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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Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation | Accept (poster) | Summary: The authors present a novel approach for SBI by utilizing neural networks (NN) to estimate a generalized cost in GBI. Their proposed method, ACE, demonstrates superior computational efficiency compared to previous approaches, without compromising competitive performance across various evaluation metrics. The a... | Rebuttal 1:
Rebuttal: We really appreciate the reviewer’s encouraging and positive comments about our work, in particular highlighting the novelty and value of our proposed contribution to SBI, our extensive benchmark experiments, and the quality of the writing and code for better communication and reproducibility.
I... | Summary: This paper studies the problem of simulation-based inference - which can is encountered in a wide range of scientific problems - where one is interested in performing Bayesian inference using simulators with implicit likelihoods. Scientific problems can have two unique properties - a) the predictive quality of... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive assessment of our work, who was very kind to note the importance, novelty, and simplicity of the contribution, as well as our effort on presenting the idea clearly and with included code for reproducibility. We enjoyed the concise and accurate summary of th... | Summary: This paper proposes amortized cost estimation (ACE) for generalized Bayesian inference (GBI) for SBI. The paper trains a neural network to approximate the cost function. The paper demonstrates results on baseline synthetic SBI examples followed by a real-world application using experimental data from the Allen... | Rebuttal 1:
Rebuttal: We thank the reviewer for noting the novelty of our work and the clear structure of the paper, and their positive score of 3s (good) in soundness, presentation, and contribution. We appreciate their requests for further clarifications and the opportunity to increase the score. We apologize for the... | Summary: The paper presents a new technique - amortized cost estimation (ACE) - that, as stated on the can, amortizes a broad class of loss functions used in generalized Bayesian inference (GBI) in place of the (log) likelihood.
After training on a moderate-to-large number of model simulations (10K-100K in the example... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s encouraging remarks regarding the timeliness and broad applicability of our work in leveraging GBI for (misspecified) SBI problems, as well as noting the high quality of execution and presentation in our paper. Furthermore, they raised several interesting questions and... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their constructive and detailed engagement with our work, resulting in many helpful comments, questions, and opportunities for clarification, as well as ideas for future work. We are especially grateful for several reviewers’ acknowledgement that the pr... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
How to Leverage Imperfect Demonstrations in Offline Imitation Learning | Reject | Summary: This paper addresses the challenge of learning good imitative policies from offline data, in which abundant imperfect demonstrations are mixed with few expert ones. Unlike previous work that measures the state-action similarity between imperfect and expert data, the present work proposes iLID, which leverages ... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of the contribution of this paper! Below are detailed responses to each comment:
---
## Q1: About the proof steps
**(1) What is the outer expectation over in the definition of $\epsilon$ and $\delta$? Do you require any assumptions on the distributions of $\mathca... | Summary: The submission presents a novel method called Offline Imitation Learning with Imperfect Demonstrations (iLID) for Offline Imitation Learning, which aims to improve policy learning from both expert and imperfect demonstrations. Compared with previous IL methods, which only consider the state-action pairs during... | Rebuttal 1:
Rebuttal: Thank you for your valuable and detailed feedback! Below are detailed responses to each comment, and new comments on them are very welcome!
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## Q1: About the notation, $\mu$
We are sorry for this typo in the preliminary section. Throughout the paper, $\mu$ is used as a distribution or overlo... | Summary: The paper addresses the problem of offline imitation learning (IL) from demonstrations that are noisy/suboptimal. To this end, the authors propose iLID which is a two-step process—a data selection step which only retains those $(s,a)$ transitions from suboptimal demonstrations which lead to states in the exper... | Rebuttal 1:
Rebuttal: Thank you for your in-depth comments and suggestions! Below are detailed responses to each comment, and new comments on them are very welcome!
**Q1: The only major concern is seeding suboptimal data with expert data. How does iLID perform across different environments when this is not done at all... | Summary: The paper proposes an algorithm for offline imitation learning on a mixture of “perfect” expert demonstrations and “imperfect” sub-optimal demonstrations. The authors provide a theoretical motivation for their approach and exhibit results on various Mujoco and Adroit tasks. The authors also conduct ablation st... | Rebuttal 1:
Rebuttal: Thank you for the reviewer's appreciation of the contribution and novelty of this paper! Below are detailed responses to each comment:
**Q1: It would be interesting to have a study of the effect of the quality of the suboptimal data on the performance of the method.**
Following the reviewer's su... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for the insightful and constructive feedback! We also thank the reviewers for their appreciation of the novelty (Reviewer K4fk), contribution (Reviewer Y7ma), and writing (Reviewers b1iR and 71T4) of our work. Per the reviewers' suggestions, we have: (1) carried ou... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Characterizing the Impacts of Semi-supervised Learning for Weak Supervision | Accept (poster) | Summary: The authors propose a design space to analyze the effects of WS + SSL, and how to combine them, and on which regimes that SSL help WS. The paper's biggest conclusion is: training using unlabeled datain SSL is mainly unhelpful, except when the main issue is bad labeling function (LF) accuracy. The design space ... | Rebuttal 1:
Rebuttal: Thanks for your time and helpful feedback! We see your comments as covering two main points, which we will respond to below:
**(W1) Testing on more label models (i.e., FlyingSquid, LIGER)**
**FlyingSquid (FS).** For our experiments, we chose label models (i.e., Snorkel/MajorityVoting) by looking... | Summary: This paper studies the impacts of semi-supervised learning (SSL) for programmatic weak supervision (WS) in a systematic way. The authors define a modular design space with three key methodological considerations, thresholding, SSL Technique, re-labeling, to study the use of SSL for WS. Their results show that ... | Rebuttal 1:
Rebuttal: Thanks for your time and helpful feedback! To respond to some of your questions and comments:
**(W1a)** *“The experimental results show that SSL can still bring benefits on many datasets - I cannot see why the paper emphasizes SSL is not necessary if it still helps.”*
We’d like to highlight tha... | Summary: This paper empirically studies the interface between (programmatic) weak supervision (WS) and semi-supervised learning (SSL). Several existing works have tried to leverage SSL techniques and other tricks to improve the performance of weakly supervised learning, since the two settings are fairly similar on the ... | Rebuttal 1:
Rebuttal: Thanks for your time and helpful feedback, we’re glad you appreciated the paper! Regarding your point about validation sets: like you said, we chose to follow the standard setups established by other works, as our main questions were about assessing various trends we saw from the WS literature. Ho... | Summary: Getting labeled training data is a bottleneck in the development of machine learning pipelines. Two families of methods that address this are weak supervision (WS), which aggregates multiple sources to produce weak labels, and semi-supervised learning (SSL), which combines a (weakly) labeled dataset and an unl... | Rebuttal 1:
Rebuttal: Thanks for your time and helpful feedback, we’re glad you appreciated the paper! To respond to your questions and comments:
**Clarity.** We really appreciate your feedback here. In future revisions, we will take care to make these descriptions more explicit in the Appendix.
**Connections to exam... | Rebuttal 1:
Rebuttal: We thank all of the reviewers for their time and thoughtful feedback! We will respond to each review individually, using this "global response" space to upload our pdf containing the Tables/Figures for new results (which are referenced in our responses).
Pdf: /pdf/eb8e35fc9f7be89cf336069b011c26c0... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper presents a systematic study of how useful SSL is in weak supervision. Specifically, the authors analyze SSL and weak supervision (WS) along three axes and explore various approaches along each axis. First, the paper analyzes what to consider as `unlabeled` data for SSL training. The second axis ref... | Rebuttal 1:
Rebuttal: Thank you for your time and helpful feedback! To respond to your questions and comments:
**Scale of study / Stronger SSL methods.** We agree that it is possible that more sophisticated SSL techniques could lead to a conclusion that SSL is more useful. Based on your feedback, we implemented and ra... | null | null | null | null | null | null |
NEO-KD: Knowledge-Distillation-Based Adversarial Training for Robust Multi-Exit Neural Networks | Accept (poster) | Summary: This paper proposes a knowledge distillation approach for defending against adversarial attacks in multi-exit networks. They have two different objectives: (i) leveraging self-distillation to improve adversarial robustness, (ii) reducing adversarial transferability among the submodules of the network. Since mu... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the time and efforts, and providing helpful comments that are also very clear. Below, we provide responses to the reviewer's comments.
### **NEO-KD shows low performance at the specific exits in some cases.**
We agree with the reviewer that in some datasets, NEO-K... | Summary: This paper proposed a know-distillation-based method to improve the adversarial robustness of multi-exit neural networks. Extensive experiments are conducted to show the effectiveness of the proposed method.
Strengths: 1. Extensive experiments are conducted to show the effectiveness of the proposed method.
2.... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the positive comments and valuable feedback. Below, we provide answers to the comments raised by the reviewer.
### **Comparison with TEAT**
The baselines in the main paper were generally the adversarial defense methods designed for multi-exit network. As the reviewe... | Summary: This paper proposed a knowledge-distillation based adversarial training method, which is designed for multi-exit neural networks. The authors propose neighbor knowledge distillation to improve the robustness against adversarial attacks, and propose exit-wise orthogonal knowledge distillation to reduce the adve... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the helpful comments, especially on the unclear aspects in the paper. In the response below, we would like to clarify all the ambiguous points raised by the reviewer.
### **Motivation of EOKD**
Multi-exit networks are highly vulnerable to simple attacks (e.g., an ad... | Summary: The paper presents a novel method called Neighbor Exitwise Orthogonal Knowledge Distillation (NEO-KD) for improving the adversarial robustness of multi-exit networks. The method's motivation lies in addressing the issue that existing knowledge distillation schemes are not ideal for multi-exit networks as they ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the time and efforts. In the response below, we provide answers to the comments raised by the reviewer.
### **Results on larger datasets**
We appreciate the suggestion. We conducted additional experiments using ImageNet with 1000 object classes. Due to the strict t... | Rebuttal 1:
Rebuttal: We appreciate all reviewers for providing constructive comments, which have greatly helped us to improve the paper.
Due to the limited content we can provide in each response, we would like to share additional experimental results that **Reviewer PqqG** and **Reviewer KNG2** suggested here. For ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Puzzlefusion: Unleashing the Power of Diffusion Models for Spatial Puzzle Solving | Accept (spotlight) | Summary: This paper applies conditional diffusion model to address the problem of spatial puzzle solving and show its real application on Cross-cut Jigsaw Puzzle (CJP), Voronoi Jigsaw Puzzle (VJP) and room layout arrangement (RLA). Unlike previous methods that rely on enumerating and verifying pairwise alignment and ma... | Rebuttal 1:
Rebuttal:
We thank the reviewer for your positive, insightful and valuable comments and suggestions which are very crucial for improving the quality of our manuscript.
---
**1. Applications of the room layout arrangement and hardness of the jigsaw puzzle solving.**
>The room layout arrangement is an eme... | Summary: This paper introduces a novel approach to puzzle solving and room floorplan arrangement by utilizing a conditional generation process based on the denoising diffusion model. The model effectively reconstructs the original polygonal coordinates, representing the spatial arrangement, during the reverse diffusion... | Rebuttal 1:
Rebuttal:
We thank the reviewer for your positive, insightful and valuable comments and suggestions which are very crucial for improving the quality of our manuscript.
---
**1. Additional details of the jigsaw part in the main paper and lack of connection between layout arrangement and jigsaw puzzles.**
... | Summary: This paper investigates the use of diffusion models to solve jigsaw puzzles. Puzzle pieces are model as a sequence of corners and the diffusion process consists in adding noise/denoising the position and orientation of the corners, conditionally to the original piece shapes. To have fragments snap into place, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for your positive, insightful, and valuable comments.
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**1. Interpretability of the metrics.**
>We thank the reviewer for pointing this out. While these metrics might not be deemed ideal, it worth to mention that for layout arrangement, as every pixel is equivalent to a ... | Summary: The paper presents a diffusion based method to tackle jigsaw puzzle solving task. This task has applications in artwork restoration, room layout estimation, etc. The paper also introduces a room layout and arrangement dataset. The authors compare their work with previous state of the art approaches and a trans... | Rebuttal 1:
Rebuttal: We thank the reviewer for your positive, insightful and valuable comments and suggestions which are very crucial for improving the quality of our manuscript.
---
**1. Section 3 is a bit hard to follow**
>We thank for the comment and will do our best to clarify our writing. We would appreciate ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable and insightful comments. We are also grateful to the reviewers for their positive comments on our work. We have addressed the reviewers points in our individual responses to each reviewer, and please let us know if there are any new questions.
Pdf: /p... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces a diffusion model for solving 3 different spatial puzzle tasks: cross-cut jigsaw, voronoi jigsaw, and room layout arrangement. Their method achieves SOTA results while being much faster than previous works, allowing them to handle larger puzzles than previous methods could. They also demo... | Rebuttal 1:
Rebuttal: We thank the reviewer for your positive, insightful and valuable comments and suggestions which are very crucial for improving the quality of our manuscript.
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**1. At zero noise level (Figs 5 and 6), the proposed method seems to be less precise at aligning the pieces compared to [6].**
>We ag... | null | null | null | null | null | null |
Topology-Aware Uncertainty for Image Segmentation | Accept (poster) | Summary: This paper proposes a framework that utilizes a probabilistic approach to extract structure-wise uncertainty estimates. This is achieved by extending the DMT to a probabilistic setting that models each structure as a sample from a probability distribution, thus capturing the intra-structural uncertainty. The p... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback! Please find our responses to specific queries below.
**Q1:** Please add a larger dataset to evaluate the proposed method.
**A1:** As recommended, we conduct these experiments. Please see 1. in the global response ‘Author Rebuttal by Authors’ above.
**Q2... | Summary: This paper proposed a topology-aware uncertainty estimation method to segment curvilinear objects. The main contribution focuses on the application of discrete Morse theory (DMT). On several public datasets, the proposed method achieves SOTA performance and the visual results demonstrate the connectivity of ve... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback! We will revise our manuscript accordingly. Please find our responses to specific queries below.
**Q1:** This paper is mainly based on [24]. The technical contribution is a bit marginal here. Please clearly state the main differences and the take-home insi... | Summary: This paper proposes a novel method for the estimation of uncertainty of the structures in the segmentation results from existing methods/models, in order to facilitate the subsequent proofreading process. To this end, it models the intra-structure and inter-structure uncertainties in two modules. While the for... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback! We will revise our manuscript accordingly and address your questions below.
**Q1:** Guarantee of GT quality/reliability?
**A1:** We use public datasets which are considered reasonably good, though are not guaranteed to be flawless. Our method can aid in ... | Summary:
This work aims to contribute to proofreading by proposing uncertain structures in a topological sense. The work proposes a method to quantify a form of structure-wise uncertainty from segmentations, where the framework explicitly models structures as samples from a probability distribution. First, the structu... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback! Please find our responses to specific queries below.
**Q1:** The work presents a real generalization of the work on DMT for segmentation [25].
**A1:** We would like to clarify that the goal of this paper is very different from [25]. [25] and other topolo... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and insightful feedback. We are encouraged that all the reviewers appreciated the novelty of the contribution, and found our work to be methodologically sound and effective.
We have uploaded a 1-page PDF where we add results of additional experiments as requ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Tree-Rings Watermarks: Invisible Fingerprints for Diffusion Images | Accept (poster) | Summary: This paper proposes a method to add the watermark to the images generated by the diffusion model during the generation process. It subtly influences the entire sampling process, resulting in a model fingerprint that is invisible to humans. In terms of the specific approach, the watermark embeds a pattern into t... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable feedback and the time you've dedicated to providing it. Below, we address specific points you raised:
> I consider the method proposed in this paper as image watermarking. My biggest concern is that the method is not practical. Image watermarking algorithms h... | Summary: This paper proposes a watermarking scheme for image generative AI based on diffusion process. The watermark is embedded in the initial noise pattern before diffusion. Detection succeeds in reversing the diffusion to get an estimate of the initial noise pattern.
Strengths: S1. The idea is simple and super ori... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable feedback and the time you've dedicated to providing it. In the limited space below, we have made an effort to address the specific points you raised:
>Some atypical wordings.
Thank you for pointing out this, we are happy update to our terminology.
>Bold cla... | Summary: This paper proposes a method for watermarking images created by diffusion models, a popular class of generative models. Whereas traditional watermarking methods operate directly on images (e.g. in pixel space or Fourier/wavelet representations), the proposed watermark is embedded in a Fourier representation of... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable feedback and the time you've dedicated to providing it. Below, we address specific points you raised:
> It is therefore reasonable to wonder whether RivaGAN might achieve better imperceptibility and robustness if trained with a lower capacity closer to that o... | Summary: This paper proposes watermarking the generated image from diffusion models by watermarking the initial noise and the reverse DDIM process is directly used as the watermark extraction. Experiments show the influence on the visual quality and robustness of the proposed method against various distortions.
Streng... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable feedback and the time you've dedicated to providing it. Below, we address specific points you raised:
> The threat model shall be clarified.
Thanks for pointing out this. We will add the following threat model section in the future version.
#### Threat Mode... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their time and for writing thoughtful reviews of our work. We've added a section about deriving P-value below and also attached a PDF containing additional figures.
Based on questions about theoretically justified threshold values, we've formalized the... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Wide Neural Networks as Gaussian Processes: Lessons from Deep Equilibrium Models | Accept (poster) | Summary: The authors investigate the infinite-width limit of a DEQ, and prove that the output converges to a Gaussian process. Their result importantly leverages the intermediary analysis of a finite-depth, finite-width DEQ. Their main technical result is that the limit of infinite width and infinite depth commute for ... | Rebuttal 1:
Rebuttal: **Response to Question 1**: Thank you for your insightful comments and questions. Limits in DEQs can commute lies in the strategic utilization of input injection and careful selection of variance parameters. Let us provide further intuition for a clearer understanding.
In DEQs, the input injectio... | Summary: This paper focuses on the DEQ (deep equilibrium) model, an infinitely deep neural network with shared weight matrices across layers. The authors show that the network approaches a Gaussian process as the network width goes to infinity, and the limit of infinite width and infinite depth commute, also, the Gauss... | Rebuttal 1:
Rebuttal: **Response to Weakness 1**: Thank you for your input. We establish the NNGP correspondence for DEQs, shedding light on why they can compete with advanced models. This is due to their tendency to exhibit Gaussian behavior, when the width is large.
One implication is its potential application to Ga... | Summary: This paper examines the infinite width behaviour of DEQs, a kind of neural network architecture that can be viewed as an infinite-depth RNN. They show that contrary to regular MLPs, the limit of infinite width and infinite depth in DEQs commutes. They back their theory up with some numerical experiments.
Stre... | Rebuttal 1:
Rebuttal: **Response to Weakness 1**: We appreciate the reviewer's insightful comments. It is widely recognized in the most recent literature [12,19,10] that as the width of neural networks approaches infinity, they exhibit Gaussian behavior, a phenomenon known as the Neural Network and Gaussian Process (NN... | Summary: This paper takes the theoretical tools for infinite width limits of fully connected deep neural networks (e.g. NNGP limits, tensor programs etc), and applies it to a deep equilibrium-type neural network. This model is seen as the depth \to infinity limit of a feedforward-type network. Convergence to a Gaussia... | Rebuttal 1:
Rebuttal: **Response to Weakness 1**: Thank you for your valuable review. We appreciate your interest in the efficiency of DEQs compared to other advanced neural networks, and your consideration of the nature of our main result.
Implicit networks [1], such as DEQs, have recently gained significant attentio... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We sincerely appreciate your thorough review of our paper and your valuable comments and suggestions. We have carefully considered each of your points and have addressed them in our separate responses to your questions.
In light of your input, we have taken significant steps to e... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Meta-Learning with Neural Bandit Scheduler | Accept (poster) | Summary: This paper proposes scheduling tasks for meta-learning using context bandits.
Strengths: 1. The idea of applying context bandits to select tasks for meta-learning is novel
2. The experimental results are strong, especially when the task distribution is skewed
Weaknesses: 1. The method is computationally inef... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable questions and comments. Here, we will try our best to address the questions and concerns in the form of Q\&A. Since we are unable to submit the improved manuscript based on reviewers' comments, we will describe these modifications on the current man... | Summary: This paper considers the problem of task scheduling in meta-learning. Under the gradient-based meta-learning framework, the authors propose BASS, which uses contextual bandits parameterized by neural networks. The tasks in each batch (arms) are selected in an optimistic manner, with the reward estimated using ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable questions and comments. Here, we will try our best to address the questions and concerns in the form of Q\&A. Since we are unable to submit the improved manuscript based on reviewers' comments, we will describe these modifications on the current man... | Summary: The paper presents a task scheduling approach in meta-learning under a contextual bandit framework. The proposed methodology, named BASS, treats each meta-learning task as an arm, prioritizing the selection of these arms according to exploration and exploitation scores. These scores are computed by a trainable... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable questions and comments. Here, we will try our best to address the questions and concerns in the form of Q\&A. Since we are unable to submit the improved manuscript based on reviewers' comments, we will describe these modifications on the current man... | Summary: This paper proposed a task scheduling framework BASS based on the status of meta-model based on contextual bandits setting. BASS addressed the performance bottleneck of meta-models by balancing exploitation and exploration and handled the data scarcity in the early stages of meta-training iterations with plann... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable questions and comments. Here, we will try our best to address the questions and concerns in the form of Q\&A. Since we are unable to submit the improved manuscript based on reviewers' comments, we will describe these modifications on the current man... | Rebuttal 1:
Rebuttal:
We would like to take the chance to thank all the reviewers for your constructive feedback and detailed comments for our work. Your suggestions can definitely make this paper a more solid one.
Here, to better resolve the questions from reviewers, we have included supplementary experiments to prov... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes to adaptively sample the meta-training tasks by optimizing the task scheduling strategy based on the status of the meta-model. The proposed method treats task scheduling as a contextual multi-arm bandit problem with a reward function balancing the exploitation and exploration. The authors p... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable questions and comments. Here, we will try our
best to address the questions and concerns in the form of Q&A. Since we are unable to submit the
improved manuscript based on reviewers’ comments, we will describe these modifications on the current
manu... | null | null | null | null | null | null |
Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure | Accept (poster) | Summary: This paper studies the stochastic monotone variational inequality problem. They propose the Accelerated Gradient - Extragradient (AG-EG) algorithm which is shown to have the optimal convergence rates for the strongly monotone VI problem.
Strengths: This papers studies the important problem of Stochastic VIs, ... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We address your comments and questions as follows.
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**Q1**: Showing that the iterates are bounded is essentially the only requirement to convert the results of papers like Chen et al. [2017] to the unconstrained setting. Can the authors describe the m... | Summary: The author(s) studied the extragradient algorithm for the separable strongly monotone VI problems. It was shown that the new analysis can achieve optimal error bounds in various settings.
Strengths: - The new analysis gives optimal error bounds in various settings, which is a decent contribution to the field.... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback. We address your comments and questions as follows.
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**Q1**: I suggest the author(s) to include a table of error bounds and assumptions to compare with existing works.
**A1**: Thank you for your suggestion. We have added a revised table comparing error bo... | Summary: The paper studies variational inequalities with separable structures (sum of the gradient of a strongly convex function and a monotone operator). This class subsumes bilinear coupling SC-SC minimax optimization and bilinear games. The authors propose an extragradient algorithm with acceleration by shifting the... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback. We address your comments and questions as follows.
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**Q1**: For bilinear problems, the algorithm does need scheduled restart to achieve optimality. The claim in the conclusion that all three lower bounds are matched in one algorithm is, therefore not real... | Summary: The work presents a stochastic accelerated gradient-extra gradient (AG-EG) algorithm for strongly monotone variational inequalities (VI) that is designed by combining extra gradient and Nesterov acceleration. The major of the work is extending the formulation to the case when the constraint set is convex but c... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback. We address your comments and questions as follows.
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**Q1**: The notation in algorithm 1 is confusing for the update equations
**A1**: In Algorithm 1, we introduce notation to distinguish different points in the AGEG method. Specifically, $z_{t-\frac{1}{2... | Rebuttal 1:
Rebuttal: Dear reviewers,
Thank you for your time and constructive comments. Based on the feedback of Reviewer iB8j and Reviewer oFAu, we have added a table comparing the complexity results and assumptions with the prior works. In addition, to address the feedback of Reviewer iB8j on empirical studies, we ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Revisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot Classification | Accept (poster) | Summary: This paper tackles the problem that classification tasks in Bayesian meta-learning meet mathematical problems with the softmax function. The previously proposed logistic-softmax function as an alternative can be optimized, but tends to exhibit inherent lack of confidence in prediction. To solve this problem, t... | Rebuttal 1:
Rebuttal: Thank you for the valuable advice. As an overview, our paper mainly focuses on two themes: 1. theoretical analysis of
the logistic-softmax function. 2. application of the logistic-softmax function to Bayesian meta-learning. We admit the second section mainly follows the research line of the Bayes... | Summary: The paper revisits the design of logistic-softmax function in the context of classification in Bayesian machine learning. In particular, the paper shows that a logistic-softmax function with a temporature could be more expressive than the conventional softmax function. However, due to the intrinsic nature of t... | Rebuttal 1:
Rebuttal: Thank you for your advice on this paper. We answer your questions below:
> Q1. Currently, the paper targets to meta-learning, but to me, the paper is about the logistic-softmax function and meta-learning is just a mere application to demonstrate. However, the paper might be strengthen more if it ... | Summary: This paper proposes to modify the logistic Softmax likelihood by including a temperature coefficient in GP-based meta learning for few-shot classification. This is motivated by the observation that prediction made by logistic Softmax likelihood do not have confidence.
Theoretically, they demonstrate that using... | Rebuttal 1:
Rebuttal: Thank you so much for your advice to this paper. We answer your questions below:
> Q1. I understand that the typical benchmark for few-shot classification uses 5 classes. Have you seen improvements with your method when there are more than 5 classes? Like 10 or 20? (Answering this question won’t ... | Summary: In this work, the logistic-softmax likelihood is redesigned, allowing control of the a priori confidence level through a temperature parameter. The modified logistic-softmax is shown to encompass softmax as a special case and induces a larger family of data distributions. By integrating this modified likelihoo... | Rebuttal 1:
Rebuttal: Thank you so much for reviewing our paper. We answer your questions below.
> Q1. The proposed modified logistic-softmax function with temperature can be fundamental for different machine learning problems, it is not clear why it is specifically used for Bayesian meta-learning for few-shot classif... | Rebuttal 1:
Rebuttal: We extend our sincere appreciation to all reviewers for their time, effort, and insightful feedback. We are encouraged by their recognition of the significance of our work in introducing an effective modification to control the confidence of logistic-softmax, uncovering novel theoretical propertie... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Path Regularization: A Convexity and Sparsity Inducing Regularization for Parallel ReLU Networks | Accept (poster) | Summary:
The authors present a theoretical analysis of optimization for path-regularized parallel ReLU networks.
They demonstrate how to represent this non-convex problem as a regularized convex problem.
While convex problems in general admit polynomial time solutions, the size of this convex
problem is exponential in... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the feedback and comments. We hope that you would consider increasing your score if your concerns are adequately addressed. Please see our responses below.
$\textbf{Responses to the comments on complexity and large problems sizes:}$
There are multiple ways... | Summary: This paper studies the problem of training parallel networks with three-layer subnetworks under path regularization. It is shown that the non-convex optimization problem of minimizing the regularized loss can be cast as a convex optimization problem, which can be solved efficiently.
Strengths: 1. The study of... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the feedback and comments. We hope that you would consider increasing your score if your concerns are adequately addressed. Please see our responses below.
$\textbf{Responses to the comments on equation (4) and Figure 2:} $
As noted in the paper, we perfor... | Summary: The paper shows how parallel ReLU networks can be trained to approximate optimality in polynomial time by convex programming when employing a path-regularization (rather than weight decay or other regularization of the network weight parameters) and a low-rank approximation of the data matrix. Some numerical e... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the feedback and comments. We will address all of your concerns regarding the paper structure and typos in the revised version. Please see our responses below.
$\textbf{Our contributions over prior works:}$
We first would like to clarify our contributions... | Summary: This study considers training the parallelized multi-layer neural networks with path-wise norm regularization. Through the duality argument, the authors reduce the regularized empirical minimization problem, which is highly non-convex, to the convex programming problem. They show that when the data matrix has ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the feedback and comments. We hope that you would consider increasing your score if your concerns are adequately addressed. Please see our responses below.
$\textbf{Responses to the comments on complexity:}$
As stated by the reviewer, our computational c... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work studies the training of 3-layer parallel ReLU networks with path regularization. Specifically, the authors show that minimizing the non-convex learning objective with an \ell_2 pathwise weight decay is equivalent to a convex program that can be solved optimally in polynomial-time complexity (Proposit... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the feedback and comments. We hope that you would consider increasing your score if your concerns are adequately addressed. Please see our responses below.
$\textbf{Our contributions over [17,29]:}$
* One of the main crucial differences is the training pr... | null | null | null | null | null | null |
A Unified Framework for Rank-based Loss Minimization | Accept (poster) | Summary: This paper presents rank-based loss as a popular replacement for empirical loss. The work develops how
optimization of rank-based loss can be done by a proximal alternating direction method of multipliers. The authors also demonstrate the algorithm's features in terms of convergence under certain conditions. E... | Rebuttal 1:
Rebuttal: We thank reviewer kXbu for the constructive comments!
**The applicability in machine learning**
It is noteworthy that rank-based loss is a highly valuable and extensively researched concept in the field of machine learning. Commonly encountered variations of rank-based losses include spectral ri... | Summary: This submission focuses on efficient minimization of a group of loss functions called rank-based losses. It proposes to consider several related losses from the perspective of a genral unified framework with a regularizer. Then, focusing on the case of monotone increasing loss functions and wealy convex regula... | Rebuttal 1:
Rebuttal: We thank reviewer G6DJ for the positive feedback and comments!
**The potential practical limitations of the proposed method**
To utilize our algorithm effectively, it is essential for the individual loss to exhibit monotonicity, as this allows us to employ the PAV algorithm to solve the $z$-subp... | Summary: This paper presents a new ADMM algorithm that focuses on three specific cases of rank-based losses. The algorithm's convergence is theoretically analyzed in the paper. Additionally, the authors conducted comprehensive experiments to compare the new algorithm with traditional approaches. The results indicate th... | Rebuttal 1:
Rebuttal: We thank reviewer GkCz for the feedback and comments!
**The confusion in notations for the loss function**
We apologize for the confusion. We should only use the definitions of $l$ and $\boldsymbol{l}$ as follows:
1. $l$: $\\mathbb{R}\\to\\mathbb{R}$: a function that represents the loss for a... | Summary: This paper proposes a unified framework for rank-based loss minimization based on the ADMM algorithm. The paper proposes to apply a pool adjacent violators (PAV) algorithm to solve one of the subproblems of ADMM. Numerical experiments show that the proposed algorithm outperforms the existing ones.
Strengths: ... | Rebuttal 1:
Rebuttal: We thank the reviewer Gyag for the feedback and comments!
Eq. (1) indeed assumes a linear model. One reason for making this assumption is that currently, many studies or experimental parts related to rank-based loss predominantly concentrate on linear models [1-3]. So far, there has been limited ... | Rebuttal 1:
Rebuttal: We truly thank all reviewers’ insightful and constructive suggestions, which helped to significantly improve our paper!
**The more experiments about Figure 1**
To explain the phenomenon observed in Figure 1(h), we conducted experiments with increased sample sizes. The corresponding results are i... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Compositional Policy Learning in Stochastic Control Systems with Formal Guarantees | Accept (poster) | Summary: This paper proposes a verifiable RL framework that learns a composition of NN policies for stochastic control systems, along with a formal supermartingale certificate for the safety probability of satisfying the reach-avoid specification. It decomposes the global reach-avoid task into a DAG with edges denoting... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. In what follows, we answer the two questions raised by the reviewer.
**Relation between RASMs and stochastic barrier functions.** On the high level, the main difference between RASMs and stochastic barrier functions is that RASMs consider reach-avo... | Summary: This paper introduces CLAPS (Compositional Learning for Probabilistic Specifications), a new method for learning a composition of neural network policies in stochastic environments, together with a formal certificate which guarantees that a reach-avoid specification over the policy's behavior is satisfied with... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. In what follows, we answer the question raised by the reviewer.
