title string | paper_decision string | review_1 string | rebuttals_1 string | review_2 string | rebuttals_2 string | review_3 string | rebuttals_3 string | review_4 string | rebuttals_4 string | global_rebuttals string | dataset_source string | conference_year int64 | review_5 string | rebuttals_5 string | review_6 string | rebuttals_6 string | review_7 string | rebuttals_7 string | review_8 string | rebuttals_8 string |
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RayDF: Neural Ray-surface Distance Fields with Multi-view Consistency | Accept (poster) | Summary: This paper proposes MucRays, which imposes ray-based neural functions with multi-view geometry consistency. This framework contains three-parts: ray-surface distance field, dual-ray visibility classifier, and multi-view consistency optimization strategy. Quantitative results of MucRays surpass the existing coo... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful comments and address the main concerns below.
**Q1: The qualitative results does not satisfy me. It seems that, MucRays has difficulty in representing thin structures. For example, in the Reception scene in Figure 4 (Appendix), the arm of the desk lamp is m... | Summary: This paper presents a new strategy to design ray-based neural representation of 3D shapes. Ray-based approaches towards shape representation is a recently emerging idea that bypasses extensive point-based evaluation required by conventional methods like signed distance function. A key missing component in exis... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful comments and address the main concerns below.
**Q1: The proposed method operates under the setting where depth maps are available for all views, which is okay, but the paper would be more complete if it discusses how bad the results would become if depth is... | Summary: The paper proposed a ray-based neural rendering method that is able to achieve good reconstruction quality from depth maps or RGBD inputs using only one network evaluation per pixel during evaluation.
The method requires two networks: a ray-surface distance network, and a dual-ray visibility classifier. In th... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful comments and address the main concerns below.
**Q1: The method requires ground truth depth information for training, which is not the case for light field works such as [57]. This can significantly limit its use cases.**
**Q2: There lacked comparisons with... | Summary: This paper proposes a framework MucRays for 3D shape representation. Specifically, the authors formulate 3D shapes as ray-based neural functions and incorporate multi-view geometry consistency to improve the performance. For the learning of multi-view geometry consistency, an auxiliary network is introduced to... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful comments and address the main concerns below.
**Q1: This paper only compares rendering time, but training/optimization time is not compared.**
**A1:** Thanks for the suggestion. The following Table compares the average training time of our method and all b... | Rebuttal 1:
Rebuttal: We appreciate all insightful comments. After carefully improving the quality of our work, we present a document containing additional experimental results. And the reponses to the comments include:
- Clarification of our dual-ray visibility classifier.
- Evaluations of our method using sparse dep... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
No-regret Algorithms for Fair Resource Allocation | Accept (poster) | Summary: The paper studies an online allocation problem where the goal is to find \alpha-fair solutions. The authors provide an algorithm that provides a constant approximate regret to this problem, along with a non-tight lower bound that improves a previous known result. The suggested algorithm is compared with other ... | Rebuttal 1:
Rebuttal: We thank the reviewer very much for their feedback, which we address below.
[$\textbf{On the experimental results}$]: The goal of our algorithm is to provide fairness (i.e., to roughly ensure that hit rates of all users are as close to each other as possible) without significantly sacrificing the... | Summary: The paper studies a general online resource allocation in which there are $m$ agents and a limited resource to be allocated over T rounds. The goal is to achieve sublinear regret for the aggregate utilities of the agents when compared to the optimal fixed offline allocation policy. In the online problem, at e... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and questions, which we respond to below.
[$\textbf{On the significance of the experimental results}$]: Although there is no major improvement in the average hit rate, the goal of our algorithm is to provide fairness (i.e., to ensure that the hit rates of ... | Summary: This work studies an abstract fair resource allocation problem. It abstracts out problems such as cache and job scheduling. The notion of fairness considered is alpha-fairness (in the range of alpha between 0 and 1), which has been previously studied and is known to encapsulate many other fairness notions.
Th... | Rebuttal 1:
Rebuttal: We thank the reviewer very much for their review and their questions, which we address below.
[$\textbf{Assumption 1}$] We agree with the reviewer and believe that it might be possible to establish similar regret bounds without Assumption 1. Please keep in mind that, for many problems, e.g., the ... | Summary: The paper consider a fair resources allocation problem in the setting of unrestricted adversary, which is called a generic online fair resource allocation (NOFRA). An OFA policy is proposed with reasonable theoretical guarantees.
Strengths: The paper present an online fair allocation algorithm, which approxi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. In the following, we address each of the comments in the same order.
1. [$\textbf{Justification for counting cumulative rewards from 1}$]: The initial value of $R_i(0)$ is set to $1$ instead of zero to make sure that the derivative of the $\alpha$-fair ut... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Back-Modality: Leveraging Modal Transformation for Data Augmentation | Accept (poster) | Summary: The paper introduces a novel approach to data augmentation that leverages recent advances in generative models. It presents a general framework and experiments with three settings for image, text, and audio tasks, showing promising results for their Back-Modality approach.
Strengths: - The idea is simple bu... | Rebuttal 1:
Rebuttal: Dear Reviewer 44qx,
We would like to thank you for your thoughtful review and constructive feedback on our paper. We appreciate your recognition of the contributions and strengths of our work. Here, we address the weaknesses and questions you have raised.
### **Weaknesses:**
1. **Experiments wit... | Summary: The paper introduced a new data augmentation technique, called back-modality. The augmentation is based on modal transformations. Specifically, instances in the original modality (e.g., image) are transformed to an intermediate modality (e.g., text), augmented in the intermediate modality and then transformed ... | Rebuttal 1:
Rebuttal: ##
We thank Reviewer QqX9 for the comprehensive and insightful review of our paper. Below, we address the specific weaknesses and questions raised in the review:
### **Weaknesses:**
**1. Decision Specific to Modalities:**
While we understand the concern regarding the decisions specific to the ... | Summary: This paper introduces a new method to perform data augmentation: Back-Modality. The augmentation process involves translating the data into another modality, perform augmentation in that modality, and translate each augmented other-modality instance back into the original modality. Each of the three steps coul... | Rebuttal 1:
Rebuttal: **Dear Reviewer Usve,**
Thank you for taking the time to review this paper and provide detailed feedback. We sincerely appreciate the insightful comments and the critical examination of our work. However, it seems that there may have been parts that were missing or misunderstood. Below we provide... | Summary: This paper proposes a pipeline called Back-Modality, which uses cross-modal generation models for data augmentation. In practice, the original data in source modality is first transformed into an intermediate modality using a cross-modal generation model. Then the typical augmentation strategy for the intermed... | Rebuttal 1:
Rebuttal: Dear Reviewer UnFZ,
Thank you for your thoughtful review and constructive feedback on our submission. We appreciate your recognition of the method's potential and your careful assessment of its strengths. In response to the weaknesses and questions you highlighted, we would like to provide the fo... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Firstly, I would like to express my profound gratitude for the time and effort you invested in reviewing our manuscript. We are deeply appreciative of the recognition most reviewers gave to our methods and experiments. Your constructive feedback is invaluable.
From the collective... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposed a method to transform data between modalities so as to augment the data. Such a method makes it possible to leverage the multiple existing modalities to generate more useful data to train the model.
Strengths: 1. The proposed method is modality-agnostic so that the initial modality can be t... | Rebuttal 1:
Rebuttal: Dear Reviewer WjzN,
Thank you for the time and effort you put into reviewing our paper. We appreciate your comments and your recognition of the strengths of our proposed method. Here, we would like to address the weaknesses and questions that you raised in your review:
1. **Presentation**: We un... | null | null | null | null | null | null |
Group Fairness in Peer Review | Accept (spotlight) | Summary: The paper describes a new algorithm for assigning papers to peer reviewers with the goal of ensuring that review assignments are in the core. Thie motivation behind this is that large conferences benefit from joining many different communities of research and thereby enabling more interdisciplinary cooperation... | Rebuttal 1:
Rebuttal:
We thank you for your useful feedback. We will incorporate all your suggestions in our revision. Please see our common answer to all reviewers with regards to your comment about the benefits of focused venues (which we fully agree with).
As for the inputs, we meant to say that we do not *need* ... | Summary: The authors consider a problem of finding reviewer assignments for conference peer review. Specifically, they aim to find a valid reviewer assignment subject to the constraint that no group of authors can achieve a (strictly) preferred reviewer assignment among themselves and a subset of their authored papers.... | Rebuttal 1:
Rebuttal: We thank you for your useful feedback.
### **Regarding subsampling**
Indeed, we subsampled sets of reviewers and papers throughout our experiments for consistency (as you noted, this was required for evaluating core violations). If you are interested, the results for USW and ESW without any sub... | Summary: The authors propose to use core concept in the context of peer review system. Potentially decreasing an overall welfare, the new paradigm offers a fairer treatment of small sub-communities, erasing an incentive to create an independent venue. The paper provides an algorithm for assigning the reviewers on a res... | Rebuttal 1:
Rebuttal: We thank you for your useful feedback. As per your suggestion, we will define the core more generally in our revision for the benefit of future work, especially given that it is indeed a concept that applies quite broadly.
Please also see our common response to all the reviewers regarding your c... | Summary: This paper proposed an approach that lets the assignment of the peer review model satisfy the core, a fairness requirement over groups of authors, so as to prevent small research communities from having the incentive to deviate and set up their own separate conferences. Through theoretical analysis, the author... | Rebuttal 1:
Rebuttal: We thank you for your review. Please see our common answer to all reviewers for motivations behind the core as well as the exact running time of CoBRA.
### **Q1 & Q3 (and related comments)**
> How optimal is CoBRA's assignment?
> The optimality of the valid assignment is not proven.
> How to... | Rebuttal 1:
Rebuttal:
We thank all the reviewers for their effort and for providing helpful reviews. We will be happy to incorporate all the suggestions of the reviewers as explained in more detail in the individual responses. Let us address two comments raised by multiple reviewers in this common response.
### **Mot... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors frame their investigation into group fairness in the peer review setting in the context of large conferences (i.e. NeurIPS, AAAI) by considering a simplified peer review model that enforces the existence of a valid reviewer assignment.
Within this framework, the authors apply the fairness notion o... | Rebuttal 1:
Rebuttal: We would like to thank you for providing useful recommendations for improving the readability of our paper. We will incorporate them in our revision.
### **Q1: Time complexity**
The worst-case time complexity of CoBRA is $O(n^3)$ and we have provided the average-case runtime in the PDF attached ... | null | null | null | null | null | null |
Implicit Manifold Gaussian Process Regression | Accept (poster) | Summary: The curse of dimensionality demonstrates in Gaussian process models in that they’re default kernel choices that often depend on the Euclidean distance between 2 points. Euclidean distance is a poor metric especially in high-dimensional settings, where we expect data points to lie on an, often unknown, manifold... | Rebuttal 1:
Rebuttal: *"I believe the presentation of the paper could be improved by a clear separation of predecessor works and original contribution: for example, Section 3.1 is almost solely composed of existing work except for the theoretical contribution of the Matern kernel convergence — an overall marginal contr... | Summary: Authors propose a novel methodology for doing GP regression which is able to learn the implicit structure from the data. This is particularly useful in high-dimensional problems, where the data lies on low-dimensional manifold. The proposed methods allows to learn the implicit manifold in a fully differentiab... | Rebuttal 1:
Rebuttal: *"it is not always clear what is a novel contribution of this work and what is a background from previous works (example, last paragraph of 2.1)."*
* Thank you for mentioning this point.
In light of this and some other comments we intend to revisit the presentation in Sections 3 and 4 to make the... | Summary: The authors propose a methodology to extend Matern processes on implicit manifolds, which are modeled by $K$-NN graphs, and the kernel relies on the set of eigenvalues/vectors of the associated graph-Laplacian. An approximation of the eigenfunctions based on the eigenvectors is provided, which together with an... | Rebuttal 1:
Rebuttal: *"the construction of the graph and how well this recovers the actual structure of the underlying manifold"*
* This is indeed a very appropriate valid concern.
We apologize for not explicitly discussing the limitations in the paper.
We sketched a paragraph on this (see general response) and aim t... | Summary: This work extends the reach of Matern Gaussian processes to additionally learning the implicit low-dimensional (unknown) manifold the data lives on - the existence of such a manifold is suggested by the manifold hypothesis. The theory draws from existing Laplacian Matern Gaussian processes (Borovitskiy et al.,... | Rebuttal 1:
Rebuttal: *"The exposition could benefit from an algorithm style pseudocode ..", ".. I am a bit confused about the order of the steps.", "What is the order of the steps in training"*
* Thank you for mentioning this.
We will emphasize the general flow of the algorithm.
To clarify, the algorithm can be summa... | Rebuttal 1:
Rebuttal: We thank the referees for their valuable summaries and insights which will help us improve the paper.
We have replied to every review with detailed comments.
An essential suggestion made by many referees was to add an explicit discussion of limitations.
One major limitation is that there is a tra... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Context-PIPs: Persistent Independent Particles Demands Spatial Context Features | Accept (spotlight) | Summary: This paper aims at estimating persistent long-term trajectories of query points in videos. For ignoring the potential benefits of 4 incorporating spatial context features, this paper argues that independent video point tracking also demands spatial context features. And this paper proposes a novel framework Co... | Rebuttal 1:
Rebuttal: > The novelty of using spatial context.
Spatial context is independent of temporal information. However, most of the existing methods mainly use temporal information.
We argue that our spatial context feature component is novel:
1. We did not naively adopt common spatial context features techni... | Summary: This paper tackled the problem of Trakcing Any Point (TAP). Given a query point and a series of video frames, output all the coordinates corresponding to the query point in the video frame. The problem is interesting and can be regarded as an extension of optical flow.
PIPs only takes the features correspondi... | Rebuttal 1:
Rebuttal: > TAP-Net also provide the occlusion accuracy (OA) metric. I am curious about how the spatial context features affect the occlusion prediction accuracy, but there is no discussion in the experiments. What is the reason?
The OA comparison is listed below:
| Method | K | MLP-Mixer Dept... | Summary: This paper presents some technical improvements to PIPs, which is a state-of-the-art method for multi-frame pixel tracking. There are two key modifications to the architecture. The first is: rather than only use the feature of the target to represent the appearance of the target, look at the cost map and estim... | Rebuttal 1:
Rebuttal: > In the experiments, it would be nice to see the "d_avg" metric reported on the TAP-Vid benchmarks, as computed in the TAP-Vid paper. Or maybe this is re-named here to A-PCK?
Yes. We just renamed "d_avg" to A-PCK here because the Average Percentage of Correct Keypoints (PCK) is a more common ter... | Summary: This work proposes a method for video point tracking. It is built upon the previous method of persistent independent particles (PIPs). The authors add context features to the source and target feature encoding in PIPs. The resulting method is called Context-TAP.
The proposed method is evaluated on multiple b... | Rebuttal 1:
Rebuttal: > Do the proposed modules work on any other method that deals with this task?
Yes, we think so because both recent point-tracking networks[1-2] are built upon PIPs, which iteratively refine the point trajectories via cost information. However, they only take the features of the query points into ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Bayesian Learning of Optimal Policies in Markov Decision Processes with Countably Infinite State-Space | Accept (poster) | Summary: This paper focuses on an online learning setting for Markov Decision Processes (MDPs) with a countably infinite number of states. It adopts a Bayesian learning perspective, assuming that the parameters of the MDP follow a prior distribution over a known parameter space. The paper proposes a Thompson-sampling-l... | Rebuttal 1:
Rebuttal: We thank the reviewer for the suggestions on the presentation of the paper. We will make changes in the final version based on the suggestions.
>Weakness 1. Necessity of assumptions.
A. Given a specific parameter class $\Theta$, the assumptions can be verified for all MDPs corresponding to $\the... | Summary: The authors study Bayesian learning of the problem of optimal control
of a family of discrete-time countable state-space MDPs governed by an unknown parameter $\theta$ from a general parameter space $\Theta$ with each MDP evolving on a common countably-infinite state space $X$ and finite action space $A$.
As t... | Rebuttal 1:
Rebuttal:
We thank the reviewer for the suggestions on the presentation of the paper and other remarks, and we will make changes in the final version. We note that the references marked with a letter are listed at the end of the response and are not included in the submission.
For discussion on the depend... | Summary: This paper present an adaptation of TSDE to parametric MDPs with unbounded state space.
The regret is sqrt{T} which is good but that is under strong ergodic assumptions and lower order terms can harm the behavior of the algorithm for small values of T.
Strengths: - The paper is sound technically. (I quickly... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive review, and reference suggestions (which we will cite). The references depicted with a number indicate a reference in the submission and the references marked with a letter are listed at the end of the response.
>Weakness 1. Necessity of assumptions. ... | Summary: The authors consider the average reward Markov decision process framework with countable state spaces. The considered objective is to perform closed-loop optimal control for a family of MDPs parameterized in a compact space; this is a particularly interesting setting as the cost function is not assumed to be b... | Rebuttal 1:
Rebuttal:
We thank the reviewer for their constructive review and references (which we will cite). We will add related definitions in the appendix of the final version. The references depicted with a number indicate a reference in the submission, whereas the references marked with a letter are listed at th... | Rebuttal 1:
Rebuttal: Below we address questions and remarks to common questions.
>Remark 1. The necessity of stability assumptions.
Stability needs to be imposed separately, so we use Assumption 3. This is due to the following reasons. In contrast to finite-state MDPs, where stability and existence of a stationary d... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Self-Supervised Visual Acoustic Matching | Accept (poster) | Summary: The authors proposed a self-supervised approach to match acoustic conditions via visual information without the need of paired audio-visual data. With this approach, the in-the-wild web data or simulated data can be utilized. They show that this approach can outperforms the state-of-the-art on multiple dataset... | Rebuttal 1:
Rebuttal: **Thank you for the valuable feedback.**
**Weaknesses:**
**1) The writing of this paper is not easy to follow and missing content, for example in line 200 it refers to Section 4 for details in "RT60 to allow generalization", which does not existing in Section 4, mostly focusing on datasets. In l... | Summary: The paper introduces a self-supervised method for visual acoustic matching (VAM), where the training samples consist of only the target scene image and audio. The paper is well-written and easy to follow. The experimental results have validated the effectiveness of the proposed approach. We have the following ... | Rebuttal 1:
Rebuttal: **Thank you for the valuable feedback and positive remarks.**
**Weaknesses:**
**1) The authors can evaluate the dereverberator's performance using SRMR with different levels of reverberation. It would also be interesting to see the results with and without the De-biaser component based on SRMR.*... | Summary: This paper addresses the task of "visual acoustic matching" (VAM): taking a source audio clip and target visual environment (i.e. an image), and modifying the source audio clip such that it sounds like the clip was recorded in the target environment. The paper proposes a self-supervised approach for training n... | Rebuttal 1:
Rebuttal: **Thank you for the valuable feedback and questions. We hope that our clarifications about the dataset contents and incorporation of the additional suggested metrics help reconsider our contribution.**
**Weaknesses:**
**W1)**
**I) There are better options than just measuring MSE on magnitude ... | Summary: This work proposes a method for visual acoustic matching, the task of processing an audio signal so the room acoustics are perceived as originating from within a certain room based on an image. While paired data is generally required for this task, the proposed approach is self-supervised and trained using “in... | Rebuttal 1:
Rebuttal: **Thank you for the valuable feedback, insights, and positive remarks.**
**Weaknesses:**
1) **Some relevant references for work in audio-only acoustic matching are omitted.**
A: Thank you for bringing these audio-only works to our attention. We are happy to cite them. Our discussion of audio... | Rebuttal 1:
Rebuttal: Thanks to all reviewers for their time and valuable feedback.
Three reviewers recommend accepting. The other two suggest additional error metrics of interest and ideas for how we plot the results (Reviewer AZuk) and easy-to-address clarifications and items addressed already in the text (Review... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work aims to perform acoustic matching with unpaired training data, i.e. observing the audio only in the target environment without samples of the same audio in the source environment. The basic process involves the common process of dereverberator and reverberator. But one key point in this work is to in... | Rebuttal 1:
Rebuttal: **Thank you for the valuable feedback and positive remarks.**
1) **Is it limited to evaluate with only two metrics, STFT and RTE?**
A: We focus on RT60 and Spectrogram loss metrics as these capture well the reverberant acoustic properties of audio, and are used in prior works for acoustic match... | null | null | null | null | null | null |
Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection | Accept (poster) | Summary: This work builds a unified framework for multi-class anomaly detection (AD) by using normal images only. It identifies the identical shortcut issue of the reconstruction-based AD methods and tries to alleviate it by augmenting the memory with hierarchical discrete iconic prototypes. A switching mechanism is em... | Rebuttal 1:
Rebuttal: Thanks a lot for your effort in reviewing!
A1: According to the reviewer's suggestion, we have implemented experiments on the VisA dataset. The performance is shown in Table 4 of the unloaded PDF. As we can see, our model surpasses the previous one-for-all SOTA method, UniAD, by 1.3\% and 0.1\% ... | Summary: This paper proposes a variational autoencoding framework for unsupervised anomaly detection. This work addresses the identical shortcut issue by preserving the typical normal patterns as discrete iconic prototypes and also overcomes the problems of prototype collapse problem.
Strengths: It designs a network f... | Rebuttal 1:
Rebuttal: A1: Thanks for your suggestions. We have revised the descriptions and figures to be clearer. Due to the page limitation, we show revised Fig. 1 and Fig. 5 in the uploaded PDF. We promise to carefully polish all the descriptions and figures in our revised version.
A2: Thanks for your suggestion.
... | Summary: This paper proposes a feature reconstruction based framework for multi-class anomaly detection, called hierarchical vector quantized Transformer (HVQ-Trans). To address the "identical shortcut" problem occurring in the reconstruction-based framework, the proposed method replaces the original encoding features ... | Rebuttal 1:
Rebuttal: Thanks a lot for your positive comments for this submission! We have tried our best to address the mentioned concerns in the rebuttal. Feel free to let us know if there is anything unclear or so. We are happy to clarify them.
A1: Yes. We want to emphasize that the different degrees of imprecise r... | Summary: This paper introduces a novel approach to multi-class anomaly detection (AD) by integrating hierarchical embedding vector quantization. To tackle the problem of identical shortcuts in the reconstruction-based AD paradigm, the authors suggest to enlarge abnormality's reconstruction residue by introducing discre... | Rebuttal 1:
Rebuttal: Thanks a lot for your positive comments! We have tried our best to address the mentioned concerns/problems. Feel free to let us know if there is anything unclear or so. We are happy to clarify them.
A1: To some extent, our vector-quantized prototype can be regarded as a special kind of memory ite... | Rebuttal 1:
Rebuttal: Dear reviewers and AC
Thanks a lot for your effort in reviewing this submission! We have tried our best to address the mentioned concerns/problems in the rebuttal. Feel free to let us know if there is anything unclear or so. We are happy to clarify them.
Best, Authors
Pdf: /pdf/9b5a29dffbf3ca6f0... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes using hierarchical vector-quantized transformer-based autoencoders for image anomaly detection. The key contribution is the use of prototypes learned using an optimal transport algorithm.
Strengths: The paper addresses a critical problem in reconstruction-based anomaly detection (good rec... | Rebuttal 1:
Rebuttal: A1: Thanks!
1) The unsupervised AD methods only utilize the normal images during training stage, while both the abnormal and normal images are utilized at the testing stage. Thus, the 'unsupervised' in AD commonly refers to inaccessible to any abnormal data as supervision at the training stage.
2... | null | null | null | null | null | null |
Convergent Bregman Plug-and-Play Image Restoration for Poisson Inverse Problems | Accept (poster) | Summary: Inspired by the No-Lips literature on the optimization of convex objectives which are not globally L-smooth, this paper adds to the PnP literature the “Bregman score denoiser” which extends the BPG algorithm by a Bregman-based prox-map along with convergence conditions despite NN-parametrized non-convex potent... | Rebuttal 1:
Rebuttal: **Weaknesses:**
- We first bring more details on our assumption of convexity of $\psi\_\gamma \circ \nabla h^\star(x)$, compared to the common assumption of nonexpansivity of the denoiser.
It is observed in different works [1,2,3] that a state-of-the-art network trained to denoise without addit... | Summary: This paper studies an extension of the Plug-and-Play (PnP) framework for solving inverse imaging problems by considering descent schemes in metrics different from L2: Motivated by the fact that some data fidelity terms such as the Kullback-Leibler divergence allow for an efficient minimization with the Bregman... | Rebuttal 1:
Rebuttal: **Weaknesses:**
- We do no expect the B-DRUNet denoiser to outperform the DRUNET denoiser. Indeed, both are based on the same architecture but the former is additionally constrained to take the specific form (25). We are thus satisfied with the fact that B-DRUNET almost reach the performance of D... | Summary: This paper develops a Bregman Plug and Play image restoration algorithm for solving under-determined inverse problems in the presence of Poisson measurement noise. The framework trains the image denoising algorithms used within PnP iterations (the "Bregman Score Denoiser") to remove noise with an exponential d... | Rebuttal 1:
Rebuttal: - We recognize that in terms of numerical performance, our algorithms compare with existing methods. Yet, as you correctly pointed out, our method stands alone in offering guarantees of convergence. We believe that the combination of comparable performance to these methods along with our convergen... | null | null | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their careful reading of our submission, and their helpful comments and suggestions. In each individual rebuttal, we tried to answer to all the objections raised by the reviewers. We give here more general remarks about aspects that multiple reviewers have... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference | Accept (poster) | Summary: This paper presents a variational framework for approximating neuro-symbolic inference. It addresses the weighted model counting (WMC) and most probable explanation (MPE) problems by introducing prediction and explanation models. Training techniques, such as output space factorization, regularized Dirichlet pr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and appreciate their support of the paper's writing and the framework's simplicity. The reviewer would like a clarification on the motivation behind the prediction and explanation model, which we provide below.
> It is unclear why the explanation model is ... | Summary: This paper introduces a variant of Probabilistic Neurosymbolic Learning that uses neural networks for approximate inference (vs. prior exponential-time exact inference). The efficacy of this approach is well-supported by various experiments.
Strengths: - The paper is well-written and overall quite clear (i.e.... | Rebuttal 1:
Rebuttal: We thank the reviewer for their supportive comments. We appreciate the detailed feedback on notation and useful questions, which we discuss below.
> Double subscripts on parameters are clunky, and their use is inconsistent
You are correct. We will update this and make it more consistent through... | Summary: The paper presents a polynomial time solution to the approximate neurosymbolic inference problem for probabilistic neurosymbolic learning problems. The approach is based on a variant of predictive processing, with a prediction model and an explanation model. The results are interesting, and successfully solve ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments and interesting questions.
> It is unclear how these experiments in controlled settings like MNISTAdd relate to robust neuro symbolic reasoning in more exciting tasks, like image classification or activity recognition. For example, factorization ... | Summary: This paper introduces A-NeSi, a fast approximate procedure for NeSy architectures
based on probabilistic logic. These architectures do not scale gracefully to
problems involving many possible worlds, and the goal of A-NeSi is to scale them
up. In short, A-NeSi employs a surrogate models (autoregressive neura... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments.
> How strongly does performance of A-NeSi depend on the complexity of the hard constraints/knowledge? Especially when knowledge encodes long-range interactions between variables (or, if you prefer, tree width). I would imagine beam search could ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and their reviews. The reviewers mention the clear writing and statement of the problem, motivation and methodology (`iEET`, `agwr`, `8FBf`), in particular, the use of the MNISTAdd example (`agwr`), the comprehensiveness of related work (`iEEt`), the descripti... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Expressive probabilistic sampling in recurrent neural networks | Accept (poster) | Summary: This paper studies circuit algorithms for sampling-based Bayesian inference in continuous-time rate-based recurrent neural networks. Specifically, it argues that using a linear readout from a noisy reservoir provides a substantial expressivity benefit relative to representing the sampled distribution directly ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s critiques and suggestions. Below we address the reviewer’s concerns point by point:
Strengths:
With respect to the two enthusiasm-limiting aspects, we believe we understand, and in our revision will add text specifically pointing these limitations out, with citations... | Summary: In this paper, the authors address the question of how the dynamics of recurrent neural networks (RNNs) can generate samples from probability distributions of interest. This is of interest to the neuroscience community, since it has been proposed that biological networks can use such sampling-based inference t... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s recognition of the significance of our work and the comments and suggestions. Below we address the reviewer’s concerns and questions.
Weaknesses:
- Regarding the application of our model to natural images:
We thank the reviewer for bringing up this point. We agree th... | Summary: - This study solves an outstanding problem involving arbitrary density sampling using stochastic neural networks.
- The authors propose a a Reservoir Sampling architecture, whereby an auxiliary recurrently-connected population facilitates the sampling from a non-trivial distribution.
- The work seems solid and... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s critiques and suggestions. Our point-by-point replies follow, together with changes we will make to the revision in light of the reviewer’s points.
Weaknesses:
- Validation in physiology experiments: This is a great question and one which we now see we should have a... | Summary: The authors proposed a revervoir-sampler network whose firing rate dynamics can sample from an arbitrary probability distribution. They first established the relationship between the sampling power of the neural dynamics and the ability of the dynamics to approximate the score function. Then they showed that t... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the thorough summary of our paper, as well as the comments and suggestions.
Weakness: We appreciate the importance of stronger comparisons with previous models, and in our revision, we will significantly expand our treatment of these in the main text. In the curre... | Rebuttal 1:
Rebuttal: We thank all reviewers for their expert review and insightful critiques, suggestions, and comments. We have written a detailed, point by point reply to each reviewer's issue or concern in the replies to individual reviewers.
In the pdf attached to this general response, we illustrate the resul... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper investigates architectural requirements for recurrent neural circuits to sample from complex distributions using diffusion models. It presents a model where traditional sampler-only networks are enhanced with additional firing-rate dynamics and a set of separate output units, called reservoir-sampler... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and suggestions. Below we address the reviewer’s concerns:
Weakness:
- Image quality is poor: It is an accurate point that the fidelity of the generated images is substantially lower than that would be realized by, for example, a U-net. However, our goal he... | null | null | null | null | null | null |
Fine-Grained Cross-View Geo-Localization Using a Correlation-Aware Homography Estimator | Accept (poster) | Summary: This paper propose to wrap the ground-view image to align with the corresponding aerial-view image using homography estimation. Firstly, a differentiable spherical transform is adopted to align the perspective between the ground-view and aerial-view images. Then a correlation-based homography estimator is prop... | Rebuttal 1:
Rebuttal: **Q1: The detailed flow of data between part 2 and part 3 of Fig 2 and code.**
**A1:** Thank you for the thoughtful question. The mentioned "part 2" refers to the computation of the homography matrix between the BEV image and its corresponding satellite image, denoted as $H^k$ in the diagram. The... | Summary: This paper addresses the problem of ground camera pose refinement by ground-to-satellite image matching. For this purpose, this paper proposes to project a ground image to the overhead view image plane by using a Homography, and then iteratively update the residual Homography between the projected overhead vie... | Rebuttal 1:
Rebuttal: Thank you for your detailed reviews. Before addressing each individual question, we want to briefly introduce the pipeline of our proposed method to better clarify the technical details of our method.
