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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
The Intelligible and Effective Graph Neural Additive Network	Accept (poster)	Summary: This paper proposes additive Graph Neural Networks. A combination of interpretable neural additive models and Graph neural networks. Through this combination GNANs are interpretable and similarly to NAMs the feature effects are visualizable. Strengths: - The paper is overall well written - The idea is simple ...	Rebuttal 1: Rebuttal: We thank the reviewer for the thoughtful comments and encouraging remarks. ## Weakness 1: The reviewer mentions that no hyper-parameters were tuned. We did tune the hyper-parameters, and the grid of hyper-parameters is presented in Appendix D3 and referred to in line 252. ## Weakness 2: The revi...	Summary: This paper introduces the Graph Neural Additive Network (GNAN), a novel interpretable graph neural network based on Generalized Additive Models. GNAN is designed to be fully interpretable through visualizations that clearly demonstrate how it uses relationships between the target variable, features, and graph ...	Rebuttal 1: Rebuttal: We thank the reviewer for the thoughtful comments and encouraging remarks. ## Weakness 1: The reviewer suggests improving the presentation of formulas in the methods section. We thank the reviewer for this suggestion, and we use it to improve the camera-ready version. ## Weakness 2: The reviewer...	Summary: The paper presents the GNAN, a model designed to integrate the interpretability of Generalized Additive Models with GNNs. GNAN aims to address the black-box nature of traditional GNNs by providing explanations through visualization. The model achieves this by learning shape functions for each feature and linea...	Rebuttal 1: Rebuttal: We thank the reviewer for the thoughtful comments and encouraging remarks. ## Weakness 1: The reviewer claims that GNAN is a special type of transformer and asked for clarifications in Question 1. We think there might be some confusion here that we are happy to clarify. We do not see GNAN as an i...	Summary: The paper proposes an interpretable by design model for graph data. The proposed model GNAN builds upon Generalised additive models and learns node representations as a distance function and feature shape functions explicitly and independently for each function. Interpretability is then offered by means of vis...	Rebuttal 1: Rebuttal: We are gratified by the reviewer’s appreciation of the novelty and interest of our work and thank the reviewer for the valuable feedback. ## Weakness 1: The reviewer claims it could be problematic to apply GNAN to large graphs with many features and asks for methods to prune the space as a pre-...	Rebuttal 1: Rebuttal: We thank the reviewers for their important questions and thoughtful feedback. Attached is a PDF file for reviewer 3kSH. Pdf: /pdf/1ddae0165b78b72f8ba15970e6074b415947e99b.pdf	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
SpeechAlign: Aligning Speech Generation to Human Preferences	Accept (poster)	Summary: This paper proposes to apply preference optimization techniques (which have proven useful in aligning text language models’ outputs to users’ preferences) to speech language models that generate sequences of discrete audio representations and then speech. The particularity of the preference dataset is that it ...	Rebuttal 1: Rebuttal: We sincerely appreciate the time and effort you have dedicated to reviewing our paper. Your insights and comments have been invaluable to refining our research. Our responses are as follows: Q1: How does the distribution gap relate to SpeechAlign? There is a distribution gap between golden AR to...	Summary: This paper introduces a method to improve speech generation in a speech language model via preference optimization. The method relies on creating a dataset of "gold" speech tokens produced by a neural codec model from a speech sample, contrasted with synthetic tokens produced by a speech generating model from ...	Rebuttal 1: Rebuttal: We sincerely appreciate the time and effort you have dedicated to reviewing our paper. Your insights and comments have been invaluable to refining our research. Our responses are as follows: Q1:The description of the preference optimization algorithms. Thank you for your valuable feedback. We ap...	Summary: This study analyzes the training-inference mismatch that occurs in codec language models, a branch of personalized speech synthesis research, and mitigates it through preference optimization methods. By avoiding the labor-intensive process of collecting human preference test results, the researchers efficientl...	Rebuttal 1: Rebuttal: We sincerely appreciate the time and effort you have dedicated to reviewing our paper. Your insights and comments have been invaluable to refining our research. Our responses are as follows: Q1: Which attributes cause this distribution difference between golden AR tokens and synthetic AR tokens? ...	Summary: The paper introduces "SpeechAlign," a method aimed at improving text-to-speech (TTS) performance by aligning speech generation with human preferences. It addresses the distribution mismatch between ground truth AR tokens and predicted AR tokens in neural codec language models. The proposed method involves pref...	Rebuttal 1: Rebuttal: We sincerely appreciate the time and effort you have dedicated to reviewing our paper. Your insights and comments have been invaluable to refining our research. Our responses are as follows: Q1: Performance Gap: The performance of the proposed method still falls short of the ground truth. We ac...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Parameter-free Clipped Gradient Descent Meets Polyak	Accept (poster)	Summary: I think this is a solid paper, interesting and relevant to the community. As somebody who has worked and published on Polyak step sizes in the past, I find the new results interesting regarding the convergence of the Polyak step under the $(L_0,L_1)$-smooth condition. That helps explain some of the surprisingl...	Rebuttal 1: Rebuttal: We thank the reviewer for your quite positive evaluation of our paper. > Some awkward or ungrammatical wording in places. We will revise them in the camera-ready version by using an English proofreading service. > Inexact Polyak Step-size convergence rate is somewhat slow. As the reviewer poi...	Summary: The work extends the convergence results for the Polyak stepsize to ($L_0$, $L_1$)-smoothness (and convex), showing a rate of $\mathcal O(\tfrac{L_0}{T} + \tfrac{LL_1^2}{T^2})$. To remove the dependency on knowing the optimal function value $f^\star$, they introduce a horizon dependent stepsize factor $1/\sqrt...	Rebuttal 1: Rebuttal: We thank the reviewer for your constructive criticisms of our paper. > The horizon dependent stepsize $1/\sqrt{T}$ (to relax knowledge of $f^\star$ to knowledge of a lower bound $l^\star \leq f^\star$), leads to a slow $O(1/\sqrt{T})$ rate, which is problematic. Since the work are also assuming k...	Summary: The paper proposes a version of the Polyak step size when the optimal value is not known. It analyses the proposed method in the convex, $(L_0,L_1)$-smooth setting, and draws a connection to gradient clipping. Strengths: The analysis of Polyak step sizes under $(L_0,L_1)$-smoothness seems to be novel, and the...	Rebuttal 1: Rebuttal: We thank the reviewer for your comments and criticism of our paper. > Comparing Thm. 2 and 5, the convergence rate drops from $O(1/T)$ to $O(1/\sqrt{T})$. Comparing convergence results of Alg. 1 to those of DecSPS, AdaSPS is somewhat unfair, as those are stochastic methods, and Alg. 1 is not! Thu...	Summary: This work made an interesting observation regarding the polyak stepsize. In particular, the main observation is that the polyak stepsize can be interpreted as doing a gradient clipping. With this observation, this work presents a new convergence guarantee of the polyak stepsize under the L_0,L_1 smoothness S...	Rebuttal 1: Rebuttal: We thank the reviewer for your positive evaluation and constructive feedback on our paper. > If you don't use the polyak step size, perhaps the parameter-free method should still achieve $O(1/T)$ convergence rate? [...] We will add the discussion we replied to for all reviewers to the revised ve...	Rebuttal 1: Rebuttal: We thank all reviewers and AC for their efforts in reviewing our paper. We appreciate all comments and criticisms for improving our paper. All reviewers point out that reducing $L$ to $L_0$ in Inexact Polyak Stepsize comes with the cost of slowing down the convergence rate to $\mathcal{O}(\frac{...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Scalable Optimization in the Modular Norm	Accept (poster)	Summary: The authors are tackling the difficult problem of try to scale the parameters of a network so that different sizes of network have similar optimisation properties. This would greatly aid in hyper-parameter tuning. They do this by introducing a new norm which is defined for the whole network. They show exper...	Rebuttal 1: Rebuttal: Dear Reviewer U3gK, Thank you for the time you spent reading and reviewing our paper. Many of the questions you had related to the masses in particular, so let us briefly discuss how we think about them. You are correct in that the masses absorb at least part of the question of determining optim...	Summary: The authors propose the “modular norm”, a norm for deep learning that can be simply recursively composed, allowing for easily recursively computing (and hence controlling) the Lipschitz constant of the network and loss gradient. They propose how to scale the gradient updates by the modular norm, and empiricall...	Rebuttal 1: Rebuttal: Dear Reviewer FbvX, We are grateful for your extremely thorough and helpful review! We’re very happy you found our paper interesting. Before we go in-depth into your comments: many of them are related to the tightness of the bounds we prove on first/second derivatives, so let us first explain ou...	Summary: The authors propose a new normalization strategy for deep models rooted in the introduction of a new framework and on feature learning considerations. The authors provide a few experimental examples as motivation, and then start introducing their framework. They formally define what a module is and its norm an...	Rebuttal 1: Rebuttal: Dear Reviewer qUST, We are grateful for your thorough and helpful review! First of all, we will heed your advice about restructuring the technical content. Second, regarding the result being lost in the literature, we are already proactively working with the community on larger-scale validations...	Summary: This paper introduces the modular norm, which is a norm designed to be adapted to neural network (NN) optimization. Specifically, an optimization process involving a gradient computed according to this norm scales with the size of the NN to be optimized. This paper provides the algorithm of computation of th...	Rebuttal 1: Rebuttal: Dear Reviewer 3bTa, We are grateful for your time spent reviewing our paper. We are sorry if the absence of the contribution list was disconcerting. We feel that our paper is chock-full of contributions since we are advancing a substantially novel perspective on deep learning optimization based o...	Rebuttal 1: Rebuttal: To all Reviewers, We are sincerely grateful for your contributions to the conference and for your feedback on our work. Reviewer FbvX commented that the paper “has challenged how I think about sharpness and curvature of the loss landscape in a deep learning context” and that they “feel fairly con...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
4Diffusion: Multi-view Video Diffusion Model for 4D Generation	Accept (poster)	Summary: The paper propose a 4D generation pipeline, namely 4Diffusion, aimed at generating spatial-temporally consistent 4D content from a monocular video.The authors design a unified diffusion model tailored for multiview video generation by incorporating a learnable motion module into a frozen 3Daware diffusion mode...	Rebuttal 1: Rebuttal: We sincerely thank you for providing insightful comments. Below, we address your constructive comments individually. W1: The paper's novelty wouldn't be its biggest strength, but training a multiview-video module is a good direction so, this point is moderately pass the bar of NeurIPS. Thank...	Summary: The paper proposes 3D-aware diffusion model trained on a curated 4D dataset for video-to-4D generation. 4D-aware Score Distillation Sampling loss is introduced to optimize 4D representation parameterized by dynamic NeRF. The proposed framework outperforms optimzation-based baselines. Strengths: - A new subset...	Rebuttal 1: Rebuttal: We sincerely thank you for providing insightful suggestions. Below, we address your constructive comments individually. W1: The proposed method requires 12 hours on A100, which is significantly longer than baselines. Our method focuses on generating high-quality, spatial-temporally consisten...	Summary: This paper tackles the task of 4D reconstruction from monocular video. It introduces a training approach for a multi-view video generative model using a synthetic dataset of multi-view videos. The architecture uses a 3D-aware denoising diffusion model previously applied to multi-view images and extends it to a...	Rebuttal 1: Rebuttal: Thank you for recognizing and valuing our work. We address your constructive comments as follows: W1: Difficult to obtain training data. We have manually curated a high-quality subset of Objaverse, which will released to support community development. Despite data limitations, our method has...	Summary: The paper proposes a 4D generation method that aims to generate 4D content from a monocular video. A video-to-multi-view-video diffusion model is presented to create multi-view videos given a monocular video, a text prompt, and a sequence of camera poses. The trained multi-view-video diffusion model is leverag...	Rebuttal 1: Rebuttal: Thank you for appreciating and acknowledging our work. We address your constructive comments below: W1: Some technical details are unclear. We will provide detailed information about the motion module in the revised version. Specifically, we incorporate a zero-initialized motion module at th...	Rebuttal 1: Rebuttal: We appreciate the detailed and constructive feedback from all the reviewers. We are pleased that reviewers recognize our contribution (Y3vx, 4Nq2) and the effort involved in filtering the dataset (4Nq2). Additionally, the reviewers acknowledge the significance (Y3vx) and interest (ioTR) of the add...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