Indeed, on p. 4 we mean “if the probability of any trajectory”. This sentence refers to trajectories sampled from the probability space of all trajectories starting in initial state $x... | Summary: This paper introduces CLAPS, a compositional method designed for learning and verifying neural network policies in stochastic control systems. By considering control tasks with specifications expressed in the SPECTRL language, CLAPS decomposes the task into an abstract graph of reach-avoid tasks. It utilizes r... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. In what follows, we answer the two questions raised by the reviewer.
**Comparison of RASMs and exponential barrier functions.** Stochastic barrier functions were introduced for proving probabilistic safety in stochastic dynamical systems, i.e. with... | Summary: The paper presents CLAPS, a compositional RL algorithm that also ensures guarantees of correctness when learning from temporal specifications. The core contribution of this work revolves around guarantees.
Prior works in compositional RL with guarantees apply to deterministic environments and/or ones with li... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. In what follows, we answer the questions raised by the reviewer.
We clarify that Theorem 5 in the Appendix only states that a trajectory satisfies a SpectRL specification if and only if it satisfies abstract reachability in the abstract graph assoc... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Text Promptable Surgical Instrument Segmentation with Vision-Language Models | Accept (poster) | Summary: This manuscript presents a clip-assisted semantic image segmentation method for surgical instruments. In terms of methodology, the proposed work can be viewed as an adaption of CLIPSeg to a domain-specific problem. Compared with CLIPSeg, a mixture of prompt strategy is used for augmenting text prompt informati... | Rebuttal 1:
Rebuttal: ### [fFdZ-W1, Q2] Take-home information for readers.
To our knowledge, we're the first to introduce text promptable method in surgical instrument segmentation.
Through problem-driven thinking, we proposed Mixture of Prompts (MoP) and Hard Instrument Area Reinforcement (HIAR) modules tailored for ... | Summary: This paper proposes a novel text promptable surgical instrument segmentation approach to overcome challenges associated with the diversity and differentiation of surgical instruments by using the large CLIP model and a text promptable mask decoder. The experiments show the effectiveness of the proposed method ... | Rebuttal 1:
Rebuttal: ### [9oei-W1, W2] Few novelties and modules already explored in traditional segmentation.
We respectfully disagree.
First, to our knowledge, we're the first to introduce text promptable method in surgical instrument segmentation.
Second, through problem-driven thinking, we proposed the Mixture of... | Summary: This paper presents a text prompt-based surgical instrument segmentation method, which is more scalable to handle the diverse targets in endoscopy videos.
Strengths: - originality: this is the first work to use text prompt for surgical instrument segmentation
- quality: the performance of the proposed methods... | Rebuttal 1:
Rebuttal: ### [Rt9K-W1] Why not validate the method on the latest EndoVis datasets and compare with the challenge winners?
Please refer to our global response for the justification of choosing Endovis2017 and 2018 datasets.
Our method actually compares with winning solutions, like TernausNet-11 [36] in T... | Summary: The paper introduces a novel approach for surgical instrument segmentation in minimally invasive surgeries. By leveraging text prompts and vision-language models, the proposed method achieves improved segmentation performance. The approach shows promise for practical use in robotic-assisted surgery.
Strength... | Rebuttal 1:
Rebuttal: ### [NUuZ-W1] Why chose $448 \times 448$ as the input image size?
The previous methods use different input image sizes ranging from $224 \times 224$ to original image size (i.e.,$1024 \times 1280$).
Given our adoption of a ViT-based image encoder, the input size must conform to ViT's patching req... | Rebuttal 1:
Rebuttal: We thank the constructive comments from all reviewers.
As reviewers say, our key idea of using textural prompts to perform surgical instrument segmentation is novel (wnjR, NUuZ) and interesting (NUuZ). Our paper is overall well organized (Rt9K) and easy to understand (9oei).
Below we first answer ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces a novel idea of utilizing text prompts and vision-language models to make surgical instrument segmentation more flexible and robust to diversity. The proposed method and custom modules achieve strong results on two endoscopic datasets.
Strengths: 1. The paper tackles an important problem... | Rebuttal 1:
Rebuttal: ### [wnjR-W1, Q1] Explain how text prompting helps with adaptation to new instruments and resolving inter-class confusions, with examples.
For the adaptation to new instruments,
unlike previous instrument segmentation methods [36, 18, 11, 47, 5, 3] that require model retraining with new data, our... | null | null | null | null | null | null |
Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes | Accept (poster) | Summary: This work proposes to fuse Deep Kernel Learning (DKL) into the Deep Mixture Point Processes (DMPP), resulting in Deep Kernel Mixture Point Processes (DKMPP) which can handle complex relationships between events and covariates in a more flexible and expressive manner. The authors also leverage the denoising sco... | Rebuttal 1:
Rebuttal: > Q1: "On lines 158-159, the authors argue that Euclidean distance in the kernel may not be a suitable measure of similarity, especially for high-dimensional inputs. I think this statement needs more clarification. If I understand correctly, the input $\mathbf{s}$ to the kernel function $k_\phi$ i... | Summary: This paper argues that there are two common approaches for modeling intensity functions: traditional and covariate based methods, and this paper focuses on the latter one. In detail, the intensity function is designed in a kernel convolution form: $\lambda(s|\mathcal{D})=\int f_w(\mathbf{u},\mathbf{Z(u)})k_{\p... | Rebuttal 1:
Rebuttal: > Q1: "The motivation for using a score-based approach is not clear. In fact, the score-based approach is a special generation model. In that case, why not use another generation model......"
"I'm confused about the reason why the authors use score-based methods."
"Actually, there are some eff... | Summary: The paper proposes an enhanced version of Deep Mixture Point Processes with a flexible neural network-based kernel. The intractable training process of the point process with deep kernel is handled by a score-matching technique with the denoising method.
Strengths: 1. The proposed deep kernel goes beyond the ... | Rebuttal 1:
Rebuttal: > Q1: "Related works are not comprehensive enough. There is plenty of work on point processes equipped with deep kernel, such as Okawa [1] and Zhu. [2]. These works can be reviewed to make the paper more comprehensive."
A: Thank you for your suggestion. Because we cannot make changes to the manu... | null | null | Rebuttal 1:
Rebuttal: We would like to express our gratitude to all the reviewers for their valuable efforts in providing insightful comments and constructive feedback. We are pleased that the reviewers have recognized the significance of our paper in solving an interesting covariate point process estimation problem, p... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Online Map Vectorization for Autonomous Driving: A Rasterization Perspective | Accept (poster) | Summary: This paper tackles the emerging task of vision-based online HD mapping. It has 2 main contributions: (1) AP_{raster}, a new metric that is shown to be better suited to evaluate methods in this field, and (2) MapVR, a plug-in rasterization and loss module to any existing vector-based online mapping system. Seve... | Rebuttal 1:
Rebuttal:
We sincerely thank you for appreciating our work as well as for your thoughtful and constructive comments, which we believe will significantly enhance the presentation of our manuscript.
### Sensitivity of $\text{AP}_\text{raster}$ to Hyper-Parameters
We agree with the reviewer ... | Summary: This work targets online map creation for autonomous driving.
The authors claim vector-based approaches exhibit artifacts due to lack of geometric supervision in current loss functions.
The main idea is to add a differentiable rasterization layer to any model that predicts vectors and add an additional seg... | Rebuttal 1:
Rebuttal:
Thank you for taking the time to review our manuscript. Your comments have provided us with valuable insights to improve our work. We have carefully considered each of your points, and we hope that our responses below could address your concerns.
### Significance of $\text{AP}_\te... | Summary: Online HD mapping is essential for autonomous driving because maps provide detailed and precise environmental information for perception and planning. Existing mapping literatures have limitations both in methods and metrics. To address them, the authors a new method called MapVR, integrating the philosophy of... | Rebuttal 1:
Rebuttal:
We are immensely grateful for the comprehensive and positive acknowledgment received. And we deeply appreciate your insightful comments and suggestions. Below we would like to respond to your queries and address your concerns point-by-point.
### Discussions on Failure Cases... | Summary: --
Strengths: --
Weaknesses: --
Technical Quality: 3 good
Clarity: 3 good
Questions for Authors: --
Confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with... | null | Rebuttal 1:
Rebuttal:
We are incredibly grateful for all the helpful comments received.
Here, we provide some additional contents that cannot fit into the nine-page manuscript. These additional contents will be included in the camera-ready version, in which one additional page of content is allowed.
&... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors explore and analyze the existing HD map framework and its evaluation metrics, and propose to adopt rasterization on top of the existing vectorization-based HD map for more precision. Also, they propose a rasterization-based evaluation metric rather than the existing chamfer-distance one. The experi... | Rebuttal 1:
Rebuttal:
Thank you for acknowledging the significance, presentation, and thorough evaluation of our work. We also appreciate your kind suggestion on computation/memory footprint comparison.
We hope the information provided below will help address your concerns. And we will include them in our fin... | null | null | null | null | null | null |
Diversifying Spatial-Temporal Perception for Video Domain Generalization | Accept (poster) | Summary: In this work, the authors propose a Spatial-Temporal Diversification Network (STDN) for video domain generalization. First, they intrdouce a spatial grouping method to summarize the spatial clues in each frame. Then, they further build up spatial-temporal relations in a multi-scale manner. Finally, they show ... | Rebuttal 1:
Rebuttal: #### **Q1. Why the proposed diversity-based modeling is important for video domain generalization (VDG)?**
RE: Our model is designed for VDG, although it is applicable to traditional video classification. The core idea of our proposed designs, including our proposed Spatial Grouping Module and Sp... | Summary: In this manuscript, the authors proposed a novel Spatial-Temporal Diversification Network (STDN) for video domain generalization. More precisely, the proposed method introduces the Spatial Grouping Module and Spatial=Temporal Relation Module to discover various groups of spatial cues within individual frames ... | Rebuttal 1:
Rebuttal: #### **Q1. Discussion about TRN [6]**
RE: Thank you, we will add discussion about this.
Although our proposed Spatial-Temporal Relation Module (STRM) is founded on TRN, they are designed for different tasks with different motivations. Our STRM is designed for video domain generalization, while ... | Summary: The paper addresses the problem of video domain generalization for classification task. The core idea of the paper is to enhance the diversity in class-correlated cues both in spatial and temporal dimensions with the assumption that in this diverse pool, it is more likely to capture the domain-generalizable fe... | Rebuttal 1:
Rebuttal: #### **Q1. About the t-SNE visualization**
RE: In Figure R1 (please see the PDF in the global response), we show an improved version of Figure 6, which includes a quantitative analysis of cluster separation. According to Figure R1, we qualitatively (t-SNE) and quantitatively (Davies-Bouldin Index... | Summary: The paper proposes Spatial-Temporal Diversification Network (STDN) for Video Domain Generalization (VDN). VDN is a new problem which is similar to video domain adaptation, but more challenging due to no unlabeled videos from target domain is provided. STDN is mainly designed into two modules: Spatial Grouping ... | Rebuttal 1:
Rebuttal: #### **Q1. Comparison with AVRNA [14]**
RE: We conduct experiments to compare our STDN with AVRNA on UCF->HMDB. Since AVRNA involves the audio modality but our work focuses on the RGB modality, we implement two variants of AVRNA for comparison as follows:
(1) Hard Norm Alignment loss (HNA): appl... | Rebuttal 1:
Rebuttal: Thanks to all reviewers for your constructive comments. We are encouraged that the reviewers found that, our work studies an important and practical problem (Reviewer m6Ue, SFFk) with a clear motivation (Reviewer JaYX), proposes a straightforward and interesting idea (Reviewer m6Ue, YkHU), present... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper presents STDN, a spatio-temporal diversification network designed for domain generalization. It introduces a spatial grouping module that effectively groups features from individual frames across different spatial frames. Additionally, a spatio-temporal relation module is proposed to model spatial-te... | Rebuttal 1:
Rebuttal: #### **Overall clarification of our idea**
We would like to clarify that, our key idea is to discover diverse class-correlated cues in videos, aiming to *alleviate the overfitting of domain-specific cues* in the source domain, NOT focus on learning domain-specific cues, as stated in our introduct... | null | null | null | null | null | null |
Deep Fractional Fourier Transform | Accept (spotlight) | Summary: This paper introduces Fractional Fourier Transform to provide comprehensive unified spatial-frequency perspectives for deep learning, and further introduces a basic operator, Multi-order Fractional Fourier Convolution. Besides, this paper experimentally evaluates the effectiveness of MFRFC on various computer ... | Rebuttal 1:
Rebuttal: **1: The research history of FRFT in many research areas.**
Thanks for the suggestion, we will make a comprehensive discussion in the research history of FRFT in many other research areas in the related work. The reference work you mentioned is quite simple and is not deep learning based, so we... | Summary: + The paper provides an implementation framework using deep learning for fractional Fourier transform.
Strengths: + Good deep algo for FRFT.
Weaknesses: - Baselines for different applications are a bit dated.
- The paper could have focused on just one application and dealt deeper.
- The writing is not that... | Rebuttal 1:
Rebuttal: **1: Baseline methods for different tasks.**
(1) Our method is not SOTA-oriented. Instead, the key of this paper is that we unlock the FRFT in deep learning paradigm, solving the biggest challenge for the popularization of FRFT: vague characteristics and missing fast implementation. Fourier tra... | Summary: The paper discusses Fractional Fourier Transform (FRFT) in the context of deep learning based computer vision methods. FRFT is a unified continuous spatial-frequency transform which reflects spatial and frequency representations of images. Based on FRFT, the paper proposes a new convolutional operator (MFRFC) ... | Rebuttal 1:
Rebuttal: **1: Properties of FRFT.**
It is true that the properties of FRFT is broad. Here, we introduce the main properties inherent in FRFT. For more properties associated with specific tasks, we believe that with our pioneer work, it will be easier to explore them in the near future. Similar case appl... | Summary: This paper proposed a fractional Fourier transform-based module, which can simultaneously exploit the information from spatial and frequency perspectives. With a fast implementation of FRFT, multi-order MFRFC module can be easily incoporated to existing convolutional networks for different tasks.
Strengths: ... | Rebuttal 1:
Rebuttal: **1: Baseline methods for different tasks.**
(1) Our method is not SOTA-oriented. Instead, the key of this paper is that we unlock the FRFT in deep learning paradigm, solving the biggest challenge for the popularization of FRFT: vague characteristics and missing fast implementation. Fourier tra... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: 1. This technique delves into novel fundamental operators for deep learning, the Fractional Fourier Transform (FRFT), exploring a new perspective of signal processing between two orthogonal domains (spatial and frequency domains).
2. This technique have implemented a fast and differentiable Multi-order Fract... | Rebuttal 1:
Rebuttal: **1: 1x1 convolutions in fractional domains.**
FRFT is a generalized and extended version of Fourier transform. For Fourier transform, spectral theory demonstrates the existence of operator duality between convolution in the spatial domain and element-wise multiplication in the spectral domain,... | null | null | null | null | null | null |
Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds | Accept (poster) | Summary: This manuscript introduces a new federated learning algorithm, EPISODE++, to accelerate the convergence speed of federated learning and provides theoretical proofs for the upper bound of convergence speed.
Strengths: The method proposed in this manuscript improves the EPISODE algorithm by addressing the issu... | Rebuttal 1:
Rebuttal: Thank you for your effort in providing valuable feedback. Below, we have individually addressed the questions in your review.
**Q1: “In Figure 1 (a) and (b), the left two figures show that NaiveParallelClip has lower training loss than other methods when the number of clients is 8, but its testin... | Summary: The authors propose a federated learning algorithm that can work under (L0,L1)-smooth function. Different from the previous work, the authors consider the partial-participant setting, modifying the previous algorithm, showing the convergence of the new algorithm, and giving a lower bound of the communication i... | Rebuttal 1:
Rebuttal: Thank you for your time and for providing helpful comments. Below we have addressed the concern you expressed in your review.
**Q1: “The key motivation is not clear to me. For me, it is hard to justify that with a uniform sampling strategy on clients, why the bias will occur?”**
To discuss the b... | Summary: The paper presents a novel algorithm for non-convex federated learning that addresses the challenges of relaxed smoothness, client heterogeneity, and client subsampling. The authors begin by discussing the limitations of existing algorithms such as SCAFFOLD and EPISODE in handling these challenges simultaneous... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and give valuable feedback. See the list below, where we have individually responded to the questions and comments in your review.
**Q1: “the scope of the study appears to be limited to non-convex federated learning.”**
Our algorithm is indeed de... | Summary: The paper investigates Federated Learning (FL) under client subsampling and data heterogeneity, focusing on functions with potentially unbounded smoothness, and introduces the proposed algorithm to address the problem, EPISODE++. EPISODE++ has demonstrated benefits including linear speedup with client numbers,... | Rebuttal 1:
Rebuttal: Reviewer xXDR (7): Thank you for taking the time to review our paper. Below we have addressed the concerns you raised during your review.
**Q1: “The experiments with N=8 may not fully reflect the actual performance with large-scale FL”**
We agree that evaluating the proposed algorithm on large-s... | Rebuttal 1:
Rebuttal: We would like to thank all of the reviewers for taking time to review and critique our work. We have provided an individual response to each reviewer, and here we provide a general summary of our additional results included in the 1 page rebuttal PDF.
**Large scale experiments**
In the 1-page re... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Pareto Frontiers in Deep Feature Learning: Data, Compute, Width, and Luck | Accept (spotlight) | Summary: This paper considers learning parity functions with neural networks and particularly studies the tradeoff between size of the network and the sample size. The paper also explore a connection between the network size the lottery ticket hypothesis. The neural network studied in this paper has a sparse, specific,... | Rebuttal 1:
Rebuttal: Thanks for the feedback and suggestions!
**(W1) Gap between SQ and SGD**: We agree with the reviewer that the SQ framework as stated only gives a lower bound dependent on total number of queries and the precision of the queries. This does not directly imply a lower bound on the sample complexity,... | Summary: In this paper, the authors study the tradeoff between various resources in feature learning---data, compute, width. The authors focus on the fundamental problem of learning parity functions using a two-layer MLP. The degree $k$ of a parity function of $n$ variables controls the hardness of the problem: general... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback.
**(W1) “Deep feature learning” terminology**: Our intent for the shorthand “deep feature learning” in the title was to refer to gradient-based feature learning in neural networks, as opposed to the NTK regime. We understand the possible unintende... | Summary: Disclaimer: My expertise in this domain is limited and understanding is highly superficial.
The manuscript presents a detailed take on addressing the tradeoffs on 4 axes, namely model size, dataset size, training epochs, and stochasticity. It offers a well-rounded analysis of the area and provides empirical a... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful comments and questions.
**(W1) Paper organization**: Thanks for the feedback and suggestions. We will improve the presentation, and add intuitive overviews of the technical proofs.
**(W2, Q2) Relation to other work on NN parity learning**: We have not seen following con... | Summary: In an attempt to explore the mechanisms behind generalization and training of neural networks and different elements that play a role in it, this paper investigates the impact of four resources of training MLPs on the famous and well-studied (n, k)-parity learning problem. The authors conduct a massive grid o... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review. Here we address the main concerns raised by the reviewer.
**(W1) “Deep feature learning” terminology**: (Copied from response to R3) Our intent for the shorthand “deep feature learning” in the title was to refer to gradient-based feature learning... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object Detection | Accept (poster) | Summary: The paper addresses the problem of semi-supervised 3D object detection (3DOD) where the aim is to train a 3DOD model using only few labelled and a lot of unlabelled data. On top of previous approaches, the authors propose to use the diffusion mechanism to enhance the pseudo labels generated by a teacher model ... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback and we address each question below.
**Q1: Diffusion applied during training or inference.**
Our diffusion mechanism is applied during both the training and inference stages. However, like most diffusion models, our Diffusion-SS3D is applied to denoise random... | Summary: This paper introduces a novel approach to enhance the accuracy of pseudo-labels and inference results in semi-supervised 3D object detection through the utilization of diffusion models. The authors propose the integration of diffusion models from two perspectives: 3D object sizes and class label distributions.... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback and we address each question below.
**Q1: Comparisons on the 20% labeled data or larger labeled ratios.**
For ScanNet, the results of using 20% labeled data are reported in Table 1 of the main paper. For SUN RGB-D, we show in the table below that our Diffusi... | Summary: In this paper, the author argues that previous 3D semi-supervised detection methods relied solely on teacher models, which cannot generate sufficiently reliable pseudo-labels. Therefore, the author proposes a method called Diffusion-SS3D. This method enables diffusion learning to remove noise from corrupted 3D... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback and we address each question below.
**Q1: Why are the pseudo-labels generated by diffusion are more reliable?**
In Figure 1 of the main paper, we illustrate the fundamental difference between the conventional framework and our diffusion model in pseudo-label... | Summary: This paper proposes a semi-supervied 3D object detection framework, named Diffusion-SS3D. Diffusion-SS3D introduces diffusion process to improve the quality of pseudo-labels. The authors perform experiments on ScanNet and SUN RGB-D benchmark to verify the effectiveness.
Strengths: + The motivation of this pa... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback and we address each question below.
**Q1: Pseudo-label quality.**
To validate whether the quality of pseudo-labels is improved, we evaluate the metrics on unlabeled training data during model training, via the teacher model that generates pseudo-labels. In t... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback.
In addition to addressing individual questions in each rebuttal, we include more example results of generated pseudo-labels in the pdf file, in comparisons with the 3DIoUMatch baseline that does not use the diffusion model like our method.
If there are any ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes a novel algorithm utilizing the diffusion model in 3D object detection for generating pseudo-labels for semi-supervised learning. Technically, it adopts the previous method of teacher-student architecture, but extends it by introdusing diffusion to generate pseudo-labels and denoising techni... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback and we address each question below.
**Q1: Farthest point sampling.**
We first note that we follow the common implementation in VoteNet and 3DIoUMatch to apply farthest point sampling (FPS) in our framework. To study the impact of FPS, we further conduct an e... | null | null | null | null | null | null |
GraphAdapter: Tuning Vision-Language Models With Dual Knowledge Graph | Accept (poster) | Summary: This paper introduces GraphAdapter, a novel adapter-style tuning strategy for vision-language models. GraphAdapter can leverage task-specific structure knowledge by explicitly modeling the dual knowledge graph. The authors validate the proposed method on 11 popular benchmarks on few-shot classification setting... | Rebuttal 1:
Rebuttal: **Q1:** The authors claimed the previous methods overlook the explicit exploitation of the structure knowledge, but the experimental results showed that the combination of text and visual adapters achieved limited gain (compared to text-only adapter). To some extent, it makes the motivation less c... | Summary: This paper proposes a new prompt tuning strategy named GraphAdapter to fuse textual and visual structure knowledge for downstream tasks. It first constructs the dual knowledge graph by taking the textual / visual features of a specific class as nodes and the cosine similarities between these features as edges.... | Rebuttal 1:
Rebuttal: We sincerely thank you for your great efforts and insightful questions.
**Q1:** About the concern on efficiency and scalability.
**A1:** Thanks for your valuable comments. We will answer the above questions from three perspectives.
(i) As the definition of efficient transfer learning in VLMs,... | Summary: The paper proposes to utilize graph learning for efficient transfer learning of large vision-language models. The graph learning consists of scene graphs of two different modalities, first one uses the textual features of prompts and the second one uses the visual features of training samples from the downstre... | Rebuttal 1:
Rebuttal: Thanks for your recognition of our work. We have given careful consideration in response to your insightful question.
**Q1:** Is there a way to utilize the semantics of visual relationships? The structured knowledge graph in its current form appears to be more of a co-occurrence statistic. e.g. ... | Summary: In this paper, the authors present an adapter-style tuning method, termed as GraphAdapter, that explicitly captures the dual-modality structure knowledge by utilizing a dual knowledge graph, leading to enhanced adapter-style transfer learning. Specifically, the authors identify two key challenges in existing a... | Rebuttal 1:
Rebuttal: We greatly appreciate your positive comment on our work, along with constructive suggestions for the improvement of our work.
**Q1:** The authors leverage GCN to integrate dual-modality structural knowledge. However, I am interested in understanding the performance of more advanced GNN mechanis... | Rebuttal 1:
Rebuttal: **We thank all reviewers and area chairs for their great efforts and insightful comments!** These suggestions and questions are significantly beneficial to our paper. We believe we have addressed all the concerns of reviewers in the rebuttal. **If you have some new questions/concerns, please let u... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
STEVE-1: A Generative Model for Text-to-Behavior in Minecraft | Accept (spotlight) | Summary: This paper proposes a generative pretraining method to learn an instruction-following agent in Minecraft by fine-tuning the VPT agent. It first trains an image-goal-conditioned policy and then leverages the foundation model MineCLIP as the bridge to map the language instruction into the image-goal space. Using... | Rebuttal 1:
Rebuttal: Thanks for the great questions and for engaging deeply with the work. We’re glad that you found the approach interesting and reasonable, and the paper easy to follow. We address your questions and comments below.
> The paper overstates its performance by claiming that "STEVE-1 can follow nearly ... | Summary: This paper proposed an instruction-tuned model for Minecraft, which turns previous RL model like VPT into goal-conditioned model. The experiment shows that the proposed method can follow nearly any short-horizon open-ended text and visual task in Minecraft.
Strengths: 1. This paper proposes a novel technique ... | Rebuttal 1:
Rebuttal: Thank you for your review and comments. We’re glad to hear that you found our work interesting and that you see it as a potential method for designing more general sequential decision-making agents. Please see below for responses to your comments and questions.
> This method is not applicable fo... | Summary: The paper introduces STEVE-1, a sequential decision-making agent designed to follow textual instructions and accomplish goals in the Minecraft environment. The authors utilize two pre-trained models, VPT (Video Pretraining Transformer) and MineCLIP, to facilitate this process. VPT is a transformer model traine... | Rebuttal 1:
Rebuttal: Thanks for your constructive questions and feedback, and for recognizing the versatility, novelty, and significance of our work on STEVE-1. Please see below for responses to your comments and questions:
> Clearer explanation on the distinction between packed hindsight relabeling and hindsight rel... | Summary: Paper presents a method to create instruction following agents in Minecraft. It starts with collecting trajectories using OpenAI’s VPT Minecraft agents, which cannot be controlled through instructions. Then some intermediate visual observations are randomly selected as visual goals. These visual goals are then... | Rebuttal 1:
Rebuttal: Thank you for your review and kind words. We are so glad that you found our work an approachable and valuable contribution to the community.
> Baselines; in the main paper, I couldn’t find any baselines or ablations other than the main ”Steve-1” model and VPT(without instruction-following). I agr... | Rebuttal 1:
Rebuttal: Thanks to all the reviewers for your time and effort during the review process. We appreciate that you found our work well written, insightful, and novel, and we’re glad that there is excitement about our approach to creating an open-ended agent by building on pretrained models.
We have responded... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Boosting Learning for LDPC Codes to Improve the Error-Floor Performance | Accept (poster) | Summary:
1. In the proposed work authors proposed
a. Neural Min-Sum (NMS) decoders
b. NMS decoder with block-wise training schedule that locally trains a block of weights while retraining the preceding block.
c. The different weights are assigned to the unsatisfied check nodes and training i... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and constructive comments.
## Experimental setup
As suggested, we’ve provided a more detailed account of the experimental setup in the revised version, including the channel type, the training Eb/N0 for the post decoder for each code, the number of hidden laye... | Summary: The paper proposes a training framework for the NMS decoder of LDPC codes in order to enhance the error-floor performance of these codes. \
The NMS decoder iterations are split into two cascaded parts, the first so-called based decoder is trained for decoding waterfall parts, and the second (post decoder) is f... | Rebuttal 1:
Rebuttal: Thank you for providing insightful comments.
## Schemes for avoiding zero loss
Thank you for your detailed feedback. Lines 224, 225 are about the Case 3. In Case 3, we use received words sampled at 4.5dB (the error floor region of the base decoder) as training samples for the post decoder without... | Summary: The author studies LDPC code's neural min-sum decoder's error floor problem, by solely change the training method:
(1) boosting learning method, which use first network to do majority of decoding to achieve waterfall region, and second network deal with small error residuals.
(2) relief deep decoding iterati... | Rebuttal 1:
Rebuttal: Thank you for your valuable comment.
## Contribution of this work
We agree with your opinion that the contribution to machine learning techniques can be seen as limited. However, we believe this manuscript aligns well with the scope of NeurIPS under the category of “application of machine learnin... | Summary: The paper proposes training methods to optimize neural min-sum (NMS) decoders that are robust to the error-floor phenomenon of LDPC codes. The proposed methods include: (1) dividing the decoding network into two neural networks and training the post network to be specialized for uncorrected codewords that fail... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and constructive comments.
## Other types of codes
The proposed boosting learning method is more effective in the error floor region than in the waterfall region. Therefore, its impact is more prominent with LDPC codes than with Polar or BCH codes, where the e... | Rebuttal 1:
Rebuttal: ### Dear Reviewers and Area Chair,
We sincerely appreciate the time and effort you've taken to review our paper.
Your insightful feedback has undoubtedly enhanced our work.
We've carefully addressed each of your remarks and inquiries, providing detailed responses for each one.
We hope that ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary:
This paper presents novel training techniques for the NMS decoder of LDPC codes aimed at enhancing performance in the error floor region. The proposed decoding methods comprise two stages: a base decoder and a post decoder designed specifically for uncorrected codewords from the base decoder. In order to tack... | Rebuttal 1:
Rebuttal: Thank you for the valuable comments and the clarification.
## Related works sharing a similar philosophy
Thank you for informing us about the related research. In those studies, they employ a two-stage decoding where the outer and inner decoders perform decoding subsequently or iteratively. Our ... | null | null | null | null | null | null |
Evaluating Neuron Interpretation Methods of NLP Models | Accept (poster) | Summary: This work investigates interpretation methods in NLP that identify which neurons in a neural network are most related to particular concepts (e.g. a specific part of speech). The key idea is to compare how consistent one method's ranking of neurons is (w.r.t. a specific concept) with that of all other consider... | Rebuttal 1:
Rebuttal: **R: …his consensus of course depends on the other considered interpretability methods, and it's not clear that the number of total interpretability methods (6) is sufficient to lead to reliable results…**
A: We acknowledge the concern. We discussed the limitation of our approach in detail in Sec... | Summary: This work evaluates six different interpretation methods from a unified perspective. The authors focus on two challenges in this field: the absence of standard metrics and the lack of benchmarks. They propose two voting-based metrics to evaluate the compatibility among these six methods. Probeless consistently... | Rebuttal 1:
Rebuttal: **R: The selected six methods need to be more novel. More recent advancements in this field may exist rather than relying on L1 & L2 regularization.**
A: We selected the widely used and established neuron interpretation methods for NLP models, drawing from the existing literature. Please see the ... | Summary: This paper provides a comparative analysis of six neuron interpretation methods utilizing diverse concepts across three distinct pre-trained models and introduces an evaluation framework predicated on voting theory. Importantly, it offers the first comprehensive examination of multiple neuron interpretation me... | Rebuttal 1:
Rebuttal: **R: re: missing references**
A: Thank you for pointing out the missing references. We are certainly open to including a broader view of the interpretation field in the paper to enhance its scope. We acknowledge that some relevant references on neuron interpretation, such as Foote et al and Bills... | Summary: This paper proposes a standardized evaluation metric and benchmark for comparing various neuron interpretation methods, based on ideas from majority voting. The benchmark is based on the hypothesis that "neurons that are commonly discovered by different interpretation methods are more informative than others",... | Rebuttal 1:
Rebuttal: **R: ….It would be nice for the authors to discuss limitations of the hypothesis…**
A: We appreciate your comment. Given the variety of methods we considered, we anticipate discovering neurons with diverse properties, including polysemous characteristics. For instance, the ElasticNet regularizat... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful comments, questions and suggestions. We have incorporated their suggestions, and answered specific concerns below each review. At a high level, we have significantly overhauled the related work (*included below for reviewers z745 and QcFR*), and defined ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise | Accept (poster) | Summary: The paper considers the problem of learning $d$-dimensional halfspaces $h(x) = \mathrm{sign}(w \cdot x + t)$ over Gaussian marginals under random classification noise (where with probability $\eta$, a sample is given the incorrect label). While the homogeneous case (where $t = 0$ and the bias $p = \frac12$) by... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort and their positive assessment. Below we reply to the questions in detail.