1. We use a spherical transform to project ground images onto a bird's-eye view (BEV). Subsequen... | Summary: The paper addresses fine-grained cross-view geo-localization task that that matches the camera ground images with a satellite image patch covering the same area to determine the geo-pose of camera. The proposed approach projects ground images onto a bird’s-eye view perspective and formulate the task as a 2D im... | Rebuttal 1:
Rebuttal: **Q1: "The use of BEV representation has been explored ..."**
**A1:** Sorry for the confusion. We'd like to clarify a point. The **BEV (Bird's Eye View) images** used in our paper are explicitly derived by exploiting the geometry of the scene. In contrast, the references [Ref1] and others utilize... | Summary: The paper proposes a homography estimation-based method for cross-view geo-localization. Contrary to existing methods that tackle the problem as a retrieval problem, the paper proposes to reformulate the problem as homography estimation of aligning the birds-eye-view against the satellite image. To this end, t... | Rebuttal 1:
Rebuttal: **Q1.1: Comparison with other homography-based methods.**
**A1.1:** Thank you for your constructive suggestion. In the introduction of our paper, we highlight that two significant challenges arise during homography estimation in the cross-view localization task. The first challenge pertains to th... | Rebuttal 1:
Rebuttal: We extend our sincere thanks to all reviewers for their insightful feedback. Our method has been recognized as "interesting" (Reviewer Ak83, ZVTo), "new" (Reviewer NjYP), and "intuitive" (Reviewer NnZj). We are gratified that the "large amounts of improvements" (Reviewer NnZj), "state-of-the-art p... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
On Separate Normalization in Self-supervised Transformers | Accept (poster) | Summary: The paper proposes a simple modification to Transformer-based self-supervised learning by having distinct normalization (parameters) for the CLS token, which captures the global information for use in downstream tasks, and the remaining tokens. The motivation is that this allows the tokens to avoid dimension c... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's positive feedback on our paper, as well as the suggestions that can improve our submission's quality. Below we try our best to address those concerns and questions from the reviewer.
## [Weakness]
**Q1: The only main weakness is that the paper could be struc... | Summary: The paper introduces SepNorm, a normalization technique for Transformer models. SepNorm separates the normalization of the [CLS] token from the rest of the tokens in a sequence, a departure from the traditional ShareNorm method that normalizes all tokens together.
Strengths: 1. By applying SepNorm, the embed... | Rebuttal 1:
Rebuttal: Thank you for the suggestion on our paper, below we address your concern accordingly.
## [weakness]
**Q1: No uncertainty/confidence/error bars on experimental results or significance testing.**
R1: As suggested, we re-run the experiments to obtain the standard derivations in the NLP tasks as it... | Summary: The authors propose to use a different normaliser to the [CLS] token and the rest of the tokens for masked autoencoders.
They motivate this as an improvement on the standard normalisation and combine it with contrastive uniformity loss.
They observe some improvements in classification tasks .
Strengths: The... | Rebuttal 1:
Rebuttal: Thank you for your effort in reviewer our submission. Below we answer the concerns and questions that you raised, please feel free to ask if you have further questions.
## [weakness]
**Q1: The contribution while important doesn't seem relevant for a larger audience.**
R1: Thank you for your fee... | Summary: The paper proposes a new method called SepNorm, which utilizes separate normalization layers for the [CLS] token and the remaining tokens, replacing the conventional single normalization layer (ShareNorm) in transformers. Experiments demonstrate the importance of applying separate normalization to the [CLS] to... | Rebuttal 1:
Rebuttal: ## [weakness]
**Q1: the statment "Our method aims to alleviate the potential negative effects of using the same normalization statistics for both token types, which may not be optimally aligned with their individual roles." is not empirically validated in the paper.**
R1: The potential negative e... | Rebuttal 1:
Rebuttal: We thank all reviewers for their effort in reviewing our submission. And we appreciate all the positive and negative feedbacks on our manuscript. We've tried our best to address all the concerns and questions raised by the reivewers. The attached pdf includes the additional experiments and visuali... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper analyzes the normalization layer that is applied to both the context tokens and the [cls] token in an input. It argues that traditional normalzation that is applied to both type of tokens, dubbed ShareNorm, is not effective since they have different roles. The paper proposes to use separate normaliz... | Rebuttal 1:
Rebuttal: We are sincerely grateful for your time and effort in reviewing our submission. We greatly appreacited your positive feedback on our proposed method, and we are glad to know that you found the paper well-written, easy to understand. Once again, thank you for your kind words and for accepting the p... | null | null | null | null | null | null |
IBA: Towards Irreversible Backdoor Attacks in Federated Learning | Accept (poster) | Summary: The paper introduces a two-phased backdoor injection framework, called IBA, for Federated Learning systems. IBA incorporates an adaptive trigger generation mechanism along with a gradual implantation process to insert stealthy backdoors into the global model. IBA enhances the efficiency and durability of the a... | Rebuttal 1:
Rebuttal: We appreciate the valuable comments. Additional experiments are at [**EXP**](https://files.fm/f/r67m5e7a3).
1. **Compare with PerDoor and Neurotoxin**
PerDoor uses the Basic Iterative Method to generate the trigger and relies on gradients of the loss w.r.t input. Alternatively, we learn a genera... | Summary: This paper studies backdoor attacks in federated learning setting. They propose a new backdoor attack method that is based on sample-specific trigger, optimized with constraints on weight norm and weight dimension, to achieve more stealthy, harder to detect and more durable. U-Net is trained to generate sample... | Rebuttal 1:
Rebuttal: We appreciate the comments and suggestions from the reviewer. The following is our response to the raised concerns.
1. **Comparison with other state-of-the-art attack methods**
As mentioned, our objective is to design a backdoor attack with efficiency, stealthiness, and durability under more p... | Summary: The authors propose a backdoor attack framework (IBA) in Federated Learning for trigger based backdoors that jointly learns a generative model for stealthy visual triggers while also planting the backdoor in the global model. They evaluate their attack on MINST, CIFAR-10, and TinyImageNet and show that it bypa... | Rebuttal 1:
Rebuttal: Thank you for the valuable comments. Additional experiments are at [**EXP**](https://files.fm/f/jh4hy9pg3).
1. **analysis of longevity**
As presented in Fig 5 (main), Fig 9 (sup.), and Tab 4 (sup.), our method can achieve extended longevity compared to DBA for both MNIST and CIFAR-10. We also co... | Summary: **Key Contribution:** This work proposes a two-staged model poisoning attack which works even with only the participation of a small number of malicious clients. Moreover, the proposed method is effective and robust against existing defenses.
The attack is evaluated on a variety of existing defenses on CIFAR-1... | Rebuttal 1:
Rebuttal: Thank you for your our time and effort reviewing the paper. We appreciate the review and comments on the paper. We would like to address the concerns as follows.
1. **Results under different number of clients**
The standard settings are inherited from other works [1, 31], which are 10 cl... | Rebuttal 1:
Rebuttal: Thank you the reviewers for the initial comments and questions. We provide the additional experimental evaluations to address the comments of the authors in this [**EXP**](https://files.fm/f/r67m5e7a3) file, including the following experiments:
- Performance when varying the number of Participati... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Max-Sliced Mutual Information | Accept (poster) | Summary: The authors propose max-sliced mutual information (mSMI), which inherits important properties of mutual information. They show that the projection in mSMI is reduced to that in CCA for jointly Gaussian. They also provide a method to estimate mSMI using a neural network, which is computationally more efficient ... | Rebuttal 1:
Rebuttal: We thank the reviewer for feedback and comments, which we address below:
**1. Inequivalence between mSMI and CCA in the non-Gaussian case:** This is an interesting question. We first note that the equivalence between mSMI and CCA in the Gaussian case hinges on the sufficiency of the cross-covaria... | Summary: The paper proposes Max-Sliced Mutual Information (mSMI) which equals the maximal mutual information between low-dimensional projections of the high-dimensional variables. The mSMI can capture intricate dependencies in the data while being amenable to fast computation and scalable estimation from samples. In ad... | Rebuttal 1:
Rebuttal: We thank the reviewer for feedback and comments, which we address below:
**1. max-SMI is incremental with respect to SMI and sliced Wasserstein measures:** Thank you for bringing this point up. We divide our response into two parts: (i) comparison with sliced Wasserstein distances, and (ii) compa... | Summary: The paper introduces an adaptation of sliced mutual information that focuses on the maximal mutual information between linear projections of low dimensionality of random variables. This measure (mSMI) has desireable properties and is approachable by neural estimation. The authors show that mSMI can be approxim... | Rebuttal 1:
Rebuttal: We thank the reviewer for feedback and comments, which we address below:
**“How can the results for fairness aware methods in Table 2 be better than fairness agnostic?”**
**Answer:** This is an excellent question. Note that the reported $\rho_{\mathsf{HGR}}$ coefficients are evaluated on test da... | Summary: This paper defines a novel measure of independence called Max-Sliced Mutual Information (mSMI) which provides a non-linear generalization of Canonical Correlation Analysis. This measure can also be viewed as a variant of Mutual Information with a better tractability in high dimensions.
In fact, mSMI is closely... | Rebuttal 1:
Rebuttal: We thank the reviewer for feedback and comments, which we address below:
**1. Bold display of both $\rho_{\mathsf{HGR}}$ values:** Thank you for this observation. The quantity that measures fairness is $\rho_{\mathsf{HGR}}(Z,A)$ and we aim to minimize it while maintaining a high value of $\rho_{\... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Semi-Supervised Domain Generalization with Known and Unknown Classes | Accept (spotlight) | Summary: This paper introduces a new methodology for Semi-Supervised Domain Generalization (SSDG). This paper proposes the Class-Wise Adaptive Exploration and Exploitation (CWAEE) method, which contains one-vs-rest classifiers, class-wise adaptive thresholds, and consistency regularization based on Fourier Transformati... | Rebuttal 1:
Rebuttal: W1: (Major) This paper didn’t follow the previous training pipeline for the baseline. In FixMatch, it trains the model 2\^20 iterations. For Cifar10/Cifar100, it is about 10k epochs. With only 80 epochs, I would say it is not the original other baseline numbers.
A: Our paper focuses on the Semi-S... | Summary: This paper focuses on the realistic scenario and proposes a semi-supervised domain generalization method. The method first explores unlabeled data by detecting known and unknown classes, and then exploits the data by adopting consistency regularization based on Fourier Transformation. The experiments show the ... | Rebuttal 1:
Rebuttal: W1: The method needs to train one-vs-rest classifiers. When the number of classes is large, the computation cost is high.
A: When training $|\mathcal{C}^l|$ one-vs-rest classifiers for $|\mathcal{C}^l|$ *known classes*, we keep the architecture and parameters of the network unchanged and only rep... | Summary: Paper considers the setting of semi-supervised domain generalization (SSDG) when both unlabeled source and target domains can contain unknown classes, i.e. not seen as labeled instances in source domains. The goal here is to learn a classifier which will be able to (i) reliably distinguish seen classes from un... | Rebuttal 1:
Rebuttal: W1: Though the proposed setting is indeed realistic, the experimental setup is limited. It would be helpful to consider WILDS or other competitive benchmarks to understand the applicability of the proposed methodology.
A: Thanks for your kind suggestions. The experiments of our paper are conducte... | Summary: The paper considers the realistic semi-supervised domain generalization setting where known classes are mixed with some unknown classes in the unlabeled training and testing data, and proposes the Class-Wise Adaptive Exploration and Exploitation (CWAEE) method. The experiments conducted on the datasets show th... | Rebuttal 1:
Rebuttal: Q1: Why does the method use the two-component beta mixture model to calculate the thresholds?
Q2: Why do the known classes have higher scores than the unknown classes? I think the authors should discuss more about this.
Q3: Will the number of unknown classes influences the performance? For examp... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning non-Markovian Decision-Making from State-only Sequences | Accept (poster) | Summary: This paper proposes an offline model-based method for learning a non-Markovian Decision Process (nMDP), i.e. the environment is Markovian but the policy is related to previous histories. In the dataset, we can only observe the sequence of states. This paper proposes a Lan-MDP algorithm, which estimates the tra... | Rebuttal 1:
Rebuttal: Thank you very much for your feedback!
> Q: Could LanMDP tackle POMDP?
Indeed, with the Markovian assumption in transition, the proposed model cannot directly extend to POMDP, where the transition is non-Markovian. However, it is worth a trial with the general modeling of latent EBM with non-... | Summary: The paper considers the problem of imitation learning from state observations in a setting where the transition dynamics are Markovian, but the (unknown) reward function and, therefore, the optimal policy are not. The problem is framed as maximizing the likelihood of the expert's state marginal based on a gene... | Rebuttal 1:
Rebuttal: Thank you for the feedback!
> Learning objective
The mixing of gradients in Eq.8 is derived from jointly optimizing two log-likelihood, Eq. 5 and $L_{online}(\beta) = \sum_{i=1}^{m} \sum_{t=1}^{T} \log p_\beta (s_{t+1}^i | s_{t}, s_t)$. We will include this explanation in the revision.
> Con... | Summary: The paper proposes a generative model for learning non-Markovian decision-making from state-only sequences, where the policy is an energy-based prior in the latent space of the state transition generator. To solve the problems, the authors develop a maximum likelihood estimation method (LanMDP) for model-based... | Rebuttal 1:
Rebuttal: Thank you for your feedback!
> Computational cost of MCMC.
We recognize this concern. But we are rather optimistic in the short-run MCMC methods [1] since in realistic tasks the dimensionality of the action space is normally small in comparison with the state space. We add experiments to measur... | Summary: The goal of this approach is to learn from state-only sequences, in the case where action labels are not present, especially in non-markovian settings. The method formulates a non-Markovian Decision Process (nMDP) with latent actions as a Latent-space Energy Based Model, showing that the inference on the model... | Rebuttal 1:
Rebuttal: Thank you very much for the feedback!
> I think there could be more analysis on the different types of non-Markovian tasks (i.e. tasks that require more long term versus short term memory). In general, more robotics focused applications would be a better showcase for this method. The context see... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for your helpful feedback!
We add two tables for some additional experiments in response to your requests. Table 1 includes a new baseline for the MoJoCo task, as well as the omitted std of the proposed model. The proposed model still exhibits comparable perfo... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a new approach to imitation learning from observation (ILfO) on a non-Markovian decision process (nMDP). To achieve this, the authors derive their own objective using maximum likelihood estimation, drawing inspiration from deep generative modeling of state-only sequences. After providing a ... | Rebuttal 1:
Rebuttal: Thank you very much for your feedback!
> [Hard to understand] this paper is hard to understand due to the lack of explanation. For example, there are various policy-like terms as $p_\theta(A|S)$, $p_\alpha(a_t|s_{0:t})$, and $p_\theta(a_t|s_{0:t+1})$. I believe $p_\alpha(a_t|s_{0:t})$ corresponds... | null | null | null | null | null | null |
DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization | Accept (poster) | Summary: This paper proposes a method to improve the performance of existing meta-heuristic algorithms using deep neural networks. At the same time, the method described in this paper makes it possible to design a high-performing meta-heuristic without requiring expert domain knowledge. In particular, the study in this... | Rebuttal 1:
Rebuttal: # Response to Reviewer d7BW
We appreciate your insightful comments and constructive suggestions. Below we present our point-to-point response.
---
### Response to Weaknesses
> W1. It would be nice to have comparative experiment results with existing NCOs for more diverse tasks, e.g., CVRP 500/1... | Summary: This paper proposes DeepACO, a generic framework leveraging deep reinforcement learning to automate heuristic designs. Specifically, DeepACO serves to strengthen the heuristic measures of existing ACO algorithms and dispense with laborious manual design in future ACO applications. Experiments demonstrate that... | Rebuttal 1:
Rebuttal: # Response to Reviewer BitH
Thank you for your time and effort in reviewing our submission, and we appreciate the fresh perspective you've offered. Below, we provide a thorough point-to-point response, trying to address each of your remarks.
> W1: The authors may want to discuss the major novelt... | Summary: This paper presents DeepACO, a neural-enhanced solution to the limitations of Ant Colony Optimization (ACO) meta-heuristics, namely, the laborious manual design of heuristic measures and their heavy reliance on expert knowledge. ACO, which is a foraging system inspired by ant colonies, deploys artificial ants ... | Rebuttal 1:
Rebuttal: # Response to Reviewer YjbH
We appreciate the time and effort you have dedicated to reviewing our submission. Your comments have provided us with a fresh perspective and have undoubtedly enhanced the quality of our work. Below we present our point-to-point response.
---
## Response to Weaknesse... | Summary: This article proposes DeepACO, which is a generic framework leveraging deep reinforcement learning to automate heuristic designs.
DeepACO serves to strengthen the heuristic measures of existing ACO algorithms.
According to the experiments, DeepACO consistently outperforms its ACO counterparts on eight COPs usi... | Rebuttal 1:
Rebuttal: # Response to Reviewer hKqX
To begin with, we are encouraged that you enjoyed reading our paper and we sincerely appreciate your insightful feedback. Below, we provide our point-to-point response.
> W1: Hyperlinks to the bibliography do not work.
We sincerely regret any inconvenience caused by ... | Rebuttal 1:
Rebuttal: # Global Response
We are grateful to the reviewers for their insightful feedback and for recognizing the merit of our paper, e.g., novelty (Reviewer YjbH, BitH, d7BW), generalizability (Reviewer hKqX, YjbH, d7BW), effectiveness (Reviewer YjbH, BitH, d7BW), excellent presentation (Reviewer hKqX, d... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Towards Distribution-Agnostic Generalized Category Discovery | Accept (poster) | Summary: This paper presents a novel real-world driven problem setting named long-tailed open-world classification (LT-OPC) setting, where a model should predict both closed-set and open-set classes with long-tailed distribution. To address such challenges, a Self-Balanced Co-Advice contrastive framework (BaCon) is pro... | Rebuttal 1:
Rebuttal: > Novelty of LT-OPC.
Please kindly refer to 'The Novelty of the Proposed Setting' in the 'General Response' column at the top.
> Discussions or comparisons with [C1, C2] are not provided.
Thank you for the reminder! We have migrated the two methods [C1, C2] you mentioned to the proposed LT-OPC... | Summary: The paper proposes a combination of contrastive learning and semi-supervised learning approach to label images in open-world classification. The classes are assumed to be long-tailed, as is the case in real-world cases. Not all the classes have a labeled example, but the number of classes are assumed to be kno... | Rebuttal 1:
Rebuttal: > Line 146: What do you mean sample number n?
The sample number **n** is a vector with size $C$, where the $i$-th element represents the number of samples in $i$-th cluster (line 144- line 145).
> Line 146: What does the symbol (n^c) mean? Are you doing "n choose c" or "n raised to c"?
$n^c$ re... | Summary: This paper tackles long-tailed open-world classification problem. It handles data imbalance in the long-tailed problem, and it also needs to handle the closed-world/open-world classifications. For open-world classifications, there are only unlabeled data. Moreover, it assumes that unlabeled data can come from... | Rebuttal 1:
Rebuttal: > The proposed problem setting is not very novel. For example, the previous work of "Large-scale long-tailed recognition in an open world" (reference [36]) handles long-tailed problems in an open world. Also the paper "Generalized Category Discovery" (reference [52]) proposes "the unlabelled image... | Summary: This paper studies the long-tailed recognition in the presence of open-set samples, which is never studied by previous works. Moreover, existing long-tailed learning methods can not be directly extended to open-set classification. To solve this problem, the authors design a new method termed BaCon, which utili... | Rebuttal 1:
Rebuttal: > The studied problem is novel. However, the motivation is insufficient. By simply integrating open-set classification methods with re-balancing strategies, the long-tailed problem may be alleviated. The authors should also compare with the simply integrated baseline methods, or explain the diffic... | Rebuttal 1:
Rebuttal: # General Response
## To All Reviewers.
Dear Reviewers:
We would like to thank you for your time and insightful comments! We have carefully read your review comments and conducted additional experiments as required to answer the questions (please kindly refer to the rebuttal PDF file and the re... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces and provides a formal definition for a real-world task, the long-tailed open-world classification (LT-OPC): it entails generating predictions for old and novel classes within a long-tailed open-world context. The proposed method incorporates a contrastive-learning branch and a pseudo-labe... | Rebuttal 1:
Rebuttal: > A different name for the problem seems less confusing.
Thanks for the advice! We will change the name to 'Imbalanced Open-World Classification'.
> Ablations studies could be more well explored.
Thanks for the advice! We will add a more detailed analysis in the ablation.
> Calling theorem 1 ... | null | null | null | null | null | null |
Empowering Convolutional Neural Nets with MetaSin Activation | Accept (poster) | Summary: The paper proposes MetaSin, a new activation function for deep learning. MetaSin essentially consist of a relu function plus a sum of parametrized sin activations functions. The MetaSin function is developed specifically to work in the domain of image prediction. The paper presents multiple experiments that su... | Rebuttal 1:
Rebuttal: **Swish as a Baseline for Classification**
As the reviewer suggested we ran the image classification experiments reported in Table 6 using Swish versions of the baselines. We report the validation accuracies, in comparison to the MetaSin and ReLU results from earlier:
| Teacher | WRN-40-2 | WR... | Summary: The paper proposes a modification of the sine activation function and show that this results in improved performance, compared to RELU and others. The new activation, called METASIN, is a superposition of RELU with several sinusoidal functions, and is motivated by the observation that RELU has a spectral bias ... | Rebuttal 1:
Rebuttal: **Distillation and ReLU Networks**
The reviewer is correct that our best results in both image resampling and denoising applications are obtained using distillation, specifically KD Bootstrapping as discussed in Section 3.1 of our manuscript. In short: the role of distillation in these experiment... | Summary: **SUMMARY AFTER REBUTTAL**: the authors have addressed most of my concerns and I have increased my score during the rebuttal phase. I believe the novelty of the paper is small if moving beyond their specialized subfield, which is why the overall score remains low.
---
The paper proposes a variant of the sin ... | Rebuttal 1:
Rebuttal: **Comparison with ensemble activations**
Throughout the paper we present comparisons against popular baselines Snake, Mish, Siren, as well as an ensemble activation we call MReLU that is similar to the Adaptive Piecewise Linear Units presented by Agostinelli et al., 2015 and mentioned in the surv... | Summary: The authors of this paper propose a new activation function, which relies on a parametrized sinusoidal function instead of only a piecewise linear function. The authors show how this function can lead to performance improvements in the setting of denoising.
Strengths: - The authors propose a novel formulation... | Rebuttal 1:
Rebuttal: **MetaSin without KD Bootstrapping from a pre-trained network**
During our experiments we had a chance to confirm firsthand the well-known difficulties associated with training *sin*-based activations, especially when utilized in convolutional networks. These difficulties may even lead to diverge... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and insightful comments. We are encouraged that the reviewers are unanimously leaning towards accepting our submission for publication. In the following we start by briefly summarizing our motivations, then address similar remarks by two reviewers about th... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Augmentation-Free Dense Contrastive Knowledge Distillation for Efficient Semantic Segmentation | Accept (poster) | Summary: In this manuscript, the authors propose a effective knowledge distillation framework for semantic segmentation task. Specifically, in addition to traditional knowledge distillation on segmentation masks as well as feature distillation, to better align the dense feature, the authors introduce contrastive learni... | Rebuttal 1:
Rebuttal: Thank you for the constructive review, and the recognition of our motivation, method and experiments. In what follows, we provide detailed responses to address your concerns one by one:
**1. Your comments on the weakness about the lack of an ablation study** “Intuitively, the proposed Af-DCD loss... | Summary: This paper focuses on knowledge distillation for semantic segmentation and introduces an augmentation-free dense contrastive loss function. The student and teacher feature maps are partitioned into patches, and both spatial and channel contrasting is performed within these local neighborhoods. For contrastive ... | Rebuttal 1:
Rebuttal: Thank you for the constructive review, and the recognition of the proposed approach and the experiments. In what follows, we provide detailed responses to address your concerns one by one:
**1. The first concern about why should the motivation/proposed method be augmentation-free**.
**Our respon... | Summary: This paper points out that existing knowledge distillation methods have been heavily relying on tdata augmentation and memory buffer, which require high computational resources and this is further amplified when it comes to segmentation task that requires relatively higher resolutions of feature maps for proce... | Rebuttal 1:
Rebuttal: Thank you for the constructive review, and the recognition of the novelty, the effectiveness, and the ablation studies of our work. In what follows, we provide detailed responses to address your concerns one by one:
**1. The first weakness** “In Table 1...ours refers to cumulatively adding all th... | Summary: This paper proposes a novel knowledge distillation methods for semantic segmentation, called Augmentation-Free Dense Contrastive Knowledge Distillation(Af-DCD). Af-DCD is a new attempt on the usage of contrastive learning in the task of knowledge distillation for semantic segmentation, which alleviate the prob... | Rebuttal 1:
Rebuttal: Thank you for the constructive review, and the recognition of our work. In what follows, we provide detailed responses to address your concerns one by one:
**1. The first weakness about the organization of reference**.
**Our responses**: **(1)** We agree with you, and will add the references to ... | Rebuttal 1:
Rebuttal: Dear Reviewers, Area Chairs, Senior Area Chairs and Program Chairs,
We sincerely thank all four reviewers for their thorough and constructive comments. We are glad that the novelty, basic experiments and performance of our work have been mostly recognized by all four reviewers.
In the past week,... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Bayesian Extensive-Rank Matrix Factorization with Rotational Invariant Priors | Accept (spotlight) | Summary: The authors consider the problem of matrix factorization of a noisy measurement in the setting of all matrices having an rotationally invariant prior. They provide a non-rigorous but comprehensive theoretical derivation of their results. They also provide a number of experiments validating there theoretical cl... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review. We address the questions below:
1. We will surely include a conclusion section in the final version, also taking into account comments made by the referees.
2. Depending on the nature of the problem, one can consider a general class of priors for the factors... | Summary:
This paper explores the Matrix Factorization problem, which involves estimating the matrices $X \in \mathbb{R}^{N \times N}$ and $Y \in \mathbb{R}^{N \times M}$ given the noisy matrix $S = \sqrt{\kappa} X Y + W$.
The focus is on the high-dimensional regime, where $N/M \to \alpha$, and the investigation incl... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments.
We agree that making the derivations mathematically rigorous is an interesting research direction. We want to bring your attention to the papers:
"Optimal cleaning for singular values of cross-covariance matrices" (arXiv:1901.05543) , "A short proof of Led... | Summary: This paper considers a matrix factorization problem in a setting where the rank of the factor matrices grow linearly with the ambient dimensions. They assume that the factors follow a prior distribution such that: (1) One of the matrix factor is symmetric, (2) Both factors and the noise are drawn from rotation... | Rebuttal 1:
Rebuttal: We thank the referee for the review and additional suggested references. We will add those (with possibly other references) in the final version.
We agree that the required assumptions are restrictive and do not necessarily hold in practice, however as mentioned in the manuscript the proposed es... | Summary: For a matrix factorization model S = \kappa XY + W, this paper proposes a method for estimating X and Y from S under the assumption that priors of X, Y, and W satisfy certain rotation invariance properties and their distributions of eigen/singular values are known. The proposed method is rather simple. First, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for positive comments. We address his/her question below:
$\bullet$ For $c=0$, the prior on spectral of $\mathbf{X}$ is symmetric, $\rho_X(x)=\rho_X(-x)$, and using the definition of Stieltjes transform one can see that indeed from eq. (7) the estimator is 0 for all eigenval... | Rebuttal 1:
Rebuttal: Numerical results on sensitivity of RIE to mismatched priors ( response to first question of Reviewer LQDn)
In figure 1, the spectral distribution of $\mathbf{X}$ is uniform on $[0,4]$, and both $\mathbf{Y}, \mathbf{W}$ are Gaussian matrices. We applied the RIE, assuming two different misspecifie... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Guarantees for Self-Play in Multiplayer Games via Polymatrix Decomposability | Accept (poster) | Summary: The paper studies theoretical performance guarantees for agents learned using self-play in multiplayer games. Self-play is a common approach of machine learning in multi-agent systems for generating unbounded quantities of training data, but agents trained using self-play may perform poorly against new agents ... | Rebuttal 1:
Rebuttal: Thank you for your review.
### Response to Questions
**Assumptions.** The main assumption we make is that self-play is performed by a no-regret learning algorithm. This means the average regret (the difference in utility between the chosen strategy and some hypothetical deviation) will get drive... | Summary: This paper asks and answers the question: "In what games does self-play (with regret-minimizing algorithms) in multiplayer imperfect-information games perform well?"
The motivation behind the question is that such algorithms, such as multiplayer versions of CFR (which has theoretic guarantees in 2-player zero... | Rebuttal 1:
Rebuttal: Thank you for your encouraging review.
### Response to Weaknesses
We think this is an excellent suggestion for our work, and we will definitely mention this. Since submission, we conducted additional experiments on a toy version of Hanabi (another game where self-play is known to perform well in... | Summary: In multiplayer games, the authors derive bounds for the vulnerability of marginal strategies trained via no-regret self-play against other, uncorrelated agents independently trained via no-regret self-play. This is done by projecting games onto the space of constant-sum polymatrix (CSP) games, which can be dec... | Rebuttal 1:
Rebuttal: Thank you for your review and interesting questions.
### Response to Weaknesses
1. We agree that it would be interesting to validate our approach on a larger suite of games. Since submission, we have conducted experiments on a toy Hanabi game and found that self-play performs poorly against new o... | Summary: This paper explores the intriguing problem of why no-regret algorithms seem to approximate well in multiplayer games, a phenomenon that has been empirically demonstrated in multiagent poker. The authors identify a structural property in multi-player games that allows performance guarantees for strategies deriv... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review.
### Response to Questions
**Practical applications.** This work can be used practically to show in which multiplayer games pre-computing a strategy via self-play is desirable. It absolutely can be used to predict whether the strategy of a no-regret algorithm ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Data Minimization at Inference Time | Accept (poster) | Summary: This paper questions the necessity of soliciting all user information at inference time, with an eye towards privacy-sensitive domains such as law, recruitment, and healthcare, where learning models typically require access to extensive and sensitive user data for accurate prediction. The authors propose in so... | Rebuttal 1:
Rebuttal: Thank you for your time and review. Below, we report our answers to your questions. Feel free to let us know if there are further questions or concerns and we'll be more than happy to elaborate.
### Comment on hyperparameter T
You are right, and we appreciate the catch. $T$ is indeed the number... | Summary: The authors address the problem of data minimization at inference time, which poses a real-world challenge in real-world applications, where users might want to hide sensitive or personal attributes. They provide an efficient algorithm to sequentially determine the appropriate attributes for an individual, wit... | Rebuttal 1:
Rebuttal: Thank you for your time and review. Below, we report our answers to your questions. Feel free to let us know if there are further questions or concerns and we'll be more than happy to elaborate.
### Comment on `The addressed problem is inherently a privacy problem ... can be inferred from the oth... | Summary: This paper considers the problem of data minimization at inference time. Consider a set of features X which consists of public features Xp, and private features X\Xp. A model has been trained with all the features X, i.e., f(X). Now, the goal is to allow for inference revealing only a subset of the private fea... | Rebuttal 1:
Rebuttal: Thank you for your time and review. Below, we report our answers to your questions. Feel free to let us know if there are further questions or concerns and we'll be more than happy to elaborate.
### Q1
Thank you for pointing out the apparent similarities between our work and existing problems i... | Summary: The authors propose that in a large number of application of machine learning reasonable model accuracy can be realized without the model having access to the entire feature set. This has implications for privacy and data-sharing, as a model that adaptively selects features to solicit would retain model perfor... | Rebuttal 1:
Rebuttal: Thank you for your time and review! If there are further questions or concerns and we'll be more than happy to elaborate.
### Empirical evaluation
We appreciate the suggestion to include a more holistic view of the classifier's performance. Below, we address your concerns and provide additional... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The work presents a method to increase privacy of ML predictions at test time by asking users to reveal fewer features. The proposed method asks for features that are maximally informative of the prediction outcome given the features seen thus far and stops when the prediction outcome can be decided. The metho... | Rebuttal 1:
Rebuttal: Thank you for your time and review! Below, we report our answers to your questions. Feel free to let us know if there are further questions or concerns. and we'll be more than happy to elaborate. We also hope that you consider updating your score, if these replies answer your questions.