The Closeness of In-Context Learning and Weight Shifting for Softmax Regression	Accept (poster)	Summary: This paper aims to investigate why transformers possess the capability of in-context learning from a theoretical perspective. Previous works have shown simplified self-attention layer’s capability of learning linear functions in context. This work conducts further research based on softmax regression, as softm...	Rebuttal 1: Rebuttal: *Q1: This paper appears to build upon previous research that explored in-context learning capabilities of transformer using linear regression, but instead opts for a softmax regression approach, lacking significant innovation and contribution.* Thank you for your comments. Different from pre...	Summary: This paper explores the relationship between in-context learning and weight shifting in the context of softmax regression for large language models. The authors delve into the mathematical aspects and study the in-context learning based on a softmax regression formulation. They present theoretical results that...	Rebuttal 1: Rebuttal: *Q1: While the paper compares the performance of a single self-attention layer with a softmax unit and a softmax regression model trained with one-step gradient descent, it would be informative to include comparisons with state-of-the-art models and methods.* Thank you for your suggestion. Ou...	Summary: This research delves into enhancing the comprehension of in-context learning through theoretical analysis. It builds on prior studies showcasing how a single self-attention layer can learn gradient steps in linear regression contexts. The authors extend this concept to softmax regression and give the upper bou...	Rebuttal 1: Rebuttal: ***Q1: The presentation is hard to follow. It's better to organize the formulation in a question-driven format, and it's unclear why bounding the single step of data transformation relates to building a connection between in-context learning and softmax weight shift. Consider optimizing the presen...	null	null	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Déjà Vu Memorization in Vision–Language Models	Accept (poster)	Summary: This work studies training data memorisation in Vision-Language Models (VLM). The paper focusses on contrastive learning with OpenCLIP, using a private Shutterstock dataset and a filtered LAION-50M, and evaluation on ImageNet. The paper proposes a method and metrics to measure déjà vu memorization, and in addi...	Rebuttal 1: Rebuttal: Thank you for a detailed review of our paper and for raising important questions. Below we would like to clarify some of the key points raised in the review. ## Distinguishing between memorization and learning To clarify, we believe a model can memorize and generalize (or learn) at the same time...	Summary: This paper investigates the issue of overfitting to pre-training data in Vision Language Models (VLMs) like CLIP. The authors conduct a comprehensive set of experiments focusing on text-to-image retrieval to evaluate this phenomenon. Their findings indicate that VLMs often memorize the data encountered during ...	Rebuttal 1: Rebuttal: We thank the reviewer for their detailed review and for acknowledging the importance of our work. Below we respond to some of the points raised. ## Long-tail issues of pretraining data Similar to Parashar et al. (2024), our work also explores the memorization in pretraining data of openclip mode...	Summary: This paper explores the concept of training data memorization within vision-language models (VLMs). The authors introduce a method to measure the degree of memorization by analyzing the fraction of ground-truth objects in an image that can be predicted from its text description. The study reveals a significant...	Rebuttal 1: Rebuttal: Thank you for your detailed review and for raising important questions that has helped us better shape our paper. Below we respond to the key points raised in the review. ## Lack of interpretability of metrics Our memorization metrics are built bottom-up from our notion of deja vu memorization f...	Summary: The paper proposes a methodology to measure memorization in vision-language models (VLMs). These measurements are based on the fraction of ground-truth objects in an image that can be predicted from its text description. The authors also explore different mitigation strategies. Strengths: - The methodology is...	Rebuttal 1: Rebuttal: We thank the reviewer for raising important questions, which has helped us improve our paper. ## Combining mitigations The main contribution of our work is a metric for evaluating memorization of VLMs. The ablation studies are done to validate the effect of key parameters and are not meant to b...	Rebuttal 1: Rebuttal: We thank all the reviewers for their thoughtful comments and for raising important questions. Here we include some new benchmark results across different compositional reasoning tasks for the various models we test in our paper. We include the answers to other questions / points raised in the indi...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Alleviating Hallucinations in Large Vision-Language Models through Hallucination-Induced Optimization	Accept (poster)	Summary: This paper addresses object hallucination in large visual language models (LVLMs), a common phenomenon that such models generate texts not consistent with images. A viable approach for this issue is contrastive decoding. By comparing logits derived from images and distorted images, visual contrastive decoding ...	Rebuttal 1: Rebuttal: # Response to Reviewer 6ngE We thank all the reviewers for the insightful comments and the recognition of our work "Novel Idea" (Reviewers #1, #3, #4) "Promising Results"(Reviewers #2, $3, #4) "Interesting"(Reviewers #1, #4) "Easy to understand"(Reviewers #1, #4). Now, we c...	Summary: This manuscript presents a novel perspective on implementing contrastive decoding for hallucination mitigation in large visual language models. In contrast to methods such as image perturbation, it achieves a quasi-min-max optimization by enhancing model hallucination followed by contrastive decoding for multi...	Rebuttal 1: Rebuttal: #### W1: The description of experimental details is weak. W1-A1: Based on your suggestions, we have revised the descriptions of the experiments in Tab.1, Tab.2, and Tab.5 as follows: Table 1 presents the experimental results on the POPE dataset across random, popular, and adversarial setti...	Summary: This paper focuses on alleviating hallucinations in Large Vision-Language Models. Specifically, the authors introduce a novel optimization strategy named Hallucination-Induced Optimization (HIO). This method amplifies the contrast between hallucinatory and targeted tokens relying on a fine-tuned preference mod...	Rebuttal 1: Rebuttal: #### W1: Novelty: motivation is similar to [1]. [1] Alleviating Hallucinations of Large Language Models through Induced Hallucinations W1-A1: We indeed adopt the same concept of induced hallucination from ICD[1], **but we also identified two key issues of it, i.e., less effective and poor gene...	Summary: This paper proposes a method for mitigating hallucinations by training an "evil" LLM to provide logits for contrastive decoding. This "evil" LLM is trained with a dataset that prefers hallucinated samples over true ones during fine-tuning. The logits from this "evil" LLM are then used for contrastive decoding....	Rebuttal 1: Rebuttal: # Response to Reviewer DdM1 We thank all the reviewers for the insightful comments and the recognition of our work "Novel Idea" (Reviewers #1, #3, #4) "Promising Results"(Reviewers #2, $3, #4) "Interesting"(Reviewers #1, #4) "Easy to understand"(Reviewers #1, #4). Now, we ...	Rebuttal 1: Rebuttal: We thank all the reviewers for the insightful comments and the recognition of our work. "Novel Idea" (Reviewers #1, #3, #4) "Promising Results"(Reviewers #2, $3, #4) "Interesting"(Reviewers #1, #4) "Easy to understand"(Reviewers #1, #4). Summary of Strengths: **R1-S1...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Stepping on the Edge: Curvature Aware Learning Rate Tuners	Accept (poster)	Summary: This paper intents to design new learning rate tuners based on insights from the Edge of Stability phenomenon. Based on the conjecture that classsical linesearch methods undershoot the edge of stability, and that this causes poor performance, the paper proposes a new learning rate tuner, that targets ceratin s...	Rebuttal 1: Rebuttal: We thank the reviewer for reading this manuscript and providing valuable feedback. We answer their comments below. > _"The proposed CDAT tuner has several limitations"_ - Our goal with this study is to underscore the interplay between sharpness dynamics and stepsize tuners. The CDAT rule serves...	Summary: The paper investigates the consequences of the sharpness dynamics on step-size tuners' design. In particular, given a learning rate $\eta$, the sharpness exhibits a progressive sharpening phase towards the Edge of Stability (EoS) threshold of $2/\eta$ , where it stays for a large part of training time. First, ...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the positive feedback and to appreciate the exploratory nature of this work. Below we answer their comments. > _"it does not outperform the baseline of a constant learning rate in the practical deep learning use-case of mini-batch optimization [...] it has an...	Summary: This papers studies the behavior of several automatic learning rate schedulers in deep learning. First, the paper studies two classical learning rate tuners - line search, and quadratically greedy (which chooses the learning rate that minimizes a quadratic Taylor approximation in the negative gradient directi...	Rebuttal 1: Rebuttal: We thank the reviewer for reading our paper and providing valuable feedback. We answer below their comments. > _"it studies optimizers which are not state-of-the-art in the first place"_ - Could the reviewer clarify what optimizer do they have in mind? We already added additional learning rate...	Summary: The paper proposes a novel learning rate tuning method, CDAT, that leverages the largest Hessian eigenvalue information during training. To illustrate the feedback loop between learning rate selection and sharpness dynamics, and to emphasize the importance of stepping on the edge of stability, the authors intr...	Rebuttal 1: Rebuttal: We thank the reviewer for their detailed feedback, and address their comments here. > _"Theoretical ground is not solid"_ - The experiments and models focus on the full batch regime just as Cohen [24, 25] did to uncover the edge of stability phenomenon. The theory is grounded in the full batch ...	Rebuttal 1: Rebuttal: We sincerely thank the reviewers for reading our paper carefully, and providing numerous insightful comments. We answer each reviewer's comments separately and provide here some answers to the main common comment. > "Experimental results are not good." (Reviewer 7YAk) > "it does not outperform ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Typicalness-Aware Learning for Failure Detection	Accept (poster)	Summary: This paper identifies overfitting of atypical samples as a potential casue of overconfidence in DNN for Failure Detection. The authors proposes a Typicalness-Aware Learning (TAL) approach that computes a typicalness score of each sample. TAL assigns dynamic logit magnitudes based on typicalness to allow flexib...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable comments and suggestions. Q1：The definition of atypical samples is vague, making it difficult to accurately determine the typicality of samples, which may affect the method's effectiveness. Please consider providing some mathematical definitions and visu...	Summary: In this work, the authors propose a new approach to failure detection from DNN predictions. They suggest that data samples can be classified as either typical or atypical. The latter includes ambiguous, out of distribution samples or ill annotated data. In order to circumvent this limitation, the authors intro...	Rebuttal 1: Rebuttal: Thank you for your valuable comments and kind words to our work. Q1： in its current form the empirical quantitative evaluation is not fully convincing as it is bounded to computer vision tasks and models with very few comparison points on large scale datasets. Thank you and the results on Im...	Summary: This paper proposes a novel training method for improving the failure detection ability of classification models. The authors argue "overconfidence" may be in part due to overfitting of a model to the one hot labels of "atypical" samples. In order to mitigate this issue, a dynamic modification of the LogitNorm...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback on our paper. Q1: Missing comparisons. A number of methods that should be compared against are missing. As CRL requires training, but SIRC doesn't, and given the length of the rebuttal period, I would be happy with a comparison with just SIRC. The compari...	Summary: The paper proposed a method for detecting incorrect prediction (Failure Detection). The main hypothesis behind the method is that cross-entropy either increases the logit magnitude or aligns the logit direction to align it with the ground label, which can cause discrepancy on atypical samples at test time. Pre...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable comments and suggestions. Q1: Most experiments were conducted on CIFAR. Results on ImageNet are inconclusive; only MSP was used as a baseline for comparison. Thank you for the reviewers' comments. The question about ImageNet is a common concern among th...	Rebuttal 1: Rebuttal: Thanks to all reviewers and ACs for the valuable comments and suggestions. We appreciate that reviewers described our work as `` well-written, intuitive, effective". We are grateful for the reviewers' positive evaluation of our work as ``well-written, intuitive, effective". Each reviewer's feedbac...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models	Accept (poster)	Summary: This paper proposes two measures to reflect the quality of the trained sparse auto-encoder when decomposing superposition features. In addition, this paper achieves approximate L0 optimization through dynamic adjustment of the p-norm and testing it on two types of board games. Strengths: The research directio...	Rebuttal 1: Rebuttal: Thank you for your thoughtful review. We’re glad that you think the research direction in our paper is very valuable. Comparison of the advantages of the two proposed measurement methods compared to reconstruction loss and sparsity: Thank you for giving us the opportunity to clarify this. In...	Summary: This manuscript applies sparse autoencoders (SAE) to detect interpretable features from autoregressive transformer models trained on Othello and chess. These controlled scenarios provide suitable testbeds, in the sense that we can extract ground truth features to measure progress in dictionary learning. Based ...	Rebuttal 1: Rebuttal: Thank you for your review. We’re glad that you enjoyed reading the paper and think it’s of high relevance to the NeurIPS community. Demonstrating transfer to natural language: Yes, we agree that this is a limitation in our paper and future work should study this. However, note that our metri...	Summary: The paper introduces a setting for evaluating dictionary learning methods (and in particular sparse autoencoders) for language model interpretability. The setting proposed is that of interpreting features learned by language models trained on data representing Chess and Othello games. This setting should allow...	Rebuttal 1: Rebuttal: Thank you for your review. We’re glad that you think that our metrics intuitively make sense and that the motivation for p-annealing is well thought out and presented. Mismatch between figures and caption: Thank you; this is a mistake and we will correct this in the paper. We have included t...	null	null	Rebuttal 1: Rebuttal: We have taken a number of steps to improve, simplify, and clarify our analyses: - Multiple reviewers raised that the distinction between low-level and high-level BSPs seemed ambiguous and subjective. To address this, we moved to a more principled division of BSPs into (1) board state BSPs, which c...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