1. (**Question 1**) We would like to remark that the optimal lower bound should have a linear dependence on $d$, since even for the simpler realizable case the sample complexi... | Summary: This paper studies the problem of learning Gaussian half-spaces with random classification noise. Given labeled data $(x,y)\sim D$ where $x$ follows the standard Gaussian distribution, a halfspace function $f$ such that $y=f(x)$ with probability $1-\eta$ and $y=-f(x)$ with probability $\eta$ and a precision ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort. We address specific questions/comments by the reviewer below.
1. (**Weakness 1**) Gap between upper and lower bound (as a function of $d$): We refer the reviewer to bullet 4 in the response to reviewer m9mP for a detailed discussion. Our work showed... | Summary: The authors provide an algorithm to learn d-dimensional halfspaces under random classification noise upto error \eps. The algorithm has time complexity O(dN/\eps^2), where N is the number of samples; and sample complexity ~ \tilde{O}(d/\eps + d/\eps^2). They also prove a lower bound, in the statistical query m... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort in providing feedback. We will make sure to polish the final version of the paper and simplify the statements where possible, as suggested. | Summary: This paper studies the PAC learning complexity of half spaces (or linear threshold functions), when the labels are flipped randomly with some probability $\eta$.
For realizable hypothesis classes, it is known that $O(d/\epsilon)$ samples are enough to learn a halfspace with $\epsilon$ 0-1 error. Under random ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort in providing feedback. Below, we provide a response to the comments and questions raised by the reviewer.
1. (**Weakness 1**) Regarding the reviewer’s comment: ‘main concern of this paper is the presentation of their technical result…’: We would lik... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and effort in reading and reviewing our paper. In particular, we are encouraged by the positive feedback and that our paper is appreciated by the reviewers in the following aspects: (i) **clear presentation and lucidity** (m9mp, BT2g, QgYm, 2pgi, Imug) (ii... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper gives new positive and negative results for the problem of learning general (nonhomogenous) Gaussian halfspaces under the random classification noise (RCN) model. The motivating question is whether there exists a polynomial time algorithm nearly achieving the (known) minimal sample complexity requir... | Rebuttal 1:
Rebuttal: We thank the reviewer for the effort and the positive assessment.
We address specific comments and questions by the reviewer below.
1. (**Weakness 2**) Regarding the reviewer’s comment ‘...not be as much emphasis on the optimality of the runtime…’: The focus of our work was to develop the first... | Summary: This paper studies the problem of learning non-homogeneous halfspaces in the presence of Random Classification Noise, where the marginal distribution is standard Gaussian in $d$ dimensions.
On the upper bound side, this work provides an efficient algorithm that achieves learning an $\epsilon$-optimal halfspa... | Rebuttal 1:
Rebuttal: We thank the reviewer for the effort and the positive feedback. Below we respond to the reviewer’s questions in detail.
1. (**Weakness 1**) Regarding the reviewer’s comment on the related works, we have added more detailed comparisons with prior works in the related work section and Appendix A i... | null | null | null | null |
Anytime Model Selection in Linear Bandits | Accept (poster) | Summary: This paper introduces AlExp, a novel algorithm to address the problem of online model selection in the context of bandit optimization. The algorithm interacts with the environment by randomly choosing a learner (linear bandit regret minimizer) each round, then the chosen learner tries to propose the optimal ac... | Rebuttal 1:
Rebuttal: Thank you for your review. Our response to your questions and concerns follows.
**"The algorithm has a strong dependency on quantity $C$, can you provide intuition on it?"** Could you please clarify which $C$ are you referring to? In the text we have $C_{\mathrm{min}}$ and $C(M, d, \delta)$. We ... | Summary: based on the time-uniform analysis of the Lasso, the anytime exponential weighting algorithm based on Lasso reward estimates with the nature of anytime regret guarantees on model selection linear bandits is developed. The result neither requires knowledge of the horizon n, nor relies on an initial purely explo... | Rebuttal 1:
Rebuttal: Thank you for your review.
**Our Contributions.** We would like to highlight the contributions of this paper as it seems to have missed the attention of the reviewer.
We address the open problem of Agarwal et al. 2017, and are the *first* to show the feasibility of the conjectured $\log M$ rate... | Summary: The paper uses tackles model selection for linear bandits with $M$ models. In particular, rewards are estimated from the $M$ models using Lasso and then EXP4 is ran on-top of these estimated rewards to update individual model probabilities. The use of Lasso over ridge regression reduces variance, leading to a ... | Rebuttal 1:
Rebuttal: Thank you for your review. Our response follows.
**Add more insight on the group Lasso loss.** Thank you for pointing this out. In the revised version, we have included an explanation of the loss, focusing on how the $(2-1)$-norm induces sparsity at the group level.
**Extending online Ridge ana... | Summary: The paper considers the problem of model selection in (lifted) linear bandits. There are M hypothesis models, each of which have a different feature map, and one of these is the true model; it is unknown to the optimizer which of the M models is the correct one. At each timestep, the optimizer chooses one mode... | Rebuttal 1:
Rebuttal: Thank you for your review! We have updated the Introduction and Experiments section, incorporating your feedback. Regarding your questions, see below.
**What is special about Lasso that makes it a suitable choice?** To obtain $\log M$ rates in online model selection, we require a reward estimator... | Rebuttal 1:
Rebuttal: We have responded to our reviewers individually. Attached is the pdf supplement, which is referred to in our responses to some of the reviewers.
Pdf: /pdf/d5897bc8b01b24854039eed68a0b5b0453f3bb13.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper considers linear bandit problem given $M$ models, or sets of feature mappings.
In this problem, it is necessary to select the appropriate action as well as the model based on the bandit feedback.
This paper provides an algorithm with an anytime regret bound of $O(n^{3/4} \sqrt{\log M} + \sqrt{n \log... | Rebuttal 1:
Rebuttal: Thank you for your review and your feedback on the notation. We have updated the text, fixing it on the instances that you mentioned. Our response follows.
**On the difficulty of model selection, lower bounds, and minimax optimality.**
Thank you for this comment. We have added a discussion on low... | null | null | null | null | null | null |
Autodecoding Latent 3D Diffusion Models | Accept (poster) | Summary: In this manuscript, a novel method for unconditional and text-conditional generative models of 3D shape and texture representations is proposed. More specifically, the authors propose to train a 3D diffusion model of radiance and RGB fields that can be trained from 2D image and object mask supervision. The met... | Rebuttal 1:
Rebuttal: We would like to thank R.nmDk for their detailed response. We appreciate their highlighting of our work’s strengths. Namely, R.nmDk (a) finds our proposed 3D Diffusion model an interesting idea in an important field of study; (b) commends our extensive evaluation on different datasets, while appre... | Summary: This paper presents a 3D generation framework that generalize to large-scale 3D dataset and articulated objects. The method comprises two parts. The first part is a 3D auto-decoder, reconstructing 3D objects from multi-view images or monocular videos. The second part is a latent diffusion model for uncondition... | Rebuttal 1:
Rebuttal: We were delighted to read R.iNSR’s review! They appreciate the workload needed to achieve large and diverse static 3D object generation as well as synthesizing articulated human heads. Moreover, they commend our proposed Robust Normalization and De-Normalization scheme and its importance for laten... | Summary: This paper proposes a diffusion model that learns to generate 3D objects, using only multi-view images or videos for training. It first trains a 3D convolutional autodecoder to embed the dataset; this maps latent vectors via a 3D feature space to voxelised scenes, and is trained for reconstruction of volumetri... | Rebuttal 1:
Rebuttal: We would like to thank R.xDwW for the informative review. We are delighted that they found our pipeline novel and our evaluation comprehensive. R.xDwW also highlights the comparison with respect to the baselines and ablation study. In the following we address R.2ZWE points:
**W1 and Q2 (Prior met... | Summary: The paper proposes a 3D autoencoder to learn a latent volumetric space on the training dataset, which can be decoded into a radiance volumetric representation for novel-view synthesis, and then learn a 3D diffusion model on the latent volumetric representation. The latent volumetric space is acquired by traini... | Rebuttal 1:
Rebuttal: We would like to thank R.2ZWE for their thought-provoking review. We are glad they appreciate that our method works on diverse datasets, both real-world and synthetic, as well as rigid and articulated objects. Let us address R.2ZWE points:
**Q4 (Training and Evaluation Protocol):** Regarding eval... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their thoughtful and detailed reviews. We are delighted to see that they found our method broadly applicable (R.Nn3Q), our modification effective (R.Nn3Q) and evaluation extensive (R.2ZWE, R.xDwW, R.iNSR, R.nmDk). It is also nice to see that reviewers a... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this paper, the authors propose an approach to learning a latent diffusion model for 3D assets from image (or video) supervision. The core is that they use an autodecoder architecture, which learns embeddings in the latent space by decoding them into a volumetric representation for rendering. Then, the auth... | Rebuttal 1:
Rebuttal: We would like to thank R.Nn3Q for their review. We appreciate their highlight of broader method application, effectiveness of our 3D autodecoder modifications and application of robust normalization. Next we answer weakness and questions:
**W1 (GAUDI Discussion):** Thank you for pointing that out... | null | null | null | null | null | null |
Bandit Social Learning under Myopic Behavior | Accept (poster) | Summary: The paper considers social learning in a two-armed Bernoulli bandit scenario, where agents sequentially arrive and pull an arm with the highest index, where the index is arbitrarily chosen to be within some confidence bound of the empirical mean of the arm, parametrized by $\eta$. This behavior subsumes greedy... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful review which raises several rather subtle issues. Let us address the stated weaknesses point-by-point.
**[W1]** What might count as “surprising” is that we managed to *prove* any/all these things, let alone in such generality. Given that such results remained unproved fo... | Summary: The paper posits a bandit social learning (BSL) problem, which consists of a multi-armed bandit (MAB) problem where at each round an arm is pulled by a newly arrived agent, as a function of the history. This is motivated by reviews on online platforms, where agents pick decisions sequentially based on past rev... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful and a largely positive review!
**[W1 and Q]** Please see “K>2 arms” in the general rebuttal. In particular, adding more arms could affect the failure probability positively, negatively, or not at all, depending on the problem instance.
Re the semantics of $N_0$: indeed... | Summary: This paper studies bandit social learning problem with two arms. Instead of aiming to design an efficient algorithm with theoretial guarantee, the authors demonstrate negative results regarding myoptic behaviors of agents. The main contribution of this paper is proving the regret lower bounds of $\eta$-confide... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful and a largely positive review!
**[W]** Re techniques, please see “techniques” in the general rebuttal. We would also like to re-emphasize the generality of allowed behaviors, please see “generality” in the general rebuttal.
**[Q]** We’ve included some experiments as req... | Summary: The paper proposes the model of social learning under myopic behavior, where a 2-armed bandit problem is considered with agents that behave myopically. Upper and lower bounds on the probability that all but \leq n agents choose the bad arm are derived.
Strengths: - The paper is well written and easy to follow... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful and explicit review. Let us respond point-by-point to the stated weaknesses, in the same order.
**[W1]** Please see “K>2 arms” in the general rebuttal.
**[W2]** Assumption (3.2) is not that strong in the theoretical sense: it merely requires $N_0$ to be larger than a ... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful reviews. Many of our points are relevant to several reviews at once.
**[SIMULATIONS: NEW]** As requested, we provide simulations to illustrate our main findings. We focus on the fundamental regime when agents are homogeneously all $\eta$-optimistic (resp., all $\eta$-pe... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper considers a social learning problem motivated by reviews on online platforms, where (myopic) users make (purchase) decisions based on historical reviews and generate new reviews in an online fashion. It considers several different user behavioral types, such as the confidence based optimistic, pessi... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful review. Let us respond point-by-point to the stated weaknesses.
**[W1]** The structure of the paper is driven (and necessitated) by the somewhat intricate collection of results, see “technical story” in the general rebuttal. Given the commonality / connections both in s... | null | null | null | null | null | null |
Your representations are in the network: composable and parallel adaptation for large scale models | Accept (poster) | Summary: The paper presents a study on the benefits of training a small cross-attention based adapter instead of performing full fine-tuning of a large VIT model. The authors propose a cross-attention layer they dub InCA that has trainable queries to cross attend to intermediate layers in the large pretrained VIT model... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and thoughtful review of our work and the insightful questions. We are pleased that the reviewer appreciates the the methodological experimentation presented in the paper, the generality of the method, and the discussion of two-stage-training. Below we addres... | Summary: - This paper proposes a new way to adapt a pretrained deep neural network for downstream tasks called InCA. InCA does not modify the intermediary representations of the pretrained network and thus doesn’t require backproping through it, which makes it memory- and compute-efficient. To use InCA, one first heuri... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review of our work and appreciate that they found InCA a simple and comprehensive approach with broad range of baselines and sound benefits. below we address the main points.
>A significant portion of the benefit of InCA comes from using a subset of the laye... | Summary: This paper proposes an efficient fine-tuning method that works parallel to the pre-trained network. Based on cross-attention between intermediate activations, InCA can generalize to various classification tasks from different domains. Additionally, the framework inherently supports class incremental learning, ... | Rebuttal 1:
Rebuttal: > The formulation of Open-InCA is not completely clear and can be better presented; However, I understood the complete idea, and I had to re-read it a couple of times for deeper understanding.
We thank the reviewer for the engaging review of this work and value their appreciation of the elegance ... | Summary: The paper presents a method termed InCA (Introspective Cross-Attention) to learn compact adapter modules for large vision models that can be used for various downstream tasks (image classification domains). The proposed approach has an advantage over the entire model finetuning due to its parameter efficiency ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the through review of our method noting the parameter/training efficiency, the signatures produced by InCA and the compactness and modularity enabled by the method as well as that InCA "can be helpful for domain practitioners". We address the points of the reviewer below.... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work presents a method called Introspective Cross Attention (InCA), which aims to identify high-performing adapter models for handling downstream tasks using large-scale pre-trained models. InCA achieves competitive performance compared to the well-established baseline of full fine-tuning, while also enab... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review of this work and are encouraged that they find the presentation to have effective context and and the method to be be presented comprehensively including in the realm of large-scale modern architectures. We address the points raised by the reviewer b... | Summary:
Summary: Firstly, I would like to kindly point out that this paper proposes an "adaptation" method; however, I believe there may be some serious concerns in multiple aspects. Firstly, the paper appears to lack innovation in its methodology. Secondly, the experimental design also seems to have some significant... | Rebuttal 1:
Rebuttal: We thank the reviewer for reviewing this work. We address each of the reviewer’s points below:
>authors hold the belief that ImageNet pre-training is the default choice worldwide, a viewpoint that is challenging to comprehend
While we consider ImageNet pre-training for some of our experiments, ... | null | null | null | null |
Learning Trajectories are Generalization Indicators | Accept (poster) | Summary: The paper introduces a novel generalization bound that incorporates trajectory information, aimed at providing deeper insights than existing methods on generalization at different points during training. The key idea is to analyze the increase in generalization error at each point in training by linearizing th... | Rebuttal 1:
Rebuttal: Thanks for your positive comment.
In the overall responce, we device a toy dataset to compare our results with previous works. We will check and fix the typos in the paper. | Summary: This paper studies the generalization of a general function class under the (S)GD algorithm with minimal assumptions.
Specifically, it gives a generalization (upper) bound based on several training trajectory characters during training: variance of the gradients, gradient norm, training loss values and the le... | Rebuttal 1:
Rebuttal: Thank you for your time. The two points you raised (1. Analysis on machine learning settings and 2. Analysis on non-neural network models) are indeed interesting and beneficial for a deeper understanding of our proposed method.
## Assumption:
The primary objective of our paper is to examine the ... | Summary: This paper present a new generation bound with moderate realistic assumptions that incorporate new information from gradients and trajectory of learning.
Strengths: 1 - the paper is well written and easy to follow.
2 - the paper does a great job comparing their results with previous literature on this topic.... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review the paper.
We have included additional experiments in the overall response, featuring a toy dataset to compare tightness, as well as experiments on ResNet18 with CIFAR-10 and WikiText-2 datasets. | Summary: This work proposed a novel generalisation error bound which takes the learning trajectory of neural networks into consideration.
Instead of focusing on the post-trained neural networks, the proposed bound bases on the parameter updates during the learning of the neural networks.
The core proof of the bound rel... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback. Based on your suggestions, we propose the following improvements to the paper:
Major 1: Indeed, Section 1.1.1 may hinder the paper's understandability. We plan to move Section 1.1.1 to a location after Theorem 3.6 and Remark 3.7.
Major 2: We will provide add... | Rebuttal 1:
Rebuttal: We thank ACs, SACs, PCs, and reviewers for the efforts and time spent in handling our paper.
Figures in the pdf. A: Result of toy dataset for Question 2 below. B: Results of ResNet18 on Cifar10. C: Results of Transformer on WikiText2.
The training config of ResNet18 on Cifar10 is the same as th... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies the connection between the learning trajectories of DNNs and their generalization when optimized using SGD. Its main contribution is that it provides a good perspective for generalization error analysis by studying the contribution of the learning trajectory. Based on their analysis of the l... | Rebuttal 1:
Rebuttal: Question 1 (Weakness 1): We provide a comparison on a toy dataset due to the challenges in calculating the bound, and the results are displayed in the overall response. Although we cannot currently determine the exact conditions for the equal sign, we will continue to investigate. Identifying such... | null | null | null | null | null | null |
Real-Time Motion Prediction via Heterogeneous Polyline Transformer with Relative Pose Encoding | Accept (poster) | Summary: This paper proposes a method to do future motion prediction for autonomous driving agents with the focus of having a computational complexity that is suited for real-time deployment. To do so, they propose a new attention mechanism, called KNARPE, and a hierarchical transformer architecture, called HPTR. By le... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you very much for your helpful comments and suggestions!
We kindly ask you to read our global response which discusses the comparison with GNN-based pairwise-relative methods and the long training time of our models.
Now in this post we answer your questions as follows.
---
... | Summary: This paper proposes a motion prediction framework HPTR. As agent-centric presentation usually has a high computational cost and poor scalability, the paper uses the transformer to encode pairwise-relative representation with K-nearest neighbor attention and relative pose encoding. It proposes a hierarchical tr... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you very much for your helpful comments and suggestions!
We kindly ask you to read our global response which discusses the comparison with GNN-based pairwise-relative methods and the long training time of our models.
Now in this post we answer your questions as follows.
---
... | Summary: This work proposes a novel method for motion forecasting which uses an efficient attention mechanism with pairwise relative representation and asynchronous updates for the static & dynamic parts of the scene. Extensive experiments on Waymo and Argoverse datasets show competitive performance to existing methods... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you very much for your helpful comments and suggestions!
We kindly ask you to read our global response which discusses the comparison with GNN-based pairwise-relative methods and the long training time of our models.
Now in this post we answer your questions as follows.
---
... | Summary: This paper introduces several ideas to boost the efficiency of marginal motion prediction: (1) represent all input entities as polylines without global pose attributes, (2) use transformer architectures but limit attention to K nearest neighbors, (3) directly use relative pose in transformer computations, (4) ... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you very much for your helpful comments and suggestions!
We kindly ask you to read our global response which discusses the comparison with GNN-based pairwise-relative methods and the long training time of our models.
Now in this post we answer your questions as follows.
---
... | Rebuttal 1:
Rebuttal: ## Global Response
We thank all reviewers for their helpful feedback.
We are glad that the reviewers appreciate our technical contributions, specifically the KNARPE attention mechanism, the asynchronous token update and the efficiency comparison.
Moreover, we are happy to see that the reviewers a... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: --
Strengths: --
Weaknesses: --
Technical Quality: 3 good
Clarity: 2 fair
Questions for Authors: --
Confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with... | Rebuttal 1:
Rebuttal: Since this review does not provide any detailed comments, we will omit the rebuttal in this case. | null | null | null | null | null | null |
Fast and Regret Optimal Best Arm Identification: Fundamental Limits and Low-Complexity Algorithms | Accept (poster) | Summary: This work focuses on simultaneously achieving regret minimization and best arm identification in multi-armed bandits, which is called Regret-Optimal Best Arm Identification (ROBAI). That is, the goal is to identify with high probability and as fast as possible / within a certain time frame the optimal arm, and... | Rebuttal 1:
Rebuttal: We thank the reviewers for the precious time they spent on reviewing our paper. We discuss the points raised by the reviewer below.
### Clarification on Pre-determined Stopping Time and Fixed-Budget ###
We thank the reviewer for raising this question, and we would like to make a clarification on... | Summary: This paper studies the classical multi-armed bandits problem with the goal to design algorithms achieving tight regret bound and tight sample complexity to identify the best arm simultaneously. To this end, three algorithms are proposed based on upper confidence exploration and lower confidence commitment for ... | Rebuttal 1:
Rebuttal: We thank the reviewers for the precious time they spent on reviewing our paper. We discuss the points raised by the reviewer below.
### Motivation of Constant-level Tight Regret but Rate-level Tight Sample Complexity And Contributions ###
Whether it is necessary for constant-level tight regret b... | Summary: The work studies how to design an algorithm with an asymptotically optimal regret rate such that it will also commit to the best arm with high probability after a stopping time (e.g., O(logT)). The paper proposes two algorithms in the Gaussian bandits setting: one with pre-determined stopping time (EOCP), whic... | Rebuttal 1:
Rebuttal: We thank the reviewers for the precious time they spent on reviewing our paper. We discuss the points raised by the reviewer below.
### Fixed-budget Setting ###
We thank the reviewer for the great question! Generally, our algorithm is not designed for the fixed-budget setting because EOCP has to... | Summary: The paper delves into the study of the 'Explore Then Commit' (ETC) policy, where the algorithm is divided into two stages: exploration and commitment. During exploration, the algorithm is permitted to switch actions, while the commitment phase restricts the algorithm to pulling only the commit arm. The primary... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the comments/suggestions and included a detailed response below. We also would like to point out a couple of misunderstandings the reviewer had in the preliminary review (see 1 and 2 below).
### Limitation of Targeting Two-armed Bandits ###
The main theorems (... | Rebuttal 1:
Rebuttal: We thank the reviewers for their precious time spent on reviewing our paper. To address the questions and concerns raised in the preliminary reviews, we present additional numerical results in the uploaded pdf File. Due to the limited time, we can only provide results with Gaussian bandits, and th... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
BiMatting: Efficient Video Matting via Binarization | Accept (poster) | Summary: The authors propose the first binarized video matting network, namely BiMatting. They first analyze the bottlenecks of the direct binarization of video matting models and propose an accurate and efficient binarization method. Compared with other full-precision neural networks and other binarization methods, th... | Rebuttal 1:
Rebuttal: > **Q1**: For the backbone with SBB, the authors should provide more detailed information. First of all, I suggest the authors give a more detailed ablation about the efficiency, including the FLOPs and the number of parameters of direct full-precision/binarized mobilenetv3 and SBB backbone. In ad... | Summary: This paper propose an efficient solution that utilizes binarization to achieve real-time video matting on for devices constrained by computational resources. The proposed BiMatting constructs shrinkable and dense topologies of the binarized encoder block to enhance the extracted representation, while sparsifyi... | Rebuttal 1:
Rebuttal: > **Q1**: The authors need to check the equations and make sure that all the notations are explained. For example, the ⊗ in Eq. (2) is not mentioned in the passage. The |W| is also remained unexplained.
**A1**: $|{\mathbf{W}}|$ means to take the absolute value of the weight ${\mathbf{W}}$, $\otim... | Summary: The paper proposes a new video matting method called BiMatting. It is based on Binary neural networks (BNNs), a more compact network to reduce the computational and storage requirements of video matting. Specifically, the authors addressed the accuracy bottleneck of BNNs by re-designing its encoder and decoder... | Rebuttal 1:
Rebuttal: > **Q1**: Although this method outperforms existing binarized video matting models, it is not yet on par with its full-precision counterpart in visual quality.
**A1**: As we present in our limitations paragraph and figures, BiMatting is not as accurate as full-precision models in certain highly d... | Summary: The authors analyzed the operations inside the deep video matting networks and proposed an efficient binarization method to greatly reduce the computation cost. Specifically, they re-designed the encoder and also sparsified the decoding process. The proposed methods are shown to outperform the existing baselin... | Rebuttal 1:
Rebuttal: > **Q1**: The training pipeline is too complicated.
**A1**: Please note that our training pipeline follows that of the baseline RVM for a fair comparison, using the code from their public GitHub repository. We do not add additional training stages or other complications. We also adopt the same st... | Rebuttal 1:
Rebuttal: We deeply appreciate all reviewers for the positive reviews and constructive feedback. All reviewers agree that our BiMatting is highly efficient and contributes to both video matting and binarization fields significantly. Your expertise and insightful comments greatly help us to further improve o... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Toolformer: Language Models Can Teach Themselves to Use Tools | Accept (oral) | Summary: The paper explores an interesting area to extend large language models (LLMs) with external tools. The authors show that LLMs can teach themselves to better utilize tools. They tested GPT-j on several tools (calculator, QA system, search engine, translator, and calendar). The experimental results well support ... | Rebuttal 1:
Rebuttal: > The experiments are only conducted on GPT-j (a non-instruction tuned model), which I believe is not enough, considering the existence of more powerful open-source LLMs such as LLaMA and Vicuna.
We completely agree with the reviewer, but at the time during which this work was conducted, neither ... | Summary: This paper proposes an approach to augment language models with the ability to call "tools" during decoding, such as a calculator, retrieval system, or machine translation system. This requires only a few human-written examples, and then uses the LM to generate a larger fine-tuning datasets constructed from ra... | Rebuttal 1:
Rebuttal: > The use of a threshold based on the likelihood assigned by the LM with and without the tool use is clever, but I also wonder whether this could be misleading in some cases. For instance, the LM may have been trained (?) on some of the CCNet data, so this may lead to an overly optimistic likeliho... | Summary: This paper proposes an innovative method for enabling Language Models (LMs) to utilize tools. The authors prompt the LM to generate API calls based on human demonstrations, which are then executed in tools. Any non-contributing API calls are filtered out. A dataset is then augmented with these API calls, and u... | Rebuttal 1:
Rebuttal: > The proposed method has some limitations. First, there's a dependency on fine-tuning when adapting the LM to new tools, which could impede broad usage and necessitate additional work.
At the time this work was conducted there was no evidence that effective tool use could be achieved purely thro... | Summary: This paper proposes a method to finetune pretrained autoregressive language models such that they learn when and how to use external tools to achieve good performance in downstream tasks. Following an in-context learning scheme, humans provide a few examples of inserting API calls at appropriate location in th... | Rebuttal 1:
Rebuttal: > From the writeup, this approach doesn't seem to generate multiple API calls in a sentence and also doesn't perform well with nested API calls. More discussion on this would be useful.
This is currently touched upon in the Limitations section: “API calls for each tool are generated independently... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and efforts to review, discuss and improve the paper. We have written responses to each reviewer in turn.
Two of the reviewers asked for more details on the types of errors we have seen in the evaluation. While a detailed and quantitative classification of ea... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Beyond Stationarity: Convergence Analysis of Stochastic Softmax Policy Gradient Methods | Reject | Summary: The paper presents a convergence (rate) analysis for a variant of policy-gradient learning of tabular-softmax policy models under finite-horizon MDP setting with discounting factor $\gamma=1$. In this variant, the policy parameters are updated in an epoch-by-epoch manner, from the last decision epoch at the ho... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you very much for your careful and constructive review which we appreciate a lot!
**Your practical concern**
There is an increasing gap between (extremely) successful practical applications of RL and solid foundations. While the approach of training all policies at once is u... | Summary: This paper proves asymptotic convergence and convergence rate of (stochastic) policy gradient descent to global optimum for un-discounted finite time Markov decision process (MDP). For the deterministic version, at each decision time, they show the error bound depends linearly on the remaining time step. For t... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you very much for taking the time to review our article and the overall positive feedback!
In order to answer the comment about your concerns about significance and novelty, in the following we will explain in more details the two main contributions of this article. One cont... | Summary: This paper studies the convergence properties of stochastic policy gradient methods for finite-state MDPs for finite horizon problems with un-discounted optimality criteria. The convergence relies on the development of a weak PL condition. In the second part of the paper, the authors then extend their converge... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you very much for taking the time to review our article and the overall positive feedback!
**1. Numerical examples**
In the main rebuttal we added graphs for the deterministic analysis in the very simple MDP problem of optimally stopping when throwing a dice 5 times. In this... | Summary: The paper analyzes the convergence of non-stationary softmax policy gradient methods where the policy is parameterized by different parameters at each decision epoch. The convergence results on REINFORCE algorithm are provided under undiscounted finite-time and infinote-horizon cases.
Strengths: - The paper o... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you very much for taking the time to review our article!
**1. Limitation to exact gradients: not true**
It is correct, that access to exact gradients is a restrictive assumption for practical applications. However, the article has two main contributions. Firstly, we extend ... | Rebuttal 1:
Rebuttal: Dear reviewers, thank you all very much for your reviews which we appreciate a lot!
**1. Stochastic vs. deterministic**
Please note that there are two contributions of the article. We will improve the abstract to make this more clear. Extending Mei2020 to the finite case and, most importantly, ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Contrast, Attend and Diffuse to Decode High-Resolution Images from Brain Activities | Accept (poster) | Summary: The paper focuses on decoding visual stimuli from recorded fMRI activity. Proposed method initially pre-trains an fMRI feature learner (and essentially a signal denoiser) on unlabeled data using a contrastive training scheme resembling to masked autoencoders. Subsequently this feature encoder is finetuned by u... | Rebuttal 1:
Rebuttal: Thanks very much for all the constructive feedback. We answer your questions and comments as follows. (Due to the rebuttal length limit, we summarize your questions.)
Q1: Why fMRI is a better choice over alternatives such as EEG for this problem?
A1: Primarily, fMRI offers significantly higher s... | Summary: The authors aim to decode the visual stimuli from neural responses by reverse-mapping the signals from functional MRI (fMRI) to the images the participants see while being scanned. The authors claimed to achieve this through a two-phase framework. In the first phase, they pre-train an fMRI feature learner insp... | Rebuttal 1:
Rebuttal: Thank you very much for the valuable feedback and for appreciating our work. Our responses to the weaknesses and questions are as follows.
Weakness 1: In Figure 3, qualitative results are shown for two datasets while bar plots are only displayed for one.
Answer 1: Thanks very much for the advi... | Summary: This paper proposed to decode visual stimuli from neural responses recorded by fMRI. First, it pretrains an fMRI feature learner with a proposed Double-contrastive Mask Auto-encoder to learn denoised representations. Second, it tunes the feature learner to attend to neural activation patterns most informative ... | Rebuttal 1:
Rebuttal: Thanks very much for all the constructive feedback. We answer your questions as follows. (Due to the rebuttal length limit, we might summarize some of your questions.)
Q1: About the selection of baselines and consideration of other models cited in related work as baselines.
A1: Thanks for your a... | Summary: The paper proposes an approach for decoding visual stimuli from neural responses (fMRI images). The rationale behind the proposed approach lies in the difficulty of learning the complex relationship between a stimuli and the neural responses to it, and the noisy nature of fMRI images.
The authors propose a mu... | Rebuttal 1:
Rebuttal: Thanks very much for all the constructive feedback and for appreciating our work. We answer your questions as follows.
Q1: To which space does v_i belong, is it a time series? Why 1D convolutional model is used to map v_i^m_1 and v_i^m_2 into embeddings?
A1: Thank you for your question regardi... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper propose a novel two-phase fMRI representation learning method to encode the visual stimuli from neural responses. It is a significant challenging task due to noisy fMRI signals and complex intricate pattern of brain visual representation. The proposed two-phase can reconstruct image stimuli from brai... | Rebuttal 1:
Rebuttal: Thanks very much for all the constructive feedback. We answer your questions as follows.