**Q1: N... | null | null | null | null | null | null |
Context-guided Embedding Adaptation for Effective Topic Modeling in Low-Resource Regimes | Accept (poster) | Summary: This paper proposes a new solution (Meta-CETM) for inferring topics on a dataset with a few available documents only. The main idea is to train the model on various tasks and then use it on a new, small dataset. The are extensive experiments on diverse topic models, in particular in this context of "few shot" ... | Rebuttal 1:
Rebuttal: Thank you for your positive and helpful comments and suggestions. Your concerns have been addressed as follows.
**Q1:** The paper is not always well presented. Here I give some examples:
- *Q1.1:* The problem formulation (2.1) is quite confusing ... For instance, the authors seem to use a self-... | Summary: This paper proposed an approach for few-shot topic modeling. The authors first question the limitations of "static word embeddings" in previous related work when transferring to new tasks, and then propose to use the "adaptive word embeddings" generated by VGAE to address this issue. Although the problem this ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your careful consideration and valuable comments. In the following, we are going to try our best to address your concerns.
**W1:** This paper aims to address the limitations ... The authors should carefully explain why the dependency graph and VGAE can reflect the characte... | Summary: The authors target the problem of multi-meaning words across different tasks for topic models, particularly under low-resource settings. To this end, they propose a variational graph autoencoder with a trainable Gaussian mixture prior to capture the distribution of task-specific word embeddings.
Strengths: Ov... | Rebuttal 1:
Rebuttal: Thank you for your careful readings and valuable comments. We believe the constructive feedback will improve the paper and increase its potential impact on the community. Regarding the weaknesses you mentioned, we respond as follows.
**W1:** The examples illustrating the applications of few-shot ... | Summary: The authors present a new neural topic model which aims to solve the problem of learning task-specific word embeddings in a low resource scenario. In addition to a somewhat typical neural TM, dependency graphs are collected using parsers, and embedded via GCNs to produce adaptive word embeddings. A topic-wor... | Rebuttal 1:
Rebuttal: We sincerely appreciate your careful consideration and valuable comments. In the following, we are going to try our best to address your concerns.
**W1:** reliance on pre-trained parsing tools. How well does this approach work in different languages, or styles/domains of text that differ signific... | Rebuttal 1:
Rebuttal: We really appreciate all the reviewers for their constructive and helpful comments. And we apologize for typos, grammar mistakes, unclear notations and missing citations. They will be corrected such that the overall writing meet NeurIPS standards. Here we briefly introduce our newly added rebuttal... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper addresses the problem of inducing neural topic models in low-resource regimes by learning adaptive word embeddings that exploit contextual grammar information. The adaptive word embeddings are learnt with a variational graph autoencoder and the topics are formed from a gaussian mixture prior of the ... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the quality of our work! We will take your suggestions to give a more detailed explanation of the baselines in the revision. Here we clarify some of your questions.
**Q1:** I can't parse eq. 10 in the evaluation: what is the superscript (1)s?
**A1:** Eq. 10 is the for... | Summary: This paper proposes a method for few-shot topic modeling. Specifically, rather than following traditional wisdoms to learn static word embeddings for all the tasks/domains, the authors allow task-specific word representations such that the knowledge from the source task can be better transferred to a target ta... | Rebuttal 1:
Rebuttal: We appreciate your constructive comments and feedback. The weaknesses have been addressed below.
**Q1:** In Eq 1, is it ${Z^{(i)}}^{\top}Z^{(i)}$ or $Z^{(i)}{Z^{(i)}}^{\top}$? What is the relationship between $\hat{A}$ and $A$?
**A1:** Since we assume $Z^{(i)} \in \mathbb{R}^{D \times V}$ in th... | null | null | null | null |
Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive Learning | Accept (poster) | Summary: This paper focuses on the problem of sequential decision-making within a hierarchical framework, where tasks exhibit a hierarchical decomposition structure, and the agent does not possess any prior knowledge of the task dependency graph. In contrast to prior hierarchical approaches that directly model dependen... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. We appreciate the encouraging comments (“the author proposes a novel self-supervised loss within the context of hierarchical decision-making”, “good empirical performance boost compared with the previous strongest model-based RL algorithms”). We wou... | Summary: This paper introduces achievement distillation, a representation learning method that is combined with PPO to obtain state-of-the-art results on the 2D crafter benchmark. First, the authors demonstrate that with simple hyper-parameter tweaks, the performance of vanilla PPO can be greatly improved. Next, detail... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful feedback. We are encouraged by the reviewer’s positive comments (“the experiments improving the performance of vanilla PPO were exciting”, “the ideas in the paper are easy to follow and straightforward in a good way”). We would like to address the questions ra... | Summary: This work introduces achievement distillation, a model-free RL method designed to discover achievements without the need for explicit long-term planning components. The proposed method comprises three primary components: two self-supervised tasks and a memory component. The self-supervised tasks, namely Intra-... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We appreciate the encouraging comments (“The authors have done a great job in implementing various relevant strong baselines”, “The paper is well-written, and the ideas are effectively presented and justified”). We would like to address your questions belo... | Summary: This paper proposes a contrastive learning approach for representation learning in the problem of hierarchical achievement discovery. The proposed method leverages previous contrastive learning loss and combines it with optimal transport. Empirical results show that the learned representation could improve PPO... | Rebuttal 1:
Rebuttal: We appreciate the review's constructive and helpful feedback. We are encouraged by the reviewer’s positive comments (“it is interesting that self-supervised representation learning can improve PPO”, “this paper is generally well-organized and easy to follow”). We would like to address the question... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and effort in providing valuable feedback. We are especially appreciative of the encouraging comments we received from each of them. To begin our response, we would like to first address some of the common concerns that have been raised by multiple reviewe... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Riemannian Residual Neural Networks | Accept (poster) | Summary: EDIT: Having read everything here. I am increasing my score slightly. However, I still think the paper is not clearly explained. It is unclear why you first introduce the method without the feature map. It seems like there are two different versions of the method and it is not clear which is which. I am also c... | Rebuttal 1:
Rebuttal: > The proposed methods seem to not really leverage the manifolds intrinsice geometry and depend entirely on the embedding on the manifold into ambient space $R^D$
A: Our approach uses intrinsic geometry (we use local coordinates, as mentioned by the reviewer).
---
> Indeed $n_i$ is defined on a... | Summary: This paper extends the well known residual network which are usually applied to Euclidean data to a variant defined on manifold. The main novelty is to replace the "addition / plus" operation in Euclidean space to exponential operation. Specifically, given an input point on a certain manifold (e.g., hyperbolic... | Rebuttal 1:
Rebuttal: Thank you for the review and the constructive comments. We appreciate that you think our idea is theoretically sound and our experiments are supportive. We address your comments from the “Weaknesses” and "Questions" sections below.
---
> The biggest weakness... closed-form exponential maps?
A: ... | Summary: The paper generalizes the ResNet layer to non-euclidean geometries by replacing the Euclidean sum with the exponential map. The theory is general and applies to any smooth metric. They propose a way to parametrize a vector field on the manifold which is more geometrically principled than the trivial vector fie... | Rebuttal 1:
Rebuttal: Thank you for the review and the constructive comments! We address your comments from the “Weaknesses” and “Questions” sections below.
---
> The definition of the vector field... are isomorphic to $\mathbb{R}^{\dim(M)}$?
A: Since our manifold is equipped with a Riemannian metric, there is a can... | Summary: The paper proposes an extension of standard ResNets called Riemannian Residual Neural Networks. The extension is done based on Riemannian manifolds as discussed in Equation (2). Some numerical results on node classification problems are presented in section 5 to show the improvements of the proposed generaliza... | Rebuttal 1:
Rebuttal: Thank you for the review and the constructive comments. We appreciate that you think our idea is interesting and has the potential to improve the performance of ResNets over datasets with chosen Riemannian geometry. We address your comments from the “Weaknesses” section below as well as your quest... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Precise asymptotic generalization for multiclass classification with overparameterized linear models | Accept (spotlight) | Summary: The paper studies the asymptotic generalization error behavior of an overparameterized linear model and under the Gaussian covariates bi-level model. In this setup, the number of data points, features, and classes all grow together. The authors manage to fully characterize the regimes of the generalization err... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review and detailed feedback.
> The considered assumptions for the distribution of features are very simplistic. Both independence and having identical Gaussian distributions are very restrictive; which highly influences the practicality of the results.
- ... | Summary: The paper titled "Asymptotic Generalization of Overparameterized Linear Models for Multiclass Classification under Gaussian Covariates Bi-level Model" presents a study on the asymptotic generalization of overparameterized linear models for multiclass classification under the Gaussian covariates bi-level model.... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review and feedback.
> How realistic is the Bi-level feature weighting model ? I am thinking instead of the source/capacity setting where I would expect a more power-law-like behavior. How different would the conclusions be?
- Because our paper focuses on... | Summary: This is a theoretical paper that gives insight into how and when an overparametrized linear classification model, for multi-class classification, can be successfully generalized. In particular, they look at multiclass classification under the Gaussian covariates bi-level model introduced by Subramamian et a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review.
> It would be helpful to comment on the limitations of the bi-level model studied.
- As mentioned in the general comment, because the main contribution of our paper is resolving the conjecture of Subramanian et al., we chose to adopt the same mode... | Summary: In their main result, Theorem 3.2, the authors establish Conjecture 3.1 which is a conjecture posed in 2022 describing the asymptotic misclassification probability of the bi-level ensemble model (Definition 1) under a sparsity assumption (Assumption 1). They provide a rigorous and tight analysis, with very cl... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and suggestions.
> In equations (36)-(40) could you explicitly write the polylogarithmic terms in the denominator, or at the very least, precisely defined them after the equation environments.
- Yes, we will include the explicit polylog factors. F... | Rebuttal 1:
Rebuttal: We thank all of the reviewers for their comments and feedback. Below, we highlight some high level takeaways which address a set of questions shared across multiple reviewers.
## Bi-level model
- Because the main contribution of our paper is fully resolving the conjecture of Subramanian et al. fro... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this paper, the authors analyze the generalization of the linear multiclass classifiers in the overparametrized regime under the bi-level model with Gaussian covariates. In particular they prove a conjecture made in a previous paper about the region (characterized by the paramters of the bi-level model) und... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and suggestions.
> While this paper does not seem to have any obvious flaws, I feel that the model analyzed in this paper is a bit too restrictive. In a footnote on page 4, the authors mention that "such models are widely used to study learning even beyond ... | Summary: By resolving the conjecture posed by Subramanian et al. (2022), the authors address the asymptotic generalization for overparameterized minimum-norm interpolation (MNI) linear multi-class classifiers under two assumptions: (1) features are Gaussian vectors and labels are generated from $1$-sparse noiseless mod... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and suggestions.
> The setting of bi-level ensemble seems a bit weird to me, especially the eq (12). Maybe the authors can offer more explainations.
- See the general comment for an overview of the bi-level model. The bi-level model is a set of simplifyin... | null | null | null | null |
GraphMP: Graph Neural Network-based Motion Planning with Efficient Graph Search | Accept (poster) | Summary: The paper studies learning-based motion planners with GNNs. To improve the performance of GNN-based previous work, this paper proposes the GraphMP, possibly applied to low and high-dimensional planning tasks. The important idea is using (1) using predicted heuristic values, (2) NCC (Neural collision checker), ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's very constructive comments and suggestions. The following is our response in order of questions and comments raised.
>Q1: KUKA7 vs KUKA14 in Table 1.
Thank you for pointing it out. The reported a bit worse success rate for KUKA7 than KUKA14 is due to random... | Summary: This paper presents an improved graph searching technique, called GraphMP. The algorithm consists of two modules, the neural collision checker and the neural heuristic estimator, which are utilised by a differentable graph-based A* module for path planning.
Strengths: 1. The algorithm is presented in detail,... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's very constructive comments and suggestions. The following is our response in order of questions and comments raised.
>Q1. Polishing Section 3.1.}
Thanks for the valuable comment. Given an RGG $G=(V,E)$, source node $v_s$ and goal node $v_g$, the prior work ... | Summary: The paper extends the previous GNN-Explorer work and replaces its greedy search strategy with A* search using a neural heuristic. Furthermore, it utilizes the neural collision checker and lazy node removal to improve the success rate and path cost. The result is evaluated based on a benchmark from 2D maze to 1... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's very constructive comments and suggestions. The following is our response in order of questions and comments raised.
>Q1: Technical novelty.
Thanks for the valuable comments. As we state in the Related Work Section, neural network-enabled motion planning is... | Summary: This paper presents GraphMP, a neural motion planner that uses GNNs and graph search techniques to do motion planning in various scenarios. GraphMP has two components: a neural collision checker that estimates the collision status of edges in a randomly sampled graph, and a neural heuristic estimator that assi... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's very constructive comments and suggestions. The following is our response in order of questions and comments raised.
>Q1: Impact of K, graph size and $\theta$.
Thank you for pointing it out. **Fig. 2-4 in Sec. 6 (Ablation Study) of Supplementary Material** ... | Rebuttal 1:
Rebuttal: **We would like to thank all reviewers for the valuable comments and suggestions.** In the attached PDF file, we include five figures as described as follows: the study on the admissibility/consistency of the neural heuristic estimator **(Fig. R1)**, the updated neural collision checker results o... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model | Accept (poster) | Summary: This paper studies the statistical nature of distributionally robust reinforcement learning under the generative model. Specifically, it studies two divergences, total variation or chi-square divergence, over the full range of uncertainty levels. The paper improves the upper and lower bounds, especially when t... | Rebuttal 1:
Rebuttal: We extend our thanks to the reviewer for their meticulous review and perceptive insights regarding future directions. It is gratifying to know that the reviewer found our results both interesting and important. In what follows, we provide our response to the reviewer's comments.
### 1. Adding dis... | Summary: 1. This paper studies the model robustness in RL via the framework of distributionally robust MDP.
2. The authors derive the sample complexity of RMDPs using a model-based algorithm called distributionally robust value iteration when the uncertainty set is measured via either total variation or chi square di... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for careful review and insightful feedback. It is rewarding to know that the reviewer recognizes the significance of our contributions --- the new techniques and interesting takeaway message. In what follows, we provide our response to the reviewer's comments.
### 1. Ex... | Summary: The research primarily focuses on the robust Markov Decision Process setting, where the objective is to learn a robust policy with the uncertainty set being measured by f-divergences using a model-based algorithm. One of the key idea of the paper lies in deriving precise sample complexity bounds for the same w... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the comprehensive feedback and recognize the significance of our contributions.
### 1. The intuition of why robust RL with TV uncertainty is easier than standard RL.
The intuition is kind of the opposite --- as the uncertainty level $\sigma$ increases, fewer samples... | Summary: This paper presents new upper and lower bounds for distributionally robust MDPs where the uncertainty set for the transition kernel is specified as a ball with $\chi^{2}$ divergence or total variation as the distance measure. The bounds significantly improve previous results.
Strengths: The paper is general... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to the reviewer for their insightful feedback and valuable comments.
### 1. The range of the discounted factor $\gamma$ that is considered in our theorems.
Recall that the discounted factor $\gamma$ determines the effective horizon length $\frac{1}{1-\ga... | Rebuttal 1:
Rebuttal: We thank the reviewers for their careful reading of the paper and their insightful and valuable feedback. Below we provide some new numerical results to corroborate the theoretical findings in this work.
### New numerical results
As reviewers suggested, we add new experiments to corroborate and d... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper focuses on the sample complexity of learning robust MDPs under generative model access, with uncertainty sets measured with TV and chi-squared divergences. The paper proposes tight upper and lower bounds for sample complexity under TV robustness and chi-squared robustness (under some range of the u... | Rebuttal 1:
Rebuttal: We thank gratefully the reviewer for various valuable suggestions and the praise of our interesting findings!
### 1. Questions about Algorithm 1 in Section 3.
Thanks for raising questions about algorithm 1. We shall address them as below:
* **Improving the specification of Algorithm 1 and its com... | null | null | null | null | null | null |
A Scalable Neural Network for DSIC Affine Maximizer Auction Design | Accept (spotlight) | Summary: AMAs is a parametric family of auctions that generalizes VCG and that is exactly DSIC (unlike mechanisms found with alternative regret-based approaches) and IR. This paper proposes a deep variant of AMAs. In particular, AMAs parameters are learned as outputs of a permutation-equivariant attention-based network... | Rebuttal 1:
Rebuttal: Thank you for your review! Your comments raise very good questions, and we will address them and the other concerns that you have raised.
**Q1: About the experiments on RegretNet-based methods.**
We understand the suggestion to include CITransNet and RegretFormer in the comparison for auctions w... | Summary: This paper introduces AmenuNet, a scalable NN for the AMA design, which ensures DSIC and IR. And the experiments demonstrate the effectiveness of AMenuNet, including its revenue, scalability, and out-of-setting generalizability.
Strengths: Originality:
1.Proposes a new automated auction design method: The pap... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments! We appreciate your positive feedback and we will address the questions you listed.
**Q1: About the comparison with industrial methods.**
> The paper does not compare the proposed method with some existing industrial methods, such as DNA, NMA, which makes... | Summary: This paper proposes a new architecture for learning auctions that are DSIC (but not necessarily revenue optimal) auctions. The authors' main contribution lies in a transformer-based permutation equivariant architecture designed to calculate the allocations, weights, and boosts variables utilized by AMA-based a... | Rebuttal 1:
Rebuttal: Thank you for your constructive review! We will address the questions you have listed.
**Q1: About the contribution**
> This paper shares similarities with the work of Curry et al. [2022] on Differential Economics for Randomized AMA auctions. However, a key distinction is that the allocation men... | Summary: Revenue maximizing strategyproof auction design with multidimensional types has proven to be extremely challenging. The lack of theoretical progress even in simple problem instances has motivated the use of machine-learning-based techniques to approximately learn high-performing auctions. One approach, typifie... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback! We value your affirmation and we will address the questions and concerns you have listed.
**Q1: About the comparison of RegretNet-based approaches.**
> There have since been many improvements on RegretNet in addition to CITransNet, with and without contexts.... | Rebuttal 1:
Rebuttal: We thank all reviewers for their careful comments and constructive suggestions. Here are our responses to some common questions in the reviews.
**Q1: About the comparative experiments on AMenuNet and RegretNet-based methods.**
Our primary focus in the experiments is to compare AMenuNet with othe... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
PanoGen: Text-Conditioned Panoramic Environment Generation for Vision-and-Language Navigation | Accept (poster) | Summary: This paper proposes a nove approach for creating an infinite number of diverse panoramic environments conditioned on text for visual-language-navigation (VLN). Specifically, the authors use stable diffusion with captions of images from a existing dataset to generate in-door panoramic views. Recursive inpaintin... | Rebuttal 1:
Rebuttal: **Response to Reviewer PpHD**
> Q1: Qualitative examples of generated data.
We include one panorama-trajectory-instruction pair in the general response pdf. As shown in the Figure, our PanoGen environment is more diverse in appearance, but still maintains good alignment in semantics with the or... | Summary: This paper proposed a data augmentation method named PANOGEN, which generates panoramic environments. The proposed method employs a recursive image inpainting technique to generate coherent panoramic environments and incorporates these augmented environments in both the pre-training and fine-tuning stages. Exp... | Rebuttal 1:
Rebuttal: **Response to Reviewer wyXn**
> Q1: Discussion of selection of image captioning model, and clarification for generating panorama from text.
**Image captioning model choice.** We choose BLIP-2 for image captioning since it has sota/good zero-shot performance on multiple image captioning benchmar... | Summary: This paper presents a new data augmentation method for VLN tasks. The proposed method first generates captions for each view and then recursively generates new images to ensure the consistency among multi-views. The authors demonstrate two ways to utilize the newly generated panorama on three benchmarks: R2R, ... | Rebuttal 1:
Rebuttal: **Response to Reviewer Wxjf**
> Q1: Consistency between panorama of two steps.
We show one qualitative analysis in the general response pdf. We could observe that though we didn’t explicitly improve consistency across steps, the general semantic information is still reasonable across steps. For... | Summary: In this paper, the authors propose to leverage the generative model to create panoramic images for agent training. A recursive inpainting method is adopted to generate 360-degree panorama views, which aims to ensure the co-occurrence of objects and enough diversity. Experiments are conducted on R2R, R4R, and C... | Rebuttal 1:
Rebuttal: **Response to Reviewer tReg**
> Q1: Novelty.
To adapt the text2image model for VLN, our main technical contributions include:
* We propose a novel inpainting way to generate consistent panorama views for VLN instead of single image views (Sec. 3.2).
* We propose a multi-modal transformer-base... | Rebuttal 1:
Rebuttal: **General Response**
We thank all the reviewers for their thoughtful feedback. We are glad that they find our work novel and creative (Reviewer vRWy, wyXn), and provides a cost-effective way to tackle the data scarcity problem for VLN and potentially more general robotics learning (Reviewer vRWy,... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a creative system-level solution for VL-navigation agent training, by incorporating the recent T2I model to help increase the data diversity while follow the context and human intuition, which facilitate effective pre-training for performing domain-specific tasks. The proposed pipeline with... | Rebuttal 1:
Rebuttal: **Response to Reviewer VRWy**
> Q1: Comparison with previous practices which avoid overfitting and increase data diversity.
First, our approach is compatible with the existing instruction augmentation approach PREVALENT[1]. As we described in L229-L231, we follow our baseline approach DUET and ... | null | null | null | null | null | null |
Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothing | Accept (poster) | Summary: This paper simultaneously addresses the label imbalance problem and uncertainty qualification capability in regression.
The authors propose to enhance the reweighting technique dealing with the imbalance problem in (Yang et al., 2021) to be applicable to VAE and combine the method with the output distribution ... | Rebuttal 1:
Rebuttal: Thank you for your constructive and encouraging comments as well as the insightful questions. We are glad that you find the problem we address ``"novel"``, our method ``"non-trivial"``/``"works well"``, our presentation ``"clear"``, and that experiments show our method's ``"superiority"`` ``"in te... | Summary: The authors propose a variational regression model for imbalanced data, which (1) borrows data with similar regression labels for variational distribution (neighboring and identically distributed: N.I.D.) and (2) utilize the conjugate distributions to impose probabilistic reweighting on the imbalanced data to ... | Rebuttal 1:
Rebuttal: Thank you for your constructive and encouraging comments as well as the insightful questions. We are glad that you find the problem we address ``"important"``/``"interesting"``, our performance ``"excellent"``, and our paper ``"easy to follow"``/``"well-written"``. Below we address your questions ... | Summary: This paper proposes a variational imbalanced regression model by taking the Neighboring and Identically Distributed (N.I.D.) assumption to solve both imbalanced regression and uncertainty estimation problems. Experiments on four imbalanced datasets demonstrate the effectiveness of the proposed method.
Strengt... | Rebuttal 1:
Rebuttal: Thank you for your constructive and encouraging comments with insightful questions. We are glad that you find the problem ``"important"`` and our N.I.D. assumption ``"reasonable"``, and acknowledge that our method improves performance. Below we address your questions in detail one by one.
**Q1: "... | Summary: In this work, authors recognize that although the existing regression models for the imbalanced datasets have been mainly developed to improve the prediction accuracy, they overlooked the quality of the uncertainty estimation. In this context, authors propose a deep probabilistic regression framework, to impro... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and insightful questions. We are glad that you find our work ``"novel"``. Below we address your questions one by one.
**Q1.1: "...ablation study...not investigate (1) whether each trick ... the stochastic latent feature (VAE) ... the posterior distributio... | Rebuttal 1:
Rebuttal: We thank all reviewers for their encouraging and constructive comments. We are glad that they found the problems we identified ``"important"``/``"novel"`` (ivhk, 9XGJ, C6MR), our idea/method ``"novel"``/``"non-trivial"``/``"reasonable"`` (cFEB, C6MR, ivhk), our paper ``"easy to follow"``/``"clear"... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
HIQL: Offline Goal-Conditioned RL with Latent States as Actions | Accept (spotlight) | Summary: The paper introduces HIQL, a hierarchical algorithm for offline goal-conditioned RL. HIQL utilizes an action-free version of IQL to learn the value function and subsequently derives both high-level and low-level policies from this shared value function using AWR. The paper asserts that the hierarchical structu... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review and suggestions for improving the work. Below, we describe how we have revised the paper to clarify parts of the paper to address the questions raised by the reviewer. We also clarify how we have already compared to baselines that use HER, and present ... | Summary: This paper introduces a hierarchical approach to address the issue of offline goal-conditioned problems, specifically when certain trajectories in the dataset have missing actions. The proposed method tackles this challenge by simultaneously learning a value function with a modified version of IQL and extracti... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review and constructive feedback about this work.
* **How to encode $s$ in $V(s, \phi(g))$?**
For pixel-based environments, we encode $s$ and $g$ separately into $\psi(s)$ and $\phi(g)$ using two *different* CNNs, $\psi$ and $\phi$. We then concatenate $\ps... | Summary: The paper identifies an issue with offline goal-conditioned reinforcement learning: namely that goal-conditioned value estimation can be noisy, so long-horizon tasks can be difficult to accomplish due to accumulating errors in value estimation. In addition, when states are so close together, there is very lit... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review and constructive feedback about this work.
* **“Novelty is not very clear to me from the given presentation.”**
One major difference between our approach and prior hierarchical methods is that we extract both policies (and even representations) from ... | Summary: This paper introduces hierarchical IQL (HIQL) for offline goal-conditioned reinforcement learning. HIQL uses the IQL algorithm to learn both a high-level waypoint policy as well as a low-level action policy; in both case, the goal-conditioned value is provided by the same value function learned using goal-cond... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review and constructive feedback about this work. As suggested by the reviewer, we ran an additional ablation experiment to study the use of AWR for both policies.
* **An ablation that demonstrates that AWR is needed for both policies**
We ablated both low-... | Rebuttal 1:
Rebuttal: We appreciate all five reviewers’ constructive feedback and suggestions for improving the work. We would like to highlight the updates we made in our responses below.
- We evaluated HIQL and baselines on $\mathbf{2}$ **additional pixel-based environments**, Roboverse and Visual AntMaze, which demo... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes HIQL a hierarchical algorithm for offline goal-conditioned RL. The approach consists in utilizing a single action-free value function to acquire knowledge about the structure and employ two policies: a high-level policy that predicts or represents a waypoint, and a low-level policy that pred... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review and constructive feedback about this work.
* **Why not use TT in Kitchen for comparison?**
As mentioned in L303, we directly took the performances of the Trajectory Transformer (TT) and Trajectory Autoencoding Planner (TAP) from Jiang et al. [1], whe... | null | null | null | null | null | null |
On the Role of Entanglement and Statistics in Learning | Accept (poster) | Summary: This paper studies quantum learning theory, in particular the relationship between the quantum version of PAC learning and the quantum version of the statistical query (QSQ) model, as well as their connection to other considerations in quantum computing, such as entangled measurements and separable measurement... | Rebuttal 1:
Rebuttal: Thank you for your comments!
Regarding your question about separating quantum vs. classical techniques: There are known separations between classical and quantum PAC for the distribution-dependent setting, for example for DNF formulas [Bshouty, Nader H., Jackson, Jeffrey C.: Learning DNF Over the... | Summary: This paper studies the power of different quantum machine learning models for learning Boolean functions, namely quantum PAC-learning (QPAC) with entangled measurements, QPAC with separable measurements, and quantum statistical query (QSQ). It has two main results. First, it shows that QPAC with entanglement m... | Rebuttal 1:
Rebuttal: Thank you for your very interesting questions/comments.
Indeed, our bound on $O(n T^2)$ is suboptimal, but in our main theorem statement we have a slightly combinatorial parameter $\eta$ and we show a bound of $O(n T \eta)$ which we show is tight for a certain concept class. We suspect that the r... | Summary: This submission investigates the relationship between learning models with access to entangled measurements, separable measurements, and statistical measurements in the quantum statistical query (QSQ) model. The authors make several notable contributions: they establish the polynomial relationship between the ... | Rebuttal 1:
Rebuttal: Thank you for your comments!
We will definitely address these and also look over the document for the next revision. | Summary: This paper considers the task of learning an unknown concept class with quantum accesses. In particular, the authors make a comprehensive comparison of the settings with entangled measurements, separable measurements, and statistical measurements in the quantum statistical query (QSQ) model, respectively, and ... | Rebuttal 1:
Rebuttal: Thanks for your comments.
While the proof techniques seem very different in both these results, we believe that both the results are related within the theme "need for entanglement in learning". Previously, it was known that QSQ measurements >= Sep measurements >= Ent measurements for all learnin... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
NeuroGF: A Neural Representation for Fast Geodesic Distance and Path Queries | Accept (poster) | Summary: The paper proposes a neural implicit representation of a 3d surface that enables querying for geodesic lengths and paths between points on the surface. A neural network is overfitted to one given surface. The input query points are embedded into high dimensional features whose euclidean distance represents the... | Rebuttal 1:
Rebuttal: ### **[Rebuttal to Reviewer Ujtd]**
### **[W1]** *Limited technical contribution due to the quite straightforward approach.*
**Response:** Thanks for your recognition of the usefulness of our core idea and technical solution. **Still, we beg to differ with the judgment that our work shows limite... | Summary: The authors propose a solution to the estimation of the geodesic distance between a pair of points (source, target) on a mesh. The proposed approach relies on an implicit field. In particular, the implicit field learns/memorizes the distance between each pair of points. The authors sample a subset of mesh vert... | Rebuttal 1:
Rebuttal: ### **[Rebuttal to Reviewer nwTQ]**
### **[W1]** *1) The input is not limited to points belonging to the surface; 2) The output geodesic path is not guaranteed to lie on the surface; 3) Have to optimize a neural network for each new input.*
**Response:** For the first issue, we argue that it is ... | Summary: This paper presents a framework to effeciently compute pairwise geodestic distances and shortest geodesic paths on the surface of a given mesh. This is accomplished by overfititng a neural learning model to a given mesh to regress the distances, paths and a SDF to reconstruct the surface. The geodesic distance... | Rebuttal 1:
Rebuttal: ### **[Rebuttal to Reviewer yBGS]**
### **[W1]** *Weakened contribution due to the overfitting setting; Extension to learn on datasets.*
**Response:** Thanks for your insightful advice, yet we beg to differ with the judgment that the overfitting setting "significantly" weakens our contribution.... | Summary: This paper develops a neural network architecture to estimate the geodesic distances and shortest geodesics between query points on a given 2D surface. It also provides a signed-distance function field evaluated at the given query points. The architecture consists of a set of FC layers followed by Pointwise ML... | Rebuttal 1:
Rebuttal: ### **[Rebuttal to Reviewer oZNk]**
### **[W1]** *Very dense sampling of ground-truth training data.*
**Response:** There seems to be a misunderstanding about preparing ground-truth pairs for training. In our experiments, we will create a simplified version of mesh model with around 20K vertice... | Rebuttal 1:
Rebuttal: ### **[Global Response]**
We sincerely thank all five reviewers for their time and efforts in reviewing this paper and providing different aspects of valuable suggestions and helpful comments. In summary, during the rebuttal period, we made the following major efforts to address reviewers' concer... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposed to employ neural geodesic fields to implicitly represent (1) geodesic distance, (2) signed distance filed, (3) shortest geodesic path query using the overfit-paradigm. Experiments are conducted to demonstrate the effectiveness of the proposal.
Strengths: • The writing of this paper is cle... | Rebuttal 1:
Rebuttal: ### **[Rebuttal to Reviewer Racd]**
### **[W1]** *Not enough shape diversity and scale of the used testing meshes.*
**Response:** We would like to remind that some of the testing models used in our experiments (such as *armadillo*, *bunny*, *dragon*) are real-world meshes, which are created fro... | null | null | null | null | null | null |
The geometry of hidden representations of large transformer models | Accept (poster) | Summary: This work investigates the geometry of hidden representations of transformer models trained via a self-supervised task on either amino acid prediction in proteins or pixel prediction in images. The work uses two tools to understand this geometry: intrinsic dimension (ID) (estimated via the TwoNN algorithm) and... | Rebuttal 1:
Rebuttal: We thank reviewer wEGg for carefully reading our manuscript for providing several points of discussion that we address below.