General bounds on the quality of Bayesian coresets	Accept (poster)	Summary: This paper studies the quality of posterior likelihood based on coreset, a subset of the full data. The quality is quantified through KL divergence between the posterior likelihoods based on coreset and full data, so the main theorems demonstrate the upper and lower bounds of KL which are of great concern. St...	Rebuttal 1: Rebuttal: Thanks for your efforts reviewing the manuscript! > Tightness of the upper and lower bounds. I am not sure how different are the bounds from each other. If upper and lower bounds are different from each other, their applications might be limited. > Please describe the gaps between the upper and l...	Summary: This paper provides new lower and upper asymptotic bounds on the KL divergence between the true posterior and the posterior obtained from common classes of coreset construction algorithms, under milder assumptions than previously used. My own research is in the area of Bayesian statistics but overall less the...	Rebuttal 1: Rebuttal: Thanks very much for your efforts reviewing the manuscript! > I don’t see any major weaknesses. Of course, it would have been desirable to see the results illustrated on more complicated empirical examples, but I understand why the authors used the examples they did. Much appreciated! > Can the...	Summary: The paper derives bounds on the quality of coresets, as measured by forward and reverse KL divergence. The lower bound is used to study importance weighted coresets, leading to the conclusion that importance weighting leads to a large (forward or reverse) KL divergence between the approximate posterior and the...	Rebuttal 1: Rebuttal: First, thank you for your very thorough review! > Several statements are made in text that seem overly strong. [...] This is a fair comment. We were thinking mostly of parametric models with conditionally independent data, where competing methods rely on asymptotic normality (either explicitly o...	Summary: The authors present general upper and lower bounds on the Kullback-Leibler (KL) divergence of coreset approximations. The lower bounds require only mild model assumptions typical of Bayesian asymptotic analyses, while the upper bounds require the log-likelihood functions to satisfy a generalized subexponential...	Rebuttal 1: Rebuttal: Thank you for reviewing our manuscript! We appreciate your kind words about its clarity, novelty, and significance. > The paper offers a theoretical explanation for the suboptimal performance of importance-weighted coreset construction [...] however, [the results in] [32] [...] raises questions a...	Rebuttal 1: Rebuttal: Thank you to all the reviewers for their efforts reviewing our manuscript. We have responded to each reviewer in the comment section below their review. Please let us know if there are any further questions!	NeurIPS_2024_submissions_huggingface	2,024	Summary: This work contributes to the approximation theory of Bayesian coresets. It establishes asymptotic lower bounds (in KL divergence) of Bayesian coreset approximation that do not require posterior normality assumptions. It also provides an upper bound of the approximation (in KL divergence) when the potentials sa...	Rebuttal 1: Rebuttal: First, thank you for your efforts reviewing the manuscript. We're very glad you found it understandable and interesting despite not being in your area! > Since this manuscript studies both the lower and upper bounds of coreset approximation, a natural question that arises is how well do the lower...	null	null	null	null	null	null
AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data	Accept (poster)	Summary: This paper presents an improved pipeline for training LLMs for code generation. Their method incorporates prompt modifications using hindsight tuning to modify prompts to better align with the associated code. In addition, they introduce improved data filtering and additional training tasks which they find giv...	Rebuttal 1: Rebuttal: W1: Concerns of the presentation. - Thank you for your valuable advice on improving our presentation! Our motivation stems from the following two considerations: 1) Existing Code LLM pre-train methods typically use multi-source data, while Code LLM fine-tuning methods focus on developing high-...	Summary: The paper presents a series of Code Large Language Models (LLMs) named AlchemistCoder, which are fine-tuned on multi-source data to enhance code generation and generalization capabilities. The authors address the limitations of previous Code LLMs that were typically fine-tuned on single-source data, which lack...	Rebuttal 1: Rebuttal: W1: Concerns of AlchemistPrompts. - Thanks for your insightful concerns! To fairly compare with other models, we provide a new version of Table 1 in the global response PDF, which includes details of training corpora sources for fine-tuned Code LLMs. Compared to methods that heavily rely on GP...	Summary: This work improves upon past work developing code LLMs with a focus on intervention on the data used to instruction tune the models. Specifically, the key insight in this work is that past works have usually relied on single source data for fine-tuning, but this can come at a drawback of quality and diversity....	Rebuttal 1: Rebuttal: W1: Missing ablations. - Thanks for your meticulous suggestions! We provide a new version of Table 1 in the global response PDF, which includes details of the training corpus used for fine-tuned Code LLMs. - Here, we reorganize the following table based on Table 4 to more clearly demonstrate t...	Summary: This paper introduces AlchemistCoder, a series of code language models fine-tuned on multi-source data. The authors propose using "AlchemistPrompts" to harmonize inherent conflicts in multi-source code corpora and incorporate code comprehension tasks into the training process. The resulting models show improve...	Rebuttal 1: Rebuttal: W1&W2: The novelty of the proposed method. - Actually, our AlchemistPrompts are entirely different from existing instruction evolution techniques in several aspects: - Different designed goals: Instruction evolution techniques are designed to "expand into a richer and more complex set of...	Rebuttal 1: Rebuttal: Dear all, We appreciate the reviewers for valuable feedback remarking our work has "simple" method (Reviewer yLAq & FM86) and "strong" efficacy with "impressive improvements" (Reviewer q6UB & fYHy & yLAq & FM86), provides "wide range of evaluations, detailed ablation studies and analyse...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Diffusion Tuning: Transferring Diffusion Models via Chain of Forgetting	Accept (poster)	Summary: This paper proposes Diff-Tuning method to encourage the fine-tuned model to retain the pre-trained knowledge. In this method, both pre-trained data and downstream data are used to train the diffusion model. Compared to standard fine-tuning methods, Diff-Tuning enhances the convergence speed and improves the pe...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the efforts in reviewing our paper. Our responses according to the reviewer's comments are summarized as follows. --- > W1: Concern about the novelty and difference from [1,2]. Thank you for your insightful thoughts regarding the distinction from existing ...	Summary: This paper explores the fundamental transfer characteristics of diffusion models and observes the monotonous chain of forgetting trend of transferability of diffusion models in the reverse process. It then proposes a simple but effective transfer approach to make the fine-tuned model retain the denoising abili...	Rebuttal 1: Rebuttal: We sincerely appreciate the time and effort you have taken to review our manuscript and for your constructive feedback. We will address the concerns you raised and revise the paper accordingly, as your comments provide valuable insights for improving our work. --- > **W1: Use more metric to show...	Summary: The paper proposes a new method to fine-tune the pretrained large-scale diffusion models for new tasks. It finds that different time steps of the denoising process of diffusion models have varying transferability. Specifically, the paper finds that "low-noise" time steps close to the end of the denoising proce...	Rebuttal 1: Rebuttal: We sincerely appreciate the careful review and insightful suggestions provided by the reviewer. Our responses to the concerns raised are detailed below: --- > W1&2: The motivation of knowledge retention and a potential varient of Diff-Tuning with knowledge distillation Thank you for your ins...	Summary: This paper focuses on transfer learning methods for diffusion models. It experimentally demonstrates and provides theoretical insights into how the forgetting trend varies with the diffusion timestep. Based on this observation, they proposes Diff-Tuning. The proposed method introduces objectives for knowledge ...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the careful review and insightful suggestions. Our responses to the concerns raised are outlined below: --- > W1: More discussion on how to set the hyperparameter $\xi(t)$ and $\psi(t)$. Thank you for highlighting the importance of hyperparameter selection...	Rebuttal 1: Rebuttal: > Common Concern #1 (raised by WW5v and x2jk): The comparison to existing literatures [1-4] Reviewer WW5v concerns about some similar phenomena have been shown in the existing literatures [1,2]. and Revewer x2jk also points out that [3,4] find gradients conflict across timesteps even in the s...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Activation Map Compression through Tensor Decomposition for Deep Learning	Accept (poster)	Summary: The paper addresses the challenge of on-device training for deep learning models, particularly focusing on the memory bottleneck caused by the storage of activation maps during backpropagation. The authors propose a method to compress activation maps using tensor decomposition techniques, specifically Singular...	Rebuttal 1: Rebuttal: [W1.1: Compression cost] Please refer to answer #3 of General answers. [W1.2: Introduced compression loss] Please refer to answer #4 of General answers. [W2: Comparison with activation checkpointing] As evidenced in Figure 1 of the PDF rebuttal file, the required memory to store...	Summary: This paper proposes a method to compress activation maps in deep neural networks using tensor decomposition techniques, specifically Singular Value Decomposition (SVD) and Higher Order SVD (HOSVD). The goal is to reduce memory requirements during backpropagation, enabling on-device learning for resource-constr...	Rebuttal 1: Rebuttal: [W1: Severe performance degradation relative to memory reduction] We are focusing on performing direct training on edge devices with extremely limited memory, so it is worth considering trading off accuracy for memory savings at an incredible rate. [W2: Implementation complexity] We have ...	Summary: [Editing to reflect my score increase from 6 to 7 after the author discussion phase.] The authors tackle the problem of memory consumption due to needing to keep realized activation tensors available between their use in the forwards pass and backwards propagation in training neural networks. They propose th...	Rebuttal 1: Rebuttal: [W1 - Originality: Missing reference] In their work Rhu et al. exploit inherent activation sparsity for compression, resulting in low memory usage and accelerated training. This is a very relevant paper with respect to our research and we will cite it in our paper, thank you for pointing it ou...	Summary: Tensor low-rank decomposition is used to compress the backpropagation process of full connection and convolution in neural networks. The main process is to decompose input activation tensors into Tucker structures using HOSVD algorithm and truncate subtensors at each mode. Experimental result find an efficient...	Rebuttal 1: Rebuttal: [W1: Confusing formulas] Thanks for your feedback, we are taking into account your comments to improve the readability. [W2: Distinguishing $U^{(k_j)}$s] We acknowledge the notation issue, taking action to fix it. Specifically, we will change the notation for factor matrices introduced ...	Rebuttal 1: Rebuttal: First of all, we would like to thank you and express our appreciation for the time and effort you have invested in reviewing our work. We are especially grateful for the highlights regarding the novelty of the proposed method (HEKd, dqZf), the theoretical groundings (HEKd, dqZf, WgAz) and the expe...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