Q1: Why is two-phase representation learning needed? What is the difference between the two phases?
A1: The two-phase design stems from the unique characteristics and challenges posed by fMRI data in the co... | null | null | null | null | null | null |
GAN You See Me? Enhanced Data Reconstruction Attacks against Split Inference | Accept (poster) | Summary: The paper introduces a new data reconstruction attack (DRA) on split inference (SI) called GLASS and GLASS++. The task of DRAs in SI is to reproduce the image of a certain user based on the intermediate output of the first part of a trained network, which is located on the user's device. GLASS uses StyleGAN as... | Rebuttal 1:
Rebuttal: We highly appreciate the invaluable and perceptive feedback offered by the reviewer. We have considered all the concerns mentioned and responded appropriately to each one.
**Answer for Weakness1, Qustion1, Qustion2, Qustion4:**
Before proceeding, we kindly ask you to consult the detailed informa... | Summary: This paper proposes GAN-based latent space search attack (GLASS) that leverages a pre-trained StyleGAN for reconstructing private data from shared representations in split inference via a two-step search in the Z space and the W+ space. Additionally, GLASS++ is proposed to learn a mapping model to produce bett... | Rebuttal 1:
Rebuttal: We highly appreciate the invaluable and perceptive feedback offered by the reviewer. We have considered all the concerns mentioned and responded appropriately to each one.
**Answer for Weaknesses:**
1. We agree that GAN inversion techniques are used in some of the study cases of Model Inversion A... | Summary: The paper proposed GLASS and GLASS++, which utilize StyleGAN to launch data reconstruction attacks against Split Inference. This is the first GAN-based reconstruction attacks against split inference, and it shows consistently better results compared with previous methods, against 7 defense schemes.
Strengths:... | Rebuttal 1:
Rebuttal: We deeply value the priceless and insightful feedback provided by the reviewer. We have taken into account all of the mentioned concerns and addressed them accordingly.
**Answer for Weaknesses:**
We thank the reviewer for the comment and would like to further clarify the adversary's knowledge ab... | Summary: The paper titled "GAN You See Me? Enhanced Data Reconstruction Attacks against Split Inference" investigates and proposes new methods of data reconstruction attacks (DRAs) against split inference (SI), a deep learning paradigm that addresses computational constraints on edge devices while preserving data priva... | Rebuttal 1:
Rebuttal: We highly appreciate the invaluable and perceptive feedback offered by the reviewer. We have considered all the concerns mentioned and responded appropriately to each one.
**Answer for Weaknesses:**
- Data Types
We appreciate the reviewer's comment. We'd like to clarify that our attack pipeline... | Rebuttal 1:
Rebuttal: The **response.pdf** contains our supplementary experiments.
Here is our detailed explanation of the experiment in the pdf:
**Detailed explanation of response.pdf/Figure-4**:
The Z space is entangled, signifying that even a small change in the latent code within the Z space can yield a large ch... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Structured Prediction with Stronger Consistency Guarantees | Accept (poster) | Summary: This work studies $\mathcal{H}$-consistency of surrogate losses for structured prediction. The authors show that non classic surrogate losses are not $\mathcal{H}$-consistent thus not Bayes-consistent. They propose two families of $\mathcal{H}$-consistent losses as extensions to existing losses and algorithms ... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our work. We will take your suggestions into account when preparing the final version. Below please find responses to specific questions.
**Questions:**
**1. The inconsistency results for each individual classic loss in Section 3 are not new but the authors see... | Summary: This work extensively studies surrogate losses for structured predictions supported by H-consistency bounds. It first shows several negative results for some widely used surrogate losses in structured predictions: no non-trivial H-consistency bound can be derived. Then it provides two new families of surrogate... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our work. We will take your suggestions into account when preparing the final version. Below please find responses to specific questions.
**Weaknesses:**
**1. The current manuscript does not have a conclusion section. Given that the results are already impressi... | Summary: In this paper, the authors study surrogate losses for structured prediction problems. They show that surrogate losses proposed in previous work are not Bayes-consistent, i.e. a sequence of hypotheses which minimises the surrogate loss may not minimise the target loss. They then introduce two families of surrog... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback and suggestions on improving the readability. We will take them all into account when preparing the final version. Below please find responses to specific questions.
**Weaknesses:**
**The exposition of the paper could be improved: While I found that the nota... | Summary: * The paper studies (Fisher, or “Bayes”) consistency in structured prediction. In particular, the focus is on non-asymptotic, quantitative bounds for common and not-so-common (i.e., new) surrogate losses, which requires different proof techniques and an approach based on “H-consistency”.
* Thm 4, Thm 5 provid... | Rebuttal 1:
Rebuttal: Thank you for your encouraging review. We will take your suggestions into account when preparing the final version. Below please find responses to specific questions.
**1. It seems the content of thms 4, 5 — on the lack of consistency — is also present in previous works (e.g., those by Ciliberto ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Fixing the NTK: From Neural Network Linearizations to Exact Convex Programs | Accept (poster) | Summary: The paper provides a connection of the convex program for gated ReLU networks to multiple kernel learning model with a weighted data masking feature map. Additionally, the paper provides a theoretical analysis of the predictive error of the proposed kernel algorithm.
Strengths: The paper provides a new framew... | null | Summary: This work considers a convex formulation of a finite-width regularised two layer ReLU network and interpretations as multiple kernel learning. This then is related to the neural tangent kernel.
The convex formulation considers cones of parameters with fixed activation along with a bound O(n^d). The gated ReLU... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the feedback and comments. We hope that you would consider increasing your score if your concerns are adequately addressed. We have addressed the issues related to generalization in the global response. Please see our responses to your other specific queries... | Summary: The paper studies gated ReLU networks with L2 regularization. The authors show that this model is equivalent to Multiple Kernel Learning with group lasso, which is a convex optimzation problem. Thus L2 regularized gated ReLU networks is equivalent to learning the NTK according to a Lasso objective and then fit... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the feedback and comments. We have addressed the issues related to complexity in the global response. Please see our responses to your other specific queries below.
$\textbf{Regarding the objective}$
We would like to clarify that one of the main results of ... | Summary: This work presents the insight that the convex formulation of training a gated ReLU network is an instance of Multiple Kernel Learning (MKL) techniques. This contrasts with the NTK limit, which becomes a single kernel learning in the infinite width limit. The main thesis is that the finite-width ReLU network i... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the positive feedback and accurate comments, and greatly appreciate the time taken by them to review our work. We are encouraged to know that you believe that our main insights warrant an acceptance. | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers and the AC for taking the time to review and assess our work.
We are encouraged to know that the reviewers believe that our findings are valuable and our main insights are sufficiently novel. We also appreciate that the reviewers found the paper well writt... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper establishes the equivalence between shallow neural networks activated by ReLU and multiple kernel learning (MKL) through the convex reformulation of shallow neural networks into gated ReLU networks. By interpreting neural networks in this way, it becomes apparent that the network may not be able to ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the feedback and comments. We hope that you would consider increasing your score if your concerns are adequately addressed. We have addressed the issues related to generalization in the global response. Please see our responses to your other specific queries... | null | null | null | null | null | null |
Predicting a Protein's Stability under a Million Mutations | Accept (poster) | Summary: The paper proposes a new method, Mutate Everything Method, to model a protein's thermodynamic stability based on mutations in a protein's sequence. A key distinguishing feature of the method is the ability to perform large amount of parallel evaluations, which significantly speed up computational efficiency. T... | Rebuttal 1:
Rebuttal: > Figure 3 is not very clear in how exactly multiple mutation evaluations are processed and aggregated. I suggest adding labels that show how a mutation changes a particular part in the sequence similar to Figure 2. [Clarity]
Figure 3 showcases how we can apply Mutate Everything to decode ΔΔG val... | Summary: The authors present the "Mutate Everything Method", a simple method that builds on top of protein representations obtained through existing models to predict the effect of single and higher-order mutations. They apply the method to representations from ESM2 and AlphaFold, and show the performance on several do... | Rebuttal 1:
Rebuttal: > Reported spearman correlations to experimental results are very low in some places, and around 0.5 at most, signifying a very weak correlation at best. This begs the question whether this is an unfortunate metric or if there's a deeper issue causing the correlation to experimental results to be ... | Summary: The authors are concerned with the task of predicting the effects of single- and double-residue mutations on the thermodynamic stability of a protein. They propose a simple method that involves passing combinations of embeddings from a pretrained model (AlphaFold2 or ESM) to MLPs. Compared to existing approach... | Rebuttal 1:
Rebuttal: > While the model is evaluated on a mixture of double- and triple-residue mutations, individual results for each group are not provided… this seems like an important omission.
Agreed. While our model performs similarly against the additive baseline on double mutations, it excels at higher-order m... | Summary: This work predicts changes in thermodynamic stability for single or higher-order mutations on top of AlphaFold2 modules. The proposed model leverages linear aggregation of mutational scores on all possible sites in the latent space to decode $\Delta\Delta G$ value for deep mutations in parallel.
Strengths: ... | Rebuttal 1:
Rebuttal: > “The innovation is incremental. The main algorithm relies heavily on the existing AlphaFold2 model.”
We experimented with multiple backbones, including AlphaFold2, ESM2, MSA-Transformer (see Table 6a). Among these backbones, AlphaFold2 happens to be the strongest performing backbone. We present... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Offline Reinforcement Learning with Differential Privacy | Accept (poster) | Summary: This paper proposes two offline RL algorithms with differential privacy guarantees. The two pessimism-based algorithms apply to both tabular and linear MDP settings. In theory, the authors prove that the proposed algorithms achieve instance-dependent sub-optimality bounds while guaranteeing differential privac... | Rebuttal 1:
Rebuttal: Thanks for your high-quality review and your support. We will reply to the weaknesses you stated.
**In the introduction, the authors used a medical example to motivate the need of considering privacy in offline RL. Why could not the data owner (hospital or doctors) do offline policy evaluation or... | Summary: This paper addresses the offline RL with Differential Privacy constraints problem. Tabular and linear MDP are considered, while both forms of DP, traditional DP and zCDP are studied. The authors cast the DP definition into the offline RL problem as a constraint for protecting trajectories. Two algorithms, DP-A... | Rebuttal 1:
Rebuttal: Thanks for your high-quality review and your support. We will reply to the weaknesses you stated.
**Globally, the paper is well-written, however, I found the section on DP-VAPVI very hard to follow, especially the algorithm.**
We apologize for this and we will improve the readability in the revi... | Summary: This paper focuses on reinforcement learning (RL) in an offline setting under differential privacy considerations. It studies the proposal and analysis of a new approach to learning policies in this specific setting, leveraging the Bernstein concentration inequality.
Reinforcement learning explores an agent's... | Rebuttal 1:
Rebuttal: Thanks for your high-quality review and your support. We really appreciate the detailed and insightful summary. For the weakness, we agree that part of the techniques (including Gaussian mechanism and Bernstein-type pessimism) have been studied. Our main technical contribution is to privatize Bern... | Summary: The paper proposes an algorithm for offline reinforcement learning with differential privacy (DP), which protects the privacy of the original information using a Gaussian mechanism based on pessimism. The motivation and ideas behind the paper are clear and meaningful. However, there are some issues with the me... | Rebuttal 1:
Rebuttal: Thanks for your high-quality review and positive score. We agree that we mainly use simulations to support our theories. Since we are taking the first step towards differential privacy under offline RL, we mainly analyze our algorithms through theory while only running simulations on toy examples.... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper focuses on the offline RL problem with differential privacy. The authors propose algorithms for offline tabular MDP and offline linear MDP with $\rho$-DP. For the first problem, the sub-optimality bound almost matches the best-existing non-private counter-part in spite of an additional term $O(\sqrt... | Rebuttal 1:
Rebuttal: Thanks for your high-quality review and positive score. We will reply to the weaknesses you stated.
**The technique novelty is somewhat limited given literature in online RL with DP.**
We politely disagree. It is true that current techniques for online RL with DP can be adapted to the offline ca... | null | null | null | null | null | null |
MoVie: Visual Model-Based Policy Adaptation for View Generalization | Accept (poster) | Summary: This work presents an approach to train model-based RL methods such that it generalize to novel views on multiple RL benchmarks. The method leverages classical STN and frozen encoder to benefit the generalization performance.
Strengths: The method is sound and simple with barely hyperparameters tuning
The de... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments and suggestions. We address each of your comments in the following.
**Q1:** The writing can be largely improved on section 2 and 3. It is unclear what the model-based RL problem is, and how to define the view generalization, how to map the mod... | Summary: This paper mainly provides a training paradigm. Utilizing the dynamic transition model of the environment during the testing phase as a supervisory signal, STN is used to quickly finetune the mapping of observed potential states, resulting in better performance of the strategy mapping trained on a single view ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments and suggestions. We address each of your comments in the following.
**Q1:** The subscripts for o and a in Formula 2 are missing.
**A1:** Thank you for catching this. We will fix this in the final version.
**Q2:** There are issues with the ba... | Summary: The paper addresses the novel problem of view generalization in reinforcement learning, where an RL agent is trained on an environment with a fixed view and then evaluated on a test environment having the exact same dynamics but observed from a different perspective. In order to address this issue, the authors... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments and suggestions. We address each of your comments in the following.
**Q1:** It would be beneficial to see a comparison of the performance decrease relative to the original view. The lack of this data makes it challenging to truly gauge the sig... | Summary: This paper focuses on improving the generalization ability of visual DRL to adapt to unseen views. The authors propose a model-based policy adaptation approach that combines spatial transformer networks with a self-supervised dynamics prediction objective to address this problem. The effectiveness of the appro... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments and suggestions. We address each of your comments in the following.
**Q1:** While the problem setting is attractive, the technical contribution is insufficient as a full NeurIPS paper. It is a straightforward combination of multiple existing w... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful comments and suggestions.
We are delighted to receive your recognition of the strengths in our work, including but not limited to the meaningful problem formulation, well-motivated and effective method, extensive experimental validation and good wr... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: Adapting policy into new view setup is an important task in RL. This paper presents MoVie, Model-based policies for View generalization to achieve the fast view adaptation of model-based policy. Specifically, they combine spatial transformer networks into the encoder models and train it during test-time using ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments and suggestions. We address each of your comments in the following.
**Q1:** The proposed method is simple yet provide stable performance gain over various methods. Experimental validation is extensive enough. But Technical novelty and technica... | null | null | null | null | null | null |
CELLE-2: Translating Proteins to Pictures and Back with a Bidirectional Text-to-Image Transformer | Accept (poster) | Summary: In this work, the authors present CellBERT-E, a transformer-based model to generate protein localization images. The model takes in the nucleus and threshold images as well as amino acid (AA) sequences as input. The images are tokenized via VQGAN tokenizer and AA sequences are tokenized via pretrained protein ... | Rebuttal 1:
Rebuttal: Thank you for the feedback, we will address the concerns below:
> ... The authors can at least include the comparison to CELL-E which the proposed method is based on.
As the reviewer agrees, this is an incredibly new field of study. As such, there are no proper baseline models with which to c... | Summary: This work proposes an image-sequence multimodal encoder to model the interdependencies between cellular image and protein sequence. The pre-trained ESM-2 protein language model is employed to extract protein sequence embeddings, and the pre-trained VQGAN is used to extract cellular image patch embeddings. A Tr... | Rebuttal 1:
Rebuttal: > Important downstream applications and baseline methods are not investigated in the experiment section.
We included a highly tangible downstream application in the discussion. There, we demonstrated the generation of new NLS sequences using in-filling. In the protein engineering space, iden... | Summary: This paper proposes a novel bidirectional transformer named CellBERT-E to generate accurate protein localization image prediction from the amino acid sequences. To solve the ignorance of the integration of sequence and image information in existing methods, CellBERT-E adopts a BERT-like architecture so that th... | Rebuttal 1:
Rebuttal: >More background knowledge on biological terms mentioned in the paper is required (or explained more clearly), e.g., what are nucleus images.
Thank you for the feedback, we have updated the language in the introduction and methods to more to clearly explain those terms.
> Although the authors'... | Summary: The authors propose a new architecture, CellBERT-E, for producing flexible embeddings that encode combinations of protein amino acid sequences and protein localization images. It can be used to generate localization images given a sequence and vice versa. Compared to its predecessor, it has many favorable char... | Rebuttal 1:
Rebuttal: >My only real criticism is that the subject matter is quite niche (even to the point where some of the significance of this is lost on me), and I suspect that it will not interest most NeurIPS readers per se. That being said, I think the three-way multimodal architecture is well-executed and poten... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Implicit Regularization in Over-Parameterized Support Vector Machine | Accept (poster) | Summary: A regularization-free algorithm for high-dimensional support vector machines (SVMs) is designed by integrating over-parameterization with Nesterov's smoothing method, which induces implicit regularization. An over-parameterized hinge loss function is constructed and true parameters are estimated by leveraging ... | Rebuttal 1:
Rebuttal: Thank you for providing valuable feedback on our paper. We will explain each point one by one. If you have new questions or ideas, please don't hesitate to let us know.
1. Statement for the smoothing term $d(\mu)=\lambda/2||\mu||^2$
We appreciate your inquiry regarding the smoothing term, as it'... | Summary: The paper proposes an iterative/implicit regularization algorithm for sparse SVM, using an Hadamard product overparametrization of the iterate $\beta = u \odot u - v \odot v$. In addition, a Nesterov smoothing of the Hinge loss (replaced by its Moreau envelope, a term that is lacking in the paper) is performed... | Rebuttal 1:
Rebuttal: Thank you for carefully reading our paper and sharing valuable feedback. Due to character limits, we can't address every question separately. We will fix all writing mistakes and unclear parts you've pointed out in the revised version. For other questions, we address them here. If you have more qu... | Summary: This paper design a regularization-free algorithm for high-dimensional support vector machines (SVMs) by integrating over-parameterization with Nesterov's smoothing method, and provide theoretical guarantees for the induced implicit regularization phenomenon.
Strengths: This paper provides a regularization-fr... | Rebuttal 1:
Rebuttal: We appreciate the time and effort you put into reviewing our work. We've reviewed your comments and summarized our responses below. Please let us know if you have any additional comments or concerns.
1. Clear specifications about the constants $c_1\sim c_4$ in Theorem 2.
Thank you for highlighti... | Summary: The paper tackles the problem of implicit regularization for classification in the context of over-parameterization. Starting from a $L^1$ regularized SVM, they voluntarily over-parameterize the feature vector $\beta = w \odot w - v \odot v $ using two vectors $w, v \in \mathbb{R}^p$.
The optimization process... | Rebuttal 1:
Rebuttal: We appreciate the valuable feedback you've provided on our paper. We will go through each point and provide explanations. If you have any new questions or ideas, please feel free to share them with us.
1. Future work about implicit regularization in Non-linear SVM.
Exploring implicit regularizat... | Rebuttal 1:
Rebuttal: We highly appreciate the invaluable feedback we received from reviewers for our work. Your comments aid us in identifying areas where we can enhance our research and make our findings clearer and more accessible to our readers. We have considered all the comments and summarized the responses below... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies the implicit regularization in over-parameterized (sparse) support vector machine. The paper by nature is an extension of Vaskevicius et al [28], applying the quadratic reparametrization on SVM (hinge loss). Due to the non-differentiability of hinge loss, Nesterov’s smoothing is applied to e... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback on our paper. We'll carefully review each point and provide explanations. If you have any new questions or ideas, please don't hesitate to share them with us.
1. More discussion of the sparse SVM.
In modern applications, we frequently encounter classifica... | null | null | null | null | null | null |
3D-LLM: Injecting the 3D World into Large Language Models | Accept (spotlight) | Summary: Due to the limited perception of 3D space by LLMs and VLMs, this paper proposes 3D-LLMs to understand spatial relationships, affordances, physics, and layout in 3D scenes. The authors generate 300K 3D-language pairs to train the 3D-LLMs, which enable better performance on various 3D understanding tasks.
Stren... | Rebuttal 1:
Rebuttal:
*Thank you for your insightful and constructive comments! We have added additional experiments and modified our paper according to your comments.*
> **Q1: Which 3D feature extractors were used for Objaverse, ScanNet, and HM3D, respectively? How many pairs were extracted for each?*... | Summary: This paper proposes a new family of 3D-LLMs that can take 3D representations as inputs and generate responses, it introduces a series of 3D-language data generation pipelines to generate a dataset of 300K 3D-Language pairs from different tasks for the training.
Strengths: The proposed approach seems to be val... | Rebuttal 1:
Rebuttal:
*We appreciate the positive and insightful comments from you! We address your concerns in detail below.*
> **Q1: The reviewer is curious whether the authors have tried to train it directly using the language-3D dataset for the 3D encoder directly without leveraging the images as ... | Summary: This paper proposes a new framework named 3D-LLM which leverages LLM to understand the 3D world. Specifically, 3D-LLM can take 3D point clouds as inputs to conduct various 3D tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, ... | Rebuttal 1:
Rebuttal:
*We appreciate the positive and constructive comments from you, which are essential for improving the paper! We have conducted your suggested experiments. We will update all results in the paper.*
> **Q1: In Table 4, the ablation study could be more comprehensive, where the baseli... | Summary: In this paper, the authors tried to leverage LLM to understand the 3D scene. Specifically, the authors use both grounding and captioning/QA datasets to tune the model. Specifically, the authors adopt the three 2D to 3D feature transformation techniques to let the model have a sense of the 3D features.
Strengt... | Rebuttal 1:
Rebuttal: We'd like to express our sincere gratitude for your thorough review of our paper. We greatly appreciate your suggestions which are crucial in improving the quality of our paper.
> Q1: Aggregating 2D features to 3D is ill-posed
Thanks for raising this concern. We want to emphasize that building ... | Rebuttal 1:
Rebuttal: We sincerely appreciate all reviewers’ time and efforts in reviewing our paper. In addition to the response to specific reviewers, here we would like to highlight our contributions and the new experiments that we add in the rebuttal.
**[Our Contributions]**
We are glad to find out that t... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Harnessing Hard Mixed Samples with Decoupled Regularizer | Accept (poster) | Summary: This paper proposes Decoupled Softmax(Eq. 4), which is an interesting improvement to the previous mixup method, which mitigates the impact of noise in mixed samples by modifying the loss.
Strengths: The proposed idea is simple and effective. The manuscript has a high degree of completion and is rich in experi... | Rebuttal 1:
Rebuttal: Thank you for recognizing our work! These two papers you have mentioned are very interesting works, but many of the differences are worth discussing.
- Although The Benefits of Mixup for Feature Learning argues the different linear interpolation parameters for features and labels can still achie... | Summary: The authors propose a new objective function with decoupled regularizer named decoupled mixup (DM) to harness hard mixed samples and mine discriminative features adaptively. This method is available on supervised learning and semi-supervised learning. Unlike the previous approaches which propose a more complic... | Rebuttal 1:
Rebuttal: Thank you for your precious time and great efforts. Your insightful suggestions and professional questions are the key to improving the quality of the paper. We will address your questions one by one and make the corresponding changes in the reversion.
---
### Answers to questions
> 1. It is con... | Summary: This paper introduced a simple strategy decoupled mixup (DM) to improve the effectiveness of Mixup and its variants. Regarding the softmax result of a mixed image with a pair of classes, one class is removed from the denominator when computing the loss of the other class. Authors provided both theoretical and... | Rebuttal 1:
Rebuttal: Thank you for your great efforts, these valuable questions and constructive suggestions, which are exactly what the paper needs. We will take your suggestions and solve your problems one by one, and all corresponding changes will be reflected in the revision.
---
### Answers to questions
>1. A... | Summary: The authors point out that while $\textit{dynamic}$ mixup methods are shown to be effective, they induce too much computational cost. To address this issue, they propose a $\textit{static}$ method called Decoupled Mixup, which utilizes the hard mixed samples. The authors suggest that the Softmax function will ... | Rebuttal 1:
Rebuttal: Thanks for your great effort and very constructive comments to help us improve the manuscript. We will address your questions one by one and make the corresponding changes in the reversion. Please note that due to compilation issues, $L$ denotes $\mathcal{L}$
---
### Answers to questions
> 1, 3, ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Spatio-Angular Convolutions for Super-resolution in Diffusion MRI | Accept (poster) | Summary: The authors utilize a parametric continuous convolution network to capitalize on the geometry of diffusion MRI. They enhance prior work (PCConv), integrating domain context and global information. They show that they obtain accurate high resolution dMRI using merely sparsely sampled data and demonstrate perfor... | Rebuttal 1:
Rebuttal: # Reviewer PJ9o Rebuttal
We thank the reviewer for taking the time to review our work and look forward to further discussions.
## Weaknesses
We will revise the second paragraph of the introduction to further clarify the specifics of b-vectors and multi-shell acquisition. As this is an applicati... | Summary: The paper presents a learning-based method for q-space interpolation in diffusion MRI. The approach constructs particular convolution operators that lend themselves to the structure of the space to be interpolated, then learn convolutions from examples. Experiments show the learned interpolation substantiall... | Rebuttal 1:
Rebuttal: # Reviewer bfmq Rebuttal
We thank the reviewer for taking the time to provide this review.
## Weaknesses
### W.1 "Should really use a 3D q-space..."
There are a myriad of diffusion models that we could have included in this analysis if we had unlimited time and space. For example, *dipy*, the ... | Summary: This paper proposes a parametric continuous convolution (PCConv) framework for Diffusion MRI (dMRI). The PCConv convolves across both spatial and angular dimensions of dMRI data. Meanwhile, the authors introduce a Fourier feature mapping, global coordinates, and domain specific context into PCConv. Experiments ... | Rebuttal 1:
Rebuttal: # Reviewer Kmgz Rebuttal
We thank the reviewer for taking the time to provide this review.
## Weaknesses
### W.1 "The PCCNN has been proposed in previous work..."
Whilst the PCCNN developed in this study is an extension of the PCConv framework proposed by [Wang et al.](https://arxiv.org/abs/21... | Summary: The paper with title: Spatio-Angular Convolutions for Super-resolution in Diffusion MRI applies established fully parametric continuous convolution network (PCCNN) to diffusion SR, demonstrating the potentials.
Strengths: 1. This paper proposes a practical method to apply parametric continuous ConvNet to diff... | Rebuttal 1:
Rebuttal: # Reviewer Vyq4 Rebuttal
We thank the reviewer for their time in providing this review.
## Weaknesses
### W.1 "The authors propose a few variations to PCCNN..."
The "Bv" modification increases the co-ordinate embedding dimensionality by splitting the coordinate $\rho_{j} - \rho_{i}$ into its c... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for taking the time to review our work in great detail. We look forward to further discussions.
The notable additions we have made during this rebuttal period include:
- A figure that visualises FODs reconstructed from different angular super-resolution model... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: **Background for ML audiences**: Multi-shell diffusion MRI (dMRI) is a 6D (3 dims of space + 2 angular dimensions + 1 radial dimension) imaging modality where each 3D voxel contains (potentially concentric) spherical signals which correlate with local white matter properties within the brain. In research setti... | Rebuttal 1:
Rebuttal: # Reviewer bdM9 Rebuttal
Thank you for taking the time to provide such a robust and thorough review, it is greatly appreciated.
## Weaknesses
### A.1 "Only ~40 subjects were used for training/validation/testing..."
Whilst only ~40 subjects were used in total, each subject constitutes a lot of ... | null | null | null | null | null | null |
On Measuring Fairness in Generative Models | Accept (poster) | Summary: The paper considers the problem of measuring fairness in generative models. In particular, the paper has two main contributions: (1) they have produced a dataset of hand labeled (sensitive attributes, SA) dataset for various SOTA generative models; and (2) they have proposed a method for estimating the expecte... | Rebuttal 1:
Rebuttal: > **Q1**: "how is $C_u$ obtained?"
**A1**: Thanks for your comment and we apologize if it was unclear. As discussed in the main paper (Sec.3), to obtain $C_u$, we strictly follow previous work (e.g., Imp-Weighting [1] and fairTL [2]). Particularly, we train the SA classifiers using the labeled da... | Summary: This paper considers the fairness measurement for generative models. The contributions of this paper are three-fold. First, the authors reveal that the existing frameworks have significant measurement errors, even using sensitive attribute classifiers. Second, the authors propose a new framework namely CLEAM t... | Rebuttal 1:
Rebuttal: >**Q1**: “On page 6, the authors say that “the probability of the counts for each output $c^T$ in Eqn. 2 (denoted by $N_c$) can be modeled by a multinomial distribution.” Does this assumption hold in practical systems?”
**A1**: Thank you for your insightful question. In Sec. 4.1, we have conside... | Summary: The authors conduct a study on the fairness of generative models. They propose a CLassifier Error-Aware Measurement (CLEAM) framework which accounts for inaccuracies in classifiers involving sensitive attributes. The authors also create a new dataset of generated images from a text-to-image generator which a... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the valuable suggestions. They are very helpful. We will clean up the abstract and introduction, shorten the discussion of CLEAM in the introduction, shift some results/mathematics to Supp., and better arrange the discussion of SA classifiers/generator as suggested by the... | Summary: The paper studies measuring fairness in generative models, which is defined as equal number of samples generated from different groups. The measurement needs a sensitive attribute (SA) classifier to predict group attribute to compute the fairness. The paper emprically finds out the error in SA classifier would... | Rebuttal 1:
Rebuttal: Regarding Summary: Thank you. Our apologies if unclear, but Reviewer’s Summary is not very accurate:
- Our statistical model is overlooked: We develop a statistical model to understand how errors in SA classifier ($\alpha$) affect the fairness measurement ($\hat{p}$). See Fig 1.b., entire Sec 4.... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable time and effort to review our work. We appreciate the Reviewers' kind comments and recognition, such as:
- "The paper is well written, with clear intuitions, illustrations, and experimental results." (Reviewer 2H4p)
- "Fairness in generative models is... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a framework for fairness measurement. It first shows that existing framework has considerable measurement errors even when highly accurate sensitive attribute classifiers are used, then propose CLassifier Error-Aware Measurement (CLEAM), a new framework which uses a statistical model to acc... | Rebuttal 1:
Rebuttal: Thank you for the valuable suggestion. We will shorten the first contribution and discuss more on our proposed method in the introduction following the Reviewer's suggestion.
$ $
>**Q1**: The introduction to the proposed method is too short and not very solid. Some theoretical support may be bet... | Summary: The objective of this paper is on measuring the fairness in generative models. There are three contributions. (i) Consideration of measurement errors of sensitive attribute (SA) classifiers in fairness measurement of generative models. (ii) A classification error aware measurement framework, called CLEAM, whic... | Rebuttal 1:
Rebuttal: >**Q1**: Putting the application of gender bias in the introduction seems to be out of place, possibly undermining the main framework CLEAM. Similarly, table 1 is also out of place, throwing numbers to the readers without explaining the setup
**A1**: Thank you for your suggestion. We will fix the... | null | null | null | null |
Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness | Accept (spotlight) | Summary: This paper presents a theoretical characterization and some theorems supporting previous empirical finidings on perceptually aligned gradients (PAG) of classification neural network models. Specifically, it provides the first rigorous definitions for the previously qualitative definitions of "PAG", and provide... | Rebuttal 1:
Rebuttal: Thanks for your insightful review! We're glad you liked the paper and found the theory intuitive and relevant. We address your questions below.
1.*“While the math (theorem 1) is nice, it does not consider adversarially chosen noise. It would be nice to mention that for normal noise with $\sigma \... | Summary: This paper studies Perceptually Aligned Gradients (PAGs), a phenomenon where the input gradients are semantically meaningful and aligned with human perception.
While this trait gained research attention, we do not truly understand it.
To this end, the paper proposes an explanation via off-manifold robustness.
... | Rebuttal 1:
Rebuttal: Thanks for your great review, and we're glad you found our paper interesting! We address specific comments below.