**Weaknesses**
*[...] Disentangling which of the conclusions are due to architecture and which are due to training method would make the work significantly stronger.*
We... | Summary: This paper presents an analysis of the internal representations of transformers trained with self-supervised learning, such as masked language modeling, from two perspectives: intrinsic dimension and adjacency structure. Experiments on two datasets - protein sequences and image data - revealed that the intrins... | Rebuttal 1:
Rebuttal: We thank reviewer kxbE for carefully reviewing our manuscript and for the constructive comments.
**Weaknesses**
*An ablation study would be incredibly beneficial in distinguishing the elements that stem from the model architecture (transformer) and those arising from the training protocol (maske... | Summary: This work focuses on characterizing the geometrical and statistical properties of data representations across the hidden layers of large transformer models. Specifically, it demonstrates the similarity in the evolution of geometric properties, such as the intrinsic dimension, between image-based and protein-ba... | Rebuttal 1:
Rebuttal: We express our gratitude to reviewer 4o8z for their appreciation of our work, for carefully reviewing our manuscript, and for the stimulating comments.
**Weaknesses**
*In line 36 (and others), the paper mentions the term "semantically rich representation." However, it is important to clarify how... | Summary: The paper studies the hidden representation of pretrained transformers via the ID (intrinsic dimension) on protein language tasks and image reconstruction tasks . The papers show that on protein LM, from input to output layer, the ID first increase to a peak, then decrease to an elbow, and finally increase to ... | Rebuttal 1:
Rebuttal: We thank reviewer K92u for reviewing our manuscript.
**Weaknesses/Questions**
*The results on iGPT is less robust (compare to protein LM). In particular, for the small model (I don’t see two peaks).*
We agree that in the small model the second peak is absent. However, the small model is signifi... | Rebuttal 1:
Rebuttal: We thank all reviewers for the detailed and diligent reading of our paper, from which we took a lot of cues on how to improve the quality of our work. We would also like to express our gratitude for the general appreciation of our contribution. We reply to common points raised by some Reviewers he... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Conditional Score Guidance for Text-Driven Image-to-Image Translation | Accept (poster) | Summary: This paper introduces a novel approach for text-driven image-to-image translation tasks. The main contribution of this work is the development of a conditional score function that takes into account both the source image and text, in addition to the standard condition with the target text. The new score functi... | Rebuttal 1:
Rebuttal: We truly thank you for your constructive and positive comments and below are our responses to the main questions.
Q1. Computational cost in terms of time and memory
In order to observe the realistic speed of each algorithm, we measure the wall clock time using a NVIDIA A100 GPU with a single ima... | Summary: This paper propose Conditional Score Guidance (CSG), where the goal is text-driven image-to-image translation by preserving the original context of a source image. They propose two novel components to achieve this: first, conditional score guidance that computes the score based on the combination of text-condi... | Rebuttal 1:
Rebuttal: We truly thank you for your constructive and positive comments and below are our responses to the main questions.
Q1. Incremental novelty
Our algorithm is somewhat related to Pix2Pix-Zero in the sense that CSG and Pix2Pix-Zero employ the cross-attention layers in the noise prediction network for... | Summary: The authors are proposing two sampling techniques in Diffusion Models; Cross Attention Mixup and conditional score guidance.Experiments show that the proposed methods show decent performance compared to baselines.
Strengths: - Qualitative results are interesting
- Reasonable motivations and methods
Weaknesse... | Rebuttal 1:
Rebuttal: We truly thank you for your constructive and positive comments and below are our responses to the main questions.
Q1. Mathematical expression of Eq. 8 from Eq. 7
As described in line 164 of the main paper, we get Eq. (8) from Eq. (7) by drawing a sample $\hat{\mathrm{x}}^{\text{src}}_t$ from $p(... | Summary: This paper proposes a new score function for text driven image to image translation. The core idea is to estimate score function conditioned on both original image and the target prompt. The score function can be decomposed into two parts, one is from the target prompt and the other one is guiding term for tar... | Rebuttal 1:
Rebuttal: We truly thank you for your constructive and positive comments and below are our responses to the main questions.
Q1. Concern about the generalizability of CSG
In the main paper, our paper focused on testing CSG on local editing tasks such as cat-to-dog, dog-to-cat, wolf-to-lion, zebra-to-horse... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their time and efforts in reviewing our main paper.
We have attached our visualization results in the document "Rebuttal_CSG.pdf", and please refer to the qualitative results.
Pdf: /pdf/afcc7b2c38b12d7ddf8be662d0f08ac4ad041395.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this paper, the authors propose a new method that can perform image-to-image translation through a pretrained text-to-image. They propose a new cross-attention map mixing technique and a new conditional score guidance function to tackle this problem. The method introduced in this paper does not require addi... | Rebuttal 1:
Rebuttal: We truly thank you for your constructive comments and below are our responses to the main questions.
Q1. Citation and comparison with DDIB [A1]
We omitted the reference for Eq. (3) accidentally since it is a basic equation introduced in the DDIM paper, which was also used in [A1]. We are sorry... | null | null | null | null | null | null |
Strategic Apple Tasting | Accept (poster) | Summary: The paper studies an online learning problem with incentives. In particular, in the model studied in the paper there is a principal who has to take decisions on a sequence of $T$ (different) agents. Each agent has a context and they can strategically disclose it (truthfully or untruthfully) to the principal in... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our work. Please find our answers to your comments/questions below.
[*While the literature revised in the paper is quite extensive as far as work on online learning with incentives are concerned, I think the paper is missing some very related works in the l... | Summary: The paper studies strategic apple tasting settings. This setting involves decision making that assigns decisions to agents who have incentives to strategically modify their input (context), and the decision maker only receives apple tasting (one-sided) feedback, where it only receives feedback for positively a... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our work. Please find our responses to your questions and comments below.
[*As in the above section, can the authors shed some light on the strategic modification effort budget?*]
Consider a lending setting in which an applicant (strategic agent) wishes t... | Summary: The paper considers contextual bandit problem in which the users can strategically modify their context for its own sake.
It provides sublinear regret algorithms by exploiting the budgeted strategic structure of the agents, against stochastically chosen agents.
It further obtains sublinear regret algorithms ag... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our work. Please find out responses to your comments and questions below.
[*What happens if the principal can also optimize over the choice of decision policy, i.e., not just using thresholded rule?*]
This is an interesting question. While we are able to o... | Summary: They consider the problem of online learning with apple tasting feedback where the sequence of arriving agents may strategically modify their features (contexts). They show how to achieve sublinear strategic regret compared to the best policy in the hindsight if agents were reporting truthfully.
Their main re... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our work. Please find our responses to your questions below.
[*It might be helpful to add a paragraph on how your bounds differ from the non-strategic case.*]
We would be happy to add such a paragraph in Section 1.1 where we discuss our contributions. At a... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies an online learning problem with one-sided (apple-tasting) feedback. At each round $t\in[T]$, an agent with a $d$-dimensional context vector $x_t$ arrives. The principal chooses a policy $\pi_t$ to map the context to binary decisions. Given $\pi_t$, the agent best responds strategically by mo... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our work. Please find our answers to your questions below.
[*What is the reasoning behind a bounded ℓ2 norm perturbation of size $\delta$ (instead of a different norm or a packing-type constraint) as the effort budget? Could you explain this in the context ... | null | null | null | null | null | null |
Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture | Accept (oral) | Summary: The paper introduces a new neural network layer which runs efficiently on modern GPUs, and exhibits strong performance against state-of-the art on several benchmarks. The layer is based on Monarch matrices, introduced in [7]. Monarch matrices use permutation matrices, and block diagonal matrices to represent d... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback on the technical contributions of our paper. We are glad that you found the GPU performance discussion insightful, and we appreciate your constructive comments on how to improve the paper.
**W1. More intuitive explanation of Monarch matrices.** Thank you for y... | Summary: This paper takes a fresh approach by addressing the issue of high complexity in current neural networks. It points out that the computational complexity of Transformers is quadratic with respect to both the sequence length and the feature dimension. Previous papers primarily focused on reducing the complexity ... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and questions. These questions have helped us improve the presentation of our paper. We provide a more detailed explanation of Monarch matrices below, which we plan to add to the paper.
**Q1. Monarch Motivation.** The motivation behind the Monarch matrix is to... | Summary: The Monarch Mixer (M2) combines MLP mixer and Conv mixer and yields a new family of mixers that is formalized in terms of Monarch matrices. the approach is novel and reminds me of an extension of [15] in their reference.
The main advantage of M2 is in its sub-quadratic computation capability. The authors eva... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and insightful suggestions. We hope that the additional experiments reported in the common response have improved the paper, and we look forward to further discussion. Here, we answer the specific weaknesses and questions raised in your review.
**W1. Evaluatio... | null | null | Rebuttal 1:
Rebuttal: # Common Response
We thank all reviewers for their time and valuable comments, which have helped us improve our paper. In this paper, we introduce Monarch Mixer, a new architecture that is hardware-efficient and sub-quadratic in both sequence length and model dimension. We demonstrate that Monarch... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Enhancing Motion Deblurring in High-Speed Scenes with Spike Streams | Accept (poster) | Summary: This paper proposed the first deblurring method that uses two types of inputs: RGB frames and spike streams. Two synthetic datasets are introduced. They are also used for training and evaluating the proposed method. This method outperforms other state-of-the-art methods on these synthetic datasets.
Strengths:... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments, summary of our paper, and affirmation of the performance. We would like to address your concerns and answer your questions here.
***1. More thorough evaluation in real-world scenarios is needed.***
Thanks for your suggestion! Please refer to Response 1 in **T... | Summary: The paper attempts to remove motion blur from high-speed scenes making use of spike streams. Most deep learning-based deblurring algorithms predict sharp frames relying only on the input blurry frames and are not robust when the blurry artifact is severe. This work proposes to use spike streams that could be o... | Rebuttal 1:
Rebuttal: ***1. What does low-resolution texture information have to do with motion deblurring? Why should it be a stronger guidance compared to high temporal resolution?***
We would like to clarify that the low resolution of spike camera is due to current hardware limitation. Our emphasis is on the textur... | Summary: This paper proposes a motion deblurring method that integrates RGB images and binary spike streams. In detail, it has a content-aware motion magnitude attention module and a transposed cross-modal attention fusion module. Experiments demonstrate state-of-the-art performance on deblurring datasets.
Strengths: ... | Rebuttal 1:
Rebuttal: Thank you for your summary, and we appreciate you for pointing out the strengths of our paper. My clarification and answers for the weaknesses you summarized are as follows.
***1. The Transposed Cross-Attention Fusion (TCAF) is close to the multi-Dconv head transposed attention (MDTA) in Restorme... | Summary: The paper proposes a novel approach that integrates the two modalities from two branches, leveraging spike streams as auxiliary visual cues for guiding deblurring in high-speed motion scenes, introducing a content-aware motion magnitude attention module and transposed
cross-modal attention fusion module.
St... | Rebuttal 1:
Rebuttal: Thank you for your positive and constructive feedback. We are encouraged that you find our method effective. We would like to address your concerns and answer your questions here.
***1. Have authors ever considered constructing a camera system to truly capture the spike data and blur images to be... | Rebuttal 1:
Rebuttal: We appreciate all reviewers for their helpful feedbacks. We are encouraged that they found our paper "well-written and technically sound" [DRAW], our method "novel" [Rugk] and "intuitive" [9w9w], and the proposed problem "new" [9w9w] and "is a promising direction for further research". We are deli... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces a novel approach that combines traditional cameras and spike cameras to address motion blur in high-speed scenes. By leveraging spike streams as auxiliary visual cues, the proposed spike-based motion deblurring model effectively extracts relevant information from blurry images using conte... | Rebuttal 1:
Rebuttal: ***1. The authors have missed a major branch of related works using Quanta Image Sensors (QIS). The related works and the comparison section should ideally have a detailed comparison with the QIS-based methods.}***
Thanks for recommending these works. We appreciate the recognition of Quanta Image... | null | null | null | null | null | null |
Explainable Brain Age Prediction using coVariance Neural Networks | Accept (poster) | Summary: This paper proposes a new framework for predicting brain age, an area of increasing interest in computational neuroscience. The authors use coVariance Neural Networks (VNNs) to develop an anatomically interpretable method that relies on cortical thickness features. Their framework goes beyond existing metrics ... | Rebuttal 1:
Rebuttal: Thank you for recognizing the novelty and key strengths of our work. We address the concerns raised in the review below.
**Additional empirical evidence.** To address the concern about empirical evidence, we have added results on the ADNI1 dataset (see pdf file attached with the global response)... | Summary: The authors propose a framework for predicting brain age by developing coVariance Neural Networks (VNN). They leverage the stability properties of VNN to first train the network using data from healthy controls to predict chronological age. Then, they perform inference using a combined dataset that includes gr... | Rebuttal 1:
Rebuttal: Thank you for recognizing the appropriateness of VNNs to the brain age prediction task and broadly to data analysis in medical domain. The concerns in the review are addressed below.
### Contributions and novelty.
The motivation to study VNNs for brain age prediction relative to other studies i... | Summary: The authors aim is to investigate the application of coVariance Neural Networks (VNNs) to brain age prediction. They train and test VNNs on the OASIS-3 brain dataset with additional data of Alzheimer's disease and cortical thickness. The results indicate an association between the biomarker (brain age predicti... | Rebuttal 1:
Rebuttal: ### Novelty, conceptual contributions, and comparisons with existing methods.
Here, we clarify the lack of conceptual clarity associated with chronological age in brain age prediction, motivation for using VNNs, and the comparisons with existing brain age approaches. These aspects are intertwined... | Summary: The paper leverages coVariance neural networks (VNNs) for brain age gap prediction in a principled statistical fashion. The paper focuses on the specific case of training on a healthy control and evaluating the gap on people with Alzheimer's disease.
------------------------------------
EDIT AFTER REBUTTAL PE... | Rebuttal 1:
Rebuttal: ### Contribution and novelty.
To understand the contributions relative to other studies in brain age prediction application, the following aspect must be recognized explicitly.
**Fallacies of focusing on performance.** The performance on chronological age prediction is a flawed metric for assess... | Rebuttal 1:
Rebuttal: We are grateful to all reviewers for their insightful feedback and appreciation of our work. We have provided individual responses to all reviewers. In this global response, we highlight the major advantages offered by VNNs over existing approaches and a summary of our response to two common conce... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Estimating the Rate-Distortion Function by Wasserstein Gradient Descent | Accept (poster) | Summary: This paper describes the elegant connection between entropic OT and estimation of rate-distortion functions, as well as proposes a novel algorithm based on Wasserstein Gradient Descent (WGD). It outlines prior methods (e.g. Blahut-Arimoto, NERD) in the same (or similar) language, and performs WGD on the two ob... | Rebuttal 1:
Rebuttal: Thank you for the detailed and insightful comments. We have corrected all the typographical issues as you suggested, and address all your remaining concerns and questions below.
> I think the references are a bit thin, especially on the level of the Wasserstein gradient for $\mathcal{L}\_{EOT}$.... | Summary: The authors propose a novel method based on Wasserstein Gradient Descent for numerically computing the Rate-Distortion function. Judging from the experiments their method seems to achieve comparable performance to other state of the art methods while having lower computational cost and being conceptually simpl... | Rebuttal 1:
Rebuttal: Thank you for the insightful review. We have incorporated all the typographical/notational suggestions as well as added a new experiment on MNIST (see PDF attached to the main rebuttal). Below we go over all the concerns and how we addressed them in our updated manuscript.
> In (15) in line 202,... | Summary: The paper proposes an algorithm for estimating the rate-distortion function for (possibly) continuous sources. This is done by presenting the R-D as an entropy-regularized optimal transport problem, and solving via Wasserstein GD. The source distribution is approximated by empirical distributions. This yields ... | Rebuttal 1:
Rebuttal: Thank you for your helpful suggestions, which have significantly improved our manuscript. Below we go over your concerns and detail how we have addressed them in our updated manuscript.
> First, the proposed Algorithm outputs the marginal distribution of the reproduction. This allows to compute a... | Summary: This paper proposes estimating the rate distortion function using Wesserstein gradient descent. Different from the classical Blahut-Arimoto algorithm in which the support points are fixed, the proposed method is able to learn the support of the optimal distribution. The authors prove finite-sample complexity b... | Rebuttal 1:
Rebuttal: Thank you for your helpful feedback. We have incorporated your suggestions to improve the writing. Below we will go over all points raised and detail how they have been addressed.
> Weaknesses:
> While this paper is technically solid, some parts of the paper is hard to read. For example
I(X;Y... | Rebuttal 1:
Rebuttal: We thank all reviewers for taking the time to review this manuscript and for providing insightful feedback, which has strengthened our submission.
We appreciate that the reviewers recognized our proposed method as “principled” (**jdNJ**), “novel” (**SDqV, GB3T, 4bjw, RV2s**), and “technically sol... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes an estimator using WGD and moving particles. This is different from prior methods that leverage neural networks to fit the unknown high-dimensional support of the optimal reconstruction distribution. The authors also note a connection with entropic OT and provide sample complexity bounds on... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback and bringing our attention to recent related work. We have followed your suggestion to cite (Wu et al. 2022, Lei et al. 2023), and added a new experiment on MNIST as well as more discussion around the computational cost of our method; also see points 2, 3, 5 ... | null | null | null | null | null | null |
Fair, Polylog-Approximate Low-Cost Hierarchical Clustering | Accept (poster) | Summary: This paper studies the clustering problem in a fair setting. It proposes an approximation algorithm that achieves polylogarithmic factors for fair and cost while also keep relative balance. This work has greatly improved the result of Knittel et al. [2023] that has an approximation factor of $O(n^\delta\text{p... | Rebuttal 1:
Rebuttal: *“This work seems to depend heavily on Knittel 2023…”*
This work does build upon the algorithm of Knittel 2023, but greatly simplifies the procedure to be more easily implemented. Moreover, our analysis removes the extra $n^\delta$ approximation factor which allows for the first polylogarithmic a... | Summary: The paper addresses the problem of fair hierarchical clustering.
Given a hierarchical clustering with a certain cost (determined by
Dasgupta's cost function), the paper shows how the hierarchy (tree) can be
efficiently modified so that fairness constraints are satisfied
and the cost of the tree increases (pr... | Rebuttal 1:
Rebuttal: *“On lines 124--126 (page 4), it is mentioned that the approximation factor is with respect to an optimal vanilla hierarchy (which doesn't consider fairness). Did the previous work also use this assumption?”*
Yes, the previous work also considers an approximation to an optimal, unfair, hierarchy,... | Summary: The paper considers finding a hierarchical clustering of (approximately) minimum Dasgupta cost under fairness and balance constraints. It appears that for any constant fairness range (i.e. the quantities a_i and b_i) they have a quasi-linear time algorithm which has a polylogarithmic approximation ratio. In fa... | Rebuttal 1:
Rebuttal: *“The way the bound is written is completely impenetrable…”*
We emphasize, as pointed out by reviewer XyRG, that for the given problem, our results require some in depth math to be fully accurate. To alleviate these issues, we will provide informal definitions and theorem statements in the respon... | Summary: This paper considers fairness in hierarchical clustering and proposes an approximation algorithm that modifies a given unfair approximate vanilla hierarchy $T$, of which the cost is bounded by $\alpha$-factor of the optimal (OPT) hierarchy tree, i.e., $cost(T)\leq \alpha \cdot cost(OPT)$, to a polylogarithmic-... | Rebuttal 1:
Rebuttal: *“I think the presentation of the paper needs to be largely modified…”*
We will revise the paper for the final draft per the suggestions of all reviewers. Notably, we will guarantee that all formal theorems and definitions have a high level intuitive explanation in the introduction to alleviate t... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Characterization and Learning of Causal Graphs with Small Conditioning Sets | Accept (poster) | Summary: Besides computational complexity, the weak spot of constraint-based causal discovery is the assumption of having an independence oracle. Especially with large conditioning sets, independence testing is difficult. This paper aims to address this limitation by restricting the number of conditioning variables and... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their detailed comments and suggestions. Please see below for our replies.
**1.** "performance gain seems to be only evident in small sample regimes"\
Note that showing our method outperforms the existing approaches in the small sample regime was the main g... | Summary: A new structure learning approach is proposed in limited data settings. In these settings, existing constraint-based causal discovery approaches struggle with statistical tests of independence. The main insight is that we can define an equivalence class of graphs based on an upper bound on the size of the cond... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their comments and feedback.
"real-world examples of learning from limited conditioning sets"\
We share the reviewer's sentiment that more experiments with real-world datasets would make the paper stronger. bnlearn repository is the main resource that caus... | Summary: The authors study the problem of causal learning with bounded conditioning sets. It is an important question that up to what set of equivalence graphs a learner can infer a causal graph when it can only perform conditional independence tests with limited size of the conditioning sets. They characterize the eq... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their constructive comments and we are happy to hear that they found our results relevant and important.
"Complexity of checking k-Markov equivalence."
This is a good question. Currently, one needs to explicitly construct k-closure graphs given the two DAG... | Summary: While I have the background to understand this paper, I'm afraid I am not well positioned to judge its novelty and significance in the causal discovery subfield. In short, the PC algorithm struggle when conditioning sets are large, so the authors propose a modification of Markov equivalence for bounded conditi... | Rebuttal 1:
Rebuttal: We would like to thank you for your feedback and insights. Our responses are below.
"this seems like competent well done work"
Thank you! We appreciate this.
"The modification of PC and Markov equivalence for bounded conditioning sets seems like a straightforward and simple modification of exis... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper aims to address the problem that in constraint-based causal discovery based on conditional independence tests, the tests become statistically unreliable with limited data when the conditioning sets are large. The proposed solution is to use only conditional independence tests for relatively small co... | Rebuttal 1:
Rebuttal: We would like to first thank the reviewer for pointing out such a relevant paper to us. Please allow us to compare our work with Spirtes (2001).
The short answer to your question "Are the main results of this paper a reproduction of Spirtes's (2001) results?" is no, they are not a reproduction of... | null | null | null | null | null | null |
Sequential Subset Matching for Dataset Distillation | Accept (poster) | Summary: This paper proposes a novel dataset distillation method called SeqMatch which focuses on extracting high-level features from later training trajectories. The authors highlight a limitation in state-of-the-art data distillation methods, which tend to condense low-level information from easy data while overlooki... | Rebuttal 1:
Rebuttal: Thank you for the comments and suggestions. We answer your questions in order.
**Q1:** The paper lacks evaluation numbers for certain settings.
**A1:** This is attributed to the notably sluggish training speed observed in the IDC[21] framework. Our experimental setup aligns with the configurati... | Summary: This work proposes a change to existing dataset distillation methods by sequentially optimizing different subsets at a time. At each iteration, the existing subset is frozen and a new subset of data is *added* to it and optimized. This method allows different subsets of the synthetic data to capture different ... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments! We sincerely appreciate the time and effort you dedicated to reviewing our work and answer your questions as below.
---
**Q1:** Algorithm 1 is a bit confusing to read.
**A1:** We have made revisions to our Algorithm 1, and have appended the revised versi... | Summary: This paper propose a new method called sequential subset matching (SeqMatch) for dataset distillation. The proposed method is designed to continuously generate synthetic images at different training (distillation) iteration. This strategy is inspired by the general mechanism of optimization, which captures cha... | Rebuttal 1:
Rebuttal: **Q1:** How was the claim for the optimization mechanism that captures low-level features in the early stages and high-level features in the later stages verified?
**A1:** The derivation of the claim has been explicitly presented in lines 161-166 of our paper. In support of this claim, it is cruc... | Summary: This paper investigates an issue with dataset distillation, where synthesized datasets tend to overly condense low-level features but fail to efficiently incorporate high-level ones. The authors argue that this is due to existing methods treating the synthetic dataset as a unified entity and equally optimizing... | Rebuttal 1:
Rebuttal: **Q1:** It's not clear how generalizable these results are to other tasks and datasets.
**A1:** Our chosen experimental framework adheres rigorously to the configurations delineated by a suite of established dataset distillation benchmarks, specifically MTT[5], DM[46], CAFE[42], KIP[34,45], IDC[2... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to all the reviewers and the Associate Chair for dedicating their time and effort to the review of our work. We appreciate the constructive questions and suggestions that have contributed to the enhancement of SeqMatch. Based on the feedback received, we have... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Maximum Average Randomly Sampled: A Scale Free and Non-parametric Algorithm for Stochastic Bandits | Accept (poster) | Summary: The authors propose a novel non-parametric algorithm for stochastic bandits, based on a sub-sampling scheme. While other algorithms using sub-sampling have been recently proposed, their approach significantly differs from these works. Indeed, instead of using sub-sampling to perform pairwise comparisons, they ... | Rebuttal 1:
Rebuttal: We thank your detailed and insightful review.
**@R4-A1) More precise literature review and clarification of MARS’s contribution**
To enhance the precision and accuracy of the literature review, we revised the "Related Work" section in the paper. The text previously present in lines 56-69 was rep... | Summary: The paper presents a new approach to develop a data-dependent upper confidence bound to replace the classical UCB based on concentration inequalities. The data-dependent bound is constructed using sub-sampling of rewards and offers a tighter estimate on the error than the classical UCB resulting in improved pe... | Rebuttal 1:
Rebuttal: We appreciate your recognition of the theoretical novelty presented in our work. To address your concerns regarding the empirical evaluation of MARS several experiments were added to the paper described below:
__@R3-A1) Enhancing Empirical Evaluation of MARS in Paper__
As proposed GIRO and PHE w... | Summary: The paper proposed a new method to compute the upper confidence bound (UCB). The new method does not require knowing or estimating the scale parameters. The property shown by Theorem 2 addresses the conservative issue of the existing methods. The practical importance of free from modeling the scale parameters ... | Rebuttal 1:
Rebuttal: We thank reviewer for insightful comments and positive feedback on the conducted theoretical analysis.
**@R2-A1) What are typical values? It is defined in Definition 1, but its intuition and usage are unclear. Also, it would be great to explain the typical value's role in proving Theorem 2 (the o... | Summary: In multi-armed bandits (MAB), when the noise distribution is known, the UCB algorithm with a carefully constructed confidence bound achieves a gap-dependent regret depending on the noise distribution. This manuscript studies the following question: when the noise distribution is unknown, is there an algorithm ... | Rebuttal 1:
Rebuttal: We thank the reviewer for insightful comments and for recognizing the utilization [Campi and Weyer, 2010] in the context of the multi-armed bandit problem.
**@R1-A1) MARS performance for non-symmetric reward distributions**
A new simulation was added to Supplementary material of the paper sectio... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable and generally positive feedback. We are encouraged that the reviewers found our work novel in terms of *“used a cute observation in [8] [...] provides a fully data-driven approach to construct the upper confidence bound, and this manuscript shows that the ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
EMMA-X: An EM-like Multilingual Pre-training Algorithm for Cross-lingual Representation Learning | Accept (poster) | Summary: This paper proposed EM-like multilingual pre-training algorithm using both supervised parallel data and unsupervised monolingual data to train an encoder model such that it can produce language agnostic representations for multiple languages. That is, embedding space of sentences of similar meanings will be th... | Rebuttal 1:
Rebuttal: Thanks for your comments.
**Q1: Unfair comparison between EMMA-X and unsupervised method (XLM-R).**
As a semi-supervised method, we have undertaken a comprehensive evaluation by comparing our results with four supervised methods (InfoXLM [1], HiCTL [2], LaBSE [3], and S-BERT [4]) in Tables 1, 2,... | Summary: This paper proposes an EM-like pre-training algorithm called EMMA-X to learn cross-lingual representations. The authors unify semantic relation classification and universal representation learning into the framework. To this end, a GMM classifier and a cross-lingual encoder is jointly trained in the algorithm.... | Rebuttal 1:
Rebuttal: Thanks for your positive comments.
**Q1: Initialization with parallel corpora weakens motivation.**
The primary goal of EMMA-X is to acquire universal semantic representations for a multitude of languages. However, it is important to note that only a limited number of languages (4%) possess para... | Summary: This paper proposes a new approach to learn cross-lingual representation learning as a universal alignment solution for any two languages without parallel data. The proposed approach resembles EM-like algorithm, which consists of a classifier to quantify the semantic similarity of two non-parallel sentences, a... | Rebuttal 1:
Rebuttal: Thanks for your constructive reviews.
**Q1: Over-claiming EMMA-X operates without parallel data.**
The primary goal of EMMA-X is to acquire universal semantic representations for a multitude of languages. However, it is important to note that only a limited number of languages (4%) possess paral... | Summary: The paper proposes EMMA-X, an EM-like approach to pretrain multilingual models. It learns the cross-lingual representation learning task and semantic relation prediction task within EM. They propose a new benchmark, XRETE, to evaluate the experiments with 12 cross-lingual tasks. The training involves two stage... | Rebuttal 1:
Rebuttal: Thanks for your positive reviews.
**Q1: Moving model details from Appendix to Main paper.**
Due to page limitations, certain model details have been included in Appendix A. We will incorporate the model details to the main paper in the next version.
**Q2: Lack of information about model paramet... | Rebuttal 1:
Rebuttal: **General Response to all Reviewers:**
Thank all reviewers for their time and efforts.
**Q1: Clarification about “Unsupervised” / “without parallel data” in EMMA-X.**
In EMMA-X, we explicitly show the use of parallel data for initializing the model in Section 3.2 and Algorithm 1. The primary go... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes to apply the EM framework to realize unified sentence representation learning with non-parallel multilingual data. This framework consists of two modules, a GMM classifier and a cross-language encoder, which are responsible for semantically related classification and cross-language unified ... | Rebuttal 1:
Rebuttal: Thanks for your positive comments.
**Q1: The reason to use GMM model as Semantic Relation Model.**
Please refer to the response to Q2 included in the “**General responses to all Reviewers**” part for details.
**Q2: The reason to form an XRETE benchmark.**
The formation of the XRETE benchmark ... | null | null | null | null | null | null |
PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning | Accept (poster) | Summary:
The paper studies the generalization and plasticity of deep reinforcement learning (RL) agents. The paper's primary contribution is a combination of the existing Sharpness-Aware Minimization (SAM) and Resetting mechanisms. The main insight is that combining SAM+Resets provides additive benefits: the former in... | Rebuttal 1:
Rebuttal: Dear reviewer mzhA,
Thank you for your constructive feedback. To address your concerns,
- We've experimented with SAM on advanced algorithms.
- We provide an explanation of the generality of our insight.
- Rectified .xxx in Table 1.
- The presentation in Table 2 will be revised to avoid misin... | Summary: This paper presents a new method to enhance sample efficiency in reinforcement learning, by integrating two existing techniques: sharpness-aware minimization (SAM) and weight resetting. It shows that SAM and resetting work in a complementary way where SAM addresses input adaptability and resetting addresses la... | Rebuttal 1:
Rebuttal: Dear reviewer dLhs,
Thank you for your constructive feedback. To address your concerns,
- We explored the DMControl Generalization Benchmark (DMC-GB). Our findings from this extended study suggest that DMC exhibits a large degree of input non-stationarity than we initially presumed.
- We recti... | Summary: This work studies the role of generalization and plasticity in sample-efficient deep RL. This paper proposes sharpness-aware minimization (SAM) to improve generalization in RL. And it provides details on how to use SAM with deep RL algorithms like SAC and Rainbow. Empirical evaluation shows that combined usage... | Rebuttal 1:
Rebuttal: Dear reviewer GE2W,
We appreciate your constructive guidance. Based on your feedback,
- We've refined our Synthetic experiments.