PPLNs: Parametric Piecewise Linear Networks for Event-Based Temporal Modeling and Beyond	Accept (poster)	Summary: This paper proposes Parametric Piecewise Linear Networks (PPLNs) for event-based temporal modeling, which emulate biological principles by representing membrane potentials as parametric mappings. The authors demonstrate how a straightforward modification enables standard multi-layer perceptrons and convolution...	Rebuttal 1: Rebuttal: Dear Reviewer, We appreciate the discussion with you previously in our submission to another venue. We have answered the questions you listed earlier but did not receive your response. May we ask whether you think our rebuttal has clarified your concerns? If so, we kindly encourage you to conside...	Summary: 1. The paper presents Parametric Piecewise Linear Networks (PPLNs), a novel neural network architecture inspired by neuromorphic principles for temporal vision inference. 2. The innovative approach of PPLNs lies in modeling the membrane potential of artificial neurons as parametric piecewise linear functions ...	Rebuttal 1: Rebuttal: Dear Reviewer, Here are the responses to the concerns in your review. We look forward to your additional input during the reviewer-author discussion period. While we are grateful for the weak accept vote, we encourage you to consider raising the rating if you deem it appropriate. Comment: Th...	Summary: This paper introduces Parametric Piecewise Linear Networks (PPLNs), a novel approach to temporal modeling inspired by biological neural principles. PPLNs represent neuron membrane potentials as piecewise linear functions with learnable coefficients, aiming to allow for explicit temporal modeling. The authors e...	Rebuttal 1: Rebuttal: Dear Reviewer, Here are the responses to the concerns in your review. We look forward to your additional input during the discussion period. Meanwhile, we encourage you to consider raising the rating if you deem it appropriate. Comment: The paper lacks a thorough analysis of the computationa...	null	null	Rebuttal 1: Rebuttal: We would like to express our sincere gratitude to the reviewers for their thoughtful evaluation of our submission. While we appreciate recognition from all reviewers, we believe there may be additional aspects of our research that warrant further consideration. Specifically, we feel that Theorem 3...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Cloud Object Detector Adaptation by Integrating Different Source Knowledge	Accept (poster)	Summary: This paper introduces CODA, a new problem, where the goal is to adapt object detectors for specific target domains using knowledge from cloud-based detectors. The paper uses CLIP to refine the knowledge from the cloud detector. A gradient alignment method is proposed to deal with the inconsistency between the ...	Rebuttal 1: Rebuttal: We thank the reviewer for the very encouraging comments like the originality of CODA, the novelty of COIN, good writing, extensive experiments and good computational efficiency for edge devices. We hope to provide satisfying answers to the concerns raised. **Q1: The definition of the new problem ...	Summary: This paper proposes a new problem in the field of domain adaptation, called Cloud Object Detector Adaptation (CODA), where a cloud model is provided to help with target detector training. A novel method termed COIN is proposed to leverage CLIP for knowledge distillation in a divide-and-conquer manner. Sufficie...	Rebuttal 1: Rebuttal: We thank the reviewer for the very encouraging comments like the originality of CODA, the novelty of COIN, good presentation, and sufficient experiments. We hope to provide satisfying answers to the concerns raised. Q1: Method Generalization Ability. A: Great suggestions. COIN can be general...	Summary: In this paper, the authors propose a Cloud Object Detector Adaptation framework, which leverages a strong detector in the cloud to extract discrimative knowledge of objects in the target domain. It is basically a mean teacher style for domain adaptive object detection. Strengths: 1 This paper considers an imp...	Rebuttal 1: Rebuttal: We thank the reviewer for the very encouraging comments that confirm the importance of CODA and the extensive nature of our experiments. **Q1: The design of this work basically follows the mean teacher style for self-distillation with EMA. Basically, the contributions are not convincing. Hence, t...	Summary: This paper proposes a novel task: Cloud Object Detector Adaptation (CODA). It discusses how to build a domain specific object detector with the help of a cloud detector, and local data from the target domain. Different from existing similar tasks, CODA does not have the full access of the cloud model, only det...	Rebuttal 1: Rebuttal: We thank the reviewer for the very encouraging comments like the novelty of CODA and COIN as well as the effectiveness of COIN. We hope to provide satisfying answers to the concerns raised. **Q1: The whole architecture should be validated on different cloud detectors. It should focus more on diff...	Rebuttal 1: Rebuttal: We appreciate all the reviewers for the constructive and positive comments e.g., the originality of CODA (reviewers nLTh, s7Cc, MA6n, FRKx, and Ht4V), the novelty of COIN (reviewers s7Cc, FRKx, and Ht4V), good organization and presentation (reviewers nLTh, FRKx, and Ht4V), experimental effectivene...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper introduces Cloud Object Detector Adaptation, in which a cloud model is responsible for detection in the target domain. The proposed framework, COIN, includes successive stages including knowledge dissemination, separation, and distillation. The target detector leverages a cloud detector and CLIP thr...	Rebuttal 1: Rebuttal: We thank the reviewer for the very encouraging comments on the originality of CODA, the effectiveness of COIN, and the overall good presentation. We hope to provide satisfying answers to the concerns raised. Q1: The challenge of identifying optimal hyperparameters. A: (1) This number of hype...	null	null	null	null	null	null
Normalization Layer Per-Example Gradients are Sufficient to Predict Gradient Noise Scale in Transformers	Accept (poster)	Summary: This paper explores efficient ways of computing the gradient noise scale (GNS) when training transformers. The main practical relevance of the GNS is that it can be used to estimate the critical batch size, where the larger batch sizes become computationally inefficient. The authors discuss different ways of d...	Rebuttal 1: Rebuttal: Thank you so much for your detailed and constructive review, and your kind words regarding the appeal and relevance of the paper’s core idea. ### On the clarity of the paper > The paper relies too heavily on McCandlish et al 2018. Despite spending significant effort on trying to summarize the re...	Summary: The paper proposes a method for efficient computation of per-example gradient norm for the broader usecase of computing gradient noise scale. Further the authors showcase the usecase of gradient noise scale (GNS) in Transformers and showcases that GNS of only normalization layers in transformer models suffices...	Rebuttal 1: Rebuttal: Thank you for your helpful review. Your suggestions will definitely help improve the paper. It is also gratifying to know that the work is well motivated and the application of per-example gradient norms to Transformers training is useful and clear. ### Regarding revising the related work > p...	Summary: This paper proposes a more efficient method for computing per-example gradient norms without significant computational overhead in terms of FLOPs and I/O cost. This method accurately estimates the gradient noise scale (GNS), useful for neural network batch size selection and scheduling. Additionally, it observ...	Rebuttal 1: Rebuttal: Thank you for your thoughtful comments and suggestions, and for positively noting the efficiency of our GNS estimator. ### Regarding the scope of the evaluations > the experiment should be much more comprehensive … only one set of experiments was conducted on a fixed model/dataset, making it unc...	Summary: This work proposes a method to compute per example gradient norms as a means to compute GNS. It shows that not all layers are necessary to estimate the GNS and that the per-example gradient norms can be computed for normalized layers without any overhead. Strengths: - This work provides an efficient technique...	Rebuttal 1: Rebuttal: Thank you very much for your thoughtful feedback, and for your support of the paper's core idea and experimental approach. ### Regarding larger-scale models > The largest model included in the case study only has 111M parameters... this work could benefit from experiments on larger architectures...	Rebuttal 1: Rebuttal: Thank you to all the reviewers for their thoughtful feedback. After reading the reviews we noted the following points we could address with additional figures: 1. Reviewers 6ptT, fwbc and 32m2 brought attention to the clarity of the work. 2. Reviewers 5nZ5 and 6ptT asked for additional experiment...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
A versatile informative diffusion model for single-cell ATAC-seq data generation and analysis	Accept (poster)	Summary: The paper introduces a new diffusion model ATAC-diff for scATAC-seq data generation and analysis. ATAC-diff using a latent diffusion model which is conditioned on latent auxiliary models to encode latent variables, and integrate GMM as the latent prior to capture genetic information. This paper introduces a mu...	Rebuttal 1: Rebuttal: Thank you very much for your detailed and insightful comments on our manuscript. Your comments clearly helped a lot to improve this manuscript! We have summarized your comments and made point-by-point responses and revisions to address your concerns. 1. Thank you for your valuable comment. Our m...	Summary: This submission introduces ATAC-Diff, a conditional latent diffusion model for scATAC-seq data. ATAC-Diff incorporates a few components to a basic diffusion model, including a Gaussian Mixture Model as a semantic prior over the latent variables and mutual information as a regularizer. ATAC-Diff is benchmarked ...	Rebuttal 1: Rebuttal: Thank you very much for your detailed and insightful comments on our manuscript. Your comments clearly helped a lot to improve this manuscript! 1. We clarify that in our approach, we utilize fragment counts rather than binary peaks to represent the scATAC-seq data. To alleviate any confusion, we...	Summary: Generating simulated scATAC-seq data is important for developing new methods and gaining a deeper understanding of the data. However, the simulation is challenging due to dropout and high noise in the data. Authors proposed a diffusion + VAE type of method to solve the problem. The general idea is first to use...	Rebuttal 1: Rebuttal: Thank you very much for your encouraging comments and the constructive advices on improving the manuscript. Your comments clearly helped a lot to improve the study. We have summarized the suggested comments (Questions) and made point-by-point responses and revisions as follows. 1. Thank you for y...	null	null	Rebuttal 1: Rebuttal: Thanks all the reviewers for their comments and constructive suggestions, which really help improve this manuscript. We have added the revised illustration of model in the PDF file. Moreover, we have computed the average latent embeddings within each cell type population and calculated the distanc...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
VeXKD: The Versatile Integration of Cross-Modal Fusion and Knowledge Distillation for 3D Perception	Accept (poster)	Summary: The paper introduces a modality-general fusion teacher that narrows the gap between teacher and single-modal student models. This framework also includes a data-driven mask generation network that creates unique spatial masks for different feature levels and tasks. These masks enhance feature distillation by s...	Rebuttal 1: Rebuttal: ### 1. Q1: Experiment on More Baselines Thank you for your suggestion. We have conducted 3D object detection KD experiments on both BEVDepth and the temporally fused BEVFormer as student models. As shown in Table 1 attached to the global response, our KD framework was applicable to both model...	Summary: This paper introduces VeXKD, a Versatile framework that integrates Cross-Modal Fusion with Knowledge Distillation for 3D detection tasks. The framework adopts a modality-general cross-modal fusion module to bridge the modality gap between the multi-modal teachers and single-modal students. Extensive experiment...	Rebuttal 1: Rebuttal: ### Q1: CenterPoint+VeXKD (L+C->L) Compared to TransFusion-L (L) Thank you for your question. To ensure fair comparisons with existing KD methods like S2M2-SSD and UniDistill, we chose CenterPoint as the LiDAR student model. CenterPoint inherently underperforms compared to TransFusion-L by 5....	Summary: This paper presents VeXKD, an innovative framework that combines Cross-Modal Fusion and Knowledge Distillation (KD) to significantly enhance 3D perception capabilities. VeXKD employs knowledge distillation on BEV feature maps, facilitating the seamless transfer of multi-modal insights to single-modal student m...	Rebuttal 1: Rebuttal: ## 1. Q1: Adaptation on other feature representation In our manuscript, the experiments were conducted on the BEV feature map. The BEV feature space has become a focal point of research in recent years due to its favorable compatibility with multiple modalities and its similar processing pipe...	Summary: This paper proposes VeXKD, a method that performs Knowledge Distillation (KD) in the BEV feature space. By distilling cross-modal knowledge from a teacher model into a single-modal student model, VeXKD eliminates the need for additional inference time overhead. The distilled student model can be adapted to var...	Rebuttal 1: Rebuttal: ### Q1: Adaptation on other feature representation In response to your valuable insights, we would like to offer some clarifications. When conducting KD, it is crucial for the feature maps of both the student and the teacher to reside within the same feature space to ensure spatial and semant...	Rebuttal 1: Rebuttal: Thank you to all the reviewers for your constructive comments and feedback, which have been invaluable in allowing us to improve our work. We appreciate the recognition from all reviewers of the main contributions of this paper, including the versatility of the proposed knowledge distillation...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Spatio-Temporal Interactive Learning for Efficient Image Reconstruction of Spiking Cameras	Accept (poster)	Summary: Spiking cameras are sensors that capture high-speed motion by firing continuous binary spike streams asynchronously. Current image reconstruction methods from these spike streams use complex architectures that overlook the collaboration of spatio-temporal information. This paper proposes an efficient spatio-te...	Rebuttal 1: Rebuttal: Thank you for your precious time and insightful comments. We first list your advice and questions, then give our detailed answers. > W1: A little confusion in Spike-Based Image Reconstruction. Thank you for your question that has brought our attention to a potential point of confusion. We woul...	Summary: This paper proposes a new method for reconstructing images from spiking camera data called STIR (Spatio-Temporal Interactive Reconstruction network). Strengths: 1. The joint motion-intensity learning architecture is innovative and addresses limitations of previous step-by-step methods. 2. Faster inference spe...	Rebuttal 1: Rebuttal: Thank you for your precious time and insightful comments. We address each concern below. > W1 & Limit2 & Q3: The overall architecture is too complex to implement or adapt. The approach still requires significant computational resources. How is the scaling achieved, and what are the trade-offs i...	Summary: In this paper, authors propose a novel method for reconstructing images from spiking camera representations. The approach involves constructing a spiking embedding representation followed by a complex network of sub-networks, the importance of each respective block being evaluated through ablation studies. Not...	