimation and tracking. The reviewers have raised several concerns about the novelty of the dataset and the licensing of the videos. However, the authors have addressed the concerns and clarified the issues. The reviewers agree that the dataset is valuable for the community and can provide new insights into animal pose estimation and tracking. Therefore
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Accept
('This paper proposes a latent point diffusion model, LION, for 3D shape generation. The model builds two denoising diffusion models in the latent spaces of a variational autoencoder. The latent spaces combine a global shape latent representation with a point-structured latent space.  Comprehensive experiments are conducted to evaluate the performance of the proposed method.  The authors address the major concerns of the reviewers and strengthen the paper by providing additional empirical results. After the rebuttal, all four reviewers reach an agreement on accepting the paper because of the novelty and the state-of-the-art performance. The AC agrees with the reviewers and recommends accepting the paper. ',

Accept
This paper proposes a hierarchical architecture for point cloud generation, which is composed of two diffusion models for shape latents and latent points. The major difference between PVD and this paper is the hierarchical architecture and its ability to reconstruct corresponding meshes even though it comes from an off-the-shelf method SAP. Both qualitative and quantitative results are performed to show the effectiveness. The paper is well written and easy to understand. It adequately discusses related works. In terms of generation quality, it truely beats several SOTA methods. 


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 'Accept (Poster)')
('The paper introduces a new method to probe contextualized word embeddings for syntax and sentiment properties using hyperbolic geometry. The paper is written well and relevant to the ICLR community. Reviewers highlight that the proposed Poincaré probe offers solid results, extensive experiments that support the benefits of the approach, and proposes a new approach to analyze the geometry of BERT models. The revised version clarified various concerns of the initial reviews and improved the manuscript (comparison to Euclidean probes, low dimensional examples, new results on edge length distributions etc.). Overall, the paper makes valuable contributions to probing contextualized word embeddings and the majority of reviewers and the AC support acceptance for its contributions. Please revise your paper to take feedback from reviewers after rebuttal into account (especially to further improve clarity and discussion of the method).',

Reject
The paper proposes a simple probe to investigate the geometric structure of contextualized word embeddings. The proposed method projects embeddings into a Poincaré subspace and then use the Poincaré distance to measure the distance between pairs of embeddings. The authors apply the proposed probe to BERT to investigate the syntactic and semantic information encoded in the embeddings. The main contribution of the paper is to demonstrate that the Poincaré probe can achieve better performance than Euclidean probes.  The reviewers agree that the paper is well-written and the experiments are well-executed. However, there are concerns about the novelty and significance of the paper