- We explored the synergies of input and label adaptation techniques in RL experiments.
- Line 128 has been corrected for clarity.
- We plan to provide a detailed expla... | Summary: Sample efficiency in RL is desirable to reduce computational and data collection costs, and is particularly critical to data-limited domains.
While off-policy methods can improve sample efficiency by training multiple passes over the same data, it faces challenges due to overfitting, which makes it harder for... | Rebuttal 1:
Rebuttal: Dear reviewer 1mez,
We appreciate your insightful questions and positive support. We have provided a detailed response to the comments which includes additional experiments on finding different synergetic combinations and integration of SAM + Reset on the advanced algorithms. Please let us know... | Rebuttal 1:
Rebuttal: We sincerely appreciate all four reviewers for their constructive and insightful comments.
The reviewers recognized the strengths of our paper as follows:
- A fresh insight into the dissection of generalization and plasticity (Reviewer 1mez, GE2W).
- The introduction of a synergistic solution: SA... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Privacy-Preserving CNN Training with Transfer Learning | Reject | Summary: This paper combines several existing techniques to achieve privacy-preserving CNN training. These techniques include transfer learning, Quadratic Gradient, mathematical transformation, and matrix-encoding method Volley Revolver.
This writing is more of a technical document rather than a research paper with... | Rebuttal 1:
Rebuttal: $\textbf{Response}$
We would like to thank the reviewers for their input. Their comments have been thoroughly considered, and altering the manuscript in accordance with these comments will significantly improve the quality of our paper in the next submission.
**C1: Is the propose method suitable... | Summary: The paper presents a method for CNN transfer learning implemented in homomorphic encryption to protect privacy.
Strengths: I'm not aware of the method being implemented in HE before.
Weaknesses: I cannot judge the machine learning aspects, but I don't see a strong novelty on the cryptographic side. The pap... | Rebuttal 1:
Rebuttal: $\textbf{Response}$
We would like to thank the reviewers for their input. Their comments have been thoroughly considered, and altering the manuscript in accordance with these comments will significantly improve the quality of our paper in the next submission.
**C1: The paper claims that some pri... | Summary: In this paper, the authors proposed a CNN training technique on the homomorphic encryption domain based on transfer learning. A gradient variant called Quadratic Gradient on homomorphic encryption was proposed. And a sigmoid function-based Softmax approximation was proposed. In addition, a new loss function fo... | Rebuttal 1:
Rebuttal: $\textbf{Response}$
We would like to thank the reviewers for their input. Their comments have been thoroughly considered, and altering the manuscript in accordance with these comments will significantly improve the quality of our paper in the next submission.
**C1: What is the security target to... | Summary: The paper employs a few heuristic methods to accelerate logistic regression training over encrypted data. The heuristics considered include: a new loss function called squared likelihood error (SLE) along with a polynomial approximation of sigmoid function, a faster gradient-descent method based on quadratic g... | Rebuttal 1:
Rebuttal: $\textbf{Response}$
We would like to thank the reviewers for their input. Their comments have been thoroughly considered, and altering the manuscript in accordance with these comments will significantly improve the quality of our paper in the next submission.
**C1: First and foremost, the title ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Tight Bounds for Volumetric Spanners and Applications | Accept (poster) | Summary: This paper researches the $\ell_2$-volumetric spanner (or $\ell_2$-well-conditioned spanning subset) for a given dataset $X$ based on the local search algorithm, in which each iteration implements a single swap to update the volumetric spanner $S$. More generally, the results are extended to $\ell_p$-norm for ... | Rebuttal 1:
Rebuttal: > For the $\ell_2$-volumetric spanner case
> (1) In the paper [Hazan, Karnin, and Meka'13] (see Theorem 1.1), the 1-approximate spanner $S$ has size $|S| = 12d$ and the running time is $O(n^{3.5} + n^3d +d^5)$.
>
> (2) In the paper [Woodruff and Yasuda'23] (see Theorem 3.4 and Corollary 3.5), th... | Summary: This paper studies the problem of constructing small volumetric spanners. Given a set S of points in R^d, (the l2 version of) the problem is to find a subset of S so that every point in S can be written as a linear combination of the subset points, with the l2 norm of the coefficients bounded above by 1. Analo... | Rebuttal 1:
Rebuttal: >The improvement in the spanner size is modest compared to prior work, and the algorithm is quite similar to known approaches (the only difference is that instead of iteratively adding elements to the spanner, each time we add a new element we also remove one), as is the analysis. I would not be e... | Summary: This paper looks at the problem of identifying volumetric spanners from a set of points \in \RR^d. The paper shows a method of local search which can be extended to find near optimal bounds of volumetric spanners, improving on previous algorithms. The authors also apply it to an application called MVEE, where... | Rebuttal 1:
Rebuttal: >The comparison of results is quite difficult to follow from the paper. In particular, it could be pretty easily explained by having a table or having a clear description of the best possible previous result and how much it was improved by through the local search method. I am also not clear about... | Summary: This paper develops and analyzes a local-search algorithm for finding volumetric spanners under norms in the regime where the number of given vectors $n$ is at least as high as the dimension $d$ of these vectors. Moreover, a runtime of the algorithm is given and the size of the algorithm's output is compared t... | Rebuttal 1:
Rebuttal: >(i.1) [Woodruff and Yasuda, 2023] - How does the notion of “distortion” in Definition 1.2 of [Woodruff and Yasuda, 2023] factor into the analysis of this paper. For example, do the coreset generated by Algorithm have high distortion when applied to the well-conditioned $\ell_2$ coreset problem?
... | Rebuttal 1:
Rebuttal:
**Message to all Reviewers.**
We thank all reviewers for their careful and constructive comments. Here we are addressing the question regarding the application of our result to machine learning and the improvement of our work over prior works. Then, we will answer all other questions in the indi... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper considers the problem of constructing a volumetric spanner: a subset of vectors which allows to represent every other vector via a linear combination with coefficients whose lp-norm is small. The algorithms are based on local search and work for any lp-norm for p>=1. As a representative result (Theo... | Rebuttal 1:
Rebuttal: > The applications to machine learning problems are somewhat weak (a few examples are given in the intro to column subset selection and sparse coding, but I am not exactly sure what the implications of the results in this paper are for the applications).
Please see our above message to all review... | null | null | null | null | null | null |
Tight Risk Bounds for Gradient Descent on Separable Data | Accept (spotlight) | Summary: This paper considers training a convex linear model with gradient descent on linearly separable data. The paper improves the upper bound of the population risk and proves a matching lower bound.
Strengths: The paper closes the gap for training convex linear models with smooth loss functions on linearly separ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and suggestions. We respond in length to all of your questions below; in case there are any remaining concerns, we will be glad to clarify them during the discussion period.
> Re novelty of techniques
In terms of upper bounds, we believe that simplicity i... | Summary: This paper examines the problem of learning linearly separable data with margin using gradient descent (GD) and focuses on establishing a population risk bound on the GD output. Previous research in this area has primarily concentrated on understanding the implicit bias and population error associated with sol... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and suggestions. We respond in length to all of your questions below; in case there are any remaining concerns, we will be glad to clarify them during the discussion period.
> “Why the authors do not focus on providing classification error bound for $C_{\ph... | Summary: This paper studies the generalization properties of gradient methods for convex, smooth and decreasing losses (e.g., logistic and polynomial losses) over linearly separable data distributions. The contributions are 1) a generalization bound based on Rademacher complexity which does not require the self-bounde... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and suggestions. We respond in length to all of your questions below; in case there are any remaining concerns, we will be glad to clarify them during the discussion period.
> 1. Re improvement over known results in literature
First, note that monotonicity... | Summary: This paper establishes tight upper and lower bounds on the population risk of linear models trained with gradient descent (GD) on linearly separable data. In contrast to previous work, the paper's result only requires a smoothness assumption on the loss function, and the upper bounds are adaptive to the loss f... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments and suggestions. We respond in length to all of your questions below; in case there are any remaining concerns, we will be glad to clarify them during the discussion period.
> Re Motivation for studying losses with diverse tail decays
The relat... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
BIOT: Biosignal Transformer for Cross-data Learning in the Wild | Accept (poster) | Summary: This paper proposes a model called Biosignal Transformer (BIOT) that allows cross-dataset learning with different number channels, sequence lengths, and missing values. BIOT consists of a tokenization module that transforms multi-channel signals into a sequence (“sentence”), and a Linear Transformer Encoder to... | Rebuttal 1:
Rebuttal: # Response to reviewer ykvi
------
###### We thank the reviewer for the constructive feedback. We have uploaded a revision and used blue to mark the new changes. Our detailed responses are as follows.
**Q1: My concern is on the scalability to biosignals with long sequences and large number of cha... | Summary: Biological signals are crucial for clinical applications, but current models are specialized for specific settings such as sampling rate and duration. The authors propose a pre-trained model that enables cross-data training that addresses differences in sensor settings such as mismatched channels, variable sam... | Rebuttal 1:
Rebuttal: # Response to reviewer XY8p
----
##### We thank the reviewer for the helpful feedback. We have uploaded a revision with the changes marked as blue. Our detailed responses are as follows:
**Q1: What are the limitations of using language modeling algorithms for biosignal tasks and considering biosi... | Summary: The authors present a general Transformer-based pipeline for learning on biosignals such as EEG, ECG and human activity sensor data. The proposed approach relies on a tokenization scheme that includes temporal and channel position information and a spectral representation of a segment of a single-channel time ... | Rebuttal 1:
Rebuttal: # Response to reviewer vQ39
-------
##### We thank the reviewer for your appreciation and constructive comments. We have uploaded a revision and used blue to mark the new changes.
**Q1: Can you give a sense of this length for the experiments of Table 2 and 3?**
Sure, it can be calculated from Ta... | Summary: The paper presents a Biosignal Transformer (BIOT) model that can be pre-trained from multiple data sources and fine-tuned on different downstream biosignal tasks. The model tokenizes diverse biosignals into unified “biosignal sentences” and adds channel embeddings and relative position embeddings to preserve s... | Rebuttal 1:
Rebuttal: # Response to reviewer ve81
###### We thank the reviewer for the constructive feedback. We have uploaded a revision and used blue to mark the new changes. Our detailed responses are as follows.
-----
**Q1: The paper does not provide motivation for why the authors picked these special datasets. T... | Rebuttal 1:
Rebuttal: We thank all the reviewers for your time and constructive feedback. During the rebuttal, we have prepared a revision and used blue to mark the new changes.
Pdf: /pdf/6f0994411972d16479ed532205913b9612d6c6e7.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting | Accept (poster) | Summary: This analysis paper demonstrates that the chain-of-thought explanations generated by LLMs do not faithfully represent the true deciding factors of their predictions. In the experiments, LLMs are steered to give intended predictions with three types of biases (Answer is Always A, Suggested Answer and social ste... | Rebuttal 1:
Rebuttal: Thanks for your review!
> What would the desired CoT explanations look like if they are faithful?
They might look as you propose, _if the model makes predictions according to the bias_. But faithful explanations that look just like those in the demonstrations are possible if the model simply doe... | Summary: The paper studies faithfulness of COT explanations. Authors perform experiments on different datasets under various setups (biased and none) to demonstrate how decisions made by the models change along with the provided CoT explanations to even justify the wrong decisions made by the model.
Strengths: 1. The ... | Rebuttal 1:
Rebuttal: Thank you for your review!
> Can authors please clarify on their definition of faithfulness and address my comment 2 made in the weaknesses section?
See “definition of faithfulness” in the global response.
---
Rebuttal Comment 1.1:
Comment: I thank the authors for providing the response to my ... | Summary: The authors present an investigation of ‘unfaithfulness’ in CoT prompting for large language models. They argue in particular that models can be predictably influenced by biasing features in the input, which the CoT “explanations” reliably fail to mention, and that they can be influenced by other factors (eg.,... | Rebuttal 1:
Rebuttal: Thanks for your review!
### Results and design
> The conclusion in 3.2 ... no inferential statistics ... present in any of the analyses in this section.
Thanks, we’ll add paired difference statistics there.
> A paired difference test is better, but still misses the multi-level nature of these ... | Summary: When using LLMs for problem solving, decision-making, and general reasoning: many prompting and reasoning strategies rely on step-by-step reasoning and/or other explicit explanations and structuring of the reasoning process. This paper studies the degree to which decisions and classifications are faithful to ... | Rebuttal 1:
Rebuttal: Thank you for your review! We’re glad that you found the experiments convincing and well-motivated.
> It's unclear whether the biases affect all classes of reasoning tasks equally, or if there are some scenarios or domains that are more strongly affected than others. Also, it is not clear from th... | Rebuttal 1:
Rebuttal: Thank you for the reviews! We’re encouraged that the reviewers agreed that our work is interesting, provocative, well-written and clear, found that our evaluations “convincingly demonstrate that the LLM's decision making is influenced by factors outside its verbalized reasoning” and thought that t... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Federated Linear Bandits with Finite Adversarial Actions | Accept (poster) | Summary: This paper studies the linear contextual bandits problem with federated learning of $M$ clients communicating with a central sever. In particular, the paper assumes adversarial finite action, and considers two cases of the communication: asynchronous and synchronous.
Following the idea of OFUL, this work ext... | Rebuttal 1:
Rebuttal: We thank the reviewer for the clear summary and for finding our paper interesting. Please see our response below with respect to your specific comments.
**Q1**: "Any lower bound of the communication cost in the synchronous case or the asynchronous case?"
**Response**: There are some most recent ... | Summary: This paper studies a federated linear bandits model, where M clients communicate with a central server to solve a linear contextual bandits problem with finite adversarial action sets and proposes the FedSupLinUCB algorithm, which extends the SupLinUCB and OFUL principles in linear contextual bandits. Both asy... | Rebuttal 1:
Rebuttal: We thank the reviewer for the interesting questions regarding the proposed FedSupLinUCB algorithm.
**Q1**: "Do the adversarial corruption actions only exist in the setting of the asynchronous case?"
**Response**: The asynchronous case contains the synchronous case as a special case. We thus des... | Summary: In this work, the authors consider the problem of Federated linear bandits with finite adversarial actions. This is the first time investigating a setting where federated clients are faced with a set of actions to choose from that changes over time in an oblivious adversarial manner, and the authors of this pa... | Rebuttal 1:
Rebuttal: We appreciate that the reviewer liked our federated linear bandit model, as well as for providing a thoughtful summary of our paper.
**Q1**: "In the experiments section, it would be interesting to see the proposed algorithms compared with other results for federated learning..."
**Response**: We... | Summary: This paper addresses the linear bandits problem in the context of federated learning. It proposes a general algorithm called FedSupLinUCB that solves a linear contextual bandit problem with finite adversarial action sets that may vary across clients. The paper also considers practical challenges in federated s... | Rebuttal 1:
Rebuttal: We thank the reviewer for the interesting questions regarding the proposed FedSupLinUCB algorithm.
**Q1**: "The introduction and preliminaries do not provide a detailed definition of the time-evolving and adversarial arm. To improve comprehension, it would be helpful to explain the impact of thes... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Natural Language Instruction-following with Task-related Language Development and Translation | Accept (poster) | Summary: The main contribution of the work is TALAR, a method for learning a vector representation of the instruction via a referential game. In TALAR, a generator creates a task vector from (state, next-state) pairs, which is then back-translated into natural language by the receiver. A translation from natural langua... | Rebuttal 1:
Rebuttal: Thank you for your time and valuable comments. We have taken every question into consideration and revised our paper to fix the typos. Please find the response below.
> Q1: Why the proposed technique would be better than parsing the instruction from humans into a structured representation that’s ... | Summary: This paper proposes the framework of TALAR, an Inside-Out learning framework for training policies that follows language instructions with reinforcement learning. The method leverages predicate representation for building a compact space of task language, and learns the generator of the task language from the ... | Rebuttal 1:
Rebuttal: We appreciate your time and effort in reviewing our paper and providing valuable feedback. We are glad that you found our presentation, soundness, and the TALAR method to be strong. We would like to address your concerns and questions as follows.
> Q1: Some baseline methods are not compared: meth... | Summary: This paper presents an algorithm that reduces the policy learning burden in a natural language-conditioned reinforcement learning framework. The authors investigate an inside-out scheme for natural language-conditioned RL and then present a new approach, TALAR, that learns multiple predicates to model object r... | Rebuttal 1:
Rebuttal: We appreciate your insightful comments and constructive feedback on our paper. We are grateful for the time and effort you put into reviewing our work. Below we address each of your concerns and questions.
> Q1: A separate training dataset is required for TALAR learning, and the cost for this see... | Summary: This work tackles the problem of learning an effective natural language (NL) instruction-following agent using RL algorithm as a goal-conditioned RL setup. The authors propose to learn a task-language (TL), which is a synthetic and vectorized representation containing the abstractive meaning of the instruction... | Rebuttal 1:
Rebuttal: Thank you for carefully reviewing our paper and providing constructive comments. We hope that our response has addressed your concerns, but if we missed anything please let us know.
> Q1: Could you elaborate the rationale behind learning the TL independent to a translator?
>
A1: The main reason... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to the reviewers and chairs for their valuable time and constructive feedback on our paper. We have carefully considered each comment and provided detailed responses. Thanks to the insightful assessments from the reviewers, we have conducted a more thorough e... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Global Structure-Aware Diffusion Process for Low-light Image Enhancement | Accept (poster) | Summary: This work introduces a low-light image enhancement based on diffusion model. By incorporating global structure-aware regularization, this work achieves more promising performances than existing methods.
Strengths: By considering the global structure during the diffusion process, this work achieves better per... | Rebuttal 1:
Rebuttal: ### **[Responses to Reviewer 2x92]**
### ***1** Response to Weakness 1 (W1): Baseline Model*
We refer the reviewer to the results of ablation studies depicted in Figure 5 on page 9, where the first case corresponds to the baseline diffusion model. It only achieves
26.02 dB, which is noticeably l... | Summary: This paper presents an innovative and efficacious approach to regularization within the domain of diffusion models, with particular utility for low-light image reconstruction. The method ingeniously takes into account the latent global structure of images. To achieve this, an image is first divided into patche... | Rebuttal 1:
Rebuttal: ### **[Responses to Reviewer U5ii]**
### ***1** Response to Weakness 1 (W1): Clustering Algorithm*
Thanks for your valuable comment. As shown in the following table, our method benefits from advanced clustering algorithms greatly, and the PSNR value is further improved by 0.7 dB, compared with K... | Summary: Aiming at better low-light image enhancement performance with diffusion models, this paper proposes a global structure-aware regularization utilizing the intrinsic non-local structural constituents of image data. An uncertainty map is incorporated into the diffusion model to ease the strict constraints on inde... | Rebuttal 1:
Rebuttal: ### **[Responses to Reviewer 9ew6]**
### ***1** Response to W1: Paper Contributions*
Thanks for the comments. We conducted additional experiments in terms of image super-resolution ($16\times$ SR on CelebA-HQ dataset) to validate the generality of our method on different tasks.
The quantitative... | Summary: This work proposes a diffusion-based low-light image enhancement framework that exhibits good performance in different benchmarks. A rank-informed regularization term during training and uncertainty-weighted loss is proposed.
Strengths: - This work proposes a diffusion-based low-light image enhancement framew... | Rebuttal 1:
Rebuttal: ### **[Responses to Reviewer gyFW]**
### ***1** Response to Weakness 1 (W1): Relation between Global Structure Modeling and Non-local Patch Rank Modeling*
It is imperative to highlight that by "global structure-aware," we are referring to the network's ability to account for various patterns, s... | Rebuttal 1:
Rebuttal: ### General Purpose
We thank all reviewers for your time, constructive comments, and recognition of our work. We believe all concerns have been clearly and directly addressed. Here we also want to summarize a few key clarifications concerning the contributions of our work.
Our **MAJOR** contribu... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents a diffusion-based framework to enhance low-light images. They propose a global structure regularization, which leverages the intrinsic non-local structural constituents of image data, besides, they introduce an uncertainty-guided regularization technique, which relaxes constraints on the mo... | Rebuttal 1:
Rebuttal: ### **[Responses to Reviewer ts6H]**
### ***1** Response to Weakness 1 (W1): Novelty of the Method*
We would like to underscore that the **MAJOR** innovation presented in our paper is the integration of global structure regularization into diffusion models. We also refer the reviewer to the **s... | null | null | null | null | null | null |
Chatting Makes Perfect: Chat-based Image Retrieval | Accept (poster) | Summary: The paper tackles the problem of chat-based image retrieval. The goal is that the system, being chat-based and powered by a LLM, engages in a conversation with the user to elicit information, in addition to an initial query, in order to clarify the user’s search intent by asking follow up questions. These ques... | Rebuttal 1:
Rebuttal: Dear Reviewer **L59s**, thank you for your insightful feedback!
**Lack of comparison with classical text-image retrieval methods:**
Please see the global part of the rebuttal and the figures in the accompanying pdf, where we address this concern.
### Answers to questions: ###
1. **“Are there ... | Summary: In this paper, the authors present a dialog-based image retrieval system and show strong performance against baseline models.
Strengths: 1. The author's attempt to augment the image retrieval process with dialogue is interesting and largely under-explored.
2. The paper is well-written and easy to read.
We... | Rebuttal 1:
Rebuttal: Dear Reviewer **34K9**, thank you for your insightful feedback!
1: **Regarding the motivation and “What type of dialogue one needs to have (given the caption) …”**:
The motivation is that, typically, a short query is not sufficient to retrieve the correct images from a large corpus, certainly not... | Summary: This study introduces ChatIR, a chat-based image retrieval system that engages in a conversation with the user to clarify their search intent and retrieve the desired image from a large corpus. The system leverages Large Language Models to generate follow-up questions to an initial image description and achiev... | Rebuttal 1:
Rebuttal: Dear Reviewer **Z6DK**, thank you for your insightful feedback!
**The absence of results on the traditional single-hop text-to-image retrieval:**
Please see the global part of the rebuttal and the figures in the accompanying pdf, where we address this concern.
Q1: Missing reference: Thank you. ... | Summary: This paper proposes a chat-based image retrieval framework, which can clarify users’ search intent. Authors design a question generation model based on LLM to generate questions based on dialog history. After user answer the question, an image retriever which is a transformer model is trained to extract text e... | Rebuttal 1:
Rebuttal: Dear Reviewer **6Lfv**, thank you for your insightful feedback!
**Lack comparison with SoTA image-text retrieval methods on image-text retrieval datasets in experiments:**
Please see the global part of the rebuttal and the figures in the accompanying pdf, where we address this concern. | Rebuttal 1:
Rebuttal:
Dear Reviewers and ACs,
We were happy to see that the reviewers have found that our paper presents: “interesting problem and proposes an interesting setup” (L59s), “process with dialogue is interesting and largely under-explored” (34K9), The strength of this submission lies in its clear and com... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement Learning | Accept (poster) | Summary: This paper proposes a new relational world model which is at an attribute level. Based on the state knowledge from the environment or unsupervised object-centric representation (OCR) learning [1,2], they collected attribute-level object-centric knowledge, thereby, based on them, they learned a relational world... | Rebuttal 1:
Rebuttal: Thanks for your feedback, it will surely improve our paper. We will answer your concerns in the following.
**Requiring the class label**
We first wanted to clarify a potential misunderstanding: we do not need labels for each new object, but we need to train a classifier that can classify the obj... | Summary: This paper proposes a methodology to tackle compositional generalization, allowing the policy to be able to generalize to previous unseen combinations of objects or compose previously learned tasks. More specifically, the authors propose the Dynamic Attribute Factored RL (DAFT-RL) framework, which involves lea... | Rebuttal 1:
Rebuttal: Thanks for the suggestions and feedback. We have run several experiments to answer your questions and we include a complete version of these analyses in the final version.
**Effect of imagination on baselines**
We conducted an additional ablation study without the imagination component for each... | Summary: The authors propose DAFT-RL, which uses object-centric representations to improve generalization to object attributes, when training world-models for model-based RL. For each class of object they learn a class template graph that maps class attributes to dynamics and rewards. The authors claim that this repres... | Rebuttal 1:
Rebuttal: Thank you for your feedback, addressing it will help us make the paper better. We address your issues one by one in the following.
**Supplementary Material**
This was intentional, we combined the main paper and supplementary material in one document for the sake of readability during the review ... | Summary: The paper presents a new framework for learning world models of environments with multi-object interactions. The presented DAFT-RL framework identifies objects as instances of classes, for which class template graphs describe how the object's dynamics and reward depend on it's attributes, and interaction patte... | Rebuttal 1:
Rebuttal: Thank you for your feedback, it will definitely improve our paper. We answer your questions inline below:
> I think it would be a lot easier for readers to parse the examples in Figure 1 if they were annotated with example attributes and parameters, e.g. position, velocity, friction coefficient, ... | Rebuttal 1:
Rebuttal: We thank all reviewers for the insightful feedback. Your suggestions and questions for more experiments will improve the quality of the paper, while the misunderstandings will improve its clarity and readability, making it more accessible to a wider audience.
We are happy about the positive feedb... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors has proposed Dynamic Attribute FacTored RL (DAFT-RL). For each class of object, class template graphs and interaction pattern graph and interaction graphs are computed. Through this template world model a policy can be learned for direct application in a new environment by just estimating the inter... | Rebuttal 1:
Rebuttal: Thank you for your useful feedback. We will answer each issue in the following.
**Code**
Following the NeurIPS policies, we will provide the code in an anonymized link as a separate message to the AC.
**Clarity, visual examples**
We will try to improve the clarity of the paper, including simpl... | Summary: The paper presents DAFT-RL, which is a object-based dynamics model for reinforcement learning that learns factorized relationships between attributes of objects. These relationships are learned in several steps. First, single-object episodes are created using a random policy, and these are used to train a mo... | Rebuttal 1:
Rebuttal: Thank you for your feedback, it will surely improve our paper, especially in terms of clarity. We answer each issue in the following.
**Interactions with single objects and end-to-end-training**
We think that in many real-world cases, one would first train an agent in simple environments (e.g. s... | null | null | null | null |
Self-Chained Image-Language Model for Video Localization and Question Answering | Accept (poster) | Summary: This work builds a joint model for temporal language grounding and video question answering. The model is built on a state-of-the-art image-language model, BLIP-2, and finetunes it in a parameter-efficient way to derive a localizer and an answerer. The localizer finds language-aware keyframes in a video and th... | Rebuttal 1:
Rebuttal: ### **Weakness 1**
> Most of the improvement compared to state-of-the-art methods comes from the use of the BLIP-2 model. The proposed method only shows small improvement over BLIP-2+concat in fine-tuning setting (see Table 1).
Our SeViLA consists of Localizer + Answerer, where both have B... | Summary: The paper presents the SeViLA framework, which addresses the limitations of existing image-language models for video question answering. It introduces two modules, Localizer and Answerer, that are fine-tuned from a pre-trained image-language model. SeViLA utilizes chaining for cascaded inference and self-refin... | Rebuttal 1:
Rebuttal: ### **Weakness 1**
> The motivation and LGDN are quite similar. While the paper mentions LGDN in the introduction and related work sections (which I appreciate), I suggest the authors further clarify the differences between the two approaches.
While LDGN selects salient frames with two di... | Summary: The paper proposes a simple yet effective framework for video localization and question answering using pretrained image-language models. The key idea is to introduce a LOCALIZER module which localizes the keyframes in a video in order to ignore irrelevant frames and better answer the question. The LOCALIZER m... | Rebuttal 1:
Rebuttal: ### **Weakness 1**
> The technical contribution of the work is relatively weak. The idea of selecting keyframes in a video for VQA is straightforward and widely explored in prior work. The proposed self-refinement strategy is also a standard semi-supervised learning approach and there's no new tra... | Summary: This paper proposes a novel framework called Self-Chained Video Localization-Answering (SEVILA) that leverages a single image-language model (BLIP-2) for both temporal keyframe localization and question answering in videos. The framework consists of two modules, Localizer and Answerer, which are parameter-fine... | Rebuttal 1:
Rebuttal: ### **Weakness 1**
> In the backward chain, the pseudo-tags are frame-level, and if a single frame of information can not give the correct answer, the Localizer is considered to have given the wrong keyframe.
We note that even allowing multi-frame in Localizer can also cause the misleadi... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and valuable comments. We appreciate that reviewers recognized:
- motivation of our localization+answering design (b5LJ, wzBa)
- novelty of our self-chaining framework design (TABX)
- strong experimental results (TABX, b5LJ, wzBa, mWtS)
- extensive ablation st... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Parallel Sampling of Diffusion Models | Accept (spotlight) | Summary: The paper introduces a new method for speeding up (reduced latency) sampling of diffusion models by sampling all time steps in parallel from some initialization and then iterating this procedure until convergence (Picard Iteration). Modifications are made for implementation efficiency such as a sliding window.... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review.
> **pick a suitably small problem and fully explore the compute/latency tradeoff by varying the window size… vary the window size all the way up to the number of denoising steps.**
Thank you for the suggestion. To better explore this tradeoff of vary... | Summary: Instead of reducing the number of denoising steps, the paper proposes to parallelize diffsuion denoising sampling via Picard iterations, by guessing the solution of future denoising steps and iteratively refining until convergence, which trades compute for speed. The authors then present ParaDiGMS to accelera... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review. However, we believe that there is a misunderstanding as to the nature of our proposed method. ParaDiGMS is the first parallel sampling method for diffusion models: computing multiple denoising steps at the same time. This is fundamentally different from existi... | Summary: The paper proposes an approach for speeding up sampling of diffusion models using Picard iterations. Multiple time steps in the sample process are predicted in parallel, iteratively refining until converging. Rather than refining all time steps at once which would not be practical, a sliding window approach is... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review.
> **It would be useful to see the effect of tolerance on sampling times/image quality. What practically is the impact of using different tolerance values?**
Thanks for the suggestion. Here are additional experiments on a sweep over tolerance values f... | Summary: The authors propose a technique to reduce the time taken to sample from a diffusion model at the expense of using more FLOPs. Roughly speaking, the authors parallelize sampling by "guessing" xt at numerous values of t simultaneously, then computing the score function for each of these values of xt in parallel,... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review.
> **less steps are used.**
Please see the shared response on “Speedup with few steps”. We present exciting developments where we implement custom multiprocessing for ParaDiGMS and are now able to see a 2.5x speedup on the most widely used setting of ... | Rebuttal 1:
Rebuttal: Thank you all for the helpful reviews. We are happy to hear that our contribution “brings new ideas to the diffusion sampling community”, is “practically useful”, and is “highly significant and…will be widely used”. We also appreciate that reviewers found the writing “easy to follow and overall we... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: Naive parallelization can let us generate multiple samples which improves throughput. However, the wall-clock time remains the same. To reduce the wall-clock time during diffusion model sampling, this paper proposes ParaDiGMS, a parallel sampling method of diffusion models based on Picard Iteration which impro... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review.
> **may not be suitable for situations where maximizing sample throughput is a priority**
Please see the shared response on “Focus on latency”. Our work indeed focuses on improving sample latency.
> **It is questionable whether ParaDiGMS ca... | null | null | null | null | null | null |
SyncTREE: Fast Timing Analysis for Integrated Circuit Design through a Physics-informed Tree-based Graph Neural Network | Accept (poster) | Summary: This papers proposes SyncTREE that uses a bottom-up and top-down graph attention network with a Tree Contrastive Loss to predict the delay and slew for IC interconnects. Compared to other GNN methods, the proposed one achieve lower prediction error across synthetic and RISC-V benchmarks.