1.*“The connection between a conditional score function and PAG was made in [1]. Despite not being used for assessing PAG, they show that a classifier trained to mimic the score funct... | Summary: The paper seeks to provide a condition that leads models to have gradients that are aligned with human perception, i.e. which highlight relevant features of the image while ignoring distractor features. The paper first proposes that for any manifold, if a model is robust to perturbations which are off-manifold... | Rebuttal 1:
Rebuttal: Thank you for your constructive review! We address specific concerns below.
1.*“It is not clear from the experiments what the signal manifold is. Line 278 talks about perturbing about 10% of the input, but it would be good to clarify this further.”*
The concept of the signal manifold arises from... | Summary: This paper explores the connection between manifold alignment, perceptually-aligned gradients, and model robustness, deriving a number of novel results that explain previously observed phenomena in the explainability and robustness literatures. Crucially the paper grounds its contributions in Bayes optimal cla... | Rebuttal 1:
Rebuttal: Thank you for your insightful review! We're glad that you liked our paper overall, including the acknowledgement of our limitations. We respond to individual questions below.
1.*“It does not seem right that the mask defining distractor vs signal features is constrained to be binary (Definition 3)... | Rebuttal 1:
Rebuttal: We thank all reviewers for taking the time to review our paper and providing constructive feedback. We are encouraged by their positive assessment of the paper, and we are committed to incorporating reviewer feedback to further strengthen the paper.
Based on the reviewers’ comments, we have cond... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper titled "Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness" investigates the phenomenon of perceptually-aligned gradients (PAGs) in robust computer vision models. PAGs refer to the alignment of model gradients with human perception, enabling these models to e... | Rebuttal 1:
Rebuttal: Thank you for your constructive and encouraging review! We are glad that you found our analyses rigorous and our explanations clear. We answer specific questions below.
1.*“One potential weakness of the paper is that the evaluation of on- and off-manifold perturbations does not cover the entire s... | null | null | null | null | null | null |
Connecting Pre-trained Language Model and Downstream Task via Properties of Representation | Accept (poster) | Summary: The paper mainly addresses two conditions that enable the representation of pre-trained LLMs to be transferred effectively to the downstream tasks which are usually different from pre-trained objectives.
1. The insensitivity of the downstream task of "super-small" probability words must be guaranteed for goo... | Rebuttal 1:
Rebuttal: Q: Verification of anchor vector using autoregressive models.
A: In our definition we allow v*_i to depend on the entire x_{-i} to capture both autoregressive models and masked language models for generality. An autoregressive model can still be a special case of the theory as their v*_i can just ... | Summary: This paper investigates the relationship between language model pretraining and downstream classification tasks. Under certain assumptions, the author theoretically demonstrated that pre-trained models can guarantee performance on downstream tasks with the existence of proposed “anchor vectors”.
Strengths: 1.... | Rebuttal 1:
Rebuttal: Q1: Large language model is not just last layer embeddings.
A: Indeed, this paper only considers the setting where one takes the last layer representation of a language model and uses that in downstream tasks without fine tuning the whole model. While this gives reasonable performance for many tas... | Summary:
This paper presents a sequence of assumptions along with their respective conclusions, which advance the objective of comprehending the intricate connection between the performance of pre-training and downstream tasks in language models.
By framing the prevailing language models as log-linear models, this pap... | Rebuttal 1:
Rebuttal: Q: Many assumptions
A: In general we agree that we make many assumptions and they may not always hold in practice. The goal of our paper is to give some theoretical understanding on how representations can be helpful for downstream applications, and unfortunately this is extremely difficult withou... | Summary: This paper explores how to connect pretraining performance with downstream task performance (i.e., binary classification). The theoretical analysis is based on token representations. The authors find the ``anchor vector'' in the representation space and bridge pretraining and downstream tasks performance based... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review. However, there is likely some serious misunderstanding which we try to clarify below:
Q: Many assumptions; some of them can fail.
A: In general we agree that we make many assumptions and they may not always hold in practice. The goal of our paper is to give s... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling | Accept (poster) | Summary: The authors propose a new method, a continuous-time transformer, ContiFormer, for irregular time series modelling. The proposed method extends vanilla Transformer to a continuous domain. In particular, the model incorporates the continuous dynamic modelling of Neural ODE with the attention mechanism of a trans... | Rebuttal 1:
Rebuttal: Thank you for your valuable suggestion. We will diligently reassess the detailed statements made in the paper and diligently bolster each claim with appropriate supporting evidence. Below we try to address your problems.
> **W1: Table 8 mTAN model sometimes outperforms ContiFormer.**
Contiformer ... | Summary: This paper introduces the ContiFormer, a novel continuous-time Transformer model designed for handling irregular time series data. In this model, the keys, queries and values are vector-valued functions indexed on time. Each input observation gives rise to a key function, given by the solution of a Neural ODE ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your comprehensive comments.
> **W1: The theoretical results should at least be formally stated and further commented on in the main paper.**
Thanks for your great suggestion! In the revised manuscript, we intend to consider your suggestion.
> **W2: L138, adapting fun... | Summary: The paper describes a continuous time extention of the transformer architecture using ODE blocks to propagate the effect of each observation individually through time. For computing attention values inner products between functions are used, where in the implementation the resulting integral is approximated. T... | Rebuttal 1:
Rebuttal: > **W1: Table 3 covers 20 datasets, hard to rigorously evaluate the average metrics.**
1. We would like to clarify that our benchmarking follows the prevalent common practice [1, 2], where the averaged metrics are over all the datasets in the UEA benchmark.
2. The experiments on irregular time-s... | Summary: The paper proposes a new deep learning model called ContiFormer to model continuous-time dynamics on irregular time series data. The paper argues that existing methods such as recurrent neural networks (RNNs), Neural Ordinary Differential Equations (ODEs), and Transformers have limitations in modeling continuo... | Rebuttal 1:
Rebuttal: Thank you for your comprehensive comments.
> **Q1: The order of computation cost one layer attention should be $O(N^2 \times 80 \times d^2)$; for the $M$ layer attention model, it could be $O(M \times N^2 \times 80 \times d^2)$.**
Thank you for your inquiry about time complexity. In Table 1 of o... | Rebuttal 1:
Rebuttal: ## General Response
We thank all the reviewers' valuable and insightful suggestions! And we are encouraged by positive comments from the reviewers, e.g.,
* Addressing import research problems with high practical value (Reviewer GCXG)
* The proposed method is novel with significant contribution in... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces ContiFormer, a continuous time transformer-based model that leverages parallelism and can handle irregularly sampled data well, thereby removing the need to transform these datasets into discrete uniform bins. This set-up incorporates the continuous dependence on the data from differentia... | Rebuttal 1:
Rebuttal: We sincerely thank you for your comprehensive comments.
> **W1: L137 how key & values initialised.**
As in L124, the input ($Q$), $K$, and $V$ are initially derived from the input variable $X$. Besides, we also explain in Equations (7) and (8) where $K_i = X_i W^K$ and $V_i = X_i W^V$. Moreover, ... | null | null | null | null | null | null |
Conformal Prediction Sets for Ordinal Classification | Accept (poster) | Summary: Conformal prediction for classification considers non-ordinal classes, which potentially produces sub-optimal prediction set size for ordinal classification. The proposed approach addresses this problem for a unimodal label distribution. To this end, the proposed approach designs a novel conformity score funct... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their thoughtful comments. Please find our response below
**Comment:**
My main concern is the unimodal assumption … Even though it is violated, I think it does not affect coverage rate.”
**Response:**
Yes, it is true that even if the unimodal... | Summary: The authors present a method for ordinal classification, COPOC, which guarantees (by functional form) unimodal prediction distributions over the ordered classes and consequently guarantees contiguous prediction sets for uncertainty estimation via conformal prediction . The authors argue that unimodality is des... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their thoughtful comments. Please find our response below
**Comment:**
I find the case for unimodality to be overstated... So more of an acknowledgement that unimodality might lead you astray would be good here.
**Response:**
We completely agr... | Summary: The authors explained the difference between their method and prior work satisfyingly.
I had a misunderstanding in my previous reading.
I'd be happy to support the paper's acceptance.
It is a minor contribution from a theoretical perspective, but I agree that practically, it's probably a better way of construc... | Rebuttal 1:
Rebuttal: We thank the reviewer for pointing us to Lu et. al. ‘22 and Yunpeng et. al. ’23, both of which we were not aware of earlier. While the motivation for both these papers (namely conformal predictions for ordinal settings through contiguous prediction sets) overlaps with that of ours, our solution ap... | Summary: The paper addresses the problem of adapting the conformal prediction methods to ordinal classification so that the predictor outputs contiguous prediction sets. Contributions can be split into two parts. The first part deals with adopting the existing conformal prediction methods to ordinal classification. The... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their thoughtful comments. Please find our response below
**Comment:**
In the experiments on real data in Table 1, the proposed method does not perform best in terms of MAE which might be attributed to the fact that true posterior might not be exact... | Rebuttal 1:
Rebuttal:
We thank all the reviewers for their detailed comments and suggestions. We have attempted to address the reviewer concerns and questions to our best including additional experimental results and figures.
Below we summarize the key points of our responses.
**Novelty of our current work an... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
NICE: NoIse-modulated Consistency rEgularization for Data-Efficient GANs | Accept (poster) | Summary: The authors propose a noise modulation and regularization scheme for GANs that reduces disciminator overfitting and improves training stability in the low-data scheme. The technique demonstrate consistent improvements when applied to several different network architectures and datasets.
Strengths: The paper c... | Rebuttal 1:
Rebuttal: # Response to Rev. 5 (ijuK)
***We thank the reviewer** for the constructive review and valuable questions that have helped us improve our work.*
## 1. Not a single generated image shown in the main paper.
We apologize. We have selected now **images on 100-shot and AnimalFace
datasets from Figur... | Summary: This paper proposed NICE, a technique that enforces the discriminator to be consistent with the same inputs under different noise modulations. The authors showed us both in theory and practice that NICE is effective at preventing discriminator overfitting and achieves superior performance in image generation u... | Rebuttal 1:
Rebuttal: # Response to Rev. 4 (m7vf)
***We thank the reviewer** for the constructive review and valuable questions that have helped us improve our work.*
## 1. The computational overhead of NICE.
Kindly notice **our computational overhead is small**. Firstly, our usage of multiplicative noise modulation... | Summary: This paper proposes a training approach called NoIse-modulated Consistency rEgularization (NICE) to improve the data-efficiency of generative adversarial networks (GANs) by addressing issues related to limited data. It introduces adaptive multiplicative noise into the discriminator to modulate its latent featu... | Rebuttal 1:
Rebuttal: # Response to Rev. 3 (LrCf)
***We thank the reviewer** for the constructive review and valuable questions that have helped us improve our work.*
## 1. The complete objective function of the proposed method should be included in the main text, rather than in the appendix.
Thank you. Absolutely. ... | Summary: This paper proposes a regularization method called noise-modulated consistency regularization (NICE) to train GANs with limited data. In this method, this paper proposes modulating the discriminator's latent features using noise and imposing a constraint on the discriminator so that the middle outputs of the d... | Rebuttal 1:
Rebuttal: # Response to Rev. 2 (v2y4)
***We thank the reviewer** for the constructive review and valuable questions that have helped us improve our work.*
## 1. What happens when regularizing the first- and second-order gradients of latent features directly?
Thank you. Consider the regularization term in ... | Rebuttal 1:
Rebuttal: # General Rebuttal
We thank the reviewers' for constructive suggestions and in depth analysis helping us refine our work. We are humbled by such a positive response, and we truly appreciate it.
\
\
**Also, kindly refer to the rebuttal PDF** (**at the bottom of this panel**) for additional figure... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors present a novel approach called NoIse-modulated Consistency rEgularization (NICE) to solve the challenge of training GANs with limited data. The experiment was conducted on reduced small-scale CIFAR-10, CIFAR-100, ImageNet, and FFHQ datasets. Additionally, they applied their method to low-shot gene... | Rebuttal 1:
Rebuttal: # Response to Rev. 1 (fQTZ)
***We thank the reviewer** for the constructive review and valuable questions that have helped us improve our work.*
## 1. What does MA refer to in Tables 1-4?
We apologize. MA denotes `massive augmentations' (including DA and ADA) first used in:
> *Generative co-train... | null | null | null | null | null | null |
Inverse Reinforcement Learning with the Average Reward Criterion | Accept (poster) | Summary: This paper addresses Inverse Reinforcement Learning (IRL) in the average-reward setting. The authors propose a Stochastic Policy Mirror Descent (SPMD) method to solve the Average Reward Markov Decision Process subproblem and use SPMD to propose the Inverse Policy Mirror Descent method to solve the IRL problem.... | Rebuttal 1:
Rebuttal: We extend our sincere appreciation for your insightful review and thoughtful comments, which greatly contribute to the refinement of our paper. We are pleased to address each of your points below:
1. We recognize the importance of providing comprehensive experiment details, including hyperparamet... | Summary: In this paper inverse reinforcement learning is studied when the teacher was using an average-reward criterion rather than discounted rewards with known discount factor. The paper proposes a stochastic first-order method starting from stochastic policy mirror descent for MDPs and continuing towards inverse pol... | Rebuttal 1:
Rebuttal: Thank you for the thorough review and insightful comments. We appreciate your time spent on the review and detailed comments on the paper. First we want to point out that our paper focuses on the theoretical development of solving the Inverse Reinforcement Learning (IRL) problem under the average-... | Summary: This paper proposes an inverse reinforcement learning (IRL) algorithm for infinite horizon average reward Markov decision processes (AMDPs). At first, the authors show the stochastic policy mirror descent (SPMD) algorithm that achieves $\mathcal{O}(\varepsilon^{-1})$ rate of convergence. Then, the authors prop... | Rebuttal 1:
Rebuttal: Thank you for your time reviewing our paper. Your points are valid with respect to the arrangements of the paper. We appreciate your keen observation and suggestion regarding recent studies on Average-Reward Markov Decision Processes (AMDPs) that may contribute to our work. While we strive to prov... | Summary: This paper aims to address the problem of inverse reinforcement learning (IRL) under the maximum entropy framework (MaxEnt-IRL) and an average reward criterion. The MaxEnt-IRL problem is formulated as a combination of an average reward Markov decision process (AMDP) and a dual IRL problem. The proposed algorit... | Rebuttal 1:
Rebuttal: We greatly appreciate your thoughtful review and the points you've raised. Your insights have been invaluable in refining our paper. Below, we address each of your comments:
1. We apologize for any lack of clarity in explaining the advantages of employing the average reward criterion over discoun... | Rebuttal 1:
Rebuttal: 1. Regarding assumptions being too restrictive:
We acknowledge that some assumptions made in the paper are sometimes too restrictive. However, our particular problem setting has a special implication for the assumptions we made. Assumption 2.1 (uniform ergodicity) is considered restrictive but of... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Model-Free Active Exploration in Reinforcement Learning | Accept (poster) | Summary: In this paper, the authors propose a way of obtaining tighter PAC bounds for model-free reinforcement learning. The new theoretical results allow the authors to propose new practical methods for exploration in both discrete and continuous state spaces. The proposed algorithms use ensembles of Q-values, and the... | Rebuttal 1:
Rebuttal: We really appreciate your review and positive endorsement of our paper's merits. We're particularly pleased that the reviewer recognizes the innovative approach to tighter PAC bounds, the quality of the writing, and the comprehensive appendix.
> How exactly is the alearotic uncertainty in the... | Summary: The paper introduces a model-free exploration approach developed on an information theoretical basis. Firstly, the lower bound on the number of samples for a near-optimal policy is estimated, and based on this lower bound, the paper develops an exploration strategy for both tabular and deep RL approaches. The ... | Rebuttal 1:
Rebuttal: Thank you for your insights and constructive feedback. We appreciate the effort you invested in reviewing our paper and have carefully considered your suggestions. Below, we respond to your queries and address the highlighted concerns.
> Currently, there is no explicit section outlining the limi... | Summary:
The authors propose a model free approach to exploration in RL, that is based around best policy identification. This technique, unlike prior work, uses stochastic approximation to learn a lower bound on the policy performance based on collected samples.
Strengths:
- The paper presents an model-free appro... | Rebuttal 1:
Rebuttal: Thank you for your insightful review and positive feedback. We appreciate the time you took to review our paper and have taken into consideration your suggestions. We address below your questions and discuss the perceived weaknesses.
> Could you please clarify where the proof and derivations of ... | Summary: This paper introduces an approximation of a lower bound on the number of samples needed to identify a nearly optimal policy directly applicable to model free RL.
They further propose a model free exploration strategy that can be applied to the tabular and continuous MDPs.
Strengths: The paper is clear and we... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and positive feedback on our paper. We appreciate the constructive suggestions and have taken them into consideration. We address below your questions and discuss the perceived weaknesses.
> Why have you used the 2k-th moments in the bound?
There are multiple... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work focuses on the exploration of reinforcement learning and introduces a novel model-free algorithm. The authors derive a new bound for the lower bound of the number of samples needed to identify a near-optimal policy. Based on that, they develop a model-free exploration strategy that is applicable to b... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and constructive feedback. We appreciate the time you took to review our paper and will address each of your comments individually.
> When the authors select $\bar k=1$ (line 211) and arrive at $\bar U_\epsilon^1$, does it remain an upper bound for $T_\eps... | null | null | null | null | null | null |
$\textbf{A}^2\textbf{CiD}^2$: Accelerating Asynchronous Communication in Decentralized Deep Learning | Accept (poster) | Summary: This work discusses the challenges and potential solutions related to training complex Deep Neural Networks (DNNs), particularly regarding the computational and communication demands. The traditional synchronous, centralized approaches to DNN training, while widely used, face limitations in terms of efficiency... | Rebuttal 1:
Rebuttal: We thank reviewer LKYk for highlighting the need for cost-effective methods enhancing communication speed in distributed training, and recognizing that our method is a step towards rendering large-scale training possible in this setting by substantially improving communication efficiency while p... | Summary: This work, A2CiD2, proposes a novel decentralized asynchronous training method that incurs only minimal overhead but effectively decouples communications and computations, accelerating pair-wise communications via a provable, accelerated, randomized gossip procedure based on continuous momentum and time. A2Ci... | Rebuttal 1:
Rebuttal: We thank reviewer fQ1m for stressing that our method outperforms SOTA approaches, both theoretically and empirically. We emphasize that this paper is **not** about implementations tricks but rather about *fundamental research* on new strategies to speedup asynchronous training. Indeed, our work so... | Summary: This paper proposes an asynchronous gossip-based algorithm for decentralized deep learning by using a continuous momentum. Experiments on real datasets are used for evaluation.
Strengths: 1. The studied problem about accelerating communication in decentralized deep learning is interesting.
2. The proposed al... | Rebuttal 1:
Rebuttal: We are glad that reviewer o6Xd finds the problem we study interesting and recognizes that our method accelerates previous algorithms both theoretically and empirically. We stress that our method is especially catered for large scale training of deep neural networks: the advantage of **asynchronous... | Summary: This work introduces a new method for decentralized optimization that leverages the notion of continuous momentum to speed up its convergence. The method is justified with theoretical analysis and large-scale experiments on the ImageNet dataset.
Strengths: * The work studies an important problem of distribute... | Rebuttal 1:
Rebuttal: We thank reviewer fkqi for acknowledging the importance of the problem studied and remarking that our theoretical and practical contributions are clear. Among those, we would like to emphasize that obtaining an accelerated rate of communication in the **asynchronous** setting is non-trivial, and m... | Rebuttal 1:
Rebuttal: We thank reviewers for recognizing that our method accelerates previous state of the art **asynchronous decentralized** methods both theoretically and empirically (reviewers fkqi, o6Xd, fQ1m) which is a good step towards addressing common challenges of large scale training of deep neural networks ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Finding Counterfactually Optimal Action Sequences in Continuous State Spaces | Accept (poster) | Summary: The authors present a method for finding c/f (optimal) action sequences in sequential decision making problems with uncertainty, with the novelty that they consider continuous state dynamics. They apply their method to the interesting setting of sepsis treatment at the end of the paper and therein demonstrate ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful and insightful comments, which will help improve our paper. Please, find a point-by-point response below.
**[Miscellaneous comments on presentation]** We would like to thank the reviewer for all their concrete suggestions regarding presentation/organization... | Summary: This paper tackles the problem of finding counterfactual action sequences in sequential decision making problems. The main difference to previous methods is that this paper regards a continuous state space instead of a discrete one, which renders previous solution methods infeasible. The authors formalize the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful and insightful comments, which will help improve our paper. Please, find a point-by-point response below.
**[Efficiency of our method]** The efficiency of our method depends on the tightness of the bounds $\hat{V}_ {\tau}$, which depends (i) on the number o... | Summary: The paper tackles the problem of finding optimal action sequences in domains with continuous state spaces with counterfactual reasoning. They propose an A* based search approach and show the efficacy of the approach on a clinical sepsis management problem.
Strengths: * The paper focuses on a very significan... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful and insightful comments, which will help improve our paper. Please, find a point-by-point response below.
**[d-separation]** Although the reviewer is correct that, in general, a do() operation on a variable (in our case, $A_t$) removes all *incoming* edges ... | Summary: This paper studies a question that is very natural for a sequential decision maker to ask itself: "how could I best improve the return I obtained in a trajectory by only changing a fixed (k) number of actions from the sequence of actions I just executed while keeping the rest of the actions fixed". Authors stu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful and insightful comments, which will help improve our paper. Please, find a point-by-point response below.
**[Lipschitz-like tools for RL algorithms]** We would like to thank the reviewer for bringing these papers to our attention, which we will cite in the ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper studies the problem of finding a counterfactual action sequence to maximize the outcome of a trajectory in an MDP characterized by a structural causal model. The focus is on continuous state spaces under a set of Lipschitz constraints. The authors show that this problem is NP hard and propose an algo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful and insightful comments, which will help improve our paper. Please, find a point-by-point response below.
**[Related work]** We agree with the reviewer that there is a rich literature on structural causal models (SCMs) that is not connected to reinforcement... | null | null | null | null | null | null |
A Unified Algorithm Framework for Unsupervised Discovery of Skills based on Determinantal Point Process | Accept (poster) | Summary: This paper focuses on unsupervised option discovery. It uses the framework of Determinantal Point Processes (DPP) with the aim of combining the advantages of variational and Laplacian-based methods, and unify the desiderata of coverage and diversity of the learned options. Empirical validation shows the benefi... | Rebuttal 1:
Rebuttal: ## Regarding the definition of option coverage (Question #1):
In our paper, coverage is a property defined w.r.t. a single option. It refers to the expected number of landmark states (i.e., clusters of states) traversed by an option trajectory. It is defined as $f(\tau)$ in Eq. (6). We view state... | Summary: This paper introduces a novel framework for unsupervised option discovery by utilizing Determinantal Point Process (DPP) to quantify and optimize both the diversity and the coverage of the learned options. The proposed unified option discovery framework captures the advantages of both variational and Laplacian... | Rebuttal 1:
Rebuttal: ## Regarding fine-tuning the hyperparameters:
As noted in the paper, the crucial hyperparameters are $\beta,\ \alpha_{1:3}$ in Eq. (4) and (9) which control the importance of each objective term, relating to diversity and coverage. Conducting a grid search on the set of parameters can be exhausti... | Summary: The paper introduces an unsupervised option discovery framework based on a combination of Determinantal Point Process and Laplacian spectral features. The main idea is to combine variational methods and Laplacian methods in order to control for coverage and diversity of the options. The proposed method has be... | Rebuttal 1:
Rebuttal: ## Regarding the Gram Matrix (Question #1):
As introduced in Section 2.2, the Gram Matrix includes the quality measures $q$ and normalized vectors $\vec{b}$ for each element in the set. From Eq. (1), we can see that the sampling probability is proportional to the squared volume of the parallelep... | Summary: This paper addresses reward-free options discovery for RL. First, it notes that prior work would prioritize either state coverage or diversity into the options discovery procedure. Hence, it proposes a new loss function that fosters coverage and diversity simultaneously by exploiting DPPs on both the trajector... | Rebuttal 1:
Rebuttal: ## Regarding loss terms (Question #1):
As noted in the first paragraph of Section 3, we need to learn an intra-option policy $\pi_{\theta}(a|s,c)$ conditioned on the option choice $c$. Each option choice should correspond to a specific policy. As a common practice in variational methods, this typ... | Rebuttal 1:
Rebuttal: ## Regarding complexity of ODPP :
The learning target of ODPP is an intra-option policy $\pi_{\theta}(a|s,c)$ conditioned on the option choice $c$. As in Section 3.3, this policy is learned with an Actor-Critic algorithm for which the Q-function is defined as Eq. (12). This Q-function contains va... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes and evaluates an approach to option discovery in reinforcement learning. The aim is to autonomously identify a set of options that are diverse and that give good coverage of the state space. This aim is achieved by using the Determinantal Point Process (DPP). The proposed approach is evalua... | Rebuttal 1:
Rebuttal: Thank you for your appreciation. We will fix the issues that you mentioned during the final submission stage, including moving analysis of the computational complexity to the main text, highlighting the ceiling performance in the learning curves, and adjusting the text size in Figure 1(d). | null | null | null | null | null | null |
On Consistent Bayesian Inference from Synthetic Data | Reject | Summary: The authors consider the use of synthetic data $X^{sync} $ created from a model $p(X^{sync}|Z, I_S)$ on a further Bayesian analysis where the analyst has access to $p(Q|X^{sync})$ and to $p(X^{sync}| Z)$, where $Q$ is the parameter.
the paper is based on equality (4)
$ p(Q|Z) = \int p(Q|Z.X^*)p(X^*|Z)dx^*$ ... | Rebuttal 1:
Rebuttal: > I am not sure the results are correct. From the presentation I don't understand the author's eq (4) or rather their comment which says
that $p(Q | X^*, Z)$
is different from $p(Q | Z)$. The reason is that the authors do not explain what is the generating model for $X^*$(In their paper the autho... | Summary: The paper works on performing consistent Bayesian inference from synthetic data under DP. The authors propose a solution that involves mixing posterior samples from multiple large synthetic datasets, proving that this technique converges to the posterior of downstream analysis under specific conditions. This w... | Rebuttal 1:
Rebuttal: > The motivation behind the research is not explicitly stated. Could you clarify the unique benefits that Bayesian Inference offers in this context? How does it enhance the study or application beyond the capabilities of other methodologies (frequentist)?
Bayesian inference is widely used in nume... | Summary: Inspired by Bayesian approaches for performing multiple imputation of missing data, this paper investigates the applicability of similar strategies for the analysis of synthetic data. Namely, the paper proposes inferring the downstream posterior of a Bayesian analysis by: generating multiple synthetic datasets... | Rebuttal 1:
Rebuttal: > One limitation of the proposed approach appears to be its reliance on the congeniality assumption (which we should not expect to hold in general). While the paper uses a simple example to illustrate that the method was still able to recover the data provider’s posterior when congeniality was vio... | Summary: The paper studies Bayesian inference based on synthetic datasets generated in a DP and non-DP setting.
The paper suggests a specific sampling approach for downstream Bayesian inference using synthetic DP and non-DP dataset. It contributes theoretical results on the convergence of the inferenced posterior (fr... | Rebuttal 1:
Rebuttal: > The balance between theory and experiments is generally very good for my taste, but I feel the experimental part (in the main paper) let the theory part down a bit. The initial experiments focus on intuition and basic insights which I string support; however once the basics have been presented i... | Rebuttal 1:
Rebuttal: ## Motivation and Contribution
Several reviewers wrote that the paper's contribution and motivation is unclear. Our motivation was
investigating whether multiple synthetic datasets could be used for consistent downstream Bayesian
inference when the real data is not available due to privacy conce... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work is sloving a interesting task, which infers the downstream analysis posterior using synthetic data. The work proved that the Bernstein-von Mises theroy applies, the method can converage to the ture posterio as the number of synthetic datasets. The experimental settings are under two examples, i.e. no... | Rebuttal 1:
Rebuttal: > Since synthetic data is generated by models which are trained using real data. So why synthetic data can improve the consisten bayesian inference is not clear. I think the paper needs more discussion about differences bewteen the real data and synthetic data.
The synthetic data does not improve... | null | null | null | null | null | null |
Model Shapley: Equitable Model Valuation with Black-box Access | Accept (poster) | Summary: This paper studies an abstract problem of model valuation.
They propose a notion of valuing models, called the *Model Shapley Value*, based on the classic notion of Shapley Value from the literature on cooperative games.
Additionally, the work proposes an abstraction for models, a *Dirichlet abstraction*, that... | Rebuttal 1:
Rebuttal: We thank Review Coej for reviewing our paper and for finding our problem "well-motivated" and the usage of the Shapley value also "well-motiavated".
We wish to address the questions as follows.
W1.
> What is the task? What is the key goal of this paper?
As pointed out by the reviewer:
> This ... | Summary:
The authors represent the model's predictive accuracy and certainty with Dritchlet abstractions and formalize model Shapley values, which measure the value of models to a given task defined by a query set.
They evaluate their work on MNIST, CIFA-10, DrugRe, and medNIST datasets.
Strengths: - The authors lay... | Rebuttal 1:
Rebuttal: We thank Reviewer bude for taking the time to review our paper, and for appreciating our studied problem ("lay out an interesting problem"), proposed approach ("a solid, well-thought-out solution"), presented results ("theoretical statements are sound, and the results of the empirical analyses are... | Summary: This paper introduces a novel approach to model comparison and valuation, utilizing a method known as Dirichlet Abstraction. The fundamental idea is to abstract the predictive behavior of different models via a Dirichlet distribution. This abstraction allows the comparison of diverse models on an equal footing... | Rebuttal 1:
Rebuttal: We thank Reviewer RTGV for taking the time to review our paper and providing very detailed feedback and comments, especially saying that our work presents "a novel approach", "is quite innovate" and "a valuable addition to the machine learning and statistical literature". We wish to address the fe... | Summary: The paper considers the problem of assigning a value to a ML model (e.g., in a marketplace with multiple models). The proposed idea is to estimate (via MLE) the Dirichlet abstraction of a model (potentially conditioned on the class) and to compute the Shapley value of a game where the value function is the Hel... | Rebuttal 1:
Rebuttal: We thank Reviewer tCDm for taking the time to review and providing such detailed feedback and comments, and for finding our paper "interesting" and "well written". We wish to address the feedback and questions as follows.
W1.
> So $\phi\_i$ will be the Shapley value only for a particular value of... | Rebuttal 1:
Rebuttal: We thank all the reviewers for taking the time to review our paper and providng the detailed comments and positive feedback:
- Our studied problem is interesting and well-motivated (Reviewers tCDm, bude & Coej);
- Our approach is novel and creative, and the quality of our research is high (Revie... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Quantum Bayesian Optimization | Accept (poster) | Summary: The paper studies quantum kernelized bandits or Bayesian optimization (BO). Classically, in every iteration t=1,2,\ldots,T, a BO algorithm chooses an arm x_t and then queries the reward function f for a noisy observation y_t=f(x_t)+\zeta_t, where f can be non-linear and \zeta_t is a sub-Gaussian noise. The goa... | Rebuttal 1:
Rebuttal:
We'd like to thank the reviewer for your insightful comments.
---
> 1. I'm a bit doubtful about the technical contributions of this paper... Such a combination, of course, can be regarded as the main technical novelty, but the question is, I have no idea whether it raises inherent difficulties ... | Summary: The paper studies the regret attainable for multi-armed bandits with non-linear reward functions when having access to a quantum oracle. For this setting they introduce the Quantum Gaussian Process Upper Confidence Bound (Q-GP-UCB) that with probability at least $\delta$ achieves regret:
- $\mathcal{O}((d\log{... | Rebuttal 1:
Rebuttal:
We'd like to thank the reviewer for your valuable comments.
---
> Could you expand more on the empirical behavior of Q-GP-UCB in the initial stage? It is a bit hard to see given the large setting of total time-steps. For example, could you comment on when this may be an issue and when not?
The... | Summary: This paper considers kernelized bandits also known as Bayesian optimization under a particular feedback model inspired by quantum computing. Under this model repeating sampling from the same point for N times reduces the noise to the level on $1/N$. That is significantly tighter than the classic setting where ... | Rebuttal 1:
Rebuttal:
We'd like to thank the reviewer for your constructive feedback.