Rebuttal 1: Rebuttal: Thank you very much for your precious time and recognition of our work. We first list your advice and questions, then give our detailed answers. > W1: Using previous models without mathematical justification. (1) Mathematical justification. We deeply relate to your concerns about the mathe...	Summary: This paper proposes a new efficient spatio-temporal interactive reconstruction network that enhances image reconstruction by jointly optimizing inter-frame feature alignment and intra-frame feature filtering in a coarse-to-fine approach. The network leverages a hybrid spike embedding representation and introdu...	Rebuttal 1: Rebuttal: Thank you very much for your precious time and recognition of our work. We first list your advice and questions, then give our detailed answers. > W1: Generalization ability under unknown or broader conditions. The commonly used generalization test approach of current spike-to-image reconstruc...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Who Evaluates the Evaluations? Objectively Scoring Text-to-Image Prompt Coherence Metrics with T2IScoreScore (TS2)	Accept (spotlight)	Summary: The authors propose T2IScoreScore (TS2), a benchmark and set of meta-metrics for evaluating text-to-image (T2I) faithfulness metrics. Compared to existing relevant benchmarks, TS2 has higher image-to-prompt ratios, which allows users to organize semantic error graphs (SEGs), where each edge corresponds to a sp...	Rebuttal 1: Rebuttal: Thank you for your thoughtful review. We are heartened to hear that you view our meta-metrics as a novel strength, and that you appreciate our comprehensive experiments that really attempted to make the strongest possible case for the QG/A metrics by analyzing multiple backends. We hope we can sat...	Summary: This paper presents a rigorous evaluation for text-to-image alignment metrics. This is primarily done by introducing a dataset with several images for each prompt, allowing the construction of semantic graphs that can be used to measure the accuracy of the alignment metrics. From the analysis on the benchmark,...	Rebuttal 1: Rebuttal: Thank you for your detailed review! We appreciate your praise of our work’s rigorous evaluation, valuable dataset contribution, sound methodology and analysis, and clear writing. We are overjoyed to hear that you really like [our paper] and hope to see it accepted. These are all very h...	Summary: The paper proposes an evaluation framework for holistically assessing text-to-image (T2I) evaluation methods. Since most of them are primarily established through simplistic correlational evidence and only compared to the CLIPScore baseline, this approach presents a more detailed way of assessment and also ben...	Rebuttal 1: Rebuttal: Thank you for your detailed review! We appreciate your recognition of our important contribution which you find interesting, that enables detailed insights and has a thoughtful discussion. We hope you will find our response to your questions about the counting system satisfactory. ### We...	Summary: The paper introduces T2IScoreScore (TS2), which aims to evaluate how good newly developed text-to-image (T2I) evaluation metrics/methods are. The authors formalize the task of evaluating t2i metrics as their abilities to order images correctly within SEGs. Strengths: 1. The authors identify a very important...	Rebuttal 1: Rebuttal: Thank you for your thoughtful review. We are heartened by your recognition of our rigorous methodology and well-designed experiments approaching the important task of T2I metric assessment. We will briefly address your weaknesses and questions: ### Weaknesses - **Scope of image-generating mo...	Rebuttal 1: Rebuttal: We appreciate all reviewers’ thoughtful and detailed analyses of our work. We are excited that multiple reviewers identified each of our work’s key strengths, including: 1. That our meta-evaluation setting is a timely and important task (vRNq) that has not been approached before and is “ofte...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
A Closer Look at AUROC and AUPRC under Class Imbalance	Accept (poster)	Summary: A widespread claim in machine learning that AUPRC is a superior metric than AUCROC for tasks with class imbalance is not strictly true. Based on this statement, this paper challenges this claim from two perspectives. On the one hand, the authors theoretically characterize the behavior of AUROC and AUPRC in the...	Rebuttal 1: Rebuttal: Thank you for your apt and constructive review! ### W1: Can the presentation be improved, particularly regarding the clarity of "prevalence"? In essence, in Theorem 3, we show that if one group of samples has a much higher outcome rate (prevalence) than another (e.g., men are more likely than wom...	Summary: The paper challenges the claim that area under the precision-recall curve (AUPRC) is a better metric for model comparison to the area under the receiver operating characteristic (AUROC) when it comes to tasks with class imbalance. The paper offers three formal results, proving i) a characterization of the two ...	Rebuttal 1: Rebuttal: Thank you for your comprehensive review and valuable comments! ### W1: Can you reduce the repetition in Section 4 and Figure 1? Thank you for this suggestion! We've condensed Figure 1's caption and Section 4. However, we maintain some overlap to ensure clear presentation of our key takeaway: unde...	Summary: The paper proves that AUPRC weights mistakes in higher score ranges higher, while AUROC weights all mistakes uniformly. This property of AUPRC can be underable in many real-life settings. It goes against the widespread belief that AUPRC is somehow “better” than AUROC in low-prevalence domains, which is a commo...	Rebuttal 1: Rebuttal: Thank you for your time, expertise, recognition of the impact of our work, and helpful suggestions. Below, we address each of your questions or concerns individually. ### W1: Why is the variance so high in the experiments? Two factors contribute: 1. We report confidence intervals spanning the 5th...	Summary: The paper considers a common in literature claim that AUPRC is “better” to use than AUROC for class imbalance datasets and attempts to prove it wrong based on theoretical results, empirical observations, and real-world experiments. The major focus of the paper is on the fairness gap that AUPRC exhibits. The au...	Rebuttal 1: Rebuttal: Thank you for your insightful and comprehensive review! Note that, for space reasons, we respond to your two questions in a "comment" rather than here in our official "rebuttal." ### W1: Why optimize for AUPRC? AUPRC is implicitly used as an optimization metric in cases where it is the target for...	Rebuttal 1: Rebuttal: We sincerely thank all reviewers for their insightful, comprehensive, and constructive feedback! We're particularly encouraged by the widespread recognition of our work's novelty, impact, and theoretical contributions, with reviewers noting things like * "I really enjoyed this read and consider...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Identification and Estimation of the Bi-Directional MR with Some Invalid Instruments	Accept (oral)	Summary: The paper addresses the challenge of estimating causal effects in bi-directional Mendelian randomization (MR) studies using observational data, where invalid instruments and unmeasured confounding are common. It investigates theoretical conditions for identifying valid instrumental variable (IV) sets and propo...	Rebuttal 1: Rebuttal: We truly appreciate your insightful and encouraging comments. Please see below for our responses. >W1. The setting is restricted with causal relations limited to being linear. We’d like to mention that, - Identifying instrumental variables in bidirectional MR, both theoretically and practi...	Summary: The authors take up a _very useful_ topic, of trying to identify instruments in models where bidirectional adjacencies exist, at least for the Mendelian randomization application. Strengths: The topic of the paper is on point--this is something we need to know more about, as bidirectional edges obviously exis...	Rebuttal 1: Rebuttal: Thank you very much for your inspiring commendation. We would like to mention that: (i) Identifying instrumental variables in bidirectional MR, both theoretically and practically, within the one-sample MR framework is a desirable but challenging research topic. We employed the **linearity assumpt...	Summary: The paper addresses the problem of estimating causal effects in bi-directional Mendelian randomization (MR) models with some invalid instrumental variables (IVs) and unmeasured confounding. It proposes a framework for identifying valid IV sets under the assumption that the IV set consists of genetic variants t...	Rebuttal 1: Rebuttal: Thank you for your insightful comments and suggestions. We have addressed the comments related to the empirical experiment and three assumptions in real-world data. Please see our responses below. >W1. ...it would benefit from a more in-depth exploration of the limitations...how violations of...	Summary: This paper studies the identifiability problem of the bi-directional Mendelian randomization (MR) model, where $X$ and $Y$ are a pair of phenotypes of interest and causes of each other, and $\textbf{G}$ is the set of measured genetic variants, which may include invalid instrumental variables (IVs). Under some ...	Rebuttal 1: Rebuttal: We appreciate your time dedicated to reviewing our paper and your thoughtful and encouraging comments. Below, please see our responses. We hope they can resolve your concerns. Note that we also summarize the main concerns of all reviewers. Please refer to the general response if interested. >**W...	Rebuttal 1: Rebuttal: We thank all reviewers for their constructive suggestions and overall positive comments, especially for the acknowledgment of our writing quality, comprehensive theoretical analysis, and empirical experimental performance. We have taken carefully the reviewers' feedback into account and respo...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs	Accept (poster)	Summary: This paper proposed a method about how to use a single LLM for both context reranking and answer in RAG tasks. Particularly, they finetuned a LLM with both ranking data and QA data with two stages. For inference, they use the trained llm to sample the top_k contexts at first, and then input them into llm to ge...	Rebuttal 1: Rebuttal: Many thanks for your comments and feedback. We discuss your raised points in the following. --- > “1. The motivation is unclear. … It is unclear about why using llm reranking itself rather than a separate reranker, maybe including the semantic gap between the reranker and llm or the larger lengt...	Summary: The authors introduce a novel approach to instruction fine-tuning large language models (LLMs) for ranking and answer generation tasks. Their approach involves two main steps: 1. Supervised Fine-Tuning: Initially, the LLM is fine-tuned on a general instruction-following dataset. 2. Ranking Task Fine-Tuning: Th...	Rebuttal 1: Rebuttal: Many thanks for your comments and feedback. We discuss your raised points in the following. --- > "1. Scoring each document individually increases the latency significantly. Have you considered scoring multiple documents simultaneously? Similar to retrieval-augmented ranking dataset, you could i...	Summary: This work proposes an effective approach that enhances existing RAG methods by introducing an additional context refinement step. This step filters out retrieved, but non-relevant contexts prior to including them as context in the input for answer generation. The authors train context reranking alongside answ...	Rebuttal 1: Rebuttal: Many thanks for your comments and feedback. We discuss your main concerns in the following. --- > “Reranking contributes only around 5% of the overall effectiveness on average (Table 4 RankRAG compared to Llama3-ChatQA-1.5-X), and the 7x computational overhead in inference time raises questions ...	null	null	Rebuttal 1: Rebuttal: We would like to thank all the reviewers for their thoughtful feedback. In addition to addressing the detailed questions in each review, we have summarized the new experiments suggested by the reviewers below: - (ze6x, Table 1,2) We have included the statistical significance test for both G...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning	Accept (poster)	Summary: The paper presents advancements in the theoretical understanding of the linear stochastic approximation (LSA) algorithm. It establishes the Berry–Esseen bound for the normal approximation of Polyak-Ruppert averaged iterates, achieving an optimal rate with an aggressive step size of $\alpha_k \approx k^{-1/2}...	Rebuttal 1: Rebuttal: We would like to thank the referee uNZU for careful reading of the manuscript and raising interesting questions. Next, we answer the issues raised. Missing References on Statistical Inference We thank the referee for provided references and will add them to the revised version of the paper. ...	Summary: The present paper studies linear stochastic approximation with martingale difference noise and diminishing step-sizes. The authors obtain Berry-Esseen bounds for the parameter sequence with Polyak-Ruppert averaging as well as a generalization of finite-time bounds for estimation confidence intervals for parame...	Rebuttal 1: Rebuttal: We would like to thank the referee ew5L for careful reading of the manuscript and valuable suggestions for presentation improvement. Next, we answer the issues raised. The analysis and main text are hard to follow Indeed, there are technical details in the main text that complicates reading, ...	Summary: ## Overview Let $Z, Z_1, \dots, Z_n$ be i.i.d. random elements with a common distribution $\pi$ over $\mathbf{Z}$. Given $A : \mathbf{Z} \to \mathbb{R}^{d\times d}$ and $b : \mathbf{Z} \to \mathbb{R}^d$, the goal of the LSA procedure is to find the unique solution $\theta^\star$ of $$\mathbb{E}\left(A(Z)\thet...	Rebuttal 1: Rebuttal: We would like to thank the referee DkFj for the work and for the positive feedback! Next, we answer the issues raised. Bottleneck of the convergence rate and counter-example or a deeper discussion on this convergence rate This is indeed a very important question. First of all, the analysis o...	null	null	Rebuttal 1: Rebuttal: We thank the reviewers for their thorough feedback. We are pleased that reviewers deemed our contributions to the Berry-Esseen bounds for Polyak-Ruppert averaged LSA and non-asymptotic bootstrap validity as new. The general question, which was raised by the referees DkFj and uNZU is rela...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Leveraging Environment Interaction for Automated PDDL Translation and Planning with Large Language Models	Accept (poster)	Summary: The paper proposes an approach to leverage LLMs and environment feedback to automatically generate PDDL domain and problem description files without human intervention. They do so by an iterative refinement approach that generates multiple PDDL problem and domain candidates based on feedback obtained from the...