Strengths: 1. The pro... | Rebuttal 1:
Rebuttal: ## Response to Reviewer #YDvu
Thank you for carefully going through the paper and pointing out so valuable questions. We hope these responses satisfactorily answer your questions:
### Q1. Ablation study.
1. Ablation study toward GAT modification.
Following the reviewer’s suggestion, we evaluate ... | Summary: This manuscript present a SyncTree to speed up timing analysis in IC design.
Strengths: -The problem that the manuscript is trying to address is important (increase speed of timing analysis)
-Evaluation and comparison to other machine learning based methods.
Weaknesses: - It is unclear are the Mean Averag... | Rebuttal 1:
Rebuttal: ## Response to Reviewer #sZvh
Thank you for your comments and the time spent reviewing the work. We try to address all the raised points in the following content.
### Q1. Clarification about MAE.
In our experiments, we treat the SPICE simulation measurements as the golden timing results. The Mean... | Summary: This paper proposes a GNN based method that specializes in timing analysis.
Strengths: * This paper is well written and organized, easy for readers to follow.
* Related background, related work that uses GNN on circuits are discussed with sufficient level of detail.
* The core problem looks well formatted.
* ... | Rebuttal 1:
Rebuttal: ## Response to Reviewer #6pFT
Thank you for your feedback and support! We hope these responses satisfactorily answer your questions:
### Q1. Model adaptation to different circuit size.
In this paper, our motivation is to devise a timing prediction model that can be applied to different circuits by... | Summary: The paper proposes a GNN model, dubbed SyncTREE, for IC's RC-tree timing analysis. Two techniques are proposed: 1) two-pass message-passing and 2) Tree Contrastive loss. Experiments of two IC designs demonstrate the best accuracy of SyncTREE over other SOTA GNN models.
Strengths: 1. Domain-specific knowledge... | Rebuttal 1:
Rebuttal: ## Response to Reviewer #ZUNz
We greatly appreciate your careful and detailed review. Here are some points we would like to clarify:
### Q1. Explanation of “Init Nodes” & “mask” in Fig. 2.
The "Init Nodes" part of Fig. 2 involves the node attributes assignment of the top-down graph after each ... | Rebuttal 1:
Rebuttal: ## General Response for Common Questions
Thanks for all reviewers' constructive suggestions, which help us find some points we didn’t explain clearly. We believe it’s necessary to make some global clarifications toward the following points:
### 1. Significance of our work.
Timing analysis is cruc... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
EDGI: Equivariant Diffusion for Planning with Embodied Agents | Accept (poster) | Summary: The paper proposes a new $\mathrm{SE}(3) \times \mathbb{Z} \times \mathrm{S}_n$-equivariant diffusion model based on the symmetriesThe empirical results demonstrate that the proposed EDGI (Equivariant Diffusion for Generating Interactions) model exhibits enhanced efficiency and superior generalization capabili... | Rebuttal 1:
Rebuttal: We are very appreciative of the reviewer’s time and effort in reviewing our manuscript. We are grateful to hear that the reviewer finds our idea to be “simple and effective” and that the reviewer thinks the addition of equivariance is indeed reflected in “improved generalization” on unseen tasks. ... | Summary: This paper proposes an enhancement to a planning/model-based RL method leveraging diffusion models. Specifically, the diffusion model is structured to be equivariant to the known symmetries of reasoning about objects in 3D space, namely translation symmetry, time shift symmetry, and permutation of object label... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive appraisal of our work! We are delighted to hear that the reviewer finds our approach to have solid motivations and arguments. We are also heartened to hear that the reviewer views our manuscript to be “clearly written” and to contain “sound experimental res... | Summary: The paper introduces the Equivariant Diffuser for Generating Interactions (EDGI), an $SE(3)\times \mathbb{Z} \times S_n$-equivariant diffusion model for model-based reinforcement learning. The proposed method maintains equivariance with spatial symmetry as depicted by $SE(3)$, the discrete time translation sym... | Rebuttal 1:
Rebuttal: Thank you for the thorough review and constructive criticism. We are glad that the reviewer thought the way that we incorporate symmetries in EDGI to be “novel and intriguing” and that the reviewer found our generalization experiments to demonstrate “convincing results”. We now address the main qu... | Summary: The paper introduces the Equivariant Diffuser for Generating Interactions (EDGI), a novel algorithm for model-based reinforcement learning (MBRL) and planning. It addresses the challenge of structured environments with spatial, temporal, and permutation symmetries, which are often overlooked by existing planni... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and nuanced comments. We are glad that the reviewer found our contribution to be novel and that it provides “a fresh perspective on symmetries in planning and MBRL”. We also appreciate the fact that the reviewer valued the “flexibility” provided by EDGI thr... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their thorough reviews and valuable feedback. We are encouraged that they found our approach of equivariant diffusion for planning “innovative” (reviewer **FPx8**), “simple and effective” (**kPhe**), and “very well argumented” (**28Jy**). In particular, the... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning Universal Policies via Text-Guided Video Generation | Accept (spotlight) | Summary: This paper introduces a novel approach to text-conditioned video generation task by treating it as a goal-conditioned RL, where the text is formulated as the goal and the multi-step image sequences in the video are the consecutive observations. The proposed method, termed Unified Predictive Decision Process (... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments – please see our response below. Feel free to let us know if you have additional questions or comments.
> Detailed Description of Modules.
We will clarify the detailed description of the modules. Our video planner seeks to construct a sequence of states that ... | Summary: This paper frames the sequential decision-making problem as a text-conditioned video generation problem. Given a text-encoded specification of a desired goal and the first frame with the initial configuration, a planner generates a set of future frames that depict planned actions. The generated video is then u... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments – please see our response below. Feel free to let us know if you have additional questions or comments.
> Overlap with Prior Work.
We believe the primary novelty of our work over past work such as Decision Diffuser is the construction of a large-scale model f... | Summary: The authors utilize the enhanced capabilities of text-guided image synthesis, a recent advancement in deep learning, to engineer general-purpose embodied agents capable of sequential decision-making.
The proposed method involves using language instructions as inputs to a text-conditioned video generation mod... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback on our work! We address your questions as follows.
> Highlighting contribution in method section
We will update our method sections to highlight the contributions, which includes (1) how to re-purpose text-to-video models designed for media and entertainment t... | Summary: A general framework, the Unified Predictive Decision Process (UPDP), was proposed in this paper. It leverages images as a universal interface, texts as reward specifiers and an independent planning module for policy synthesis. Powerful diffusion model was adopted into the framework of UPDP to generate authenti... | Rebuttal 1:
Rebuttal: Thank you for the detailed review. Please find our response below.
> Novelty of diffusion models for UPDP
We agree that frame conditioning and temporal super-resolution are not new in video diffusion models, but adapting them to control and hierarchical planning have not been done before. To our... | Rebuttal 1:
Rebuttal: We thank reviewers for their positive reviews and feedback. Reviewers noted that the paper was well written (Reviewer voju, 5Y9t), insightful (Reviewer eKLN), and had strong experiments (Reviewer VoKS). Reviewers voju, VoKS, 5Y9t had some concerns about novelty which we address below.
## Novelty
... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Active Reasoning in an Open-World Environment | Accept (poster) | Summary: This paper introduces Conan, a new benchmark for evaluating active reasoning in an embodied/open-world environment where an agent acts as a detective and must answer questions about the actions and intentions with the traces of another agent (a vandal). The experiments benchmark a RL + vision-and-language base... | Rebuttal 1:
Rebuttal: Dear reviewer:
> "have more examples about traces and reasoning steps"
See pdf attached.
> "Are the questions in Conan focused on testing abductive reasoning? Some heuristic exploration baselines"
Good exploration, ie, the heuristic ideal explorer recovers the vandal's trajectory, is enough fo... | Summary: The paper introduces Conan, an interactive open-world environment for evaluating the active reasoning abilities of agents. In Conan, agents need to answer questions by actively seeking for evidence and acquiring new knowledge in a setting of incomplete information.
Conan is formulated as a detective game. Firs... | Rebuttal 1:
Rebuttal: Dear reviewer:
Thank you very much for your thoughtful and detailed review. We are pleased that you found this work interesting and unique from existing benchmarks.
> "It is not clear how challenging the task is and how good the performance of the models reported in the paper is. A human study ... | Summary: This paper proposes a benchmark for "active reasoning" titled Conan, where, instead of passively answering questions from provided context, an agent must interactively explore its environment to discover information. The authors differentiate such a task from so-called "passive reasoning" tasks such as video-l... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you very much for your thoughtful and detailed review.
> "Missing negative control baselines."
Thank you for your insightful feedback. We have tried our model with empty visual inputs as a negative control baseline as your suggestion and we show the results on "goal" spl... | Summary: To address the gap in handling incomplete-information questions, this paper introduces an interactive open-world environment called "Conan." The purpose of Conan is to motivate and evaluate agents' active reasoning ability by requiring them to explore, gather evidence, and combine knowledge to solve complex sc... | Rebuttal 1:
Rebuttal: Dear Reviewer:
Thank you very much for your review and positive rating!
> "Some questions presented in the Figures/Tables can be answered without exploring the environment. "
No. All of the questions have multiple possible answers and need to be inferred from traces in the environment. From the ... | Rebuttal 1:
Rebuttal: To all reviewers:
We are sincerely appreciative and grateful for the time each of you have spent reading our work and giving useful, thoughtful and constructive feedback. The feedback is substantial and quite helpful for improving our paper. In particular, we would like to thank reviewers for ac... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces Conan, an interactive environment as a benchmark to evaluate agent’s active abductive reasoning abilities to answer questions in an incomplete (or partial) information scenario. Because of partial information, the model requires further exploration in the scene to answer the questions whi... | Rebuttal 1:
Rebuttal: Dear Reviewer:
Thank you very much for your thoughtful review! We would like to discuss your concerns here:
> "look like a toy benchmark with limited real-life applications"
It is true that Conan is synthetic and may appear simplistic at first glance. However, the primary goal of Conan is not to... | null | null | null | null | null | null |
Hierarchical Open-vocabulary Universal Image Segmentation | Accept (poster) | Summary: In this paper, the authors target open-vocabulary setting and propose a universal framework for open-vocabulary semantic/instance/panoptic segmentation. The whole framework is DETR-like. And to deal with the discrepancies between the thing and stuff classes, the authors ultilize independent decoders for thin... | Rebuttal 1:
Rebuttal: We appreciate your invaluable insights and thoughtful comments. In the following sections, we address the questions you have raised:
**[W1] New knowledge introduced to UNINEXT and performance comparison with UNINEXT on open-vocabulary and part-segmentation benchmarks** \
We thank the reviewer for... | Summary: This paper presents HIPIE, an open vocabulary image segmentation model that produces segmentation from text prompts. The authors propose to decouple the segmentation of “thing” and “stuff” due to the differences in their semantic and geometric properties. By training on an additional part-level dataset, HIPIE ... | Rebuttal 1:
Rebuttal: We appreciate your invaluable insights and thoughtful comments. In the following sections, we address the questions you have raised:
**[W1] How does the hierarchical segmentation process occur in the proposed model? Is the model inherently hierarchical in its architecture? How to demonstrate the ... | Summary: The paper proposes a unified method for open-vocabulary universal image segmentation and detection methods. A text-image fusion module takes both the image features and text features and then sends the fused results to the decoder. Several designed choices are presented and compared here. The model utilizes th... | Rebuttal 1:
Rebuttal: Thank you for your insightful inquiries, and we will provide detailed responses to each of them below:
**[W1] Concern about HIPIE's pre-training on Object365, which is unfair.**
With regard to the use of large datasets, we'd like to highlight that our research is centered on universal models cap... | Summary: The paper propose to disentangle the representation learning and decoding for things and stuff and unify multiple segmentation tasks with different granularity (whole, part, subpart) and text formulation (reference text or category only). Extensive experiments are carried out to validate its effectiveness.
St... | Rebuttal 1:
Rebuttal: We appreciate your invaluable insights and thoughtful comments. In the following sections, we address the questions you have raised:
**[W1] Open-vocabulary results with and without the assistance of CLIP for inference**
Nice question! We sincerely appreciate the reviewer for highlighting the abs... | Rebuttal 1:
Rebuttal: We extend our gratitude to the reviewers for their valuable feedback. Their insights have significantly enriched our work.
We are heartened by YNZH's recognition of our paper, where YNZH highlights the significance of our approach in *"unifying different segmentation tasks and benchmark them, whic... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Bringing regularized optimal transport to lightspeed: a splitting method adapted for GPUs | Accept (poster) | Summary: This paper adapts the Douglas-Rachford splitting to solve a wide range of sparsely-regularized optimal transport problems efficiently using GPU-parallelizable operations. The contributions are as follows:
1) Adapt the Douglas-Rachford splitting to handle regularized OT problems with sparsity-inducing penaltie... | Rebuttal 1:
Rebuttal: Thank you for the time and effort you invested in thoroughly reviewing our paper. Your feedback is very valuable to us, as it helps us improve our paper. We are grateful that you highlighted some typos in the manuscript - we will make sure to revise it according to your comments in upcoming versi... | Summary: The paper develops an approximation algorithm for solving the optimal transport (OT) problem with a general class of regularizers (named "sparsity promoting", but more precisely characterized by not penalizing sparse solutions). The Douglas-Rachford splitting algorithm is used, extending the previously introd... | Rebuttal 1:
Rebuttal: Thank you for your questions and your feedback on our paper! We believe that we have been able to address all your concerns, as detailed below.
We agree that there is room for improvement to make the theory section clearer. The 1/epsilon convergence rate of DR-splitting is nothing that we derive,... | Summary: The paper presents an extension of the Douglas-Rachford algorithm in [23] to regularised optimal transport. It describes conditions under which sparsity-inducing regularisations result in sparse solutions and convergence rates of the resulting estimates.
Implementation on GPU, as well as gradient routines, are... | Rebuttal 1:
Rebuttal: We are grateful for the time you invested in reviewing our paper. Your feedback means a lot to us - thank you!
We have rerun our experiments with a PyTorch version of RDROT. Naturally, this results in a performance drop for our algorithm, but the experiments still show that our algorithm is faste... | Summary: In previous work [23] (DROT), the Douglas-Rachford splitting was applied to solving unregularized optimal transport (OT). The idea is to split the original variable into two variables $X$ and $Z$, where $X\ge 0$ and $Z$ prescribed row and column sums. The current paper extends this idea to regularized OT. Comp... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and constructive feedback. We are grateful for your positive words about our work but, quite naturally, disagree with the statement that the algorithmic contributions are limited, or that this should be reason enough to reject the paper. Let us elaborate.
We b... | Rebuttal 1:
Rebuttal: Dear AC and reviewers,
Thank you for the time you invested in the peer-reviewing process. The input you provided has both been insightful and inspirational for us.
The reviewers have recognized many strengths and novelties of our contributions, including:
- the generality of the algorithm for ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Paxion: Patching Action Knowledge in Video-Language Foundation Models | Accept (spotlight) | Summary: The paper tackles the known issue of CLIP-like models acting similar to bag-of-words models, in which structured information (e.g. relations between objects) is not really captured, so for action recognition the information leveraged is mostly object and scene (e.g. a person besides a guitar vs a person playin... | Rebuttal 1:
Rebuttal: We thank Reviewer BUR1 for the constructive comments. We are glad that you find our paper to be innovative and well-written. We will address your comments and questions in the following paragraphs.
### Little information about the “patching data”
We appreciate the reviewer’s suggestion to include... | Summary: In this paper, the authors introduce an interesting ActionBench, aiming to handle action antonym and video reversal problems. To remedy the problem in well-trained VidLM, the authors propose the DVDM objective, along with knowledge patcher and fuser. Extensive experiments demonstrate the effectiveness of the n... | Rebuttal 1:
Rebuttal: We thank Reviewer CJDm for the constructive comments. We appreciate that you find our ActionBench to be novel and interesting. We will address your comments in the following paragraph.
### Complicated motivation for the DVDM objectives
We appreciate the reviewer's suggestion, and will revise sect... | Summary: The manuscript presents an Action Dynamics Benchmark (ActionBench) with three new evaluation metrics for existing video-language models. Through ActionBench, the authors find that existing video-language models essentially rely on recognizing objects to recognize actions. Hence, a parameter-efficient component... | Rebuttal 1:
Rebuttal: We thank Reviewer SP6c for the constructive comments. We are glad that you find our benchmark to be inspiring and our paper to be well-written. In the following paragraphs, we will address your comments and questions.
### Limited downstream tasks for evaluation
We completely agree that evaluating... | Summary: This paper addresses the task of improving video-language understanding models, which a particular emphasis on their ability to align described actions to video segments and also their ability to model temporal dynamics. The authors first propose ActionBench, which is a modification of two datasets (Ego4D, SS-... | Rebuttal 1:
Rebuttal: We thank Reviewer ou3W for the detailed and constructive comments. We appreciate your acknowledgment of the value of our proposed probing tasks and the DVDM objective. We will address your comments and questions in the following paragraphs.
### Limited impact on downstream tasks
1. First, we woul... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Adaptive SGD with Polyak stepsize and Line-search: Robust Convergence and Variance Reduction | Accept (poster) | Summary: This paper presents two step-sizes, AdaSPS and AdaSLS, and theoretical analyses of PSGD with AdaSPS/AdaSLS. It also presents numerical results to support the analyses. The contribution of the paper is to provide AdaSPS and AdaSLS step-sizes.
Strengths: The strength of the paper is to provide two step-sizes, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the remarks and criticisms. We are a bit puzzled by your score. We provide clear answers to your questions below.
Although we do not claim any contribution to the deep learning scenarios, we do provide DL experiments with our practical version of AdaSPS. Please see Appe... | Summary: The paper aims to propose robust methods that achieves optimal rates in both
strongly-convex or convex and interpolation or non-interpolation settings.
Specifically, they propose AdaSPS, a modification of $SPS_{max}$ with
an AdaGrad-like denominator (replacing the gradient norms with function values) in
the st... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive remarks. We provide answers below. (For space limit, We omit the big O notation and write V-SGD for Vanilla SGD, AGN for AdaGrad-Norm, IC for individual convexity, ISC for individual st-convexity and PCV for potential camera-ready version).
> W1. Paper o... | Summary: This paper introduces Adagrad-norm type update into stochastic line search (SLS) and stochastic Polyak step size (SPS) (namely AdaSLS and AdaSPS) that guarantee convergence in non-interpolating convex scenario with a $\mathcal{O}(\frac{1}{\epsilon^2})$ rate. Then it shows that AdaSLS and AdaSPS converge linea... | Rebuttal 1:
Rebuttal: We thank the reviewer for the remarks and criticisms. Before responding to each point, we kindly want to highlight our first contribution to address your main concern.
**We focus on the settings where the underlying interpolation condition is unknown to the users.** Having a **robust** (that can... | Summary: This paper proposes two new variants of SPS and SLS, called AdaSPS and AdaSLS, which provide convergence in non-interpolation settings for convex and strongly convex functions when training over-parameterized models. AdaSLS requires no knowledge of problem-dependent parameters, and AdaSPS requires a lower boun... | Rebuttal 1:
Rebuttal: We thank the reviewer for the remarks and suggestions on our paper. You can find our replies below:
> Comparison with SARAH and PAGE
We thank the reviewer for mentioning PAGE. We will include the discussion on PAGE in the potential camera-ready version. We would like to highlight that **there is... | Rebuttal 1:
Rebuttal: Thanks to all reviewers for examining our manuscript and help with improving our paper. We appreciate the constructive comments from the reviewers, and we address all raised issues via individual comments.
We like to highlight that:
- **We propose the first adaptive methods that simultaneously ac... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Stack More Layers Differently: High-Rank Training Through Low-Rank Updates | Reject | Summary: This paper proposed a low-rank way of training LLMs which allows more flexibility than most if not all popular low-rank training methods. It's a parameter-efficient way which only consumes limited gpu usage so it has the potential to be applied to larger models.
Strengths: 1. The rationale behind the idea is ... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback and thorough review of our paper. We sincerely appreciate your insights and would like to address your concerns. Here's our response to your comments:
Regarding your statement about PEFT methods: the goal of this paper is to demonstrate that parameter-efficien... | Summary: This paper focuses on low-rank training techniques and introduce ReLoRA that uses low-rank updates to train a high-rank network (<=350M parameters). The main idea is to employ LoRA during training and "restart" it in order to artificially increase the rank, which is a nice idea. The difficulty remains in the o... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Your comments have helped clarify certain areas of the paper that we need to address. Here's our response to your points:
### Clarification on Pre-training vs. Fine-tuning:
It seems there is some confusion regarding the distinction between pre-training and fine-tuning... | Summary: This paper introduces a novel approach, ReLoRA, for training large-scale neural networks. Recognizing the limitations of conventional low-rank matrix factorization (LoRA) in training high-performing transformer models, the authors propose ReLoRA that employs a high-rank network training through multiple low-ra... | Rebuttal 1:
Rebuttal: Thank you for your assessment of our work. We really appreciate your feedback and suggestions! Here's a response to your questions:
### Trainable Parameters and Rank 'r' of LoRA
You're correct about the parameter counts. We provide the total number of parameters (60M, 130M, etc.) in the paper. T... | Summary: The paper proposes an extension LoRA. The main insight in the paper is LoRa ca be initialization multiple times during training layers and this in the end will produce a high rank update. The authors show that it is quite challenging to re-intialize the layers mostly due to the internal initialization state o... | Rebuttal 1:
Rebuttal: We appreciate your thorough review and the feedback provided. Thank you!
To address your questions, we’ve performed the following additional experiments:
### 1. Performance Similarity in Table 3:
You pointed out the similarity in performance between the second to last row in Table 3 and our bas... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort the reviewers dedicated to reviewing our paper. Your feedback, ranging from detailed concerns to constructive suggestions, has been instrumental in guiding us to refine and clarify our work.
Following the NeurIPS rebuttal policy, we attach a single-page... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Transforming to Yoked Neural Networks to Improve ANN Structure | Reject | Summary: The paper introduces a new method called YNN that transforms traditional ANN structures into yoked neural networks, promoting information transfer and improving performance. The authors analyze the existing structural bias of ANN and propose a model YNN to efficiently eliminate such structural bias. In their m... | Rebuttal 1:
Rebuttal: Thank you very much for your precious comments.
1>Please refer to the pdf file of Author Rebuttal by Authors. I have organized the contribution of the work seriously. I hope it can answer some of your questions. All information is in the picture. If the image is small, please enlarge it.
2>GNN o... | Summary: This paper proposed a module called YNN that could exchange the information of the neurons within the same layer. The proposed module can be combined with MLP. The experiments on several small-scale datasets show that their method achieves good performance compared to previous networks.
Strengths: - The motiv... | Rebuttal 1:
Rebuttal: Thank you very much for your precious comments.
1>Please refer to the pdf file of Author Rebuttal by Authors. I have organized the contribution of the work seriously. I hope it can answer some of your questions. All information is in the picture. If the image is small, please enlarge it.
2>If th... | Summary: This paper propose a 'yoked' neural architecture where neurons at the same level are bidirectionally linked. They claim that optimizing this complete graph is superior to current deep neural network architectures that impose a structural bias due to the transfer of knowledge in a way that prevents structural b... | Rebuttal 1:
Rebuttal: Thank you very much for your precious comments.
1> Please refer to the pdf file of Author Rebuttal by Authors. I have organized the contribution of the work seriously. I hope it can answer some of your questions. All information is in the picture. If the image is small, please enlarge it.
2>I n ... | Summary: The paper proposes Yoked Neural Networks (YNN) - an extension of neural networks, which, when calculating the value of a node, in addition to the information from the previous layer of the network, uses information from the nodes on the same layer (i.e. it "yokes" nodes from the same layer together).
Strengt... | Rebuttal 1:
Rebuttal: Thank you very much for your precious comments.
1>Please refer to the pdf file of Author Rebuttal by Authors. I have organized the contribution of the work seriously. I hope it can answer some of your questions. All information is in the picture. If the image is small, please enlarge it.
2>A lin... | Rebuttal 1:
Rebuttal: Pleat refer to the pdf file. I have organized the contribution of the work seriously. I hope it can answer some of the questions.
All information is in the picture. If the image is small, please enlarge it.
Pdf: /pdf/61536891bd8db60b040e0517cd622d7e0869af19.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this paper, the authors propose a novel neural network model that exploits a connection between the nodes of a layer. The authors’ goal is to develop a model that overcomes the structural bias posed by the classical layer structure of the NN. To do this they propose to consider the model as a bidirectional ... | Rebuttal 1:
Rebuttal: Thank you very much for your precious comments.
1>Please refer to the pdf file of Author Rebuttal by Authors. I have organized the contribution of the work seriously. I hope it can answer some of your questions. All information is in the picture. If the image is small, please enlarge it.
2>The m... | null | null | null | null | null | null |
Kiki or Bouba? Sound Symbolism in Vision-and-Language Models | Accept (spotlight) | Summary: The Kiki-bouba effect is a well studied phenomena in humans to consistently associate sharp and smooth objects with certain phonetics. This paper explores whether such an effect is present within image and language models. Specifically it looks at Stable Diffusion (a text to image generative model) and CLIP (a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and comments. We will clarify wording issues and add requested citations in a revised version.
Regarding unimodal (text-only) models, we refer to our investigation of these models in the supplementary material (supp Sec 3.1), where we test encod... | Summary: The work's goal is to study whether sound symbolism is reflected in vision-and-language (VL) models like CLIP and Stable Diffusion (SD). The work proposed a method called **zero-shot knowledge probing**, and verified a sound symbolism phenomenon like **kiki-bouba effect** by evaluating the outputs from CLIP an... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and comments. We address the reviewer’s question regarding tokenization in our global response (item C), and the concern about multilingual results in our global response (item B) and in the response to reviewer NTGx.
To address concerns about t... | Summary: The authors present an investigation of the phenomenon of ‘sound symbolism’ in a pair of cutting edge Vision-and-Language Models. Sound symbolism is an intriguing phenomenon whereby the meaning of a word can be in part traced back to the way the word sounds. It is an important phenomenon in psychology and ling... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and comments. We address the reviewer’s concerns regarding our user study in our global response (item A). In particular, we reiterate that we believe the user study to not be critical to our results, and we are willing to remove or replace it u... | Summary: This paper determines the extent to which pretrained vision and language models encode phonetic information associated with sharp or round objects. Prior research has shown a cross-lingual tendency to associate sounds with shapes in human studies. In this work, the authors investigate whether this holds for a ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and comments.
To investigate whether sound symbolism may exist in a multilingual V&L model, we test the Kadinsky [1] multilingual text-to-image model (which uses multilingual CLIP) with our methodology, on four geographically and linguistically... | Rebuttal 1:
Rebuttal: We thank the reviewers for their constructive comments. We respond here to shared concerns as well as items raised by reviewers JJeH and Cwqz, referring to individual responses where more information is given.
**(A) Survey – reviewers JJeH, Cwqz, gvCK**
The main focus of our work is automatic pr... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion | Accept (spotlight) | Summary: This paper aims to solve the task of 3D instance segmentation by leveraging pre-trained 2D instance segmentation models. The authors propose a novel approach to lift 2D segments to 3D via a neural field. This idea is not completely new [38, 51], but the authors propose a contrastive loss that replaces the Hung... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments which have helped us improve our paper!
---
**Response to Weakness 1**: We are thankful for the reviewer's detailed feedback on the notation and presentation.
* We will clarify that $u$ is the pixel location.
* $\Theta$ should be $\Theta:\mathbb{R}^3\right... | Summary: This paper introduces a novel Contrastive Lift method for 2D segment lifting to 3D reconstruction and instance segmentation. The authors fuse multiview representations obtained from pre-trained 2D models into a unified 3D neural field. They propose a scalable slow-fast clustering objective function that enable... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback!
---
**Response to Weakness 1**: Lines 278-288 of the main paper provide an empirical analysis of the time complexity and training speed of our method and compares it to the linear-assignment matching-based method. In summary, the complexity of our proposed ... | Summary: This paper utilizes contrastive learning for lifting 2D segments to 3D and fuses the learned embedding by means of a neural field representation, namely Contrastive Lift. The authors further propose a slow-fast clustering objective function, which makes the method scalable for scenes with a large number of obj... | Rebuttal 1:
Rebuttal: Thanks for providing feedback and taking the time to review our work!
---
**Response to Weakness 1**: We report the mIoU and PSNR of our method (and also for Panoptic Lifting and SemanticNeRF) in Table 2 of the **supplementary material**. In Tables 1 and 2 of the main paper, we only report PQ$^\... | Summary: The proposed method tries to solve the problem of reconstructing a 3D scene together with the underlying instance segmentation. Prior work required either GT tracking data or concurrent work a less efficient way to assign instances. From a set of images a Neural Radiance Field is reconstructed together with a ... | Rebuttal 1:
Rebuttal: Thank you for taking the time to study our work and provide thoughtful feedback!
**Response to Weaknesses:** Thank you for suggesting the idea to assess the performance of our approach on outdoor scenes, e.g. *KITTI* or *KITTI-360* datasets. We agree that it would strengthen the paper. Unfortunat... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and valuable feedback. We are pleased to see positive responses from all reviewers, who acknowledge the novelty [*zMUp*,*RRt9*], design [*KUUX*,*4dod*,*Vjqu*], efficiency [*4dod*,*RRt9*] and performance [*Vjqu*,*RRt9*] of our approach as well as the useful... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper studies the 3D object instance segmentation inside a 3D NeRF space. Specifically, to train the model, conservative loss between features generated by slow and fast NeRF models are computed to 1) maximize the feature distance between different semantic regions, 2) minimize the feature distance within ... | Rebuttal 1:
Rebuttal: Thank you for taking the time to read our paper and provide feedback!
---
**Response to Weakness 1**: We do evaluate on real scenes from ScanNet [8] dataset (please refer L211-212 and Table 1). We also evaluate using Replica [41], which comprises real 3D scans, and Hypersim [36], which is synthe... | null | null | null | null | null | null |
Anonymous and Copy-Robust Delegations for Liquid Democracy | Accept (spotlight) | Summary: The paper studies fractional allocation rules when each agent indicates a ranking over the agents that agrees to represent her, to overcome impossibility results of deterministic rules. The authors consider two different rules which are shown that are equivalent. They also provide a polynomial time algorithm f... | Rebuttal 1:
Rebuttal: The choice of top-rank priority is motivated by the proof of the impossibility theorem for the non-fractional setting (Section 1). Consider the example instance with one delegating voter $v_1$ and three casting voters $s_1$, $s_2$, $s_3$, where $v_1$'s first delegation preference is $s_1$ and her ... | Summary: This paper primarily examines two equivalent fractional delegation rules for Liquid Democracy (transitive delegate voting): Mixed Borda Branching and Random Walk Rule. The main contribution of this paper is the equivalence between these two rules in the generalized setting of fractional delegations. This resul... | Rebuttal 1:
Rebuttal: **Q1**: Am I correct in saying that ignoring the isolated voters ultimately violates copy-robustness?\
**A:** No.\
Before justifying our answer, we want to clarify the handling of isolated voters. The motivation for introducing ranked delegations is to mitigate the risk of having isolated voters, ... | Summary: The authors study liquid democracy with fractional (i.e., splittable) delegations. They extend previous work by Brill et al. on delegation rules that satisfy anonymity and copy-robustness, and demonstrate that two delegation rules (mixed Borda branching and the random walk rule) that were previously thought to... | Rebuttal 1:
Rebuttal: A possible explanation could be the following reinterpretation of the rules, motivated by the nature of the algorithm. In the last step of the algorithm we solve an absorbing Markov chain on a partially contracted delegation graph, where the outgoing edge probabilities of each contracted vertex de... | Summary: The paper studies liquid democracy where voters can delegate their votes to others instead of casting them directly. Within this framework, some voters act as casting voters, while others delegate their votes. Delegation rules determine how casting voters are chosen for each set of delegating voters. However, ... | Rebuttal 1:
Rebuttal: If accepted, we are happy to use the additional page to elaborate on the connection to semisupervised learning, in particular, by providing an in-depth discussion of [1] and [2].