---
> The formulation and analytical techniques are similar to those of [32] in the case of linear bandits. That to some extent limits the novelty and contributions.
We have clarified our technical novelty and contributions (espec... | Summary: This paper studies Bayesian optimization with quantum reward oracles where the reward function $f$ lies in an RKHS space with the squared exponential kernel, and at every iteration after input is selected, we can access a quantum unitary oracle and its inverse that encode the noisy reward distribution. In such... | Rebuttal 1:
Rebuttal:
We'd like to thank the reviewer for your insightful comments.
---
> However, I am concerned about the novelty of the used techniques in this paper... the regret upper bound of $\mathcal{O}(\text{poly}\log T)$ for their proposed BO algorithm has the same order as that of [32] which is designed f... | Rebuttal 1:
Rebuttal:
We'd like to sincerely thank all reviewers for your constructive feedback and for appreciating our contributions.
For example, Reviewer Hp2A and Reviewer 5kMD have acknowledged that our paper "introduces the first quantum BO algorithm", Reviewer pGF9 has commented that our work "may be of broader... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
PTQD: Accurate Post-Training Quantization for Diffusion Models | Accept (poster) | Summary: The paper introduces PTQD, a Post-Training Quantization framework for Diffusion models.
PTQD analyzes the influence of quantization noise on diffusion noise.
The method suggests separating the quantization noise into noise correlated and uncorrelated with the full-precision reverse diffusion.
The correlated p... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for the valuable comments.
**Q1: Contribution and impact in deterministic sampling.** 1) We acknowledge that the efficacy of our contributions encounters constraints for deterministic sampling, as pointed out in line 205 of the paper. However, in the deterministic case, we ... | Summary: The paper suggests a method for post-training quantization of diffusion models. The method consists of a factorization of the quantization noise into a correlated and an uncorrelated part, and then addressing each component separately, either by linearly regressing for the correlation coefficient, or incorpora... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for the valuable comments.
**Q1: Methodical/result-based differences between this work and Q-Diffusion.** In terms of methodology, Q-Diffusion designed a calibration data collection method and applied the PTQ method BRECQ [i] to the diffusion model. In sharp contrast, we in... | Summary: The authors propose a new post-training quantization method for diffusion models titled PTQD that disentangles the quantization noise into correlated and uncorrelated parts regarding its full-precision counterpart, and demonstrate that PTQD generates as much high-quality samples as its full-precision counterpa... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for the valuable comments.
**Q1: The speed of PTQD in real time.** We have measured the latency of matrix multiplication and convolution operations in quantized and full-precision diffusion models using an RTX3090 GPU, as presented below. Both floating-point and quantized o... | Summary: The paper proposed the quantization scheme for Diffusion models, where they disentangled the quantization noise into the correlated and uncorrelated parts;
Then, they incorporated the correlated part into diffusion-perturbed noise and calibrated the denoising variance schedule to absorb additional variance int... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for the valuable comments.
**Q1: It does not seem to show noticeable improvement compared to existing works.** 1) It is essential to consider that the absolute performance **improvement is closely related to the precision** of the model. When higher bitwidths are employed, ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable feedback. Overall, our work has been well recognized as it "is well-organized and clearly presented" (Reviewer i5nq), presents a novel idea" (Reviewer oS4i) and "obtains impressive results" (Reviewer MVe5). We have summarized and addressed the main concern... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces PTQD, a novel method designed to tackle issues arising when applying existing post-training quantization techniques directly to low-bit diffusion models. The proposed approach disentangles quantization noise into its correlated and residual uncorrelated components regarding its full-preci... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for the valuable comments.
**Q1: Include comparisons with recent DDPM variants and evaluating PTQD with other post-training quantization methods on diverse image datasets in the experiments.** Table D in the rebuttal PDF presents the results on a **new dataset CelebA-HQ** o... | null | null | null | null | null | null |
Detection Based Part-level Articulated Object Reconstruction from Single RGBD Image | Accept (poster) | Summary: The paper presents a novel task focused on the reconstruction of multiple articulated objects, considering part-level shape, pose, joint parameters, and part-instance association, using only a single RGBD image. The authors propose an effective detect-and-group strategy that harnesses the part-level representa... | Rebuttal 1:
Rebuttal: Thank you for your feedback and questions!
## Novel viewpoint of the reconstructed shapes
We promised to add novel views in camera ready. For reference, we show the novel viewpoint of the reconstructed shape in Fig. 5 and Fig. 7 of the main paper in Fig. 4 of the attached material. Our method qua... | Summary: This paper presents a new method for 3D semantic instance reconstruction at the part level from a single RGB-D image. This method follows a top-down manner by first detecting object parts using 3DETR, where each instance part's bounding box will be predicted. The point cloud located inside each bounding box co... | Rebuttal 1:
Rebuttal: Thanks for your detailed review!
## Effectiveness of the KPF module and comprehensive analysis
The primary source of improvement in shape reconstruction accuracy is our part-level reconstruction approach enabling the reconstruction of articulated objects with various part counts, which is the mai... | Summary: The paper presents a detection-based reconstruction method for articulated objects along with estimating part-level 6D object poses, sizes, and joint parameters. The paper uses 3DETR as the backbone predicts all of the above quantities while treating this problem as a supervised learning approach given labeled... | Rebuttal 1:
Rebuttal: Thank you for your feedback!
## Targeting articulated objects with >5+ joints
The trained model works reasonably for complex target instances like >5+ joints, as shown in Fig. 2 of the attached material, especially when all parts are clearly visible from the given view. However, from a certain v... | Summary: The paper proposes a method for man-made articulated objects reconstruction from a single RGBD image across different object categories. The method is based on a detect-then-group pipeline, using kinematics-aware fusion for addressing false negatives.
Strengths: The proposed method addresses the challenging ... | Rebuttal 1:
Rebuttal: Thank you for your review and questions!
## Joint state error
We show the joint state error compared with OPD in Table 3 in the main paper, denoted as “State.” We have also added the joint state error comparison against A-SDF-GT-2 in the table below. Note that A-SDF-GT-2 can only be evaluated aga... | Rebuttal 1:
Rebuttal: # Global comment
We thank all the reviewers for their thoughtful feedback. We are encouraged that the reviewers having identified our paper making a good contribution (**mcMG**, **Y464**, **MJHC**, **VtGS**), and the proposed to be interesting (**MJHC**), intuitive and effective (**mcMG**), workin... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes an end-to-end trainable method for reconstructing multiple articulated objects from a single RGB-D image, consisting of detecting parts, reconstructing part-level shapes, and estimating poses, bounding boxes as well as kinematic parameters. The parts are grouped into instances later. The aut... | Rebuttal 1:
Rebuttal: Thanks for reviewing our paper!
## How to choose revolute origin?
As pointed out, any point on the line formed by GT revolute origin and GT joint axis is the correct revolute origin. Thus for evaluation, we measure the minimum distance (MD) between the GT axis line and the predicted revolute orig... | null | null | null | null | null | null |
Beyond NTK with Vanilla Gradient Descent: A Mean-Field Analysis of Neural Networks with Polynomial Width, Samples, and Time | Accept (poster) | Summary: This paper studies the global convergence of gradient descent for training two-layer networks in learning a high dimensional quartic function. The authors show that GD converges when the sample size $n = O(d^{3.1})$ and the width of the neural network grows at most polynomially in d. The authors also showed th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review, and for mentioning that “this is an excellent paper on the non-asymptotic mean field analysis of GD for training two-layer neural networks.”
# Extending to more general activations
The framework that we use for analyzing the population gradient fl... | Summary: This paper studied the projected gradient flow on two-layer neural networks in the mean-field regime with polynomial width and quartic activation function. With data sampled uniformly on the sphere, the authors proved that to learn a single-index model with an even quartic link function, this neural network ne... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback and for noting that our work “presents interesting insights into more accurate models of neural network training.” We now address the reviewer’s questions. We will incorporate presentation-related comments in the revision and include a simulation t... | Summary: This paper studies the statistical efficiency of the projected gradient dynamics on the sphere for (polynomial-width) two-layer neural networks under the mean-field regime. In particular, this work proves the sample complexity of $O(d^{3.1})$ for learning the single-index model with an unknown quartic link fun... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and for noting that our “results can be of interest to the readers and are technically sound.” Below we address the reviewer’s questions and concerns.
# Generality of Activation Function
We agree with the reviewer that our activation/target function devia... | Summary: Analyzed the (projected) gradient flow dynamics of a two-layer neural network in the mean-field regime in learning a specific degree-4 single-index target function. The main contribution is a polynomial-time convergence guarantee and a sample complexity that outperforms kernel methods. This differs from the na... | Rebuttal 1:
Rebuttal: We thank the reviewer for their very helpful comments, and for noting that our submission “definitely tackles a challenging and interesting problem.” We now respond to each of the reviewer’s concerns.
# Discrete Time
We were able to extend our results to time discretization, and we will include ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Expert load matters: operating networks at high accuracy and low manual effort | Accept (poster) | Summary: This work supposes a real-world setting where misclassified examples are reviewed post-hoc by human experts, and offers a near-optimal trade-off between such examples (the expert load) and classifier accuracy. The authors propose to use a curve of confidence versus expert load, using the latter as a sliding sc... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating the proposed method and we are happy to answer to the raised questions.
AUCOCLoss (Equation 3), exploits the definition of AUCOC (equation in Section 3.2.2). Specifically, to build a differentiable loss out of this definition, we employ KDE to define E[c|r]p... | Summary: The authors present a "confidence operating characteristic" curve to represent tradeoff between accuracy and numbers of samples delegated to human experts. To maximize the area under this curve, the authors propose a new loss. The authors run classification experiments on computer vision and medical image data... | Rebuttal 1:
Rebuttal: # Comment about active learning and related work.
We believe there is a misunderstanding, as the proposed paper is not performing active learning. Active learning and our work have two inherently different goals. Active learning aims to include the human expert in the loop **during training**. In... | Summary: The paper aims to address the trade-off between model accuracy and model confidence results. The authors propose a novel loss function called AUCOC, which maximizes the area under the confidence operating characteristic curve. They evaluate the performance of their approach on various image classification and ... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the importance of the task and appreciating the simplicity of the proposed method. We are happy to address the raised concerns.
# Comment about additional datasets.
We would like to point out that the presented article is not primarily an empirical study, ... | Summary: This paper proposes a new loss, AUCOC loss, to improve the networks accuracy and prediction confidence. The loss aims to reduce the number of errors made by the algorithm and thus also the number of delegated samples to domain experts. The proposed loss focuses on maximizing the area under the COC curve durin... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating the proposed work and helping us improve the explanations. We are happy to address the raised doubts and questions.
# Comment about employed architectures and additional experiments on CIFAR100 and Tiny-ImageNet.
We thank the reviewer for pointing this out... | Rebuttal 1:
Rebuttal: We thank the reviewers for taking the time to read our paper and for providing insightful feedback and input.
In each rebuttal we addressed the individual concerns of the reviewers and we are happy to respond to any additional doubts or questions.
In the PDF uploaded in this "general" response, w... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Unified Approach for Maximizing Continuous DR-submodular Functions | Accept (poster) | Summary: In this work, the authors present a framework for maximizing continuous DR-submodular functions over a range of settings and Oracle access types. To achieve this, they employ a variant of the Frank-Wolfe algorithm which yields either the first guarantees in some cases or comparable results to the SOTA in other... | Rebuttal 1:
Rebuttal: Thank you for your time in reviewing our paper.
**Weaknesses**
1. For this concern, there are two points we would like to highlight.
(1) First, we would like to gently push back on the statement mentioned in the concern that "the main contribution of this work is on avoiding computationally ex... | Summary: This paper studies offline constrained DR-submodular maximization in 16 different settings (monotone/non-monotone, down-closed/general convex constraint, gradient/value oracle access, and exact/stochastic oracle) and provides a unified approach to solve all 16 cases with the same Frank-Wolfe algorithmic framew... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in reviewing our submission. We reply to your questions in order below. We also include a discussion on novelty and significance in a general rebuttal box above.
1. Thank you for pointing this out.
We first discuss the technique.
The technique used in that pap... | Summary: The paper studies maximizing stochastic DR-submodular functions under 16 different settings, which depend on 1) whether the function is monotone or not, 2) the feasible region is a downward-closed or a general convex set, 3) gradient or value oracle access is available, and 4) or the oracle is exact or stochas... | Rebuttal 1:
Rebuttal: Thank you for time and efforts in reviewing our submission. Below we first provide a response to the points you raised in the "Weaknesses" section and then reply to your questions in order.
**Weaknesses**
1. In the "global rebuttal" above, we explain the novelty of our work in more detail and r... | Summary: This paper considers the problem of maximizing a continuous DR-submodular function over a convex set, under various settings for the objective (monotone/non-monotone), constraint set (downward-closed/includes the origin/general set), and oracle access types (deterministic/stochastic gradient/value oracle) -- 1... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and helpful suggestions. Below we reply to your questions in order.
1. You are correct. In Lemma 5, the ratio $d/2\delta$ in the expectation should be replaced by $k/2\delta$ where $k = dim(A)$.
Similarly, in Algorithm 1, the ratio $d/2\delta$ used in line 4 s... | Rebuttal 1:
Rebuttal: We highlight our technical contributions (in addition to contributions in obtaining new guarantees for numerous offline and online settings as well as unifying algorithm design and analysis among several prior works).
1. **Our procedure is the first Frank-Wolfe type algorithm for analyzing monoto... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds | Accept (spotlight) | Summary: The paper concerns Gaussian processes on manifolds. The authors present theorems for contraction rates for Matern Gaussian processes defined intrinsically and extrinsically through and embedding in a higher-dimensional Euclidean space. The authors shows that the rates are asymptotically equal in the two settin... | Rebuttal 1:
Rebuttal: Thank you very much for your review, and especially for the comment that you found our exposition "very clear" in spite of the technical nature of the subject! Below we comment on one of the points.
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*"since the theorems are not in the main paper, one could perhaps consider if a longer format... | Summary: This paper studies the contraction rate(s) for both the intrinsic and extrinsic Mat\'ern Gaussian process in compact Riemannian manifold. The authors proved that the (optimal) rate in both cases is $\frac{2 \min(\beta, \nu)}{2 \nu +d}$, where $\nu$ is the smooth parameter of the Mat\'ern process, $\beta$ is th... | Rebuttal 1:
Rebuttal: Thank you very much for your thorough review of our work and for the very encouraging comments! Below we address key questions:
**Further work:**
(1) *Nugget and prior on $\sigma_\epsilon$*
* Thank you for this question! In our work, the main reason we assume $\sigma_\epsilon$ is fixed is becaus... | Summary: This paper establishes bounds on the contraction rate of matern processes on Riemannian manifolds. The authors study three variants:
1) intrinsic matern process
2) truncated intrinsic matern process
3) extrinsic matern process
and show that in each case, the optimal contraction rate can be achieved, which matc... | Rebuttal 1:
Rebuttal: Thank you very much for taking the time to review our work! Thank you for the encouraging comment that work addresses a fundamental research problem! Below we address the questions, some of which we had to partially quote due to character limits:
---
*".. intuition for why nu is the smoothness p... | Summary: This paper investigates the theoretical properties and performance of Gaussian processes in machine learning, particularly when applied in geometric settings such as Riemannian manifolds. It compares intrinsic and extrinsic methods, with the former directly formulated on the manifold of interest and the latter... | Rebuttal 1:
Rebuttal: Thank you very much for the accurate summary of our work and for your review! If you have any further questions or comments, please feel free to post them - we are happy to provide further information or clarification where needed. If not, thank you very much for your time in reading our work! | Rebuttal 1:
Rebuttal: We would like to thank the referees for their summaries and pertinent observations that will help us to improve the final version of the paper. Most of the comments were about the format, additional references, identifiability and insights regarding the definitions of the different processes; we h... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Towards Higher Ranks via Adversarial Weight Pruning | Accept (poster) | Summary: This paper proposes a novel Rank-based PruninG (RPG) method for network pruning that maintains the ranks of sparse weights in an adversarial manner, leading to high-rank topology and improved performance. The proposed method is evaluated on various datasets and tasks, including image classification, object det... | Rebuttal 1:
Rebuttal: Dear Reviewer GHQR,
Thank you very much for your review. Here are our reponses to the weaknesses and questions you raised:
1. *The RPG method is not performant at low sparsities, impairing low-sparsity applications*: We admit the limitation of the RPG pruning methods under low-sparsity regimes, b... | Summary: This work propose a novel objective for performing element-wise pruning of DNN model. The work identifies the loss of weight rank as the key factor influencing the performance of DNN when pruned to high sparsity. This issue is tackled by including a rank loss in the pruning criteria, so that weight elements co... | Rebuttal 1:
Rebuttal: Dear reviewer P9XJ,
Thank you very much for your review. Here are our responses:
Q1. *The author should provide a comparison of accuracy-speed tradeoff of the proposed method and previous structural pruning results.*
A1: Thanks for your suggestion. Here we attach a table comparing the CPU speed... | Summary: This paper proposes a new weight pruning method for compressing Convolutional Neural Networks (CNNs) called Rank-based PruninG (RPG). The RPG method consists of two steps: first, the low-rank approximation error for the weight matrices is minimized using singular value decomposition, and second, the weight mat... | Rebuttal 1:
Rebuttal: Dear reviewer jsvG,
Thank you very much for your review. Here are our responses:
Q1. *Additional computations and operations can make the proposed method less practical for large-scale networks or real-time applications.*
A1: In fact, these extra overheads only accounts for a small proportion o... | Summary: This paper proposes a novel unstructured pruning method, trying to maximize the matrix rank while trying to remove as many model weights as possible. The paper first demonstrates the phenomenon that unstructured pruning may degrades to structured pruning at large sparsity ratios, which is closely related to th... | Rebuttal 1:
Rebuttal: Dear reviewer yERC,
Thank you very much for your suggestions. Here are the answers to the questions you raised:
Q1. *RPG will make the unstructured pruning less adaptable to hardwares.*
A1: Sorry for ambiguities in the paper. "Structured pattern" does not necessarily mean the acceleration-frie... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Minimax Risks and Optimal Procedures for Estimation under Functional Local Differential Privacy | Accept (poster) | Summary: The authors consider the a local version of functional DP / Gaussian DP, and establish minimax rates of convergence for mean estimation and density estimation under this privacy paradigm. They highlight how functional/Gaussian DP is more conducive to LDP than approximate DP given it’s tight compositional prop... | Rebuttal 1:
Rebuttal: **Q**: A seemingly glaring problem with results such as Corollary 2 is the privacy level, $\mu$, is not made explicit in the rate. However the constants in the theorem do depend on $\mu$. The results of Duchi et al make the dependence on epsilon explicit, and it is quite substantial (i.e. taking s... | Summary: The authors study the problems of mean estimation and density estimation under functional local differential privacy (FLDP). In particular, they are interested in deriving minimax (rate-) optimal estimation procedures and privacy mechanisms for these problems. Their results include analogues to Le Cam's bound ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful comments and insightful feedback.
**Q1:** Can the authors provide more interpretation of the general versions of Le Cam's and Assouad's bounds (Theorems 1 & 3)? Even some simple intuition such as, when would we expect the lower bound to be non-negative, et... | Summary: The paper proposes a local version of functional differential privacy (FDP), and finds the minimax rate of mean estimation and non-parametric density estimation under local FDP. This paper amounts to an extension of the main results of Duchi et al. (2018) from epsilon-LDP to local FDP.
Strengths: Originality:... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful comments and insightful feedback.
**Answer to Q1 on $\kappa$:** (i) We examined a continuous range of lower bounds relative to $\kappa$ with the purpose of investigating the *gradual shift* in optimal utility associated with privacy constraints. It was mot... | Summary: This paper investigates the minimax risk achieved under functional local differential privacy (FLDP) constraints, and particularly under Gaussian local differential privacy (GLDP). The authors first introduce lower bounds for univariate mean estimation under FLDP using Le Cam’s method. Under certain assumption... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful comments and insightful feedback.
**Q1:** Constants matter in differential privacy. In the current state of the paper it is clear that asymptotically the rates are the same, however it is hard to get an intuition of the constants. The plots however provide ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Look Ma, No Hands! Agent-Environment Factorization of Egocentric Videos | Accept (poster) | Summary: The paper addresses challenges in using egocentric videos for robotics tasks, specifically the issues of occlusion and visual mismatch between the human hand and a robot end effector. To address these problems, this work proposes a factored representation of the scene that separates the agent (human hand) from... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and thoughtful feedback. Please see our response below and refer to the supporting figures in the rebuttal PDF.
> It seems mostly Ienv is being used; where does the agent representation come into play? In application 3, I assume you're using Ienv to predict the G... | Summary: This paper proposes to use agent-environment factorization of egocentric videos to facilitate various downstream tasks (e.g., object detection, 3D reconstruction, affordance prediction, etc.). The authors leverages a pipeline to achieve agent-environment factorization. It consists of first a segmentation model... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and thoughtful feedback. Please see our response below and refer to the supporting figures in the rebuttal PDF.
>One major concern on this paper is on its technical contribution. The proposed VIDM contains limited technical novelty as it is a basic segment-then-i... | Summary: This work proposes the use of a factored agent and environment representation to handle two ego-centric video problems introduced by human hands : 1. They occlude objects of interaction and induce a domain gap between the data available for learning (egocentric videos) and the data seen by the robot at executi... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and thoughtful feedback. Please see our response below and refer to the supporting figures in the rebuttal PDF.
> In figure 2, what is the meaning of the big f and g? Does they stand for different functions? If so, what is the purpose of drawing them in that way?... | Summary: The following work presents a factorized approach for video-based egocentric tasks. Specifically, they propose to break down the video feed into separate environment-only and hands only feeds. Intuition behind this formulation is that change in appearance of the hands may constitute a domain gap when the sourc... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and thoughtful feedback. Please see our response below and refer to the supporting figures in the rebuttal PDF.
> While the superiority of their video inpainting formulation is demonstrated only within the hand-removal task of ego-centric videos, the language used... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their valuable and insightful comments. Attached to this post is a single page pdf containing 3 figures: B1, B2, and B3. B1 is a diagnostic visualization showcasing VIDM’s ability to intelligently copy pixels from previous frames. B2 visualizes failure mode... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes agent-environment factorization (AEF) as a representation for egocentric videos. AEF consists of 2 parts: the hand segmentation as agent part, and video inpainted environment part. The former uses an off-the-shelf hand segmentation while the latter is from finetuning an inpainting diffusion ... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and thoughtful feedback. Please see our response below and refer to the supporting figures in the rebuttal PDF.
**Clarifications about improvement in the video inpainting model**
First, to clarify, VIDM includes a) an architectural modification on top of an image... | null | null | null | null | null | null |
An Empirical Study Towards Prompt-Tuning for Graph Contrastive Pre-Training in Recommendations | Accept (poster) | Summary: The paper presents an empirical study on the application of prompt-tuning for graph contrastive pre-training in recommendation systems. The authors propose a method that combines graph neural networks (GNNs) and contrastive learning to enhance the performance of recommendation models. The key idea is to levera... | Rebuttal 1:
Rebuttal: Thanks for your comprehensive reviewing and recognising our contributions. We sincerely appreciate your valuable comments and suggestions. We would response to your comments and concerns in the following.
W1. The paper assumes readers have a strong understanding of graph contrastive learning and ... | Summary: This paper proposes a prompt-enhanced framework for GCL-based recommender named CPTPP. At the core of CPTPP is a personalized user prompts generation framework that summarizes user profiles in graph recommender systems. The generated user prompts are then integrated with pre-trained user embeddings when applie... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and questions. We will revise our paper according to them. Now, we would like to respond to your suggestions and questions.
W1. CPTPP relies on exploiting historical interaction records, adjacency matrix, and high-order user relations for generating personalized ... | Summary: This paper proposes a prompt-tuning approach for GCL-based recommender systems. A framework consisting of a GCL module, a prompt generation module and a recommendation module is developed. Both ablation study and hyper-parameter study are conducted.
Strengths: - This paper studies an interesting research prob... | Rebuttal 1:
Rebuttal: Thanks for your review. We would like to respond to your comments and questions in the following.
W1. The improvements over existing baselines are not significant. For example, SimGCL outperforms the proposed CPTPP approach on Gowalla dataset w.r.t NDCG@20.
Thanks for your conscientious and deta... | Summary: This paper proposes a prompt-enhanced framework for GCL-based recommender system, called CPTPP. CPTPP reforms the existing GCL-based recommendation methods with the prompt tuning mechanism to fully exploit the advantages of GCL in the pre-training phase instead of combining the contrastive loss with downstream... | Rebuttal 1:
Rebuttal: Thanks for recognising the contribution and novelty of our research work. We appreciate your suggestions and questions, and we will revise the manuscript according to your comments. The following is our response to your comments.
W1. Generating prompts for users in recommender systems has been pr... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models | Accept (poster) | Summary: This paper studies the unstructured pruning problem for vision transformer models. The Correlation Aware Pruner (CAP) is proposed. CAP takes into account weight correlations and achieves a new state-of-the-art result.
Strengths: - The proposed method achieves strong experimental results. For the first time, V... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback!
**1. Applicability to GPUs:**
In Appendix H.3, we provided results for pruning with hardware-supported 2:4 sparsity pattern [1]. This semi-structured sparsity pattern can lead to speedups of tensor operations on the Ampere and Hopper GPU architecture. One... | Summary: The submission proposes a second-order method for unstructured pruning of neural net parameters, to leverage the efficiency gains of sparsity. It uses an optimization based on the empirical Fischer matrix to find saliency scores, that are used for the order in which weights are pruned. They present an algorith... | Rebuttal 1:
Rebuttal: Thank you for your feedback, which we address in detail below.
> iv) Largely superseded by Optimal BERT Surgeon in a lot of settings
In short, we emphasize that the scalability of both methods you reference (Optimal BERT Surgeon (OBS) and CAP) is essentially the same, while CAP is significantly... | Summary: This paper proposes to consider the correlation between pruned elements in pruning deep neural netowkr models. The paper provides an efficient algorithm to distangle the correlation into a sparse regression problem, and propose a fast solver to find the solution. Further exploration is performed on the learnin... | Rebuttal 1:
Rebuttal: Thank you for your very insightful feedback!
Regarding scaling, currently, CAP can scale easily to models with hundreds of millions of parameters, with reasonable block sizes and reasonable runtime (< 30 minutes / pruning step on 1 GPU). Yet, you are right in that it would be challenging to scale... | Summary: The paper proposed a Correlation Aware Pruner (CAP) , a new unstructured pruning framework capable to prune models to high sparsity. It takes into account weight correlations. To do this, the paper reformulate the OBS multi-weight pruning problem: when using the empirical Fisher approximation, the problem of f... | Rebuttal 1:
Rebuttal: Thank you for your review and comments!
It is true that, traditionally, sparsity is harder to leverage for computational speedups. However, unstructured sparsity is now supported with speedups on CPU, and 2:4 semi-structured sparsity is supported with speedups on NVIDIA GPUs. Our method can crea... | Rebuttal 1:
Rebuttal: We thank the reviewers for their feedback and comments on our work.
Below is the summary of the main concerns and questions addressed in our rebuttal:
**1. Difference between WoodFisher (Optimal Brain Surgeon) and CAP.**
In the response, we emphasized that our contribution is not another appro... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
ReSync: Riemannian Subgradient-based Robust Rotation Synchronization | Accept (poster) | Summary: The paper concerns synchronization of observed rotations with incomplete and corrupted observations. The authors construct the method ReSync that is a gradient-based algorithm for solving the problem. The paper describes the context, prior results on the synchronization problem, and the algorithm, and presents... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the supportive and valuable comments. Should the reviewer have any further concerns, please inform us during the reviewer-author discussion period so that we can respond timely.
---
Rebuttal Comment 1.1:
Comment: Thanks for the reply. My scoring has not ch... | Summary: This paper presents a theoretical study for robust rotation synchronization with a least-unsquared minimization formulation over the rotation group.
In particular, this paper proposes a two-step algorithm called ReSync, where the first step uses spectral initialization to generate an initial guess and the sec... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the supportive and valuable comments. We address the concern below.
**A. Guarantees with additive noise.** Yes. It is possible to show the convergence results against additive noise, in which the algorithm will converge to the neighborhood of the ground-tr... | Summary: This work proposes to solve the rotation synchronization problem using Riemannian subgradient method with spectral initialization. The proposed formuation is sum of absolute deviations, which is robust to outliers. Exact recovery guarantees are provided under uniform corruption model (the graph is Erdos Renyi ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the supportive and valuable comments. We address the concerns in a point-by-point manner below.
**A. Stability to noise.** Yes. It is possible to show stability and convergence results against additive noise, which is our ongoing work. The initialization a... | Summary: The paper proposes a Riemannian subgradient based algorithm for the robust rotation synchronization (RRS) problem. RRS involves recovering the absolute rotations of objects from the possibly corrupted/noisy relative rotations between pairs of objects. The problem setting involves two ratios: q \in [0,1] denote... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the valuable comments. We address the concerns in a point-by-point manner below.
**A. Missing observations (Q1 in concerns regarding theory).** Since formulation (2) only relies on available observations, we are allowed to assign the missing entries as $\m... | Rebuttal 1:
Rebuttal: Dear ACs and Reviewers,
This global response contains our one-page supplementary PDF of the rebuttal. All additional figures are included in this file. Please find it in the attachment.
Best regards,
Authors.
Pdf: /pdf/9f8df648769c053a74671597593fa33f1893e658.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Non-Rigid Shape Registration via Deep Functional Maps Prior | Accept (poster) | Summary: The method proposes an unsupervised pipeline to solve 3D shape-to-shape registration. Firstly, the two shapes are aligned by a pre-trained orientation regressor. Then, a soft-correspondence is obtained by a Point Feature extractor, optimized using a Deep Functional Maps schema, and considering several unsuperv... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments and the recognition of our contributions. Below we address the comments:
**Applicative context of the proposed method:**
We actually follow the same scheme of adding noise perturbation as DiffFmaps [30].
We highlight our new results reported in Rebuttal Ma... | Summary: In this paper an unsupervised non-rigid shape registration method is proposed. The proposed method combines intrinsic spectral mapping (i.e. based on the deep functional map framework) together with extrinsic deformable shape registration (i.e. a deformation graph) to enable unsupervised 3D deformable shape m... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments and the recognition of our contributions. Below we address the comments:
**Modules are separated in the pipeline and lack connection:**
Thank you for the constructive comment. We agree that integrating shape matching and shape registration in a more assoc... | Summary: The paper describes a method for corresponding a 3D triangle mesh to a point cloud of a similar (possibly articulated) shape. The two-stage process first corresponds the two shapes in a high-dimensional feature space, then corresponds them again using geometric features while deforming the source closer to th... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments and the recognition of our contributions. Below we address the comments:
**Improve paper presentation:**
Thank you for the suggestions on improving the presentation of the paper, we would be happy to incorporate all of them in the future revision. Regardi... | Summary: This paper proposes an unsupervised framework for non-rigid shape registration. The proposed method deforms source mesh towards the target point cloud, guided by correspondences induced by high-dimensional embeddings learned from deep functional maps. Empirical results show that the proposed method achieves st... | Rebuttal 1:
Rebuttal: Thank you for all the insightful comments. First of all, we would be happy to improve the presentation as suggested, as well as to fix the inconsistent titles in the future revision. Below we address the comments:
**Novelty:** We acknowledge that many components in our pipeline are inspired by e... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their time, effort, and insightful comments on our manuscript. We are glad that all the reviewers recognize our promising results on various benchmarks. We are encouraged by the recognition that our formulation is reasonable and that our writing is clea... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies the problem of non-rigid shape matching. The problem is first decomposed into learning (rigid) orientation of shapes and then learning shape matching on aligned shapes. For the later, it proposes to learn it with a sequential pipeline, consisting of various modules, that is also optimized... | Rebuttal 1:
Rebuttal: We thank you for the constructive comments on our motivation and novelty. Below we address the comments.