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive comments. Due to character limit constraints, below we summarize and answer the main questions raised by the reviewer. Q1: Assumption 2 may be unrealistic. What if the domain description lacks a constraint? We put the first steps towards using the env...	Summary: The paper presents an approach that leverages LLMs to generate PDDL domain and problem files from natural language descriptions, and refine them iteratively based on environment interactions. In particular, it proposes an Exploration Walk (EW) metric that provides feedback signals to guide the iterative refine...	Rebuttal 1: Rebuttal: We thank the reviewer for the feedback. However, we respectfully disagree with the criticisms around our core contributions and problem setup, as we believe there are misunderstandings about our work. The reviewer seems to believe that we only use the scalar feedback provided by the EW score and...	Summary: This work talks about generating PDDL domain and problem files with LLMs. Specifically, it improves existing frameworks, particularly Guan et al. [8], in terms of increasing the degree of automation & eliminating the need for human corrective feedback. The core contribution of this work is the EW score. To com...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive comments. Below, we address the main questions raised by the reviewer: **W1: The domain model sampling/generation is done in a relatively simple way. The feedback message could be more informative than just indicating the inexecutable step or action in a p...	Summary: This work presents an approach for modeling planning environments via PDDL generation using LLMs and environment feedback, without relying on human intervention. This is achieved by an Exploration Walk (EW) metric to measure domain similarity and guide domain refinement, and an iterative rectifying method that...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive comments. Below, we respond to the weaknesses and questions raised by the reviewer. **W1: Section 4.2 - demonstration of the brittleness of PDDL generation can be made more realistic such as additionally including hallucinated object identifiers or actions...	Rebuttal 1: Rebuttal: We thank the reviewers for their constructive feedback. We are encouraged that the reviewers find the automated PPDL generation problem to be important (cVUW, wCSx) that our exploration walk metric to be novel and promising (cVUW, wCSx, zxRN, iKF8), appreciate the analysis we did in the paper (iKF...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Visual Decoding and Reconstruction via EEG Embeddings with Guided Diffusion	Accept (poster)	Summary: This paper presents an end-to-end EEG-based visual decoding framework that includes two stages: a brain encoder for EEG feature extraction and a "generator" for producing reconstructed images. The experiments demonstrate effective results in retrieval, classification, and reconstruction tasks across two datase...	Rebuttal 1: Rebuttal: Thank you for your careful review and comments. Below please find our point-to-point responses to your comments. Q1. “The pipeline is already well-established in the field.” Please kindly see the relevant answer in Global rebuttal: Q1 and Q2. **Q2. How the VAE is supposed to provide low-le...	Summary: The paper presents a end-to-end EEG-based visual reconstruction zero-shot framework, featuring the Adaptive Thinking Mapper (ATM) and a two-stage EEG-to-image generation strategy. This method achievies state-of-the-art performance in classification, retrieval, and reconstruction tasks, and significantly advanc...	Rebuttal 1: Rebuttal: Thank you for your careful review and comments. Below please find our point-to-point responses to your comments. **Q1. “First, its motivation is based on the signal differences between EEG and fMRI, concluding that EEG's performance limitations are due to constraints in decoding and reconstructio...	Summary: The study proposes an end-to-end EEG\MEG-to-image reconstruction framework, consisting of a tailored brain encoder ATM to project neural signals into the shared subspace as the clip embedding, and a two-stage image generation block. The model achieves successful cross-subject EEG\MEG decoding and SOTA performa...	Rebuttal 1: Rebuttal: Thank you for your careful review and comments. Below please find our point-to-point responses to your comments. Q1. “I think it hasn’t been clarified in the main text whether....” Regarding 3.2 to 3.5: We present the bar graphs of performance in the **Section 3.2 EEG Decoding Performanc...	Summary: This paper proposes a learning framework to decode images from EEG signals. It introduces a tailored brain encoder, the Adaptive Thinking Mapper, which projects neural signals to the clip embedding space. Subsequently, a two-stage image generation strategy is applied to produce images, progressing from blurry ...	Rebuttal 1: Rebuttal: Thank you for your time and thorough comments! Below please find our point-to-point responses to your comments. Q1. How did you align the channel heterogeneity? Is it a zero-shot approach? Regarding Section 3.1: ”To verify the versatility of ATM for embedding electrophysiological data, w...	Rebuttal 1: Rebuttal: Dear Area Chairs and Reviewers, We express our profound gratitude for the comprehensive feedback and comments on our manuscript. This paper receives relatively serious differentiation of Weakly Accept, Weakly Accept, Reject, and Reject during the review period. We are excited about the consensus ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Computing the Bias of Constant-step Stochastic Approximation with Markovian Noise	Accept (poster)	Summary: This paper studies stochastic approximation with constant stepsize and Markovian noise. The authors provide a characterization of the bias (i.e., the difference between the expectation of the iterate and the desired limit), show that Polyak-Ruppert averaging can help reduce the variance but not the bias, and n...	Rebuttal 1: Title: h(theta)=theta is ok Comment: In the question, the reviewer asks if using h(theta)=theta is ok because this function is not bounded. This function can indeed be used because we assume that theta remains bounded. Note that one could also replace this assumption by a weaker assumption on the moments (s...	Summary: This paper studies the asymptotic bias in non-linear stochastic approximation algorithms with Markovian noise and fixed step-size. Upon applying the averaging technique of Polyak and Ruppert, the authors identify that, in general, the bias is of the same order of the step-size. The main source of bias is chara...	Rebuttal 1: Title: answers to comments Comment: - about bounded theta: the reviewer is correct by suggesting that we can replace the assumption of "bounded theta" by an assumption that would control the probability that theta is "far" from theta^*. For instance, a bound on the higher moment of theta would work. - abo...	Summary: The paper studies non-linear stochastic approximation scheme ($\theta_n$) driven by a uniformly geometrically ergodic MC $(X_n)$. Moreover, it is allowed the evolution of the Markovian noise $X_n$ to depend on $\theta_n$. The authors study the asymptotic behaviour of the last iterate, Polyak-Ruppert and Richa...	Rebuttal 1: Title: answer on hiw to obtain more general results Comment: Dear reviewer, Thank you for your detailed ans positive review. Here are some answers to your comments / questions: - yes, the bound of alpha^(3/4) of Theorem is probably not optimal. This Theorem could in fact be a called a corrolary of Theorem...	null	null	Rebuttal 1: Rebuttal: The authors would like to thank all reviewers for their detailed and constructive reviews. All reviews are correct and suggest interesting improvements that will be invluded in the final version. Most of the comments or questions are answered below each review.	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Statistical and Geometrical properties of the Kernel Kullback-Leibler divergence	Accept (poster)	Summary: This paper proposes the regularized kernel KL divergences. With the kernelization, the authors derive closed form formula and makes it computationally cheap to optimize using gradient methods. Theoretically, a new finite sample estimation convergence bound is derived. Numerical simulations suggest this propose...	Rebuttal 1: Rebuttal: We thank the reviewer for careful reading the paper and for his relevant suggestions. We have addressed each of your comments below. Please don't hesitate to let us know if you have any further questions. - Weakness: "Proposition 1 still assumes $p$ is absolutely continuous with respect to $q$...	Summary: The authors generalize and analyze the kernel Kullback-Leibler divergence introduced by Francis Bach a few years ago. The main contributions are 1) use a skew divergence (like in JS) tinstead of KL consider non-absolutely continuous divergences, 2) provide an explicit formula of the divergence for discrete me...	Rebuttal 1: Rebuttal: Thank you for your feedback and time. We have addressed each of your comment below. If you have any further question, please don't hesitate to let us know. - Weakness: " The gradient flows built via kernel methods do not seem to perform particularly well, perhaps due to the well-known lack of exp...	Summary: Comparing probability measures is a fundamental task in many statistical and machine learning problems. One of the metrics that has been ubiquitously used is Kullback-Leibler (KL) divergence. KL contrasts the information contained in two probability distributions. Statistical learning using KL divergence invol...	Rebuttal 1: Rebuttal: Thank you for your careful reading and relevant suggestions. We have addressed your comments point-by-point below. Please don't hesitate to let us know if you have any further questions. - Question: "The decreasing property of $KKL_{\alpha}$ in Proposition 2 needs the condition that $p$ is absol...	Summary: ### Summary: In this paper, the authors study kernel KL divergences (Equation 1). First, they extend the definition of kernel KL to a new setting via regularization that works for distributions with disjoint support (Equation 2). In Propositions 2,3, they prove the approximation results for the regularize...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback and time. We have addressed your comments point-by-point below. Please don't hesitate to let us know if you have any further questions. - Weaknesses: "the title does not reflect the contributions of the paper" Reply: We acknowledge the title does not ...	Rebuttal 1: Rebuttal: We sincerely thank all the reviewers for their positive comments on our paper, as well as for their relevant suggestions and questions. We adress here the general comments and questions. If we have adequately addressed the reviewer's concerns, a re-evaluation would be greatly appreciated. For any...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling	Accept (poster)	Summary: This paper develops a novel solution to address long-sequence tasks by parameterizing global convolutional kernels. In addition, the paper presents a simple idea, yet it achieves excellent results. Moreover, this paper's clear presentation makes it straightforward to understand. The baselines mentioned in the ...	Rebuttal 1: Rebuttal: We thank the reviewer for supporting the acceptance of our paper and appreciating our thorough evaluation and analysis. We address the reviewer's only question below: ## Runtime Comparison Please see our Author's rebuttal at the top of the page and the attached pdf. Our results show that MRConv i...	Summary: The paper presents MRconv, a novel type of global convolution layer designed for long 1-D sequence modeling. MRconv is built on an efficient and effective parameterization that produces normalized multi-resolution convolutions. The authors conduct comprehensive empirical evaluations on several benchmarks, incl...	Rebuttal 1: Rebuttal: We appreciate the reviewer's useful feedback and address their weaknesses and questions below. We hope this resolves any of the outstanding concerns. ## 1. Results on NLP Although NLP is an important area of study, we have yet to explore the application of MRConv in its current form to language mo...	Summary: This paper proposes MRConv, a new way to parameterize global convolutional kernels for long sequence modeling. MRConv pads all sub-kernels to the same length and aggregate outputs of sub-kernels with batchnorm and linear rescaling. Three different kernel initializations are explored. Experiments on Long Range ...	Rebuttal 1: Rebuttal: We thank the reviewer for their support of our paper and their insightful feedback which leaves plenty of room for future work. Below we answer the questions raised by the reviewer. ## 1. Comparison with SSMs As mentioned in our introduction, SSMs such as S4 and S4D can be represented equivalently...	Summary: This paper introduces reparameterized multi-resolution convolutions, a multi-resolution approach for the parameterization of global convolutional kernels for long sequence modeling. Their idea is to view long convolutional kernels as the combination of kernels at multiple resolutions, each with the same number...	Rebuttal 1: Rebuttal: Thank you for your overall supportive review of our work. The reviewer asked several insightful and detailed questions about our proposed method, which we will respond to in order. ## 1. Complexity analysis We agree with the reviewer that, compared to inference complexity, our training complexity ...	Rebuttal 1: Rebuttal: We thank all reviewers for their insightful and detailed reviews. The feedback provided has significantly helped to improve our work and solidified some of the claims in our paper. Please see the attached one-page pdf for convergence plots and runtime experiments as discussed in our rebuttals....	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper tries to tackle the training challenge of long convolutions with reparameterized multi-resolution convolutions (MRConv), which parameterizes global convolutional kernels for long-sequence modeling. The authors introduce learnable kernel decay to learn expressive long-range kernels that perform well ...	Rebuttal 1: Rebuttal: We thank the reviewer for pointing out several points of inclarity in the existing manuscript, which we seek to address below: ## 1. Novelty of MRConv Whilst structural reparameterization has been used in computer vision, we believe that its application in long-sequence modelling has not yet been...	null	null	null	null	null	null
Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood	Accept (poster)	Summary: This paper proposes to train neural networks that are more amenable to pruning, using a Bayesian approach. In particular, they specify separate priors over individual parameters rather than a shared global prior, which allows some parameters to be regularised more than others. They maximise a Laplace approxima...	Rebuttal 1: Rebuttal: We thank you very much for your detailed and helpful feedback. We really appreciate your suggestions and will incorporate them in the revision. Weaknesses: > The more complicated KFAC with non-scalar prior approximation does not appear to be empirically [...] However, it is still an interesti...	Summary: This paper proposes to sparsify a neural network using Bayesian principles by optimizing the marginal likelihood (SpaM). Specifically, the authors use weight/node/layer-wise Gaussian priors over the weights and learn the corresponding scales by maximizing the marginal likelihood during training using Laplace a...	Rebuttal 1: Rebuttal: Thank you for your evaluation of our work and your constructive comment that shows a good understanding of the field and your readiness to increase the score if your concerns and questions are adressed. We would like to further explain our motivation behind using Laplace approximations. **Weaknes...	Summary: The paper introduces a new pruning technique named SpaM, which leverages Laplace approximation and Bayesian marginal likelihood for approximating the posterior in Bayesian Neural Networks. This approach includes two main components: the Gaussian prior variance and OPD, a pruning method that utilizes the Laplac...	Rebuttal 1: Rebuttal: Methodological Clarity: > Computation/Storage challenges We discuss computational and storage costs of SpaM in depth in the main text but also in appendices D.4 & D.7. A diagonal approximation costs as much to store as a parameter vector and KFAC costs roughly twice as much so we incur mini...	Summary: The authors present a framework for assessing the sparsifiability of a parametric model, a measure of how many parameters can be pruned without severely affecting the modelling performance. In essence, the authors suggest training a Bayesian neural network (BNN) using the marginal likelihood estimated via the ...	Rebuttal 1: Rebuttal: Weaknesses > While using the marginal likelihood as the training objective in the context of network pruning seems original, it seems somewhat incremental. Thank you for the chance to expand more on the novelty offered with our approach and scalability compared to established works. In fact, ...	Rebuttal 1: Rebuttal: Thank you for the constructive review and feedback. We appreciate the opportunity to clarify and elaborate on the decisions made in our work. One shared comment across two reviews was the choice of the Laplace approximation and the use of the Gaussian distribution. **Choice of Laplace Approximati...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Zero-Shot Tokenizer Transfer	Accept (poster)	Summary: The paper presents a novel approach towards separating the tokenizer from the language model (LM). All modern LMs are trained with a fixed tokenizer, which prevents them from generalizing well to unseen or rarely seen tokens. Furthermore, the bounding to a specific tokenizer prevents models trained with differ...	Rebuttal 1: Rebuttal: Thank you for your feedback and ideas toward extending our approach! > Experiments with TinyLlama show a similar trend to that of the Mistral results. However, I think it's important for the reader to see that the approach works with different models; I would recommend moving Appendix G to the ma...	Summary: The authors propose zero-shot tokenizer transfer, a new task where the goal is adapt language models with unseen tokenizers. They propose to tackle this problem by training a hypernetwork that can directly predict the embeddings for any given tokenizer, and such a hypernetwork is trained by sampling various to...	Rebuttal 1: Rebuttal: Thank you for your encouraging feedback! > The pretrained hypernetwork is specific to one particular LLM. As a result, this one-time cost needs to be paid for every LLM that wants to benefit from zero-shot tokenizer transfer. Practically, this may not be as efficient as simply retraining the embe...	Summary: This paper presents a way to perform "zero-shot" construction of embeddings for a new target tokenizer. It proposes a hypernetwork based approach that learns to take in the embeddings of tokens generated by tokenizing the target token with the original tokenizer and produce the embedding of this token. The aut...	Rebuttal 1: Rebuttal: Thank you for your feedback, and for recognizing the strengths of our approach. __Distinction between ZeTT and generalization to unseen tokens.__ The task we address is Zero-Shot Tokenizer Transfer since the hypernetwork and the base model have not seen the target tokenizer, that is, the specific...	null	null	Rebuttal 1: Rebuttal: We thank all reviewers for their feedback and reviews. In response to the concerns about generalization to unseen tokens by reviewer mdD9 we have conducted additional experiments to quantify the overlap between the tokenizers seen during hypernetwork training and the target tokenizers. We also ra...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
ROBIN: Robust and Invisible Watermarks for Diffusion Models with Adversarial Optimization	Accept (poster)	Summary: This paper presents a novel watermarking technique for diffusion models that is robust against input transformations and invisible to the human eye. Unlike existing methods that rely on post-processing or perturbing the initial noise, this technique actively injects the watermark signal during the intermediate...	Rebuttal 1: Rebuttal: > Q1. Could the method maintain its efficacy against a combination of attacks and reconstruction attacks? As suggested by the reviewer, we have evaluated ROBIN under both conbination attacks and reconstruction attacks. (1) We randomly selected various combinations of the six attacks outlined i...	Summary: This paper discuss ROBIN, robust and invisible watermarks. The method proposes to embed the watermark during intermediate steps of the sampling process of diffusion model, by optimized prompt guidance signal w_p, the model was able to embed the invisible watermark into the generated content without losing the ...	Rebuttal 1: Rebuttal: > Q1. The optimization of $w_p$ and $w_i$ in lines 13 and 14 seems not to be consistent with Algorithm 1. The definition of $\epsilon_\theta(x_t^*,t,\psi(p),w_p)$ should be moved to the main text. (1) In the original manuscript, Lines 5 and 6 in Algorithm 1 indeed correspond to the minimization o...	Summary: This paper addresses watermarking in text-to-image diffusion models. Its main contributions are: 1) Embedding the watermark in the later stages of the diffusion process; 2) Introducing a text prompt guidance signal. These components collectively achieve better watermark robustness and image quality. Strengths...	Rebuttal 1: Rebuttal: > Q1. The robustness improvement over Tree-Ring is minor. (1) Compared to Tree-Ring watermarks, ROBIN improves the robustness from 0.975 to 0.983 on Stable Diffusion, effectively reducing the error rate by 32\% (from 0.025 to 0.017). Achieving further improvements on an already high AUC of 0.97...	Summary: This paper aims to balance robustness and concealment for image watermarking generated by diffusion models. The authors propose a novel method that actively hides stronger watermarks while ensuring their imperceptibility. They introduce a two-step process: first, embedding a robust watermark in intermediate di...	Rebuttal 1: Rebuttal: > Q1. Clarify the motivation for preserving semantic, given that the sematic alterations caused by Tree-Ring remain faithful to the original text prompt, as evidenced by a negligible drop in FID. (1) FID cannot evaluate the semantic faithfulness of the generated images to the orignal textual prom...	Rebuttal 1: Rebuttal: We sincerely thank the anonymous reviewers for their valuable and constructive comments and suggestions. And some figures are contained in the attached pdf. Pdf: /pdf/fbba4b69164ffdc7f53a167b5e53d768bdb82d61.pdf	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise	Accept (poster)	Summary: The authors consider the problem of fitting a single neuron with respect to distributional uncertainty modeled by a $\chi^2$ divergence. The authors prove convergence to within a constant factor of the optimal value. The proof of this results proceeds by calculating lower and upper bounds on a duality gap. Unf...	Rebuttal 1: Rebuttal: On our problem formulation and the perceptron algorithm: We would like to clarify that our goal is not to understand the perceptron algorithm. We design a new method that is provably efficient and accurate for a much more challenging learning problem than the perceptron algorithm was designed...	Summary: This paper addresses the problem of learning a single neuron in a distributionally robust setting, where both the input distribution and labels can be adversarially perturbed. The authors propose an efficient primal-dual algorithm that achieves a constant-factor approximation to the optimal loss, with respect ...	Rebuttal 1: Rebuttal: Thank you for your question. While [Dia+22a] and [Dia+22b] have made significant contributions to learning a single neuron robustly to adversarial label noise, our work additionally addresses robustness to distributional shifts, which introduces unique and complex challenges not covered in those p...	Summary: The authors study the problem of computing a distributionally robust optimization with \ell_2^2 loss functions with sample distribution p, and a chi-square regularizer that ensures that p is close to a given p0 for a single d-dimensional neuron with monotone lipschitz activation function. Their main contributi...	Rebuttal 1: Rebuttal: Thank you for your comments. On the intricacies of proving Theorem 3.1 (Main Theorem): Broadly speaking, the proof of this theorem relies on two technical contributions as we discussed in Section 1.3: 1. Concentration of the target distribution: Recall that the target distribution ...	Summary: This work considers the labels with noise and possible distribution shifts of the data. The authors aim to minimize the model's loss on a worst-case distribution from a set of distributions close to the reference distribution, which is defined as ambiguity set. The activation functions used to produce labels a...	Rebuttal 1: Rebuttal: Thank you for your comments. Line 261, page 7: The variable $\hat{p}_i$ in Algorithm 1 (line 261 on page 7), is updated in Line 7 of the current iteration and used in Line 5 of the subsequent iteration. Memory cost and runtime: The memory cost and runtime analysis is standard for the...	Rebuttal 1: Rebuttal: We thank all the reviewers for their valuable time and feedback. We are encouraged by the reviewers’ positive comments about novelty (3Jnc) and importance of the problem (ehoc, aZHR). Here we address some common concerns and give more detailed responses to the reviewers’ specific concerns bel...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
SymILO: A Symmetry-Aware Learning Framework for Integer Linear Optimization	Accept (poster)	Summary: This paper proposes the SymILO framework for solving combinatorial optimization problems (using machine learning). Building on traditional supervised learning, this method leverages the inherent symmetry structure of the problem to guide the model in learning the true patterns. The author also show its perform...	Rebuttal 1: Rebuttal: Thank you for the valuable comments. We apologize for any misunderstanding caused by our presentation and will address your questions below. Please note the top-most "author rebuttal", a brief summary, and part of the response is put there. ___Question 1: ... the permutation group ,..., is o...	Summary: The article examines the problem of predicting solutions to MILPs with a large number of symmetric solutions. An integer linear program (ILP) is symmetric if its variables can be permuted without changing the structure of the problem. The authors propose a new formulation of supervised learning for problems wi...	Rebuttal 1: Rebuttal: Thank you for the detailed review and valuable comments. We address each weakness and question below. __W1: Applying classical approaches.__ Thank you for raising the insightful comments. Below is our clarification. Our framework includes two parts. - a learning part: a GNN model that predicts...	Summary: This paper aims to enhance solution prediction methods for ILPs that contain certain types of symmetry. The main methodological contribution of the paper is a loss function that considers symmetry, along with an optimization algorithm for it. In particular, the loss function applies a permutation to each optim...	Rebuttal 1: Rebuttal: Thank you for your detailed review and valuable comments. We address each question below. ___Question 1: suggestion of presenting downstream approaches earlier___ We appreciate your valuable suggestion and already added a short paragraph in the Introduction to explain the downstream part. Meanwh...	Summary: The paper provides an ML-based framework, SymLo, for solving MILPs that leverages symmetries of ILPs to improve ML performance. SymLo takes into account symmetric groups on the solutions and formulates a learning task with respect to the model parameters and the permutations that belong to the symmetric group ...	Rebuttal 1: Rebuttal: ___Weakness 1: evaluating the method on an additional task___ Thank you very much for listing potential tasks that we can consider in our experiments. Regarding the relevance, we have cited them in our revised manuscript. We agree that evaluating on a third task could improve our work's contrib...	Rebuttal 1: Rebuttal: Thanks all reviewers for their detailed review and valuable comments. In the global rebuttal, we would like to supplement some common clarifications. ___Part I. Clarifications on some notions___ --- 1. Permutation : A permutation is a bijective mapping from a set $I^q=\\{1,\\dots,q\\}$ onto ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Exploring Adversarial Robustness of Deep State Space Models	Accept (poster)	Summary: The paper investigates and evaluates the effectiveness of traditional Adversarial Training (AT) methods on recently emerged State Space Models (SSMs). The paper finds that pure SSM is not a suitable choice for AT and attention-based SSM learns the adversarial feature more effectively. Based on such insights, t...	Rebuttal 1: Title: Response to Reviewer a6wJ Comment: Response to W1: Really greatful for the valuable comments from the reviewer. To ensure a more comprehensive and thorough assessment, we introduced a dataset with a larger size and more classes, Tiny Imagenet, for evaluation. The results are shown in the **Table...	Summary: This paper investigate the adversarial robustness in deep state space models (SSMs). They provide both empirical and theoretical analysis of the SSMs' performance under the adversarial perturbation. They find that fixed-parameterized SSMs are limited in their adversarial training benefits due to output error b...	Rebuttal 1: Title: Response to Reviewer PGjA Comment: Response to W1: Really appreciate the valuable feedback from the reviewer. To facilitate a more comprehensive assessment, we conducted evaluations on the tiny imagenet dataset, which has more classes and larger image sizes. The results, as shown in **Table 1 of...	Summary: This paper presents a comprehensive analysis of the adversarial robustness of Deep State Space Models (SSMs). The authors evaluate various SSM structures under different adversarial training (AT) frameworks, specifically examining how different components contribute to adversarial robustness. They observe that...	Rebuttal 1: Title: Response to Reviewer eBVC Comment: Response to W1: Really appreciate the insightful comments from the reviewer. To ensure a fair evaluation, RO should be assessed under the same attack strategy, so we primarily judge RO based on RA on PGD-10. Our "Best" checkpoint is determined by the RA under ...	null	null	Rebuttal 1: Rebuttal: We are grateful for the comprehensive and professional feedback from the reviewers. It is both pleasing and encouraging to see that they have recognized our work as novel (R1), of high quality (R1), and of significant importance (R1, R3). They have also noted the clear logic in our...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