## Q1
**Short** Indeed, we think that some formulations in [1] are ambigious and give the impression that the paper wo... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude to the reviewers for taking the time to review our submission. In the following, we address each of the reviewer's comments and concerns individually.
## References
[1] Fita Sanmartin et al. "Directed Probabilistic Watershed." (2021)\
[2] Couprie e... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies algorithms for assigning delegation weights in liquid democracy in the setting where participants indicate a set of trusted delegates, along with a ranking describing their preferences over these delegates. Notably, the algorithms it considers permit fractional delegations - i.e., voters hav... | Rebuttal 1:
Rebuttal: **Q:** Was it previously known whether the random walk rule satisfies these three axioms simultaneously?\
**A:** No. For both copy-robustness and confluence, the question whether the random walk rule satisfies (reasonable generalizations of) the axioms were open. In particular, in order to prove t... | null | null | null | null | null | null |
Lie Point Symmetry and Physics-Informed Networks | Accept (poster) | Summary: This study proposes a new loss function for PINNs that imposes the symmetry of PDE.
Strengths: PINN loss comes from the equation, but the proposed loss comes from the property of the equation. The idea is insightful.
Weaknesses: The additional loss increases the computational cost. With the same computatio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their useful feedback! Below we answer their questions and comments.
**R**: The additional loss increases the computational cost. With the same computational budget, one can increase the number of evaluation points ($N_r$?)
**A**: This is true. We have included experim... | Summary: In this paper, a method for finding solutions to differential equations that represent physical phenomena using neural networks is considered. In particular, a method that takes symmetry into account is proposed. Specifically, the authors consider an infinitesimal generator that represents the symmetry of the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments. Below we respond to their individual questions and comments.
**R**: Can the computation of symmetry be automated by using computational algebraic software (e.g., Mathematica, Maple, Sigular)?
**A**: Yes, computational algebraic software can be... | Summary: The work proposes a generic method to incorporate Lie point symmetry into physics-informed neural networks (PINNs) by augmenting loss function. The method leverages automatic differentiation as other PINNs do because the condition for symmetries is written using differentials. The authors demonstrated the mode... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback! Below we respond to individual questions and comments.
**R**: … It makes the contribution of the work look limited because most of the mathematical contributions directly come from Olver (1986), and the results from the symmetry model are not good enough... | Summary: This paper proposes to enhance Physics-Informed Neural Networks (PINNs) by incorporating local Lie-point symmetry into them. It is achieved by introducing an additional symmetry loss term, which requires analytic computation of the PDE’s symmetries.
This loss term is designed to encourage orthogonality between... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback! Below we respond to their questions and comments.
**R**: The proposed method needs to compute the symmetries analytically first, before integrating them into the loss function. Would there be a way to automate this part?
**A**: Yes, computation... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful and constructive feedback! We are happy to see that they found the idea to be motivated (R-qo6e, R-vAL8), insightful (R-KJvK) and novel (R-vAL8, R-5AKh), the empirical results to be compelling (R-vAL8), and the presentations of the paper to be clear (R-5... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes adding a custom loss function to training physics-informed neural networks (PINNs) to force symmetry requirements when learning solutions to PDEs. A given partial differential equation (PDE) is associated with a Lie group that acts on the space of solutions of the PDE that leaves this space ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback. Below we respond to the questions and concerns raised in the review.
**R**: The paper would benefit significantly from a more thorough ablation study. Specifically, an exploration of the effects of incrementally adjusting the relative weigh... | null | null | null | null | null | null |
Hybrid Search for Efficient Planning with Completeness Guarantees | Accept (poster) | Summary: In this paper, the authors propose a hybrid technique to speed up the planning tasks. The novelty is that the completeness is guaranteed. I agree that guaranteeing completeness is a good property for a learning-based algorithm.
Strengths: 1. The paper is well-written.
2. The novelty of the contribution is m... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments and questions.
>I would say that the words "hybrid search" is not a suitable expression for the proposed algorithm. There have been too many "hybrid" algorithms. Currently I haven't come up with a suggestion, but the authors can think about it.
... | Summary: The paper introduces a method, called hybrid search (or HIPS-\epsilon) that combines high-level planning (with subgoals generated by a learned model) with low-level search. Subgoals allow for more efficient search, but existing subgoal-based methods are prone to errors, which can lead to failures in finding so... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments and questions.
>Are all results statistically significant?
>Could you run the test on more instances to reduce the error or provide some argument why the results for TSP are meaningful?
We will incorporate the information about statistical sign... | Summary: The authors present an idea of enriching a classical hierarchical search pipeline with an exhaustive low-level search. This approach guarantees the completeness of the search and offers practical advantages, including slightly better success rates in the tested environments and stronger out-of-distribution eva... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments and questions.
>I am not convinced that the completeness is a major concern itself.
>
> l.15 Why should we care so much about completeness
We see completeness as a worthwhile problem to tackle due to four main reasons:
1. Without completeness, ... | Summary: The paper considers solving complex planning problems with discrete action spaces and develops a novel hybrid search scheme that combines high-level sub-goal oriented search (aka hierarchical planning) with a complete low-level search scheme. The latter embodies a classical exhaustive search scheme that only c... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments and observations.
> The main novelty seems to consist in adding the behaviour cloning policy to select low-level actions.
We want to emphasize that not only do we add a new behavior cloning policy to efficiently solve a known problem of subgoal... | Rebuttal 1:
Rebuttal: We want to thank all reviewers for taking the time to review our work and give feedback. Your insightful comments and observations are vital for improving the paper. We are grateful to the reviewers for appreciating the empirical results (all reviewers), the intuitive main idea (v7TG), clear motiv... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Kissing to Find a Match: Efficient Low-Rank Permutation Representation | Accept (poster) | Summary: The authors propose a provably exact and an approximative method for calculating permutation matrices requiring much lower memory than previous approaches. The algorithms are based on Kissing numbers and describe row relationships as their cosine value. This allows for representing the problem with significant... | Rebuttal 1:
Rebuttal: Regarding the request for comparison to baseline methods, please refer to the general response to all reviewers, where we have shown comparisons to baseline methods within the functional maps framework [30].
#### Question 1
L191 is translation not considered on purpose?
#### Answer 1
Yes, we didn... | Summary: This paper proposes a formulation for representing high-dimensional permutation matrices. The basic idea is to express the n x n permutation matrix as an elementwise non-linear function of a product of low-rank matrices V,W (n x m). The authors introduce the kissing number theory which gives the minimum number... | Rebuttal 1:
Rebuttal: Regarding the request to include conceptual baselines to compare in the point cloud example, we have included experiments that can be found in the general response to all reviewers under the topic "Comparisons with Permutation Baselines in [30]". These experiments include comparisons in accuracy, ... | Summary: The paper proposes a novel approach for representing permutation matrices with low-rank matrix factorisation. The method employs Kissing number theory to find the minimum rank necessary to represent the target matrix. This is often quite a bit lower than the rank of the original matrix, so allows for a more ef... | Rebuttal 1:
Rebuttal: We would like to thank you for your very positive response to our work. We have added an analysis of the relationship between $k$ and the optimization time to the general response. | Summary: This work addresses the problem of estimating large permutation matrices by approximating them with a low-rank factorization follow by a nonlinear mapping, thereby reducing the storage complexity from O(n^2) to O(n).
The main contribution of the paper is i) a theoretical derivation and proof of the minimal re... | Rebuttal 1:
Rebuttal: #### Question
Is there a relation between the Kissing number and the ReLU? Is it conceivable that there exists another non-linear function that allows an even lower rank factorization while still being able to represent that exact permutation matrix?
#### Answer
The ReLU merely serves as a type o... | Rebuttal 1:
Rebuttal: We thank all reviewers for their comments that helped us improve the presentation of our work. In the following, we address the concerns that have been raised, and we provide additional experimental results:
# Comparisons with Permutation Baselines in [30]
We conduct an additional experiment whe... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a novel way to decompose a permutation matrix into two low-rank matrices so that a significant amount of space can be saved to store a permutation. Then the authors further implement such decomposition to solve practical tasks of point cloud alignment, assignment problem and shape matching... | Rebuttal 1:
Rebuttal: #### Question 1
Kissing number is based on a threshold of arccos(0.5), does this mean that if we switch to a smaller angle, we can fit more vectors in a unit sphere, thus save more space to store a permutation? If so, why don't we do so?
#### Answer 1
Yes, it is correct that the threshold does not... | null | null | null | null | null | null |
Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms | Accept (poster) | Summary: When using re-parameterization (RP) gradient estimators for policy gradient methods (PGM) the
optimisation landscape becomes chaotic and non-smooth, with exploding gradient variance.
This paper examines RP gradient estimators for policy gradient methods.
First, the authors theoretically examine RP PGM.
Their t... | Rebuttal 1:
Rebuttal: We thank the reviewer for identifying our work's soundness and technical contributions. The valuable comments have helped us improve our manuscript. Below are our specific responses to the questions raised by the reviewer:
---
**Weakness 1: Clarity of the figures in experiments.**
- We sincerely... | Summary: This paper studies differentiable model-based reparametrized policy gradient methods (RP-PGMs) with a particular focus on mitigating policy gradient variance and bias under longer horizon model rollouts to benefit agent learning and convergence. The paper introduces theorems on the bounds of policy gradient va... | Rebuttal 1:
Rebuttal: We thank the reviewer for identifying our work's soundness and technical contributions. The valuable comments have helped us improve our manuscript. Below are our specific responses to the questions raised by the reviewer:
---
**Weakness 1: It would be interesting to investigate the impact on gra... | Summary: This paper examines reparameterization policy gradient methods in model-based reinforcement learning. It investigates the relationship between the convergence rate, bias and variance of reparameterization policy-gradient, smoothness of the model, and approximation error. Based on the theoretical analysis, it f... | Rebuttal 1:
Rebuttal: We thank the reviewer for identifying our work's soundness and technical contributions. The valuable comments have helped us improve our manuscript. Below are our specific responses to the questions raised by the reviewer:
---
**Weakness 1: The application of spectral normalization to deep networ... | Summary: The paper theoretically analyzes the reparameterization policy gradient estimator’s bias and variance in reinforcement learning optimization and provide results characterizing the optimization convergence using such gradient estimators. It then proposes to apply spectral normalization (dividing the linear weig... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments. Below are our specific responses to the questions raised by the reviewer:
**Weakness 1: Eq. 4.1 and 4.2 are plain tautology and provides no information the backward recursive structure of the gradient estimators.**
- Eq. 4.1 and 4.2 (or A.9) depic... | Rebuttal 1:
Rebuttal: We conduct additional experiments to address Weakness 3 and Question 2 raised by **Reviewer epDC**, and Weakness 1 raised by **Reviewer eACF**. The results can be found in the attached PDF file.
Pdf: /pdf/4cba79755888bc2253b1ece1389ad1d63673623b.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
The Emergence of Essential Sparsity in Large Pre-trained Models: The Weights that Matter | Accept (poster) | Summary: This paper presents a comprehensive study on the induced sparse patterns across multiple large pre-trained vision and language transformers. The authors propose the existence of "essential sparsity" and present intriguing findings on abrupt sparsification during the pre-training of transformers and the effect ... | Rebuttal 1:
Rebuttal: Many thanks for identifying the significance of our work and finding it promising and practical considering the massive scale of the recent large-scale models. We additionally appreciate that you found that our experiments are quite insightful, have surprising findings, and counter-intutive observ... | Summary: This research paper focuses on the following notion for large pre-trained models: "essential sparsity", the idea that a sharp drop in fine-tuning performance occurs after one-shot pruning relative to the level of sparsity. The authors propose that large and overparameterized models can be pruned without additi... | Rebuttal 1:
Rebuttal: We would like to thank you for your time to review our work. We would like to address all the weaknesses pointed out by you point-by-point below:
**1. Experiments are too experimental and hard to be convinced that these are general results:** We would like to highlight that sparse neural network... | Summary: This paper defines essential sparsity and conducts various experiments to analyze the sparsity property of pre-trained models.
Strengths: 1. This paper investigates the potential of directly sparsifying the pre-trained models
2. CV and NLP models are both explored.
Weaknesses: 1. Evidence on large models is ... | Rebuttal 1:
Rebuttal: We would like to thank you for your time to review our work. We appreciate your feedback and would like to address the concerns you raised regarding our work's weaknesses. However, we believe that there might be some key contributions that may not have been fully acknowledged in your assessment. W... | Summary: The paper postulates existence of "Essential Sparsity" in large pre-trained transformer models. The "Essential Sparsity" is defined by the paper as a sparsity threshold beyond which further pruning of weights leads to a large performance drop. It considers (1) one-shot pruning and (2) lottery ticket pruning of... | Rebuttal 1:
Rebuttal: We would like to thank you for your time to review our work and glad that you find our work important and that some of our observations are very interesting. We would like to address all the weaknesses pointed out by you point-by-point below:
**1. Existence of sparsity in transformers in both lan... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their time to review our work and offering important suggestions. In this pdf, we attach some key experiments requested by reviewers which further strengthen the impact of our work. We summarize our results as follows:
* **[Requested by Reviewer QH66 a... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper delves into the natural sparsity of large-scale pretrained transformers that could be found without additionally training the models with sparsity optimization targets. The authors find that when simply low-magnitude weights are zeroed out, 1) up to a certain sparsity level, the pruned transformers... | Rebuttal 1:
Rebuttal: Many thanks for identifying the significance of our work and finding it promising and practical considering the massive scale of the recent large-scale models. We additionally appreciate that you found that our experiments are quite solid and our observations can lead to opening up several researc... | null | null | null | null | null | null |
Sample-Efficient and Safe Deep Reinforcement Learning via Reset Deep Ensemble Agents | Accept (poster) | Summary: Overfitting in deep Q-learning agents is a recent topic of interest in the RL community, and several methods have been proposed to mitigate this problem, including data augmentation (DrQ), random ensembles (RedQ, DroQ), and resets (Nikishin et al.). This paper builds upon prior work on periodically resetting w... | Rebuttal 1:
Rebuttal: **Regarding "Related works"**
- We will include related works regarding overfitting problem in RL in the final manuscript, as the reviewer recommended.
**Regarding "Sample efficiency and performance collapse with respect to the number of ensemble agents"**
- We have conducted additional experi... | Summary: This paper combines the resetting method proposed by (Nikishin et al., 2022) as a remedy to the primacy bias affecting deep RL algorithms with the use of ensembles of agents. The proposed RDE method, apart from generally improving performance, has the goal of minimizing the regret associated to a learning agen... | Rebuttal 1:
Rebuttal: **Regarding "Inconsistent behavior"**
- It is entirely true that a different policy can be chosen at each time step. While this might result in inconsistent behavior, such inconsistency doesn't negatively affect exploitation since our method primarily relies on off-policy learning (the resetted ... | Summary: The work proposes an extension to the resetting strategy proposed by Nikishin et al. The extension intends to mitigate the catastrophic performance collapse often observed for the simple resetting strategy while keeping the properties that help avoid the primacy bias.
To this end, the work makes use of ensembl... | Rebuttal 1:
Rebuttal: **Regarding "Presentation"**
- The subsection on 'Off-policy RL' is necessary in our work for two reasons. Firstly, the reset methods depend on an off-policy algorithm, as a recently reinitialized RL agent needs training using experiences generated by the previous RL agent. Secondly, this sectio... | Summary: This paper proposes a novel reset-based method that leverages deep ensemble learning to address the primacy bias issue in deep reinforcement learning. The authors construct N-ensemble agents and reset each ensemble agent sequentially to prevent performance collapses and improve sample efficiency. The proposed ... | Rebuttal 1:
Rebuttal: **Regarding "Results on safe RL benchmark"**
- As described in the common response, we have conducted two additional experiments on the safe RL benchmark to show the effectiveness of the proposed method. The corresponding results are presented in Figure 1 of the rebuttal PDF file. These results c... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable comments.
In this paper, we propose a novel method that incorporates ensemble learning into the resetting method to harness diversity gain and mitigate performance collapse. We provide various experiments on both standard and safety RL benchmarks, as wel... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation | Accept (poster) | Summary: This work aims to make RL robust to distribution shifts by limiting their reliance on spurious correlations between state features. This is achieved through a new RL algorithm designed in a variant of Robust MDPs extended to include a more structured uncertainty representation.
Strengths: * This paper addres... | Rebuttal 1:
Rebuttal: We gratefully thank the reviewer for valuable suggestions and the recognition of our proposed problem setting. In what follows, we provide our response to the reviewer's comments.
### **Q1. Change the term "spurious correlation" to "spurious state correlation".**
Thanks for raising this point. We... | Summary: The paper proposes a state-confounded (SC-) and a robust-state confounded (RSC-) MDP formulation to account for setups where a confounder satisfying the backdoor criterion confounds the states. The RSC-MDP setup assumes that the confounder lies in an uncertainty set which is part of the MDP parameters, and th... | Rebuttal 1:
Rebuttal: We gratefully thank the reviewer for recognizing our contributions to problem formulation and the creation of a useful benchmark! We provide our response below:
### **Q1: The connection between the proposed problem formulation (robust SC-MDP) and the empirical algorithm.**
The empirical algorithm... | Summary: This paper aims to address the spurious correlation challenge that arises in RL. Such correlation is typically useless to decision-making but may be learned by agents, which leads to failure in applying to unknown test cases. To this end, the authors proposed a novel RSC-MDP framework that models the spurious ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for valuable suggestions and the praise of our systematic formulation and experiments. In what follows, we provide our response to the reviewer's comments.
### **Q1: Does the proposed RSC-MDP formulation cover the targeted spurious correlation problems in the real worl... | Summary: The paper studies how to develop more robust reinforcement learning algorithms when spurious correlation exists in the observation space. The paper studies the problem from a causal perspective and present robust state confounded MDP as the problem formulation. The paper proposes an algorithm to solve this pro... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to the reviewer for their insightful feedback. We are glad to know that the reviewer recognizes the novelty of our contributions, the clarity of our problem formulation, and the empirical algorithm that sufficiently shows the advantages compared to baselines.... | Rebuttal 1:
Rebuttal: We thank the reviewers for their careful reading of the paper and their insightful and valuable feedback. We provide new experimental results and discussions to answer some common questions raised by reviewers.
### **(1) Add a new baseline [1], which also tackles spurious correlation in RL.**
|E... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation | Accept (poster) | Summary: Self-attention mechanisms serve as pivotal components in the domains of natural language processing and computer vision. Previous kernel function based self-attention variants, which are predicated on Mercer kernels, tend to overlook the inherent asymmetry of the attention matrix in the vanilla self-attention.... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments and the appreciation on the novelty of our work. We address your concerns point-wisely below.
***R.1 Concept of primal optimization with KSVD in eq.(6) and connections with self-attention.***
The modeling and optimization of KSVD on self-attent... | Summary: This paper proposes a new understanding of self-attention in transformers via asymmetric Kernel Singular Value Decomposition (KSVD). In particular, the authors formulate a primal-dual representation of self-attention for maximizing the projection variances in the attention outputs and then derive a new attenti... | Rebuttal 1:
Rebuttal: We thank the reviewer's high appreciation of our work and the insightful comments, which will be addressed point-wisely below.
**R.1 More large-scale experiments**
As suggested, we test on ImageNet-1K and WikiText-103, both showing our promising potentials. Ours achieves the same accuracy as bas... | Summary: This paper proposes a Primal-Attention method to realize self-attention blocks in Transformer with a kernel matrix. It firstly explains the relationships between self-attention and asymmetric kernel matrix. Secondly it formulates self-attention in the form of kernel SVD and derive its primal and dual represent... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments. Below, we address the raised two main concerns in detail.
***R.1 Incorporating projection scores involving left singular vectors of the asymmetric attention kernel matrix $K$.***
We provide detailed explanations and empirical evidences below. Rele... | null | null | Rebuttal 1:
Rebuttal: Dear Program Chairs, Area Chairs, and Reviewers,
First of all, we would like to thank you for your time and valuable comments, which help improving our work.
In this work, we provide a new framework to interpret self-attention in Transformers via asymmetric Kernel Singular Value Decomposition (... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Distributionally Robust Bayesian Optimization with $\varphi$-divergences | Accept (poster) | Summary: The authors extend the framework of distributionally robust Bayesian optimization to the case where the distribution distance notion amounts to $\phi$-divergences, which encompasses the Kullback-Leibler divergence, total variation and $\chi^2$-divergence. In particular, the paper aims at providing a computati... | Rebuttal 1:
Rebuttal: Thank you for your review and pointing out the computational superiority of our method with the phi-divergence generalization. Indeed, while our work attempts to alleviate the finiteness of contexts assumption, we provide some clarification and discussion below regarding the discretization argumen... | Summary: In this paper the authors extend the domain of distributionally robust bayesian optimization (DRBO) as introduced by Kirschner et al. to the case of distributions with continuous support. The focus on the case of $\phi$-divergences and show that for these problem then DRBO problem can be reformulated in closed... | Rebuttal 1:
Rebuttal: Thank you for your positive review and noting our key contributions. Regarding our numerical experiments: since existing methods only focus on finite context sets, we showcase the benefits of our methods on such datasets. We then present an additional experiment on a continuous context where $p_t$... | Summary: This work studies distributionally robust Bayesian optimization (DRO-BO) problems with $\varphi$-divergences which cover $\chi^2$-divergence, total variation distances and KL divergence. The authors show that the minimax DRO-BO problem has an equivalent minimization problem, and propose an algorithm for solvin... | Rebuttal 1:
Rebuttal: Thank you for your time in reviewing and positive comments towards our work. | Summary: The paper proposes a new approach for Distributionally Robust Bayesian Optimization. The paper address the problem of data shift in phi divergence which generalizes better than previously studied types and subsumes other known divergences categories including chi^2 divergence, Total Variation, and the extant K... | Rebuttal 1:
Rebuttal: Thank you for your review and inclination to accept this paper. We will include additional motivation for contextual BO and hope that the below answers address your concerns regarding the experiments.
---
Question: “It is not clear if, for the experiments in figures 3 and 4, the \epsilon was var... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their time and efforts in reviewing our work. The reviewers are majority in acceptance of the work, as they have noticed our main contribution which is to provide “a theoretical analysis that reduces the computationally intractable problem of data shift in ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper provides distributional robustness in the context of the Bayesian Optimization (BO) problem. Although there is existing work in this field, such work is rudimentary, and the authors develop a more general theory that works with generic $\varphi$-divergence-based ambiguity sets. The proposed algorith... | Rebuttal 1:
Rebuttal: Thank you for your review and generally positive inclination of our paper regarding the clarity and novelty of our work. We apologize for the lack of references with respect to DRO and we will fix this. Indeed, Theorem 1 at a technical level may not be novel however its application Bayesian Optimi... | null | null | null | null | null | null |
Efficiently incorporating quintuple interactions into geometric deep learning force fields | Accept (poster) | Summary: The paper introduces a new method for molecular modeling, QuinNet, which incorporates five-body interactions using only dihedral angles. The authors first introduce relevant concepts related to machine learning force fields and related work in the field related to a variety of equivariant models. Next, the pap... | Rebuttal 1:
Rebuttal: We thank the reviewer for his/her comments and will address each point in our response accordingly.
### Weakness 1:
* As the experimental results indicate, five-body interactions do not have a substantial impact on small molecules. To demonstrate the significance of these interactions, it is esse... | Summary: In this work, the authors propose to incorporate features from five-body interaction into machine-learning force field models and develop QuinNet. To efficiently incorporate such high-order information, the authors are motivated by the topology of many-body interactions and design sophisticated components. Exp... | Rebuttal 1:
Rebuttal: We thank the reviewer for his/her comments and will address each point in our response accordingly.
### Weakness 1:
* In our experiments, we employ Chignolin, a protein system [1,2], which offers quantitative evidence regarding the impact of five-body interactions, aligning with the conclusions i... | Summary: This paper aims to incorporate 5-body interactions into geometric deep learning models. They first analyze the topology of 5-body interactions and identify three 5-body angles. Then they propose an efficient way to incorporate these 5-body information into models. The complexity of the proposed QuinNet is stil... | Rebuttal 1:
Rebuttal: We thank the reviewer for his/her comments and will address each point in our response accordingly.
### Questions 1:
* **4-body interactions are not complete for modeling molecular interactions.** As stated in Ref [1], "it is unclear how many descriptor elements are actually needed in order to ma... | Summary: This paper introduces a machine learning force field that is a neural network with explicit interactions for up to 5-body terms. The authors evaluate the model on a couple of public datasets and show demonstrate the competence or superiority of this new model compared to the state of the art in this field.
S... | Rebuttal 1:
Rebuttal: We thank the reviewer for his/her comments and will address each point in our response accordingly.
### Weakness 1:
* We address your concern through both theoretical and practical analyses. In the official comment part, we present the time complexity analysis and comparisons of inference time an... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Online Control for Meta-optimization | Accept (spotlight) | Summary: This paper studied the problem of meta-optimization through the perspectives of online control. In this paper, the meta-optimization problem is thought as a sequence of episodic optimization problem and the performance is measured by the regret against the best static optimizer belonging to a convex class. By ... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our work and the valuable feedback. We first address the point of simple experiments, and then address other comments one by one below.
**please also see pdf for all reviewers, as it contains experiments**
We would like to emphasize that this i... | Summary: This paper proposes a framework for optimization whose goal is to learn the best optimization algorithm from experience, and gives an algorithmic methodology using feedback control for this meta-optimization problem. The authors derive new efficient algorithms for meta-optimization using recently proposed cont... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our work and their valuable feedback. We are happy to address the weaknesses:
1. We will provide explanations and intuitions for the ideal cost in the main paper, for clarity and completeness.
2. We have an additional experiment on MNIST classif... | Summary: This paper proposes an online control method to meta optimization problem. The authors formulate the problem into a robust control problem and leverages non-stochastic control framework to achieve convex relaxation. The regret guarantees are derived theoretically and experiments show its superiority compared w... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our work and the valuable feedback. Given your suggestion, we have added a proof-of-concept experiment for neural network classification on the MNIST dataset, with Adam as one of the baselines. Additional comments and details on the experiment ar... | Summary: The paper considers a new framework, meta-optimization. To solve this problem, the authors propose an online control formulation with linear time-varying dynamics. Moreover, a novel algorithm is proposed to solve meta-optimization and shown to enjoy sublinear regret under both the quadratic loss and convex smo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive evaluation of our work and the helpful suggestions. To address the weaknesses, we are very happy to clarify the online optimization formulations in the revision, and include more techniques that are used in proving the main theorems.
For the questions:
1. T... | Rebuttal 1:
Rebuttal: **please see attached pdf**
We thank all reviewers for their time and valuable feedback. One common suggestion is including more complex experiments, for example with neural networks. Since the main contribution of this paper is theoretical, the experiments are proofs-of-concept to show the poten... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Structured Neural Networks for Density Estimation and Causal Inference | Accept (poster) | Summary: This work studies the impact of imposing known conditional independence structure in fully-connected neural network architectures, notably in the setting of autoregressive normalizing flows. The independence is imposed by masking the weight matrices in the linear layers, similar to the MADE approach. The masks... | Rebuttal 1:
Rebuttal: We thank reviewer LoZH for their detailed feedback, especially for highlighting the clarity and soundness of our approach to enforce conditional independences in NNs.
We begin with the Zuko repo, which is relevant to our work. First, are there manuscripts or research results using Zuko that we co... | Summary: The authors propose a novel method for constructing structured neural networks (strNN) that are able to respect causal independencies between variables. Formal constraints for weight mask creation are discussed and evaluated empirically for an exact method and a greedy algorithm. The conditioned strNN are furt... | Rebuttal 1:
Rebuttal: We thank reviewer icwN kindly for their encouraging and constructive feedback, especially for highlighting the novelty and efficacy of our matrix factorization approach to enforcing exact independence structures in arbitrary NNs.
> The example shown in Figure 1 decomposes the network into two sep... | Summary: In this paper, the authors present a neural network architecture that can fulfill the bayesian DAG conditional independencies needed for normalized density estimation.
In this work, the authors start from a binary lower adjacency matrix that encodes the independencies of a bayesian network DAG. Then they intro... | Rebuttal 1:
Rebuttal: We would like to warmly thank Reviewer Wq9i for their encouraging feedback, especially for highlighting our method’s emphasis on data efficiency and the novelty of the causal effect estimation application.
The reviewer raises a good point on how broader comparison on other datasets would make our... | Summary: This paper introduces structured neural networks such that the resulting neural network represents the factorization of a given Bayesian network. For doing so, each layer of the neural network is masked and the product of the masks of all layers must be the same as the adjacency matrix of the DAG representing ... | Rebuttal 1:
Rebuttal: We would like to thank reviewer Tj4q for their feedback and insightful comments, especially for highlighting the simplicity of our matrix factorization approach and our contribution to the causal inference application.
First, we hope to provide some further clarification on the questions posed by... | Rebuttal 1:
Rebuttal: We thank all reviewers for your engagement and thoughtful comments. We have addressed specific concerns and questions in individual rebuttals, but here we **highlight two experiments added based on the feedback**:
1. We extend Section 5.3 with experiments on **continuous normalizing flows (CNF)**... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss | Accept (poster) | Summary: In this work, the authors focus on improving the generalization ability of the top-K recommendation model by a proposed principled Adversarial InfoNCE loss (AdvInfoNCE). Existing contrastive learning based methods usually lack considering the tailored inductive bias (such as hard negatives and false negatives)... | Rebuttal 1:
Rebuttal: **Response to Reviewer $\color{green}{\text{1hVj}}$**
We gratefully thank you for the valuable comments. To address your concerns, below we provide the point-to-point responses.
>**Comment 1 + Question 1: Intuitive example**
We value your insightful comments. To better clarify the role of $\d... | Summary: This paper is on collaborative filtering (CF) enhanced by contrastive learning (CL). The authors point out that the adoption of CL into CF is suboptimal due to challenges such as the issue of out-of-distribution, the risk of false negatives, and the nature of top-K evaluation. They also note that current CL-ba... | Rebuttal 1:
Rebuttal: **Response to Reviewer $\color{purple}{\text{qmEP}}$**
Thanks so much for your time and positive feedback! To address your concerns, we present the point-to-point responses as follows.
We have carefully revised our paper, taking all your feedbacks into account.
>**Comment 1: Clarify the relat... | Summary: This paper studies contrastive learning (CL) in collaborative filtering (CF) for top-k recommendation. In particular, it focuses on the CF-tailored challenges for CL, and then presents adversarial infoNCE (AdvInfoNCE) loss. This loss dynamically assigns hardness to negative instances and incorporates a fine-gr... | Rebuttal 1:
Rebuttal: **Response to Reviewer $\color{orange}{\text{PLbw}}$**
We thank the reviewer for the thorough and valuable feedback. To address your concerns, we present the point-to-point responses as follows. We have carefully revised our paper by taking into account all your suggestions. Looking forward to mo... | Summary: Current losses for collaborative filtering struggle to handle the issue of unobserved user-item pairs. Typically, the approach is to treat unseen pairs as negatives while seen pairs as positives, but this is somewhat problematic because unseen pairs could just be unobserved positives. The authors propose an ... | Rebuttal 1:
Rebuttal: **Response to Reviewer $\color{red}{\text{kSe8}}$**
We appreciate your comments, some of which inspires us to greatly improve our paper.
Below we provide the point-to-point responses to address your concerns and clarify the misunderstandings of our proposed method.