**Novelty, especially compared with DPC:** We argue that our approach is *fundamentally* different from DPC in the following perspectives:
1. Our feature extractor for point clouds is learn... | null | null | null | null | null | null |
Self-supervised Graph Neural Networks via Low-Rank Decomposition | Accept (poster) | Summary: The paper argues that when dealing with self-supervised learning tasks, utilizing propagation-based GNNs as encoder inevitably encounters two serious issues, i.e., failing to capture local property due to global parameters and lacking the ability on handling homogenous networks without label information. To th... | Rebuttal 1:
Rebuttal: **Q1. The reason why the low-rank decomposition-based GNNs preserve local information is not presented clearly.**
R1. The characteristic of local information preservation is from both the matrix/tensor construction and low-rank decomposition. Firstly, both the matrix and the slice of the tensor, ... | Summary: The encoder for self-supervised graph neural networks is investigated. The authors identify the weaknesses of capturing local property and handling heterophilic networks in existing propagation-based encoder. They observe that the obtained node representations possess low-rank characteristics and tend to meet... | Rebuttal 1:
Rebuttal: ***Q1. The relationship between the proposed method and existing self-supervised GNN is not clear. Existing self-supervised learning relays on encoder for supervised learning and an objective function. However, the proposed method does not need objective function. Therefore, it is important to fi... | Summary: This paper proposes to alleviate the issues in propagation-based self-supervised GNN using the low-rank matrix/tensor decompositions. Firstly, it points out existing self-supervised GNNs can not capture local information and handle networks with heterophily due to the global learnable parameters and the lack o... | Rebuttal 1:
Rebuttal: ***Q1. What are the differences between unsupervised learning and self-supervised learning. Although I like the idea of seeking representation using low-rank decomposition, I think it is an unsupervised learning. It may be different from self-supervised one. Could you explain their differences.***... | Summary: This paper studies the self-supervised graph learning for node classification tasks.
The authors make an observation that traditional propagation based GNNs loose discriminative information
via node property averaging. To address this issue, the authors propose a novel LRD-GNN method that
encourages low rank p... | Rebuttal 1:
Rebuttal: ***Q1. Justification for selecting exactly M similar ego-networks for every node?***
R1. Thanks for your insightful question. Selecting exactly M similar ego-network is the compromise of the model’s expressive power and generalization ability. It is natural that different nodes should select diff... | Rebuttal 1:
Rebuttal: We would like to express our sincere appreciations to the reviewers for their insightful comments and compliments to our paper. The PDF contains the figure of results on preventing over-smoothing issue.
Pdf: /pdf/2ca8f4a676785b7d0c3cf91049f71adad35293d9.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
LayoutGPT: Compositional Visual Planning and Generation with Large Language Models | Accept (poster) | Summary: The paper highlights the challenges faced by existing models in generating objects with specified counts, positions, attributes, and sizes, and emphasizes the need for compositional skills that can effectively arrange components coherently, accurately reflecting object specifications and interactions.
The aut... | Rebuttal 1:
Rebuttal: We thank the reviewer for pointing out ambiguities in the manuscript, and we’d like to clarify a few misunderstandings below.
>* Weakness 1-1: Reasoning ability of current T2I models
We did not claim that existing visual generative models are not equipped with various reasoning skills. It is a... | Summary: The paper propose LayoutGPT that can generate visual arrangements of objects using the input prompts, providing a way to collaborate with visual generative models for compositional layout based image generation in both 2D and 3D. Experiment results show that such a method can largely improve layout-based gener... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing suggestions for improvements and would like to clarify a few misunderstandings in the response.
> * Weakness 1: Technical contributions of LayoutGPT
We respectfully disagree that our contribution is limited. We have never claimed that LayoutGPT contributes to... | Summary: This paper introduces LayoutGPT, a training-free approach that injects visual commonsense into LLMs and enables generating plausible 2D images and 3D scenes conditioned on layouts based on text conditions. Specifically, the authors experiment with four variants of GPT models: Codex, GPT-3.5, GPT-3.5-chat and G... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and insightful comments.
> * Weakness 1: 3D layout planning with detailed description
Thanks for your suggestions. Please note that 3D-FRONT does not have ground truth captions for the rooms. Besides, existing work mostly conditions on room types or floor plan... | Summary: This paper proposes LayoutGPT, a method to compose in-context visual demonstrations in style sheet language to enhance the visual planning skills of LLMs. As the first work to use LLMs to generate layouts from text conditions, LayoutGPT can generate plausible layouts for 2D images and 3D indoor scenes, includi... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback.
> * Weakness 1: straightforward application and in-depth analysis of spatial reasoning ability
One of our main contributions is to combine style-sheet program synthesis with LLMs and in-context learning as the CSS style language inherently share similarity w... | Rebuttal 1:
Rebuttal: # General Response
We thank all reviewers for their constructive feedback and comments. We would like to address reviewers’ common concerns in the following general response:
> * Quantitative and qualitative results regarding attribute binding **(Reviewer ykMz & HVHZ & uKyZ)**
We would like to... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Mildly Constrained Evaluation Policy for Offline Reinforcement Learning | Reject | Summary: The paper investigates the problem of policy constraints in offline reinforcement learning (RL) settings, and finds the phenomenon that milder constraints on policies during training can lead to better performances at inference tests. The proposed component MCEP can be added on existing algorithms including TD... | Rebuttal 1:
Rebuttal: > I think one major critique of the paper ….
>
We appreciate reviewer nJEQ for their kind comments and review for our manuscript.
We apologize for the misunderstanding of “not even consistent”, which is probably caused by our presentation. We argue that the experiment results of “6 out of 9” a... | Summary: This paper proposes Mildly Constrained Evaluation Policy (MCEP) for offline reinforcement learning to address the issue of excessively restrictive constraints for action selection during test time inference. MCEP uses a more constrained target policy for value estimation and another less restrictive policy for... | Rebuttal 1:
Rebuttal: We thank the reviewer FqHz for their feedback and comments of our manuscript.
> Since $\pi_e$ does not participate in the policy evaluation, I think line 7 of Algorithm 1 can be removed and $\pi_e$ can be extracted from Q after actor critic learning to save computational cost. The contribution of... | Summary: This work addresses the issue of excessive policy constraints in stabilizing value estimation within the offline RL paradigm. A separate target policy is used solely for evaluation and stabilizing value estimation, which is more constrained than the "evaluation policy." The evaluation policy does not participa... | Rebuttal 1:
Rebuttal: We appreciate reviewer sDxq for their review and kind comments for our manuscript.
> While the results show promise, they do not indicate substantial improvements across many environments, and there is some inconsistency observed. The method shows a decrease in performance in the medium-expert D4... | Summary: Offline reinforcement learning (RL) methods frequently involve a policy constraint to mitigate error propagation when learning the Q function. Generally, a single constraint strength is used throughout training. This paper proposes instead to use different constraint strengths for learning the target policy, w... | Rebuttal 1:
Rebuttal: We appreciate reviewer wQCk for their review of our manuscript.
> The algorithm introduces an additional hyperparameter that requires tuning, which is already a challenge in offline RL.
>
Compared to policy constraints methods, the proposed MCEP method has an extra hyperparameter for the constr... | Rebuttal 1:
Rebuttal: ## Summary of Positive Feedback
We would like to thank the reviewers for their thorough reviews and detailed feedback. It appears reviewers have perceived various aspects of this work's contribution.
- reviewer nJEQ commented the studied problem *"is an important problem"* and the proposition of o... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Is This Loss Informative? Faster Text-to-Image Customization by Tracking Objective Dynamics | Accept (poster) | Summary: Text-to-image generation models offer fine-grained control over synthesized images, but fast adaptation to smaller datasets or new concepts remains a challenge. Existing efficient adaptation methods suffer from long training times, hindering practical applications and resource usage. This work addresses the is... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and insightful suggestions! Allow us to address your concerns in a below response:
>It is imperative to provide supporting evidence to justify the necessity of adaptive step choices. For instance, analyzing the outcomes and plotting the distribution of selected... | Summary: This paper studies the training dynamics of a few state-of-the-art txt-to-image personalization methods and proposes an early stopping criteria while fine-tuning the base models to speedup their customization. They evaluate this criteria on Dreambooth, Textual inversion, and custom diffusion on 18 concepts and... | Rebuttal 1:
Rebuttal: Thank you for taking the time to provide detailed feedback on our work. Please find our responses to your concerns and questions below:
>The whole idea of the paper is not very novel, and looks more like an analysis paper.
To the best of our knowledge, **we are the first to to study the optimizat... | Summary: This paper argues that customization techniques for diffusion-based text-to-image generation models train for longer than is needed. This is because the training loss of diffusion models is often not informative -- i.e., often looks like stationary noise -- so practitioners tend to use a fixed (often excessive... | Rebuttal 1:
Rebuttal: Thank you for your review! We would like to address your concern in the following response:
>How does L_det vary across concepts/models/methods?
Different behavior of $L_{det}$ for different methods can be observed in Figures 2, 10 – 12 of our work. In Figure 2 of the PDF attached to our general ... | Summary: This paper studies the training dynamics of popular text-to-image personalization methods (such as Textual Inversion, DreamBooth, and Costom Diffsuion), aiming to speed them up by an early stopping approach which allows the model to optimize or fine-tune for fewer iterations. A key observation is that most con... | Rebuttal 1:
Rebuttal: Thank you for a detailed review! We would like to answer your concerns about our work below:
>Good visual results in generating an image similar to the input image do not mean perfect identity and detail perservation for personalized text-to-image generation.
We agree that the CLIP image score m... | Rebuttal 1:
Rebuttal: Dear reviewers, we deeply appreciate the time and effort you devoted to the review of our paper. We are glad that multiple reviewers recognize the motivation that drives our work (**5cki**, **DXLz**, **rpQb**), the depth of our analysis (Gk9z, DXLz), and the simplicity of DVAR combined with its ef... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Randomized Approach to Tight Privacy Accounting | Accept (poster) | Summary: The authors consider the problem of bounding the privacy loss for a composition of DP mechanisms. This problem is well-studied in the literature and the particular setting here is when the mechanisms are Gaussian or have Gaussian sub-routines but the privacy loss is measured through the original (approximate) ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments!
**Q1 [The Importance of Privacy Accounting in Regime of small $\delta$]**
**A:** **Regarding the common rule of setting $\delta = 1/n$:** Setting $\delta$ around $1/n$ implies a maximum $1/n$ likelihood of a privacy guarantee failing for an indiv... | Summary: Authors introduce a new privacy accounting method to characterize the privacy loss random variable. The work reduce the classical privacy accounting problem into mean estimation problem following the previous work and give a Monte Carlo solution. The work provides detailed analysis of the proposed method and ... | Rebuttal 1:
Rebuttal: We thank the reviewer (again) for the very positive feedback!
**Q** *“The only problem I still keep is about whether the method will suffer from the dimension curse when deriving the prv samples for a general distribution.”*
**A:** This is a great comment that actually points to one of the impor... | Summary: The paper proposes a privacy accounting method called estimate-verify-release (EVR), whose basic principle is to convert an estimate of a privacy parameter into a formal privacy guarantee. The mechanism works by verifying whether the estimated privacy guarantee holds, and then releasing the query output depend... | Rebuttal 1:
Rebuttal: We thank the reviewer for the very positive feedback!
**Q1 [Fully formal version of DP-SGD with EVR paradigm]** *“The paper places a lot of emphasis on the fact that we can't naively use an estimated privacy parameter as the truth, because DP is a strict guarantee, and this makes perfect sense. B... | Summary: The authors propose EVR framework for privacy accounting. The core idea is to estimate the privacy budget, verify whether the budget is approximately met, and then decide whether to release the result or halt. The workhorse is a Monte-Carlo verifier (also used as an accountant through binary search). The empir... | Rebuttal 1:
Rebuttal: We thank the reviewer for the very positive feedback!
**Q [How to deal with halting in practical usage?]**
**A:** In Section 4.4, we developed techniques for ensuring the false negative rate (i.e., the rejection probability) is around O(delta) when the proposed privacy parameter delta^est is clo... | Rebuttal 1:
Rebuttal: We thank all of the reviewers for their detailed and valuable comments. We are pleased that all the reviewers expressed a positive view of our work! We considered the reviews carefully and modified our paper accordingly. We have answered other questions in the individual responses. Here’s a summar... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Locality-Aware Generalizable Implicit Neural Representation | Accept (poster) | Summary: This paper studies the problem of generalizable implicit neural representation. The proposed method combines a transformer encoder with a locality-aware decoder to predict the output with the feature modulation through multiple frequency bandwidths. Experiments are performed on image reconstruction, few-shot n... | Rebuttal 1:
Rebuttal: **[Locality: Definition and Intuitive Examples]**
We will include the following additional explanation of the concept of locality for a better understanding of readers. Although it is challenging to provide a formal definition of ‘locality’, we can provide its conceptual description and intuitiv... | Summary: Generalizable implicit neural representation (INR) can represent multiple data instances with a coordinate-based neural network, of which weights or intermediate features are modulated using instance-wise latent codes. A significant constraint of current generalizable INR is their struggle to localize and capt... | Rebuttal 1:
Rebuttal: **[Standard Transformer Attention for Selective Token Aggregation]**
Although the standard transformer’s self-attention can be used to predict the modulation vector for a coordinate input, we adopt cross-attention due to the computational efficiency. Using self-attention with the concatenation ... | Summary: This paper enhances the performance of generalizable INR via improving its locality awareness. The transformer encoder feed on patchs of an image and produce latent tokens with local information, and later extracted as modulation vectors. The proposed INR decoder with selective token aggregation and the multi-... | Rebuttal 1:
Rebuttal: **[The Portion of Localized Latents in an INR]**
The localized latents account for 9% of INR parameters (=65,536/725,446), which is a small portion of INR. Since the shape of extracted localized latents is 256x256, we emphasize that the size of localized latents is equivalent to **one weight mat... | Summary: This paper tries to improve the expressive power of neural implicit function modulation by enhancing its ability to localize and capture fine-grained details of data samples. A novel framework for generalizable INR is proposed that combines a transformer encoder with a locality-aware INR decoder. It further ut... | Rebuttal 1:
Rebuttal: **[More Evaluation Metrics to Emphasize Local Details]**
We compare our framework with IPC [19] to emphasize local details using HF-PNSR-r, which calculates PSNR only using the high frequency components of an image. We have also considered other evaluation metrics such as rFID, SSIM, LPIPS durin... | Rebuttal 1:
Rebuttal: We appreciate all reviewers's constructive comments to improve our paper.
We have tried our best to sincerely respond to all concerns and questions.
Pdf: /pdf/478c6702ee396bc1650a5de17ccae4d9ddfee984.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper aims to enhance the performance of generalizable implicit neural representation locality-aware model designs. A transformer encoder is applied to convert image patches into latent tokens, which the proposed locality-aware decoder composed of the selective token aggregation and the multi-band feature... | Rebuttal 1:
Rebuttal: **[The Idea of Locality]**
In our study, “locality” describes a concept that the features in a data instance have potentially a high correlation with each other, where the distance between their corresponding coordinates is close. For example, in a 2D image, pixels located close to each other ... | Summary: The paper focuses on the task of training a single coordinate-based neural network to represent multiple scenes or instances. There are two main technical contributions that improve the quality of these generalized representations: (1) a Transformer-based encoder that extracts localized features of each target... | Rebuttal 1:
Rebuttal: **[Improvement of Introduction]**
As per suggestion, we will revise our manuscript to clarify the terminologies in the Introduction for better understanding as follows. The latents refer to the outputs of our Transformer Encoder, corresponding to the positions of learnable input tokens. The inpu... | Summary: The paper tackles the important problem of bridging the gap between generalizable implicit neural representations (INR) and per-sampled trained ones. The core hypothesis is that previous generalizable INRs failed to capture local details in the global latent code due to their inductive bias. The idea of the pr... | Rebuttal 1:
Rebuttal: **[Broad Introduction]**
We will revise the Introduction by adding the following specific description of our transformer encoder. Specifically, we will add the detailed explanations about the use of cross-attention in selective token aggregation to Lines 43-44. The cross-attention is used for ea... | null | null |
Multi-Prompt Alignment for Multi-Source Unsupervised Domain Adaptation | Accept (poster) | Summary: This paper proposes an interesting pipeline for large pre-trained model-based UDA, and realizes a latent subspace tuning for continuous adaption.
Strengths: 1. The motivation behind this article is very meaningful, and the proposed method supports the motivation well.
2. In terms of methodology, the design o... | Rebuttal 1:
Rebuttal: > *Using CLIP as the backbone may limit the ability of MPA on only classification tasks, how to extend MPA to other tasks, like segmentation, referring, etc?*
As a matter of fact, there are many other CLIP-based segmentation/referring works [1] [2] [3], all of which used CLIP as backbones. Theref... | Summary: This paper introduces prompt learning to multi-source unsupervised domain adaptation (UDA). Firstly, individual prompts for each source and target pair are learned using a contrastive loss. Then, MPA aligns the learned prompt by an autoencoder-based step with an L_1 constraint to generate consistent results fo... | Rebuttal 1:
Rebuttal: > *Regarding the prompt design part in sec3.2, can it only be realized by following Ge[10]?*
There are other ways of implementing the prompt structure. For example, we had considered training just a naive soft prompt for each source-target domain pair, followed by concatenating them and apply an ... | Summary: This paper deals with the multi-source domain adaptation problem. It proposes to tune the designed domain-invariant prompts and domain-specific prompts to enable the domain adaptation ability. Generally, the training consists of two objectives, i.e., the individual prompt learning objective and the de-noising ... | Rebuttal 1:
Rebuttal: > *In LST, I don't think it is reasonable to use randomly initialized representations as the input of the back-projector to perform prompt reconstruction, as the latent representations don't subject to the same distribution. And there is no empirical evidence to show such a way really works.*
As ... | Summary: The paper proposes an extension of [10] (Domain Adaptation via Prompt Learning Ge et al., 2022) to the multi-source UDA set-up.
(i) Distinct soft prompts are learnt via contrastive loss for each source-target pair; each source-target prompt is composed of class-wise source- and target-prompts. In the target d... | Rebuttal 1:
Rebuttal: > *Lack of technical novelty. The proposed framework is a straightforward extension of the work by Ge et al. to the multi-source setting.*
We respectfully disagree. As shown in Table 1 and 2, directly applying Ge's method to MDA produces limited performance. This is because their method lacks str... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Regularized Behavior Cloning for Blocking the Leakage of Past Action Information | Accept (spotlight) | Summary: This paper introduces Past Action Leakage Regularization (PALR) for blocking the leakage of past action information during behavior cloning. Concretely, PALR focuses on the problem when a BC agent simply remembers the past actions rather than learning a generalized behavior, which gives a degenerated policy. S... | Rebuttal 1:
Rebuttal: We deeply appreciate your constructive and insightful comments.
**1. Novelty in our work**
We think that although the HSCIC regularization indeed holds significance within our work, our contribution extends beyond the introduction of the HSCIC regularization term: in the paper, we introduced a n... | Summary: This paper solves the past action information leakage problem in behavior cloning. The paper first formally defines this problem, and then provides some potential regularization methods. After careful analysis of the pros and cons of each method, the authors decide to use HSCIC and validate its performance on ... | Rebuttal 1:
Rebuttal: We hold your thoughtful viewpoints on our work in high regard.
**1. Effectiveness in complex task**
To validate past action leakage regularization is effective on complex tasks, we conduct an experiment on CARLA environment. Please see our general response and Table A of PDF.
**2. Applicability... | Summary: This paper proposes to use HSCIC (Hilbert-Schmidt Conditional Independence Criterion) to alleviate the leakage of information from past actions in behavior cloning from observation histories. The advantage of HSCIC compared with information-theoretic regularization is it can compute in closed-form and does not... | Rebuttal 1:
Rebuttal: We value your considerate insights and invaluable suggestions concerning our work.
**1. Comparisons with other non-regularization approaches**
Thank you for the pointers to the missing important related work. We recognize that both of your suggestions are valid to compare with our approach. Duri... | Summary: This paper aims to solve the problem of negative action leakage from past observations in the context of imitation learning. This is specifcally applicable to imitation learning when considering a history of observations. The paper argues that an information theoretic (entropy or MI based) regularization requi... | Rebuttal 1:
Rebuttal: We appreciate your positive feedback.
**1. Applicability to sequence modeling architecture**
We acknowledge the potential effectiveness of our approach in sequence modeling architecture. To explore this further, we have applied our regularization to the Decision Transformer architecture you sugg... | Rebuttal 1:
Rebuttal: # **General Response**
We sincerely thank the reviewers for their insightful and detailed comments. Below, we address key questions and feedback that have been consistently raised by the reviewers. If there are any aspects that still need clarification or elaboration, we are more than happy to ad... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper addresses the information leakage problem of imitation learning with observation histories. To this end, the paper measures the leakage of past action information based on conditional independence and proposes Past Action Leakage Regularization (PALR) for behavioral cloning (BC). The experiments sho... | Rebuttal 1:
Rebuttal: We are grateful for your constructive and enlightening comments.
**1. Comparison to other methods**
We appreciate your comments on alternative methods and their potential effectiveness. However, we would like to clarify some distinctions between the alternative methods in [1] and [2] and our pro... | Summary: This paper proposes Past Action Leakage Regularization (PALR) to resolve the copycat problem in behavior cloning (BC) methods:
1. mathematically defines the past-information-leakage problem.
2. introduces PALR to formalize the methods.
3. uses Hilbert-Schmidt Conditional Independence Criterion(HSCIC) to mea... | Rebuttal 1:
Rebuttal: We appreciate your constructive and insightful feedback.
**1. Assumption on ideal imitator**
Thank you for bringing up this important point. To clarify, in our problem setting of imitation learning from observation histories, we make the assumption that the control-relevant (state) information c... | null | null | null | null |
SQ Lower Bounds for Non-Gaussian Component Analysis with Weaker Assumptions | Accept (poster) | Summary: This paper provides an SQ lower bound for the problem of distinguishing the standard Gaussian distribution from a distribution with a single non-Gaussian component, under relaxed assumptions compared to what was previously known. In particular, in prior work, similar SQ lower bounds were established in the cas... | Rebuttal 1:
Rebuttal: We thank the reviewer for their effort and feedback on our work. We would like to address the following questions/comments.
1. Regarding the reviewer’s point that the main result seems to contradict the algorithm in prior work [ZSWB22] and [DK22].
If we take the one-dimensional distribution $A$ ... | Summary: This work studies non-Gaussian component (NGCA) analysis in the context of the statistical query model. In NGCA the task is to distinguish a standard multi-variate Gaussian distribution from a distribution that is standard Gaussian in all but a random direction $w$ and equal to a one-dimensional distribution $... | Rebuttal 1:
Rebuttal: We thank the reviewer for their effort and positive assessment of our work. We would like to address the following questions/comments.
1. Regarding the reviewer’s point:
“While the quantitative improvement for the list-decoding lower bound is non-trivial, it appears only in a very restricted regi... | Summary: The paper considers the SQ-hardness of non-Gaussian component analysis. The main result of the paper is a statement of the hardness without an assumption required by results stated in previous works: finite chi-squared distance between the non-gaussian distribution and the standard normal. Two applications are... | Rebuttal 1:
Rebuttal: We thank the reviewer for their effort and positive assessment of our work. We would like to address the following questions/comments.
1. Regarding the reviewer’s point:
“It is stated in the paper that the main result is "near-optimal". Can some elaborations be made on what is the "optimal" resul... | Summary:
The paper discusses SQ lower bounds for Non-Gaussian component analysis. A very influential result by [DKS17] has established an SQ-lower bound suggesting d^m time as long as the non-Gaussian component's distribution A, satisfies (a) that the first m moments of A match the m moments of N(0,1) and (b) the \c... | Rebuttal 1:
Rebuttal: We thank the reviewer for their effort and positive assessment of our work. We would like to address the following questions/comments.
1. Regarding the reviewer’s point:
“A weakness is perhaps that the SQ lower bound technique seems to be very tailored to NGCA, as opposed to the Feldman et al 20... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and effort in providing feedback. We are encouraged by the positive comments, and that the reviewers appreciated the paper for the following: (i) **importance** (YVEY), and (ii) **clarity** and **quality of writing** (YVEY, oUue). We would like to address the ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Res-Tuning: A Flexible and Efficient Tuning Paradigm via Unbinding Tuner from Backbone | Accept (poster) | Summary: This paper provides a unified framework (called Res-Tuning) to combine different efficient tuning methods. It introduces an unbinding form that integrates existing methods and allows combination flexibility. A memory-efficient variant is also introduced for the sake of training memory efficiency. Experiments a... | Rebuttal 1:
Rebuttal:
Dear Reviewer hNoJ,
Thank you for the acknowledgement of our contributions and your valuable comments. We address your concern as follows:
**Q1: Comparisons with previous works**
We would like to first point out that the unified formulation of existing PETL approaches in an unbinding form is ... | Summary: This paper proposes a new tuning paradigm, dubbed as Res-Tuning. They first introduce the basic building blocks of foundation models and then unbind three popular tuners from foundation models. They provide theoretical and empirical evidence to support their structural disentanglement. By detaching from the fo... | Rebuttal 1:
Rebuttal:
Dear Reviewer DEjH,
Thank you for the acknowledgement of the proposed method and experiments. We address you concerns as follows:
**Q1: Typos.**
Thanks for spotting the errors. We will carefully fix them and polish the writing in our revisions.
**Q2: Code release.**
Limited by our organizat... | Summary: This paper shows some existing parameter-efficient tuning methods can be decoupled from backbones, which can be formulated as a unified Res-Tuning model. Furthermore, the authors conduct empirical experiments to seek the optimal Res-Tuner. Additionally, a memory-efficient variant of Res-Tuning is introduced by... | Rebuttal 1:
Rebuttal:
Dear Reviewer nLwc,
Thank you for your time and helpful comments. We address your concerns below:
**Q1: The contribution of the unbinding formulation.**
The essential contribution of the unbinding formulation is to abstract existing PETL methods into a unified formulation of a frozen operation... | Summary: This paper proposes an unbinding formulation of parameter-efficient methods and further leverage structural disentanglement to develop a memory-efficient variant.
Sufficient experiments on both visual discriminative and text-to-image generative tasks are performed.
Strengths: 1. novel implementation.
2. good ... | Rebuttal 1:
Rebuttal:
Dear Reviewer Yhf3,
Thank you for the acknowledgement of the proposed method and experiments. We address your concerns as follows:
**Q1: Novelty of Res-Tuning and similarity to MAM-Adapter in terms of analysis on existing methods.**
We totally agree that the priority of the manuscript is the ... | Rebuttal 1:
Rebuttal: Dear all,
We would like to express our gratitude to our reviewers for their valuable comments. For positive comments,
- memory-efficiency of Res-Tuning-Bypass (R-mYTv, R-nLwc),
- sufficient and strong experiments (R-mYTv, R-Yhf3, R-DEjH, R-hNoJ),
- well-structured and easy to follow (R-mYTv, ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper pays attention to Parameter Efficient Tuning and propose a unified framework namely Res-tuning. More importantly, based on the proposed unified framework, this paper constructs a memory optimization scheme similar to the LST in the language model. Several experiments are performed to validate the ef... | Rebuttal 1:
Rebuttal:
Dear Reviewer mYTv,
Thank you for the acknowledgement of our contributions and your valuable comments. We address your concern as follows:
**Q1: Novelty of the unbinded Res-Tuning framework.**
As mentioned in the general response, the novelty of the unbinded Res-Tuning framework when compared ... | null | null | null | null | null | null |
Distributed Inference and Fine-tuning of Large Language Models Over The Internet | Accept (poster) | Summary: The paper proposed cost-efficient inference and fine-tuning methods for LLMs on geodistributed devices in a consumer-grade network. The motivation is that, by pooling together idle compute resources of multiple research groups and volunteers, we could make LLM research and applications accessible to broader co... | Rebuttal 1:
Rebuttal: Thank you for reviewing the paper and leaving valuable feedback. We address the raised concerns below.
> How to do fine-tuning under the proposed setting is not that clear, although the authors wrote one paragraph to explain the fine-tune part. Inference is clear and relatively simple, but how to... | Summary: The paper discuss about an important application problem of distributed inference for large language models. Given the size and inference requirement for large language models, and the constraint of hardware resources, the authors put forward utilizing idle GPUs in network to sped up the inference, providing d... | Rebuttal 1:
Rebuttal: Thank you for reviewing the paper and leaving valuable feedback. We address the raised concerns below.
> server utilization discussion - the paper lacks coverage regarding the resource utilization of different GPU servers, given the distributed and heterogeneous computing setting
Resource utiliz... | Summary: The objective of this study is to facilitate the operation of Large Language Models (LLMs) using commodity hardware over the internet. However, such hardware can often be characterized by high unreliability and latency issues in networks. To mitigate these challenges, the paper introduces a dual attention cach... | Rebuttal 1:
Rebuttal: Thank you for reviewing the paper and leaving valuable feedback. We address the raised concerns below.
> I found that Table 2 is a bit vague to follow. Please eborate the metrics steps/sec and tokens/sec per user. For example, the difference between step and tokens and between clients and users.
... | Summary: This paper presents a system designed for decentralized inference and fine-tuning of large language models over distributed hardware, which allows users to efficiently run LLMs without requiring high-end hardware. The system leverages pipeline-based model parallelism, distributing model layers across nodes. Ad... | Rebuttal 1:
Rebuttal: Thank you for reviewing the paper and leaving valuable feedback. We address the raised concerns below.
**Weaknesses**
> The innovative aspects of the paper are not sufficiently elaborated upon. For example, I would like to know if there are any outstanding advantages compared to the latest resea... | Rebuttal 1:
Rebuttal: We thank all reviewers for taking the time to study our paper and leave valuable feedback. We are glad that the reviewers appreciated the motivation behind our work (*8b6R, zN15*), our analysis of LLM inference challenges (*2M8A*), the soundness of the proposed system for geo-distributed inference... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Exact Representation of Sparse Networks with Symmetric Nonnegative Embeddings | Accept (poster) | Summary: This paper extends for by Chanpuriya et al (2020) for networks with homophilous and heterophilous edges. They show that their model is able to yield interesting results and they give theoretical underpinnings as well.
Strengths: The model is straightforward but seems to work well. Due do its relative simplic... | Rebuttal 1:
Rebuttal: Thank you for your review and detailed questions, which we address piecewise.
*On connections to multiplex networks and spectral methods for signed networks*
Thank you for bringing up these interesting connections. Indeed, in some sense, signed graphs seem to capture the idea of heterophilous co... | Summary: This paper proposes and studies a community-based factorization model for exact representation of sparse networks. The authors extend a prior result on exact factorization which is based on logistic principal component analysis (LPCA), and show that an LPCA factorization can be converted to the proposed commun... | Rebuttal 1:
Rebuttal: Thank you for the review. We appreciate the recognition of our conceptual and theoretical contribution. We are thankful for the suggestions on improving clarity – we will incorporate them. We address the criticisms, which mainly concern the empirical piece.
>There is a clear gap between the exact... | Summary: This paper proposes to use the logistic PCA model to represent. Paper's main contribution is theoretical – improving the embedding dimensionality bounds from maximum degree to the arboricity of a graph. Overall, I believe the contribution is significant, but currently the paper does not highlight why too much ... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review and suggested directions for improvement. We address your criticisms and suggestions as follows.
*On connections of our theoretical results to effective resistance, sparsification, curvature, and more*
Thank you for suggesting these connections. We will look i... | Summary: This paper concerns several main results/themes:
- the authors show a theoretical result on a prior model known as LPCA. Their result indicates that exact factorizations for graphs under the LPCA model is possible under a bounded arboricity assumption, which is more generally applicable than the prior degree... | Rebuttal 1:
Rebuttal: Thank you for the positive review. We address some points you raise below.
>From a theoretical and originality perspective, the advances that they make over the prior result (the arboricity vs degree assumption) appears rather marginal (similar to Chanpuriya 2020).
We believe that, while this wo... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their time and helpful suggestions. We address each reviewer with an individual reply. Here, we post the PDF of rebuttal figures that are referenced in these replies.
Pdf: /pdf/3fc7ee135670d9dff2dccaebb77a19f5aba91c10.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data | Accept (spotlight) | Summary: This work proposes a structure-free graph condensation paradigm to distill a large-scale graph into a small-scale graph node set without explicit graph structures. The node attributes of the obtained small-scale condensed graph-free data could encode topology structure information. And the condensed node set c... | Rebuttal 1:
Rebuttal: **Response to Reviewer 4KcB**
Thank you for taking the time to review my work and providing your valuable feedback. We are so encouraged by your positive comments on our originality and clear clarity and we appreciate your recognition of our research significance. The following are our detailed r... | Summary: This paper studies an interesting problem. The authors proposes a new paradigm for reducing the size of large-scale graphs without explicit graph structures. The proposed SFGC, encodes topology structure information into node attributes in synthesized graph-free data. Extensive experiments demonstrate the effe... | Rebuttal 1:
Rebuttal: **Response to Reviewer PLzT**
We sincerely appreciate the time and effort you dedicated to reviewing our work. We have carefully considered your comments and suggestions. Following the instructions for the rebuttal, we have sent the source code via an anonymous link to the AC. We appreciate it if... | Summary: This paper presents a new method to condense a training graph into a smaller number of disconnected nodes, such that a GNN trained on these nodes performs similar to one trained on the original graph at test time.
Strengths: **Originality.** Structure-free condensation has been reported before (GCOND-X), but ... | Rebuttal 1:
Rebuttal: **Response to Reviewer yKD6**
We sincerely appreciate your thoughtful review of our paper. We are glad to hear that you recognize the significance of graph condensation research, as well as encouraging comments for our work. We have carefully considered your comments and suggestions, and the foll... | Summary: This paper studies the problem of reducing the size of a large graph dataset while preserving task-relevant information.
It introduces a new methodology to distill large-scale real-world graphs into smaller synthetic graph node sets by disregarding graph structures to create condensed graph-free data. The app... | Rebuttal 1:
Rebuttal: **Response to Reviewer KcU6**
Thanks for sharing your thoughts and questions with us. We greatly appreciate your valuable suggestions on discussing more on the practical application and complexity of our proposed SFGC method. We have taken your suggestions into careful consideration and we have p... | Rebuttal 1:
Rebuttal: **Common response to all reviewers**:
We thank all reviewers for their thorough review and valuable suggestions. We are delighted that our contributions have been positively acknowledged, including:
**(1) Novel and interesting problem of structure-free graph condensation paradigm for practical ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces a structure-free graph condensation method designed to distill large-scale graphs into small-scale graph-free data while preserving comparable expressiveness. The proposed method, named SFGC, achieves this by condensing the graph topology into an identity matrix, effectively embedding the... | Rebuttal 1:
Rebuttal: **Response to Reviewer 8VHW**
We are glad that you recognize the significance of graph condensation. We have carefully considered your thoughtful comments and suggestions, and the following are our detailed responses.
**W1: Clarification on GCOND’s graphless variant GCOND-X**:
Our SFGC has diff... | Summary: This paper proposes a graph dataset condensing algorithm with a main idea of creating a new format of graph representation that does not explicitly include edge information. The authors suggest a new graph kernel and training algorithm with trajectory for online gradient to achieve graph condensation. Experime... | Rebuttal 1:
Rebuttal: **Response to Reviewer U4b1**
We sincerely appreciate the time and effort the reviewer dedicated to reviewing our work, and we are pleased to learn that our proposed SFGC is interesting and well-founded to the reviewer. The following are our detailed responses to the reviewer’s thoughtful comment... | null | null | null | null |
ASIF: Coupled Data Turns Unimodal Models to Multimodal without Training | Accept (poster) | Summary: This work proposes a method to obtain multimodal representations from pretrained unimodal models and a multimodal dataset, and demonstrates the effectiveness of the method on the zero-shot classification task. The method is to map candidate images and texts into the same representation space, which consists of... | Rebuttal 1:
Rebuttal: Thanks for the review; we provide some brief comments on the points raised.
“*I'd like to see more discussions on the limitations or impacts of the size of representation vectors.*”
Thanks for pointing this out. Indeed, the large dimensionality prevents a straightforward application of ASIF in t... | Summary: The paper proposes a method to align vision and language without learning a parametric model. Main idea: having a support set of image-text pairs, the structure of the visual data should match the structure of the language data. More precisely, the distances from one query image to all images in the support se... | Rebuttal 1:
Rebuttal: Thanks for the review. We appreciate the feedback and would like to offer some brief responses to the points raised.
“*Can this approach can be used for other downstream tasks, like VQA, or captioning?*”
We are indeed conducting preliminary experiments to use ASIF for captioning in a subsequent ... | Summary: This paper proposes ASIF, transferring independently pre-trained image/text encoders to the classification task without further finetuning. The proposed method only needs a small amount of paired image-text data as anchors, and represent new data samples using the relative representation to the anchor samples... | Rebuttal 1:
Rebuttal: Thanks for the review, we hope to have addressed all points:
On the Weaknesses:
1. “*ASIF still requires pre-trained single-modal models which are already trained on large amounts of image or text data samples.*”
The crucial difference is that unimodal data may come untied and even without any ... | Summary: This paper presents a novel approach to aligning text-image modalities without any training. The method is based on the assumption that images and their captions have similar relative embeddings, even when trained independently. By leveraging paired multimodal data, relative representations can be computed wit... | Rebuttal 1:
Rebuttal: Thanks for the review, we provide some brief comments on the points raised.
“*More comprehensive analysis of similarities between features from text and image encoders to convince the readers*”
Thanks for the suggestion, in the appendix we conduct an analysis of the two feature spaces that looks... | Rebuttal 1:
Rebuttal: We thank the reviewers for showing keen interest in our ideas, and for their thorough and quite valuable comments. This general response summarizes the main points raised and how we have addressed them. Specific answers are then provided to each reviewer in response to their remarks.
1. **Empiri... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes to leverage single-modal pre-trained text & image encoders and a relatively small image-text dataset to create a CLIP-like open-vocabulary visual recognition model without training. The authors claimed that the proposed model ASIF is more training efficient, more interpretable, and can easi... | Rebuttal 1:
Rebuttal: We invite further dialogue with the reviewer in the next phase to foster an updated assessment of our research that builds upon the discourse. We also thank the reviewer for the interesting points regarding the interpretability and scalability/inference tradeoffs
"*The only empirical results this... | null | null | null | null | null | null |
Batch Bayesian Optimization For Replicable Experimental Design | Accept (poster) | Summary: The paper introduces a batch Bayesian optimization method for the setting where experiments can be very noisy, and therefore it is common practice to repeat many experiments. The framework allows for both the selection of the experiment design and how often each experiment is replicated. The authors introduce ... | Rebuttal 1:
Rebuttal:
We thank the reviewer for your constructive feedback.
---
> - The main framework seems to fit a homoskedastic GP to the mean of the replicated data... (and) a second homoskedastic GP is used to model the variance. However, I would argue the most naive and natural solution is to simply use a het... | Summary: This paper introduces an algorithm for selecting both sampling locations and the number of replications in the context of heteroskedastic Bayesian Optimization. The authors propose to use Thompson sampling for batch candidate selection and a scheme for determining the number of replications for each element of... | Rebuttal 1:
Rebuttal:
We thank the reviewer for your detailed and insightful comments.
---
> Theoretical results: comparision with the theoretical regret bound achieved by uniform sample allocation.
For uniform sample allocation (i.e., we replicate every input a constant $n_0\leq\mathbb{B}$ number of times), the ef... | Summary: The paper proposes a batched Bayesian optimization with heterodescadic noise and Thompson sampling. Further, it proposes an extension where not only function is minimized but also a robust variant objective which also incorporates observational noise variance. It provides some regret analysis relying on prior ... | Rebuttal 1:
Rebuttal:
We thank the reviewer for your insightful comments.
---
> The proof techniques used here are classical used in prior works... but the theoretical results are only of marginal interest to the community since they are straightforward extension of prior techniques.
Although we have used some proo... | Summary: The authors propose three methods for Bayesian Optimization under the constraint of batch sampling and with significant heteroscedastic aleatoric uncertainty assumed. Their methods BTS-RED-Known assumes knowledge of the variance function, BTS-RED-Unknown does not assume the variance function is known and fits ... | Rebuttal 1:
Rebuttal:
We thank the reviewer for your valuable feedback.
---
**Clarification on Batch Selection and the Heuristic:**
> ...the batch structure is only accounted for in the heuristic regarding replicating the same point too many times...
> ...it is not clear to me that the heuristics introduced to ens... | Rebuttal 1:
Rebuttal:
We'd like to thank all reviewers for your insightful comments, and for acknowledging our contributions.
Specifically, we are encouraged to hear that our methods are "easy to implement" (Reviewer QJyu and Reviewer 2mxf) and hence practical, and that our contributions are "of interest to the Bayes... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Deep Insights into Noisy Pseudo Labeling on Graph Data | Accept (poster) | Summary: This paper aims to provide in-depth insights into pseudo labeling (PL) in the context of graph learning models. The authors first present an error analysis of the PL strategy, demonstrating that the error is bounded by the confidence threshold of PL and the consistency of multi-view predictions. Furthermore, t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading and insightful comments. Following are the responses regarding your concerns.
For the figures in the rebuttal, please check the PDF file in the global response of the author rebuttal, located at the top of this page.
> 1.There might be some mistake ... | Summary: Pseudo labeling is significant for GNN. This paper first theoretically analyzed the effect of pseudo labeling by showing the error bound and the convergence property. Then, accordingly, the paper proposes a cautious pseudo labeling based on confidence and multi-view consistency. The experimental results demons... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments. Please find our response which addresses your concern.
1.
Thanks for your agreement that our paper is well written and this is really a good question. Our analysis of the PL strategy focuses on the task of graph learning. We make assumptions re... | Summary: The paper provides an error bound for pseudo labeling on graphs. Moreover, the authors propose a cautious pseudo labeling method and validate it through experiments.
Strengths: 1. The paper presents good experimental results, demonstrating the effectiveness of the proposed cautious pseudo labeling method.
2.... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We sincerely appreciate your efforts in providing us with a detailed review. We have carefully considered all of your insightful suggestions and corrections, and we have incorporated them into the latest version of our draft. We have addressed eac... | Summary: The article discusses noisy pseudo labeling (PL) on graph data and proposes a new cautious PL methodology (CPL) to improve the graph learning process. The authors conduct experiments to evaluate CPL strategy for link prediction on various datasets and apply it on popular models in node classification task. The... | Rebuttal 1:
Rebuttal: We are most thankful for your thoughtful assessment, and glad to communicate with you on all your concerns:
> Were there any specific assumptions made when applying the proposed approach to the benchmark datasets? It would be helpful to understand the compatibility of the approach with different ... | Rebuttal 1:
Rebuttal: The revised main scheme and the required figures of the experiment from **Reviewer 4kBQ** are shown in the supplementary PDF file.
Pdf: /pdf/d5128af85bb960c2667055f5ef0d921d281e4d23.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
On the Minimax Regret for Online Learning with Feedback Graphs | Accept (spotlight) | Summary: This paper considers a classic problem of online learning with feedback graphs, which interpolates the full-information feedback and the bandit feedback. Specifically, the authors consider the case where the feedback graph is undirected, meaning that if node $i$ can observe node $j$, then node $j$ can observe ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. As mentioned in our answer to Reviewer TWzC, we hope the case of directed feedback graphs could be addressed by extending the proposed techniques. Regarding your question, OMD could be used in place of FTRL with the same techniques presented in this work. Wi... | Summary: The paper investigates no-regret online learning algorithms performing under the feedback graph model. More precisely, at each round $t$ a learner selects an action (out of set of possible actions) incurring the cost associated with the specific action at the specific round. The actions are additionally vertic... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. As mentioned in our answer to Reviewer J2zm, we agree that the directed feedback graphs case is an interesting future direction, which we hope could be addressed by extending the proposed techniques.
---
Rebuttal Comment 1.1:
Comment: Thank you for your re... | Summary: Two classical learning problems are learning with experts and multi-armed bandits. In both problems, a learner interacts during $T$ rounds with a set of $K$ actions by selecting an action at each round. After each round, the loss/reward at that step is revealed for all actions (resp. only for the selected acti... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We remark that obtaining the key result of Lemma 1 was an obstacle for prior work in the way of obtaining improved guarantees using $q$-Tsallis entropy with feedback graphs. Although we focused on the case of time-varying graphs in our lower bound construc... | Summary: The paper studies the online learning under partial observations given by an underlying graph structure. This model is a common generalization of the bandit model (where the feed graph just contains self loops) and the full information model (where the graph is a complete graph with self loops). This model has... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. It is indeed true that computing, or even approximating, $\\alpha$ is computationally intractable in general. Please note that we do address this issue in Section 4, where we not only generalize our approach to time-varying feedback graphs with possibly di... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Towards Robust and Expressive Whole-body Human Pose and Shape Estimation | Accept (poster) | Summary: The paper focuses on improving whole-body pose and shape estimation from monocular images, a task that often struggles with complex, real-world scenarios. The authors argue that the performance of these models is significantly impacted by the quality of the predicted bounding box, such as the scale and alignme... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for acknowledging our extensive experiments and in-depth ablation study, and recognising the efficacy of our methods. We will polish the paper and add the clarifications below in the revised version.
Below we would like to provide point-to-point responses to addre... | Summary: This paper addresses the task of whole-body pose and shape estimation, including human mesh, hand gestures, and facial expressions, from monocular images. The author identifies the impact of predicted bounding box quality on the accuracy and reliability of existing methods. Based on this observation, a novel f... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for acknowledging that we present novel modules, and recognising that our extensive qualitative and quantitative results are solid and convincing. We will polish the paper and add the clarifications below in the revised version.
Below we would like to provide poi... | Summary: The paper introduces RoboSMPLX, a method for whole-body 3d human pose and shape estimation from a monocular image. Motivated by the poor robustness of existing methods, especially w.r.t. the quality of bounding boxes, three components are proposed: 1) a localization module, 2) a contrastive feature extraction ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for your insightful and constructive feedback. We will polish the paper, add the experiments and make the clarifications in the revised version.
**Q1: "Evaluation seems inconsistent and incomplete. It is unclear why some competitors are omitted in different experim... | Summary: This paper proposes a method to improve the robustness of whole-body pose and shape estimation, which mainly contains three components: 1) localization module to give the network awareness of location and semantic part; 2) contrastive feature extraction module to predict consistent representations under differ... | Rebuttal 1:
Rebuttal: **Q1: "literature review of robustness in vision tasks, especially in pose estimation tasks should be included"**
**A1:** Thank you for your suggestion of a comprehensive literature review. We would like to emphasize that **prior works on 2D pose estimation are different from our 3D pose and shap... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for your constructive feedback and recognitions of this work, especially for acknowledging that the problem is well motivated and meaningful [p9Xq, bkwo, go2c, 4HYU, Uf3X], the experiments are comprehensive [go2c, 4HYU, Uf3X], and the proposed modules are effec... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This submission proposes RoboSMPLX for whole-body pose and shape estimation. RoboSMPLX incorporates three modules, including a localization module, a contrastive feature extraction module, and a pixel alignment module. The localization module is aware of the location and semantics of body parts so that croppin... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for acknowledging the proposed method is clear. We will polish the paper, add the experiments and clarify below points in the revised version.
**Q1: "The proposed localization and pixel alignment modules have very limited contributions to the community, as these o... | null | null | null | null | null | null |
Deep Recurrent Optimal Stopping | Accept (poster) | Summary: The paper purports to develop a framework of optimal stopping that generalizes the previous approaches by incorporating non-Markovian settings and using a Bayesian network formulation.
Strengths: The only strength of this paper in this reviewer's opinion is the fact that it tackles an important problem.
Weak... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's concerns regarding the definitions in the paper. Our treatment is closely related to the approach in the following references.
**[27]** A. N. Shiryaev, "Stochastic Disorder Problems"
**[Poor]** H. Vincent Poor, "An Introduction to Signal Detection and Estimation"
*... | Summary: Optimal stopping is the problem of choosing a time to take a given action based on sequentially observed random variables in order to maximize an expected payoff. Previous works used Deep Neural Networks to find the optimal stopping time (e.g. Backward Induction method), however, as the authors mentioned, thes... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating the motivation, contribution, presentation, and organization of the paper. The main concern seems to be with regard to the vast body of work on PDE based optimal stopping approaches, specifically with regard to solving American options.
We hope that have fu... | Summary: The paper proposes an RNN-based approach for optimal stopping which is based on a Bayesian inference view of optimal stopping. The proposed model can be trained with direct optimization via policy gradients, or with expectation-maximization (EM). These two appraoches are shown to be equivalent. This new RNN-ba... | Rebuttal 1:
Rebuttal: We appreciate the reviewers comments. To better help clarify the novelty and significance of the paper and approach, we have included context in the global comments to all reviewers. We hope this helps the reviewer in appreciating the contribution and to reconsider the rating.
Specific questions ... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers and appreciate the concerns raised. Here we address concerns regarding the significance of our contribution. We respond to each reviewer individually in specific comments.
While there is a vast body of work on optimal stopping problems in the Markovian setting, the literatu... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Hierarchical Integration Diffusion Model for Realistic Image Deblurring | Accept (spotlight) | Summary: This paper proposed a novelty Hierarchical Integration Diffusion Model for deblurring task. By incorporation multi-scale latent priors, the proposed method achieved SOTA performance on both synthetic and real-world datasets.
Strengths: 1. The proposed method achieved SOTA performance.
2. The ablation study is... | Rebuttal 1:
Rebuttal: ## Response to Reviewer w6Fg (denoted as R4)
`Q4-1:` The authors should add more discussion about the difference between DiffIR. The difference is mainly using multi-scale latent prior. Also, the motivation: "since the advantages of regression-based methods in distortion accuracy, we integrate DM... | Summary: This paper presents the Hierarchical Integration Diffusion Model (HI-Diff) for realistic image deblurring. The HI-Diff utilizes diffusion models to generate multiscale priors in the latent space, which are integrated hierarchically into the deblurring process to improve the results. Experiments are conducted... | Rebuttal 1:
Rebuttal: ## Response to Reviewer 5Ars (denoted as R3)
`Q3-1:` It's unclear why authors adopt Diffusion Models (DMs) to model the prior. For one blurry image, the corresponding blurry prior should be deterministic instead of a distribution generated by DMs.
`A3-1:` Thanks for your question. We explain it ... | Summary: The authors propose a new image deblurring model, Hierarchical Integration Diffusion Model (HI-Diff). The HI-Diff uses the diffusion models to produce priors in a highly compacted latent space, and is integrated into the deblurring process hierarchically with the proposed hierarchical integration module (HIM).... | Rebuttal 1:
Rebuttal: ## Response to Reviewer ymRm (denoted as R2)
`Q2-1:` The feature prior is the key of this work. However, the paper lacks a specific analysis of the prior.
`A2-1:` Thanks for pointing it out. We provide an analysis of the prior.
1. We compare the similarity of priors generated on different input... | Summary: The paper introduces the Hierarchical Integration Diffusion Model (HI-Diff), a novel approach for realistic image deblurring. It combines a diffusion model and a regression-based model, performing the diffusion process in a compact latent space to generate informative priors for deblurring. These priors are in... | Rebuttal 1:
Rebuttal: ## Response to Reviewer mQcX (denoted as R1)
`Q1-1:` The paper does not show and explain the superiority of diffusion models over other models in this task.
`A1-1:` Thanks for pointing it out. We explain and conduct experiments to show the superiority of diffusion models (DMs).
1. **Explanation... | Rebuttal 1:
Rebuttal: ## Response to all reviewers and area chairs for a brief summary
Dear reviewers and area chairs,
We thank all reviewers and area chairs for their valuable time and comments.
We are encouraged that:
1. Reviewer mQcX and Reviewer ymRm agree that our method is novel.
2. Reviewer ymRm thinks our ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Bayesian Learning via Q-Exponential Process | Accept (poster) | Summary: This paper proposes a generalization of Besov processes to higgher dimensions that satisfies the stochastic process constraints that previous works could not satisfy. This is achieved by finding the right radius density function so that the corresponding elliptic distribution satisfies Kolmogorov's extension t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the nice comments and the generous support.
We have three CT examples in Section 4.2: the Shepp–Logan phantom is of size $128\times 128$ and the other two human body parts CT images are of size $512\times 512$. They are pretty standard sizes for images in machine learnin... | Summary: Motivated by the correspondence between Gaussian Process priors and ridge regularization for non-parametric regression problems, the authors in this paper develop a stochastic process prior, namely the $\textbf{Q-exponential (Q-EP) process}$, which can correspond to $\ell_q$-regularization. Specifically, by st... | Rebuttal 1:
Rebuttal: Thanks for raising good points in the "Weaknesses" and "Questions" sections. Q-EP is proposed as a nonparametric prior for flexible Bayesian models including regression, classification, density estimation, inverse problems, etc. The motivation is to impose more regularization than Gaussian process... | Summary: As a generalization of GP, it is important to construct a stochastic process (prior) that can express various degrees of smoothness. As such process, the Besov processes have been proposed but they are defined in the form of a series expansion, and the corresponding probability distribution is not given in an ... | Rebuttal 1:
Rebuttal: We thank the reviewer for supporting the contribution and the potential impact of q-EP. We agree with the reviewer that the correlation strengthen of Besov process can be configured through the choice of basis functions $\{\phi_\ell(x)\}$, as spelled out in Equation (12). But it is just less strai... | Summary: This paper proposed a new random process prior that corresponds to estimating parameters with $\ell_q$ penalty. The process, named Q-EP, can be used to provide a shaper penalty than the standard Gaussian process. Empirical experiments show the practical use case for Q-EP.
Strengths: The derivation of Q-EP loo... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's critics. As mentioned in the introduction, the novelty lies in the first probabilistic definition of Besov process (which is widely used in imaging analysis and Bayesian inverse problems) with explicit specification of correlations and tractable prediction formula.
Fo... | Rebuttal 1:
Rebuttal: We thank all the anonymous reviewers for their careful reading, constructive advices and critics. All the reviews acknowledge the novelty of the proposed q-EP as a nonparametric prior and its potential impact in statistics and machine learning applications. Some demand clarification and presentati... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors propose the 'q-exponential process', a stochastic process interpretation of Lq function regularization, which can induce sparsity in the solutions to optimization problems and can be used as a functional prior for Bayesian applications in time series regression, image reconstruction, and other appl... | Rebuttal 1:
Rebuttal: We thank the reviewer for the suggestion on more background about Besov processes. In addition to the existing introduction which includes its mathematical definition, we will elaborate more on its implication and applications on imaging analysis.
We also appreciate the reviewer's advice on expan... | null | null | null | null | null | null |
On Calibrating Diffusion Probabilistic Models | Accept (poster) | Summary: This paper investigates calibrating diffusion models. They notice that the expected data score equals zero. Since models not usually do not learn this correctly, they introduce a calibration term to subtract from a learned score. The new objective now includes the expected model score and performs better compa... | Rebuttal 1:
Rebuttal: Thank you for your valuable review and suggestions, we have uploaded a rebuttal PDF.
***W1: It is unclear how the expected values are saved for continuous t and adaptive sampling schedules***
In our work, we present two methods for implementing calibration: post-training computation and dynamica... | Summary: The paper proposes a simple procedure to improve the calibration of pre-trained diffusion models.
Diffusion models have achieved strong practical performance, but its score estimation is often seen as a black box. The paper sheds some light on the issue, by detecting the inherent lack of calibration of the c... | Rebuttal 1:
Rebuttal: Thank you for your supportive review and suggestions, we have uploaded a rebuttal PDF.
***W1: About Section 3.4***
Thank you for your insightful analyses, which are greatly helpful to us. Actually, we conducted preliminary trials on incorporating a loss of $\\|\\mathbb{E}\_{q\_{t}(x\_{t})}[\\bol... | Summary: This paper introduces a general calibration technique for diffusion probabilistic models (DPMs). The authors derive a time-dependent calibration term that is independent of any particular input under different model parameterizations. This calibration term can be computed in advance and repeatedly used for sam... | Rebuttal 1:
Rebuttal: Thank you for your supportive review and suggestions, we have uploaded a rebuttal PDF.
***W1 & Q1: The stability of the proposed approach and FID results of AFHQv2 64×64, FFHQ 64×64, and ImageNet 64×64***
Indeed, the gain of our calibration trick is proportional to the degree of `uncalibration’ ... | Summary: This paper presents a simple way to calibrate an arbitrary pretrained diffusion probabilistic model (DPM), which can reduce the score matching loss and increase the lower bounds of model likelihood. The authors observe that the stochastic reverse process of data scores is a martingale, from which concentration... | Rebuttal 1:
Rebuttal: Thank you for your valuable review and suggestions, we have uploaded a rebuttal PDF.
***W1: Solely relying on model likelihood and the FID score for measuring generative performance***
First, we need to clarify that Table 3 is an ablation study on *the number of samples used to estimate the cali... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive feedback, and we have responded to each reviewer individually. We have also uploaded a rebuttal PDF that includes:
- **Table A**: Assessing sample quality using FID and other performance metrics including sFID, inception score (IS), precision and reca... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper makes an observation regarding the reverse process of diffusion probabilistic model, noting that the data score term is a martingale with respect to this process. A key contribution of the paper is a theorem to this effect and the associated proof. Leveraging this observation, the authors propose a ... | Rebuttal 1:
Rebuttal: Thank you for your supportive review and suggestions, we have uploaded a rebuttal PDF.
***W1 & Q1: Providing a clearer commentary on the improvement of image quality***
In **Figure A** of the rebuttal PDF, we provide examples demonstrating that our calibration could reduce ambiguous generations,... | null | null | null | null | null | null |
Holistic Transfer: Towards Non-Disruptive Fine-Tuning with Partial Target Data | Accept (poster) | Summary: In this paper, the authors tackled "holistic transfer" task in which imcomlete target data that only cover a part of class labels are given to fine-tune a pre-trained model. To gain generalization ability of the model to unseen-class data in the target domain, the proposed method adopts several techniques in t... | Rebuttal 1:
Rebuttal: **The manuscript lacks several important topics such as related work and limitations.** Due to the page limit, we leave the related work and limitations in the Supplementary (as mentioned in L343). We apologize if it was unclear, and we will clarify it in the final version.
**Lacks discussion and... | Summary: - This paper introduces a new setting: partial target data. A model is pretrained on a source domain with a set of classes. The model then has access to labels from a target domain, but only a subset of the classes. The goal is to do well on all classes (including the remaining, unseen classes) on the target d... | Rebuttal 1:
Rebuttal: Thank you for the positive and constructive feedback.
**Batchnorm seems to do well.** We note that our ultimate goal is to achieve high overall accuracy, not merely unseen accuracy. Although BN (stats only) *maintains* the unseen accuracy well on OfficeHome, it cannot effectively improve the seen... | Summary: This paper studies a learning problem, called Holistic Transfer, which involves the adaptation of a pre-trained source model capable of classifying a wide range of objects, to a target domain using data that covers only a partial label space. To solve this problem,
Strengths: 1. Distribution shift exists ever... | Rebuttal 1:
Rebuttal: **It is more important to convince me the new setup is valuable. Now the introduction is too short and evidence is not convincing.** We apologize for not making the motivation clear. We provide a detailed motivation as follows. We will incorporate it in the final version to expand the introduction... | Summary: This paper proposes "Holistic Transfer" as an important problem and also provides some solutions to it. "Holistic Transfer" handles the situation when adapting a pre-trained source model (e.g. with 1000 classes) to a target domain (e.g. with 100 classes), but there are only data for part of the target domain ... | Rebuttal 1:
Rebuttal: Thank you for the positive and constructive feedback on our paper.
**The paper assumes that both the "seen" and "unseen" classes from the target domain are subsets of the classes in the source domain. It may happen that some "seen" classes in the target domain are not in the classes of the source... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable comments. We are glad that the reviewers found the proposed problem “interesting”, “important” (Reviewer HMzM), and “valid and practically useful” (Reviewer tg8m, CeTn); the proposed method “works well” (Reviewer HMzM, tg8m, 9DJT, CeTn, 9DJT); the experime... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a problem of fine-tuning the source model with partial target data, where the source and target distributions have covariate shifts and the target test data contain classes unseen in the target training data (also called Holistic Transfer, HT, in this paper). It proposes Leave-Out Local SGD... | Rebuttal 1:
Rebuttal: **The proposed problem does not seem very interesting or practical … too many constraints and assumptions … Table 1 actually contains two different settings …** We want to reiterate that the proposed problem is fairly *practical* and *realistic*. It considers a *practical* scenario where *an end-u... | null | null | null | null | null | null |
DiViNeT: 3D Reconstruction from Disparate Views using Neural Template Regularization | Accept (poster) | Summary: This paper proposes a two-stage framework for neural 3D reconstruction from disparate views via neural templates regularization. In the first stage, a network is trained for predicting shape templates. After that the volumetric surface reconstruction network with depth and SDF constraints is trained with the t... | Rebuttal 1:
Rebuttal: Thank you for the valuable comments.
> **Q1 - Performance on dense-view scenarios**
As addressed in Q2 in the global response and in the paper, our current implementation uses MLPs for the reconstruction task, and as such, for a fair comparison, we compare against the MLP representation of MonoS... | Summary: This paper proposes DiViNet for sparse multi-view reconstruction, which specifically targets on sparse input as few as three disparate RGB images. The key is to regularize the reconstruction process by learning a set of neural templates as surface priors, which is basically a set of 3D gaussian functions with ... | Rebuttal 1:
Rebuttal: Thank you for the valuable comments.
> **Q1 - Generalization ability of the Template Prediction Network**
This has been addressed in Q1 in the global response.
> **Q2 - Visualization of learned templates and COLMAP reconstruction**
Please see Fig 4 in the rebuttal pdf for the visualization ... | Summary: This paper addresses the problem of surface reconstruction from spase input views. The authors adopt a SDF representation parameterized by an MLP. They propose learning a set of neural templates (in the form of 3D Gaussian functions) to serve as anchors in the reconstruction process to help stitch the surfaces... | Rebuttal 1:
Rebuttal: Thank you for the valuable feedback.
> **Q1 - Generalization ability of the template prediction network (TPN)**
Please refer to Q1 in the global response for the generalization of the template prediction network.
> **Q2 - Training details**
In addition to the main paper, all the training deta... | Summary: This work presents a volume rendering-based sparse view neural surface reconstruction method. For the hard sparse view reconstruction ,the authors propose to learn neural templates as surface priors to guide the learning of neural fields. The results on DTU and Blended MVS are better than NeuS and MonoSDF .
S... | Rebuttal 1:
Rebuttal: Thank you for the valuable comments.
>**Q1 - No comparisons to SparseNeuS**
We reiterate that our framework is designed to excel at reconstruction from sparse (i.e., few in number) *and* wide-baseline/disparate (i.e., little overlap) view images. Due to the latter criterion, we did not show comp... | Rebuttal 1:
Rebuttal: We thank all reviewers for their insightful comments. It is encouraging to see that the reviewers find the addressed problem important (R_4rRP), with a novel (R_jSFe, R_4rRP, R_hUMT) and technically sound (R_bjQu) proposed solution that circumvents the requirement of explicit cues (R_4rRP) and/or ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors propose a framework for sparse view 3D reconstruction from disparate views. A two stage approach is presented for reconstruction of a scene from posed sparse images. In the first stage a template is predicted from the sparse images, represented by a number of parametric 3D gaussians. The second sta... | Rebuttal 1:
Rebuttal: Thank you for the encouraging comments.
> **Q1 - Assumption on 3D information for stage 1**
Yes, currently, our template prediction network requires losses with respect to COLMAP reconstructed point cloud for it to learn the surface priors effectively. Hence, the quality of the learned templates... | null | null | null | null | null | null |
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