If you have additional question... | Rebuttal 1:
Rebuttal: We are delighted to see the contributions of our paper have been acknowledged by the majority of the Reviewers. Specifically, we appreciate the Reviewers' recognition of our motivation, theoretical analysis ($\color{blue}{\text{ikze}}$, $\color{orange}{\text{PLbw}}$, $\color{purple}{\text{qmEP}}$,... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a principled AdvInfoNCE loss for CF methods to improve generalization ability. It utilizes a fine-grained hardness-aware ranking criterion to assign weights for unobserved user-item interactions. In this way, it can enable better distinction between different negative, thus mitigating the i... | Rebuttal 1:
Rebuttal: **Response to Reviewer $\color{blue}{\text{ikze}}$**
We sincerely thank you for your time and valuable comments. Your main suggestions about considering additional CF-backbones help us substantiate wide applicability of AdvInfoNCE.
> **Comment 1: Additional CF-based backbones** - "Experiments c... | null | null | null | null | null | null |
Learning Layer-wise Equivariances Automatically using Gradients | Accept (spotlight) | Summary: Edit: Rating updated from 6 to 7 after rebuttal.
The goal of the paper is to learn an interpolation between non-equivariant and equivariant models. The authors introduce different convolutional and non-convolutional linear layers, optionally being sparsified via factorizations or a smooth spatial basis. The b... | Rebuttal 1:
Rebuttal: Thank you for your feedback and help to improve the paper.
> learns to select between pre-specified models with different levels of equivariance. However, this downside is shared by a line of prior work + scales to multiple symmetry groups at once
The approach is general in that it can work with... | Summary: While (group) convolutions encode strict symmetries into neural network architectures, this paper presents a method for representing flexible symmetry constraints and learning the degree of symmetry automatically (through marginal likelihood objectives). Their method builds on residual pathways to represent ea... | Rebuttal 1:
Rebuttal: Thank you for your feedback and help to improve the paper.
> empirical results are limited
Even when strict symmetries are desirable, our method shows that such favourable architectures can be discovered automatically, whereas the standard MAP objective cannot. Our proposed method allows automat... | Summary: This paper proposes a neural network architecture and gradient-based training algorithm for modeling approximately equivariant functions. The architecture builds upon residual pathway (Finzi et al., 2021), where each layer of network is parameterized as an additive combination of constrained equivariant path a... | Rebuttal 1:
Rebuttal: Thank you for your feedback and help to improve the paper.
> In Table 1-3, in addition to test performance, it would be nice if I could see how the models (over)fit to training data.
Thank you for raising this. We agree with the author that providing training performance is important given the m... | Summary: The work proposed an automatic way to learn equivariance in each layer by finding a balance between the equivariant layer and the unrestricted fully connected layer. Unlike the previous work on soft equivariance, the work proposed to learn the balance between them via Bayesian model selection using gradients. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback.
> motivation for finding equivariance balance
Symmetry discovery from data is a well-recognized task and is actively studied [1,2,5,6,7]. The key motivation is that the true symmetry may be unknown, not specified, or that the optimal balance of equivarian... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Sequential Predictive Two-Sample and Independence Testing | Accept (poster) | Summary: [Update: After reading the other reviews and the rebuttal, I have increased my score from 7 to 8]
The paper provides an algorithm for sequential testing of the homogeneity (two-sample) and independence hypothesis.
The core idea is that one learns a classifier to distinguish $P$ from $Q$ (in case of two-sampl... | Rebuttal 1:
Rebuttal: We thank the reviewer for constructive feedback on the paper. Below, we address in detail issues that have been raised in the review.
> The main innovation over Pandeva et al [2022] is that the present work uses adaptive betting fractions?...hard to clearly see the parallels and differences to Pa... | Summary: The authors propose methods of two-sample and independence tests in the setting of sequentially released data. Theoretical and empirical evaluations of the proposed methods are included.
Strengths: The authors propose algorithms for sequential two-sample and independence tests. The setting is interesting and ... | Rebuttal 1:
Rebuttal: We thank the reviewer for constructive feedback on the paper. Below, we address in detail issues that have been raised in the review.
> Readability can be improved. The introduction part is so long that I understand the contributions of the paper. I think the authors should split Section 1 into 2... | Summary: This paper proposes two sequential predictive two-sample tests based on betting, one is constructed by the payoff function $W\cdot \mathrm{sign}[g(Z)]$ for the misclassification risk, and the other is by the payoff function $W\cdot g(Z)$ for the squared risk. The limiting growth rate and the expected growth ... | Rebuttal 1:
Rebuttal: We thank the reviewer for constructive feedback on the paper. Below, we address in detail issues that have been raised in the review.
> Variance of the growth rate is not given ... it is actually important in the derivation of the testing power.
We kindly disagree with the reviewer regarding thi... | Summary: The work considered the problems of sequential nonparametric two-sample and independence
testing. The researchers propose a novel approach, which overcomes the issues of kernel-based testing, such as finding an appropriate kernel for high-dimensional or structured data like text and images. The authors empiric... | Rebuttal 1:
Rebuttal: We thank the reviewer for constructive feedback on the paper. Below, we address in detail issues that have been raised in the review.
> ...more technical details about the principle of testing by betting strategies...key concept in the paper but the author does not elaborate it too much in the pa... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We wanted to thank you for your time and for your valuable feedback! We hope that our responses address many/most of the existing concerns. We also attach a PDF file that contains the results of several additional experiments you asked for. If you have any additional questions, we... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: Kernel-based nonparametric two sample and independence tests performance can break down in the cases of complex data like images. The paper proposes sequential two sample and independence tests based on the misclassification rate.
Compared to kernel-based tests, the proposed tests, which use CNNs, reject the... | Rebuttal 1:
Rebuttal: We thank the reviewer for constructive feedback on the paper. Below, we address in detail issues that have been raised in the review.
> Some discussion of the practical computational complexity of the approach compared to kernel-based tests would improve the paper. How do the runtimes compare?
B... | null | null | null | null | null | null |
Function Space Bayesian Pseudocoreset for Bayesian Neural Networks | Accept (poster) | Summary: This paper presents an alternative approach to the construction of Bayesian pseudocoresets by considering the quality of function space approximations. Specifically, they seek to minimise the KL-divergence between function space approximations of posteriors conditioned on the Bayesian pseudocoreset, and the tr... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive comments. For the weaknesses, please refer to our general responses [G3, G4, G5].
---
Rebuttal Comment 1.1:
Comment: Many thanks for your response.
Regarding [G4]. The Jacobian approximation is not the only approximation used---indeed, the use of the J... | Summary: The following work proposes to a new way to construct Bayesian Pseudo-Coresets. Particularly, the authors propose to optimise the KL-Divergence between posteriors associated with real data and synthetic in function space rather than the parameter space of large networks. The main argument posed by the authors... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive comments. We respond to the individual comments below:
**[W1, Q1]** Thank you for the valuable suggestion. We will consider reorganizing the paragraphs during the paper revision.
**[Q2]** I agree with your point that the statement 'function space itself ... | Summary: This paper introduces a novel approach called Function Space Bayesian Pseudocoreset (FBPC) for constructing Bayesian pseudocoresets for Bayesian Neural Networks. A Bayesian pseudocoreset is a compact synthetic dataset that summarizes essential information from a large-scale dataset and can be used as a proxy d... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive comments. We respond to the individual comments below:
**[W1]** Thanks for your insightful comment. We will include the relevant discussion in the paper. As you pointed out, inducing points in Stochastic Variational Gaussian Processes and the functional B... | Summary: This paper combines the works of Kim et al. (2022) and Rudner et al. (2022) to propose a Bayesian coreset learning method based on function space variational inference. The authors find that this leads to coresets with improved accuracy/NLL compared to (Kim et al., 2022) on CIFAR and Tiny-ImageNet benchmarks a... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive comments. We respond to the individual comments below:
**[W1]** We agree that our method extends the discussion in Kim et al. [1] to function space posterior. However, we want to emphasize additional significant contributions in our work.
* Firstly, we h... | Rebuttal 1:
Rebuttal: We express our gratitude to all the reviewers for their valuable and insightful feedback. They have acknowledged the clarity in our method description (R-sWjp, R-9hB5) as well as our coverage of relevant prior works (R-9hB5). The reviewers have also recognized the paper's strong motivation and the... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Greatness in Simplicity: Unified Self-Cycle Consistency for Parser-Free Virtual Try-On | Accept (poster) | Summary: This work proposes a virtual try-on method. At inference time, no parser information (e.g., pose, segmentation map) is needed; given a person's image and a garment, the method is able to output a try-on image. To learn this model with a dataset of paired person and garment images, a deformation network is lear... | Rebuttal 1:
Rebuttal: > Thank you for your diligent efforts and valuable suggestions.
---
### Weaknesses:
- **W-A1:** Thanks for your comments. Datasets consist of paired garment-person images $(g,p)$, where $p$ wearing $g$. Current methods use the person representation $I_p$ and $g$ to find dense spatial correspondenc... | Summary: This paper proposes a new parser-free virtual try-on network (USC-PFN) to use only unpaired images as input to generate realistic try-on results.
To address the core warping problem in virtual try-on, it models the deformation field estiamtion by using the Markov Random Field. To train the try-on generator by... | Rebuttal 1:
Rebuttal: > Thank you for your diligent efforts and valuable suggestions.
---
### Weaknesses:
- **A1:** Thanks for your comments. Datasets consist of paired garment-person images $(g,p)$, where $p$ wearing $g$. Existing methods use the person representation $I_p$ and $g$ to find dense spatial correspondence... | Summary: The paper addresses the challenges in generating high-quality virtual try-on images, specifically focusing on non-rigid garment deformation and unpaired garment-person images. Existing methods rely on disentangling garment domains with the aid of "teacher knowledge" or dual generators, which can limit the qual... | Rebuttal 1:
Rebuttal: > Thank you for your diligent efforts and valuable suggestions.
---
### Weaknesses:
- **W-A1:** Thanks for your comment. Our method significantly outperforms both RT-VTON (CVPR 2022) and SDAFN (CVPR 2022), which are more advanced than PFAFN. Moreover, our method outperforms PFAFN in KID and SSIM. ... | Summary: The paper presented a system for image based virtual try-on. The main contribution consists (1) a parser free virtual try-on network trained with unpaired data; (2) a MRF based deformation estimation network; (3) a cycle consistency based training method. The paper performed experiments on the VITON Zalando da... | Rebuttal 1:
Rebuttal: > Thank you for your diligent efforts and valuable suggestions in the peer review process.
---
### Strengths:
**S-A:** Thanks for your comments. There is a significant difference between our main idea and the disentangled cycle consistency approach [A]. The differences are summarized as follows:
... | Rebuttal 1:
Rebuttal: > We sincerely appreciate the diligent efforts of the reviewers. We propose a novel parser-free self-cycle consistency framework, USC-PFN. To validate the effectiveness, robustness, and generalization of this architecture, we have added the following supplementary experiments:
1) Qualitative expe... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors present a virtual garment try-on method. A cycle-consistency loss allows for self-supervision, i.e. the method does not require paired data (of the same person wearing different garments) as supervision. Unlike a previous method [2] that also uses cycle consistency, the same network weights are use... | Rebuttal 1:
Rebuttal: > Thanks for your diligent efforts and valuable suggestions. We have provided both quantitative and qualitative results of our garment deformer on VITON and a high-definition VITON-HD (512×384) datasets (see https://github.com/anony-conf/results-USC-PFN).
---
### Weaknesses:
- **W-A1:** The differ... | null | null | null | null | null | null |
Uncovering the Hidden Dynamics of Video Self-supervised Learning under Distribution Shifts | Accept (spotlight) | Summary: The authors studied the behavior of six popular self-supervised methods in response to various forms of natural distribution shift. And the study uncovers a series of interesting findings and behaviors of video self-supervised learning (VSSL) methods. The experiments and results are beneficial for the video re... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time and providing such valuable feedback. We are happy to note that the reviewer finds our arguments well-grounded with the experimental results.
> Effect of data and model size
- In **Table S6 (Appendix D)**, we notice that using a larger dataset of mor... | Summary: The paper studies the generalizability of many self-supervised training approaches in the video domain. The paper uses 17 tasks to completely test different aspects of the models, including view-point change, temporal modeling, and open set generalizability.
Strengths: 1. Most of the cutting-edge video pre-tr... | Rebuttal 1:
Rebuttal: We thank the reviewer for sharing such thoughtful reviews. We are happy to find the overall positive feedback provided by the reviewer.
> It's good to have a hyperparameter table in the appendix; if the authors have swept hyperparameters for each method?
We would like to point out that we have... | Summary: The paper proposes a set of benchmarks to assess different robustness properties of video representation learning models, including contrastive, non-contrastive, and generative models. The paper trains multiple of these models using a common training protocol and reports multiple empirical findings on differen... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing such valuable feedback and a thorough review. We are glad to note that the reviewer finds the 'Highlights' at the end of each section useful and finds our work extensive and valuable.
> Do the improvements originate from the improved OoD or from the difference ... | Summary: This works studies various video SSL methods under distribution shifts. 6 different video SSL methods (SiMCLR, MoCO, BYOL, SimSiam, DINO, MAE) are trained on Kinetics-400 under the same experiment settings (e.g. ViT-B, fixed number of epochs, etc) and then evaluated on various distribution shifts (e.g. viewpoi... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing such valuable feedback. We are happy to note that the reviewer finds our work useful to the community.
> Experiments on different architectures or model scaling
We have now conducted experiments on video ResNet-50 given its strong performance in video SSL [72... | Rebuttal 1:
Rebuttal: We sincerely thank the review committee for their time and for providing constructive feedback. We are happy to see the overall engaging comments given by all the reviewers and glad to note that all reviewers find our work valuable to the community. We have carefully addressed all the concerns rai... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Deep Optimal Transport: A Practical Algorithm for Photo-realistic Image Restoration | Accept (poster) | Summary: This paper considers the high perceptual image restoration problem. The authors propose a method to control the tradeoff between the perceptual quality and distortion (e.g. MSE) of a pretrained restoration model. The mehtod is developed based on a recent theory [1] on the tradeoff between MSE distortion and pe... | Rebuttal 1:
Rebuttal: > Lack of theorectical novelty. The proposed method is based on the theory in [1] (see Section 3) and appears to be more of an extended evaluation of the results in [1]. Besides, the adopted appraoch that performs transport in the latent space is also not new and borrowed from existing work.
The ... | Summary: This paper presents an image restoration algorithm targeting at further restore the processed images that have been restored by pre-trained restoration models. As most of the restoration tasks use MSE as the main criteria for restoration, the restored images tend to be blurred to achieve better PSNR performanc... | Rebuttal 1:
Rebuttal: > As the proposed method uses a few images for optimal transport computing. How to guarantee that the distribution of test data is aligned with the training data. Does the selection on the training data have an impact on the performance of restoration?
Our experiments showed that the **class of i... | Summary: This paper presents an image restoration algorithm that builds upon a trained network to further minimize MSE.
To achieve this goal, an optimal transport was approximated by a linear transformation in the latent space. Visual results show clear improvement by using the proposed approach.
Strengths: - The ide... | Rebuttal 1:
Rebuttal: > `[Summary]` This paper presents an image restoration algorithm that builds upon a trained network to further minimize MSE
We would like to clarify that the main goal of our algorithm is actually to improve perceptual quality. As a side-effect, we discovered empirically that we could extend the ... | Summary: The paper proposes an image restoration algorithm that can control the perceptual quality and/or the mean square error (MSE) of any pre-trained model, trading one over the other at test time.
Strengths: 1. The method is plug-and-play, requires only a few samples, and does not require further training.
2. The ... | Rebuttal 1:
Rebuttal: > (4) LPIPS is generally regarded as a perception metric in image restoration tasks. Since FID, IS and KID are not very stable, they are generally not used in image restoration tasks. Just looking at PSNR, SSIM and LPIPS, the method doesn't seem to achieve a good distortion-perception tradeoff.
P... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this paper, the authors propose a few-shot algorithm to obtain higher quality restored images of the given model like VAEs and diffusion models. Specifically, the optimal transport map in the latent space is computed through the representations of the real images and the reconstructed images. By applying th... | Rebuttal 1:
Rebuttal: > In line `116-117`, what's the meaning of $x^*$ and $\hat{x}_0$ ?
$x^*$ is the MMSE estimate, being the posterior mean. This solution gives the best MSE distortion performance, but at the cost of being of poor visual quality. $\hat{x}_0$ on the other hand, is the Dmax solution - being of perfec... | null | null | null | null | null | null |
Neural Lad: A Neural Latent Dynamics Framework for Times Series Modeling | Accept (poster) | Summary: The authors propose a new neural ODE framework, Neural Lad (Neural Latent dynamics model), that decomposes the differential function in three components: a NN differential function, an attention-based network, a time-dependency function and a graph convolution network for spatial correlations. They evaluate t... | Rebuttal 1:
Rebuttal: We thank the reviewer's recognition and valuable comments on the contribution of our work. We address the concerns in the following.
**Weakness Part:**
**Quality:**
**Q:** The mechanism of time-dependent component
**R**: The effectiveness of the designed time-dependent function $h_w(t)$ could... | Summary: This paper addresses the problem of characterizing the local change of observed signals and ignoring inherent periodical property in time series forecasting tasks. A new neural ODE-based framework is proposed with 1) a decomposable latent space for time-dependent dynamics and 2) an attention-based design for l... | Rebuttal 1:
Rebuttal: We thank the reviewer's recognition and valuable comments on the contribution of our work. We address the concerns in the following.
**Weakness Part:**
**Q:** "I feel that the approach is somewhat incremental from the perspective of the methodology in that it is an extension of the previous wor... | Summary: This paper presents a new framework for modeling time series using a controlled latent neural-ODE-based dynamics model. The proposed latent dynamics function uses a special factorized structure, which effectively disentangles the influences of time (via a periodic basis expansion to capture periodic patterns),... | Rebuttal 1:
Rebuttal: We thank the reviewer's recognition and valuable comments on the contribution of our work. We address the concerns in the following.
**Weakness Part:**
**Q:** "The idea itself is not the most novel, involving a simple factorization and model architectures drawing cues from popular ones already e... | Summary: This paper propose a novel neural ordinary differential equation framework for time series modeling. The main contribution is in the design of latent dynamic function $F(\cdot)$ which can be decomposed into hidden state dynamics $f_\theta(z_t)$, time-dependency with periodical and trend property $h_w(t) $, and... | Rebuttal 1:
Rebuttal: We thank the reviewer's recognition on the novelty and contribution of our work. We address the concerns in the following.
**Weaknesses Part**:
**Q**: Analysis on the computational complexity/cost is missing
**Reply**: We add the complexity analysis and computational time comparison with othe... | Rebuttal 1:
Rebuttal: We thank all the reviewers' recognition and valuable comments on our work. We have carefully responsed the concerns raised for each reviewers, including clarification of the novelty, adding more exprimental results. We attached the additional experimental results as a pdf file for further check. ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Riemannian Laplace approximations for Bayesian neural networks | Accept (poster) | Summary: This paper develops a Riemannian Laplace approximation, which is a Laplace approximation that takes into account the Riemannian geometry of the loss surface. The contributions of this paper are as follows: i) showing that such a loss-aware Laplace approximation is better able to capture the true posterior (and... | Rebuttal 1:
Rebuttal: ## Response to Reviewer dyBq
We would like to thank the reviewer for their positive consideration. We appreciate the time and effort spent in reviewing our paper. We addressed all the remaining concerns below.
**Point 1 / Weaknesses**
> *“Even though the paper is generally very well written, I d... | Summary: The paper presents a Laplace approximation for Bayesian neural networks that adapts the covariance to the local geometry of the loss, effectively overcoming the quadratic approximation of the loss. The authors report competitive performance with the standard Laplace approximation (both Monte Carlo sampled and ... | Rebuttal 1:
Rebuttal: ## Response to Reviewer BssD
We thank the reviewer for the positive consideration of our work and constructive feedback. We appreciate the time spent to review our paper. We address all your concerns below and we refer to the general comment for the questions regarding scalability.
**Point 1/ W... | Summary: This paper presents a novel Laplace Approximation for Bayesian Neural Networks. A key insight is to examine the local loss landscape with a Riemannian metric, which is determined by the gradient of the log posterior. Using this metric and an exponential map, a Laplace Approximation technique is developed to dr... | Rebuttal 1:
Rebuttal: ## Response to Reviewer am4X
We would like to thank the reviewer for their thoughtful consideration of our work. We appreciate the time you took to review our paper. We have taken the time to address all the points raised under Weaknesses.
**Point 1/ Weaknesses**
> *"...without referring to the ... | Summary: The Laplace approximation offers a practical posterior but is limited due to the symmetry of the weight space it is parameterised in. The method proposed to improve posterior quality by adapting the posterior shape through a Riemannian metric that is determined by the log-posterior gradient.
Strengths: * Pra... | Rebuttal 1:
Rebuttal: ## Response to Reviewer CqAb
We thank the reviewer for the positive consideration on our work. We also appreciate the time taken to review our paper. We addressed all concerns you highlight under the Weaknesses and Questions sections.
**Point 1 / Weaknesses**
> *... Fig. D.4 seem off. It would ... | Rebuttal 1:
Rebuttal: ## General Comment to all reviewers
We would like to thank the reviewers for their thoughtful comments, positive considerations and suggestions for improving the paper. We appreciate that you found our work novel, well-written, with potential impact for the community, and inspiring for follow-up w... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Asymmetric Certified Robustness via Feature-Convex Neural Networks | Accept (poster) | Summary: This paper is based on the following elegant observation: Consider the case of binary classification in which we learn a function $f: \mathbb R^e\to \mathbb R$ and classify a point based on thresh-holding $f$ at 0. Assume $f(x)=g(\phi(x))$ where $f:\mathbb R^m \to \mathbb R$ is convex and $\phi$ is Lipschitz. ... | Rebuttal 1:
Rebuttal: Thank you for your kind complement on the exposition of our paper. We appreciate your constructive suggestions, which we address below.
1. "Can you find an example where your method does better than an SVM in terms of either accuracy or robustness?":
Certainly, we have shared the relevant script... | Summary: This paper tackles the asymmetric nature of classification and adversarial attacks intrinsic in most real-world scenarios that have a live and motivated attacker: that attacks are unidirectional, and proposes a general-purpose technique for specifying a certifiably robust defense in such scenarios. This is don... | Rebuttal 1:
Rebuttal: Thank you for your comments and questions. We have addressed them below, and revised the manuscript accordingly.
1. "The experimental section...":
As mentioned in our response to Reviewer qP9X, we have revised the Experiments section to satisfy your suggestions. In particular, we have moved the ... | Summary: This paper considers a specific problem in binary classification task, where one class is recognized as a ‘sensitive’ class, which needs to be certified for robustness. The authors propose a special network called feature-convex neural network, which combines a Lipschitz network and a convex ReLU network. Expr... | Rebuttal 1:
Rebuttal: We greatly appreciate your positive feedback on our paper's presentation, proposed problem/approach, and theory.
1. "The proposed feature-convex network may limit classification power...":
In practice, we find our clean accuracies to be on par with the state-of-the-art robust classification base... | Summary: The paper is about certified robustness of neural networks. In particular the authors explore the concept that there is typically a one-sided attack that needs to be certified since adversaries have certain goals that only work in certain directions (e.g., classify a spam email as ham). The authors call this... | Rebuttal 1:
Rebuttal: Thank you for your constructive suggestions. We have addressed all of your comments and questions below, and revised the manuscript accordingly.
1. "The authors use... long sentences":
We have identified and revised some of our lengthier sentences: "Specifically, we assume..." (line 59); "We cha... | Rebuttal 1:
Rebuttal: We sincerely thank the Reviewers for their insightful comments and valuable suggestions. We are happy to see that 3/4 reviews are generally positive on our work, with primarily presentation-focused concerns that we address below. Reviewer g6eM has indicated their willingness to update their score ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective | Accept (poster) | Summary: The paper presents FourierGNN for multivariate time series forecasting from a pure graph perspective, performing matrix multiplications in Fourier space, which has not been investigated so far. They design a new hypervariate graph structure to consider spatiotemporal dynamics unitedly and reformulate the graph... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and valuable suggestions. In the following, we provide a detailed response to address all of your concerns.
**W1**
Since it is difficult and unfair to analyze the time complexity and parameter volumes of comparative methods that have different architectures, ... | Summary: This paper has studied a popular and important problem, i.e., modeling the intricate spatial and temporal dependencies among multivariate time series for accurate forecasting. To overcome the main limitation that existing works always separately model spatial and temporal, this work designs a new hypervariate ... | Rebuttal 1:
Rebuttal: We appreciate your positive comments. We would like to respond to your comments as follows.
**Q1. How to properly handled redundant or unwanted correlations in the hypervariate graph?**
As stated in **Section 4.1 The Pure Graph Formulation**, we propose the hypervariate graph to connect all vari... | Summary: The paper addresses the time series forecasting problem.
The authors propose a model that represents each scalar
observation as a node in a (fully connected) graph,
encodes the nodes and finally regresses the future
observations on all previous encoded observations
(via a fully connected layer). As encoding th... | Rebuttal 1:
Rebuttal: We appreciate your review. Hope our response can address the misunderstandings or concerns.
**w1**
1. The node features of the hypervariate graph are $X \in \mathbb{R}^{n \times d}$, where $n=NT$ is the number of nodes, $d$ is the number of features, $N$ is the number of variables, and $T$ is th... | Summary: In this paper, the authors study a problem in GNN-based multivariate time series (MTS) forecasting, i.e. modeling spatial correlations and temporal dependencies in the same time. In particular, the authors do not follow previous works on regarding the input as T graphs and capturing temporal dependencies betwe... | Rebuttal 1:
Rebuttal: We appreciate your positive feedback and valuable suggestions. In the following, we provide a detailed response to address all of your concerns.
**W1.**
Thanks for your suggestion. We provide more clear details to clarify the experimental settings of baselines in Appendix E.2. In the experiments... | Rebuttal 1:
Rebuttal: Dear Reviewers, ACs, and the SAC:
We thank all reviewers for their valuable comments. We response to all comments of reviewers; in particular, after carefully considering ndBv's comments, we realize there might be some potential misunderstandings. We've tried our best to clarify these misundersta... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
ATTA: Anomaly-aware Test-Time Adaptation for Out-of-Distribution Detection in Segmentation | Accept (poster) | Summary: The manuscript considers dense OOD detection under domain shift. The manuscript shows that the contemporary methods for dense OOD detection experience performance drop under domain shift and propose an adaptation framework to mitigate the issue. The proposed framework has two steps. The first step determines w... | Rebuttal 1:
Rebuttal: Thank you for recognizing the novelty and applicability of our work. We have added results on the SMIYC benchmark, inference time analysis, and a refined Figure 2 in our general response and attached PDF. We appreciate the reviewer for pointing out missing relevant works and will include them in t... | Summary: This paper proposes ATTA (Anomaly-aware Test-Time Adaptation), which introduces test-time domain adaptation (TTA) for anomaly segmentation. As a result, anomaly segmentation can be performed well even in a harsher environment where domain shift and semantic shift occur simultaneously. To create an environment ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and detailed feedback. Unfortunately, there appears to be some misunderstanding regarding our method, which we will clarify in the following responses.
## W1: Clarification of the FS Static -C dataset
We would like to clarify that our comparison is indeed fair ... | Summary: This paper focuses on the challenging task of open-set semantic segmentation (i.e., dense out-of-distribution (OOD) detection) with domain shift. It proposes a dual-level test-time adaptation framework to overcome domain shift and semantic shift simultaneously, which leverages low-level feature statistics to d... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and recognition of our work. To address your concerns, we have revised Figure 2 to better highlight the key components of our proposed framework and the overview of our methodology. The updated visualization can be found in the attached PDF. Additionally, w... | Summary: The papers deal with two levels of domain shift in semantic segmentation, namely the domain shift on the semantic pixel level and the domain shift on the image level. The paper argues that the current dense out-of-distribution (OOD) detection methods are particularly vulnerable in the presence of image-level d... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for insightful feedback and recognition of the novel aspects of our work. We are committed to improving the clarity in our final version, and we intend to release the code after the double-blind stage. Below, we respond to the specific concerns raised:
## W1: Clarity
-... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and constructive comments. In the following, we address some shared concerns in this general response and answer each individual question by replying to each reviewer. We also include an additional PDF containing a revised Figure 2, some examples showing t... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The proposed method considers the OOD sample with domain shift and semantic shift. They address the problem of current OOD approaches that often ignore to handling domain shifts. They introduce an anomaly-aware test-time adaptation method that
jointly tackles domain and semantic shifts. The experiments on dif... | Rebuttal 1:
Rebuttal: Thank you for recognizing our contribution and for your thoughtful and detailed feedback. We have presented our results of SMIYC and analyzed the inference time of our method in the general response. Please find our responses to your other concerns below:
## W1: Detailed Figure 2 - Model Overview... | null | null | null | null | null | null |
Differentiable Neuro-Symbolic Reasoning on Large-Scale Knowledge Graphs | Accept (poster) | Summary: This paper integrates rule-based reasoning and knowledge graph (KG) embedding to enable effective and efficient knowledge graph reasoning. The key idea is to use probabilistic soft logic (PSL) to assess the agreement between the inferred triples and weighted rules, based on the embedding representations of ent... | Rebuttal 1:
Rebuttal: Q1. Why distinguishing $I^-_q$ and $I^+_q$ are important to DiffLogic?
A.
Thank you for your question.
The importance of differentiating between the notations $I^-_q$ and $I^+_q$ is twofold: knowledge representation and logical reasoning.
1. Knowledge Representation: As introduced in subsection... | Summary: The paper presents a novel approach for the knowledge base completion task by combining embedding-based and rule-based approaches in a neural symbolic framework. The rule-based component uses probabilistic soft logic to encode rule truth values as continuous values. The overall framework follows an EM approach... | Rebuttal 1:
Rebuttal: Thank you for your questions. We will first answer your questions, and then address the weakness.
Q1. The initialization method for the knowledge graph (KG) embeddings is random, right? Is there a performance change if you try to initialize the embeddings using pre-trained KB models?
By default... | Summary: This paper introduces differentiable logic approach, DiffLogic, based on the probabilistic soft logic (PSL) representation. Efficient training of DiffLogic is enabled through the introduction of a grounding technique that iteratively identifies important ground formulas required for inference, and additionall... | Rebuttal 1:
Rebuttal: Thank you for your question. We will first answer the question raised by the reviewer which concerns the first weakness, then we will respond to weakness 2 and weakness 3.
Q1. Can you explain this method and its performance in the context of recent similar methods? This method seems somewhat comp... | Summary: This paper proposed a differentiable framework - DiffLogic. Instead of directly approximating all possible triples, the author design a tailored filter to adaptively select essential triples based on the dynamic rules and weights. The truth scores assessed by KG-embedding are continuous, so the author employ a... | Rebuttal 1:
Rebuttal: Q1. The most impressing advantage of Neuro-Symbolic methods is they are interpretable. But I do not see that.
A.
The interpretability of neuro-symbolic methods primarily stems from their logic formulas, using rules to infer new facts is interpretable because a rule is an implication from premise ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes a framework called DiffLogic for neuro-symbolic reasoning on knowledge graphs. It balances accuracy and efficiency by selecting essential triples based on dynamic rules and weights. The framework uses a continuous Markov logic network named probabilistic soft logic (PSL) for end-to-end diffe... | Rebuttal 1:
Rebuttal: Q1.
Can you provide a more thorough literature review?
A.
Thanks for your suggestion.
We will do a more thorough literature review, including recent GNN-based methods. We will also add RNNLogic and RLogic as baseline methods in our experiments. See Q3 for more details.
Q2.
Can you provide a de... | null | null | null | null | null | null |
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