OTTER: Effortless Label Distribution Adaptation of Zero-shot Models	Accept (poster)	Summary: This paper introduces a novel approach to address the issue of label distribution mismatch in zero-shot models, which is a common problem due to the imbalance in the pretraining datasets. The proposed method uses optimal transport to adjust the predictions of pretrained models based on an estimated downstream ...	Rebuttal 1: Rebuttal: We are grateful to the reviewer for noting the strengths of our work and providing useful comments. The reviewer appreciated our work, recognizing the novelty of our method, its solid theoretical foundation, and extensive empirical validation. * On dependence on estimated label distribution: ...	Summary: Zero shot classification suffers from label distribution mismatch. This paper suggest to adjust pretrained model prediction via optimal transport. By showing the optimal transport prediction is equivalent to the bayes optimal classifier's output theoretically and good model performances on various experimental...	Rebuttal 1: Rebuttal: We appreciate the thoughtful comments and references. We will include links to proofs and will clarify the statements corresponding to the reviewer's questions in our updated draft. * On the proposal saying that Bayes optimal classifier can be derived through optimal transport: The statement ...	Summary: Zero-shot models such as CLIP suffer from imbalanced predictions inherited from the uncurated pre-training data. This paper proposes adjusting the predictions of zero-shot models via optimal transport. This method requires only the downstream data and the label prior of the target distribution. Under the assum...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for their kind words, constructive feedback, and useful suggestions. We appreciate the reviewer recognizing the novelty of our work and its strong theoretical foundation. We plan to include the suggested related works and fix typos. * **On additional related works...	null	null	Rebuttal 1: Rebuttal: ### Common Response We thank all of the reviewers for their kind comments and feedback. Reviewers recognized the strengths of our paper: * OTTER provides a novel and elegant solution to deal with label distribution using optimal transport. (Reviewers t3Bz, t22L) * OTTER offers **theoretical re...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Neural Localizer Fields for Continuous 3D Human Pose and Shape Estimation	Accept (poster)	Summary: This paper proposes Neural Localizer Field, a continuous field of point localizers, for localizing any point of the human body in 3D from a single RGB image. The method enables mixed-dataset training using various skeleton or mesh annotation formats. The method has three main parts: a point localizer network, ...	Rebuttal 1: Rebuttal: We thank Reviewer R9xH (R4) for the assessment and questions. R4 considers the idea "interesting and novel" and finds that we do "a good job in explaining" the motivation and technical details. R4 further sees the performance as "impressive" on "extensive benchmarks and experiments". > Lack of di...	Summary: The authors proposes a Neural Localizer Field (NLF) to learn a continuous representation of the canonical human pose by learning to predict a set of functions that map a query point in the canonical human volume to a point in the human posed space, given a single rgb image. By introducing a meta-learning archi...	Rebuttal 1: Rebuttal: We thank Reviewer YzeS (R3) for the review and questions. R3 considers that our "insight is simple, clearly explained and well motivated", and finds the quantitative metrics "impressive" and "convincing". > Its not clear if the performance is derived from just a few data sources or all of them i....	Summary: This paper focuses on 3D human pose and shape estimation from a single RGB image. The main insight is, to avoid the influence of a fact that different human pose dataset defines different skeleton in their annotations, this paper proposes a point-based representation to ensure the model can learn from many dat...	Rebuttal 1: Rebuttal: We thank Reviewer M7cf (R2) for the suggestions. R2 notes that we performed "extensive ablation studies", which are seen as "helpful" for future model design decisions, and further praises our strong qualitative results with good pixel alignment. > If we use the same training dataset but remove t...	Summary: This paper deals with the task of 3d human pose estimation. It contains three main contributions: 1. A hypernetwork that takes as input a point in a 3d body volume (in a canonical pose) and outputs the weights of a network (a single layer, really) that, when applied to the features of a vision backbone, is ab...	Rebuttal 1: Rebuttal: We are glad that R1 sees our idea as "novel", further acknowledging the novel aspect of putting together such a "super-dataset". R1 further praises the model quality as "strong across the board" and foresees significant community impact. Importance of the hypernetwork and datasets: see the global...	Rebuttal 1: Rebuttal: We thank all reviewers for their thoughtful suggestions and questions. Their assessments are unanimously on the positive side, recommending acceptance. R1 (Dcsh) sees both our architectural idea and our extensive dataset combination as "novel" and foresees significant community impact. R3 (YzeS) f...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
QVAE-Mole: The Quantum VAE with Spherical Latent Variable Learning for 3-D Molecule Generation	Accept (poster)	Summary: This paper proposes a fully quantum VAE framework, QVAE-Mole, for 3D molecule generation. It introduces a quantum encoding scheme and adopts a von Mises-Fisher distributed latent space. A conditional version, QCVAE-Mole, is also presented for property-specified generation. Experiments show that the model outpe...	Rebuttal 1: Rebuttal: Thank you for acknowledging our work and your suggestions have been immensely helpful. Below is our detailed response. > W1: I think some background knowledge needs to be described in a bit more detail, at least it should be in the appendix (For some laymen like me). Thank you for your sugg...	Summary: This paper introduces a Variational Autoencoder (VAE) with a von Mises-Fisher (vMF) latent space for 3D molecular (conditional) generation. This approach leverages the capabilities of quantum computing, particularly within the Noisy Intermediate-Scale Quantum (NISQ) era, to achieve efficient and effective mole...	Rebuttal 1: Rebuttal: Thank you for acknowledging our work and your suggestions have been immensely helpful. Below is our detailed response. > W1: Formulas 8 and 9 can be represented using more rigorous mathematical notation. Thanks for your suggestion and we will revise this. The von Mises-Fisher (vMF) distribu...	Summary: The authors introduce the first Variational Autoencoder (VAE) and Conditional Variational Autoencoder (CVAE) formulated entirely as parameterized quantum circuits (PQC), as opposed to hybrid methods that combine learnable parameters in quantum circuits with classical learnable parameters. In previous hybrid mo...	Rebuttal 1: Rebuttal: Thanks for your review and feedback, below is our detailed response. Due to word count limitations, part of the answer and references are included in the Official Comment below. > W1: About novelty (SQ-VAE (https://arxiv.org/abs/2205.07547) and 3D-QAE). Thanks, and we would like to humbly po...	Summary: The paper aims to realize 3D molecule generation on quantum hardware and proposes quantum parameter circuits for 3D molecule generation. The 3D coordinates and atomic types are explicitly encoded as the initial quantum state and input into the network. The paper selects the classic generative model Variational...	Rebuttal 1: Rebuttal: Thank you for acknowledging our work. Your rating has provided us with great encouragement, and your detailed feedback and suggestions have been immensely helpful. Below is our detailed response. Due to word count limitations, part of the answer and references are included in the Official Comment ...	Rebuttal 1: Rebuttal: # General Response by Authors We express our gratitude to all the reviewers for dedicating their time and providing valuable comments. They acknowledged that our work is well-structured (VsUh, 5Ebq, hKnP), contributive (VsUh, hKnP), effective (VsUh, 5Ebq), and presents a novel approach (hKnP). Wh...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
SCaR: Refining Skill Chaining for Long-Horizon Robotic Manipulation via Dual Regularization	Accept (poster)	Summary: This paper focuses on skill chaining for long-horizon robotic manipulation tasks. A dual regularization method is proposed to tackle this problem, including an adaptive sub-task learning scheme that enhance intra-skill dependencies, and a bi-directional adversarial learning mechanism for reinforcing inter-skil...	Rebuttal 1: Rebuttal: We sincerely thank you for acknowledging the strength of our work, e.g., reasonable idea of dual regularization and extensive experiments. We address the comments and questions below. Regarding real-world robotics experiments. * Thank you for pointing this out. Due to hardware limitations, w...	Summary: In this work, the authors present SCaR or Skill Chaining via Dual Regularization, an algorithm for both learning a set of subtasks as well as how to sequence them well. The method consists of two major components, AES regularization for learning the skills from demonstrations and real world interaction, as wel...	Rebuttal 1: Rebuttal: We sincerely thank you for acknowledging the strength of our work, e.g., the natural bi-directional regularization idea, interesting experimental setups, and effective experimental validation. We address the comments and questions below. Regarding the AES component. - We introduced AES regu...	Summary: The paper proposes regularization strategies to enhance inter-skill and intra-skill consistency in skill chaining. Skill chaining is the problem of learning a sequence of sub-task policies chained together to achieve the goal, given expert demonstrations and rewards. The sub-task partition is defined manually ...	Rebuttal 1: Rebuttal: We sincerely appreciate your constructive feedback and thank you for acknowledging the importance of the skill chaining, the high level of our proposed regularization strategies, and our experiments! We address your comments and questions below. Regarding the reliance on existing methods: - W...	Summary: The paper introduces the Skill Chaining via Dual Regularization (SCaR) framework, designed to enhance skill chaining in long-horizon robotic manipulation tasks by applying dual regularization techniques during skill learning and chaining. The framework addresses the error accumulation issue in traditional skil...	Rebuttal 1: Rebuttal: We sincerely thank you for acknowledging the strength of our work, e.g., the innovative dual regularization, comprehensive evaluation and practical impact. We address the comments and questions below. Regarding the discussion of limited task generalizaiton. * Thank you for pointing this out...	Rebuttal 1: Rebuttal: Global response Dear Reviewers, Thank you very much again for your helpful comments. We appreciate your recognization of our work and would like to engage with you in our responses to your questions/comments. We hope that our approach will provide a simple and solid baseline for future resea...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
LACIE: Listener-Aware Finetuning for Calibration in Large Language Models	Accept (poster)	Summary: This paper tackles LLM miscalibration (overconfidence in wrong answers) with a listener-aware fine-tuning method. The authors hypothesise that LLMs are overconfident for two reasons: a lack of knowledge of what is correct, and a lack of pragmatic grounding (knowing how your utterance is perceived by a listener...	Rebuttal 1: Rebuttal: Thanks for your comments and questions, and for highlighting our “excellent experimental setup” and positive results. We’ve sought to address each point in the review below: 1. Highting decrease in recall/accuracy due to abstention in the intro We agree that this is an important point to hig...	Summary: This paper focuses on addressing the calibration of implicit (e.g., tone of expressions) and explicit confidence markers of LLMs when providing answers in conversations. This paper proposes a method, LACIE, to cast calibration as a preference optimization problem for QA tasks. In particular, the preference dat...	Rebuttal 1: Rebuttal: Thanks for highlighting the importance of our research problem and our empirical improvements. We have sought to address the remaining questions/comments below: 1. Improvements on additional baselines Thanks for your suggestion of non-pragmatic baselines. To clarify, our main preference tun...	Summary: This paper explores a fine-tuning strategy for optimizing confidence calibration in LLM outputs. In contrast to prior work, the authors fundamentally define this as a pragmatic problem, where performance improvements are measured based on listener's correct inference that affects their downstream task performa...	Rebuttal 1: Rebuttal: Thank you for appreciating our human evaluation, and our analyses – we will address the remaining questions/comments in more detail below 1. **“...Do you qualitatively/quantitatively find that justifications that can clearly be inferred to be incorrect (e.g., based on personal learning experience...	Summary: This paper tackles the overconfidence problem (represented in texts, such as `I am 100% sure that') in LLM generations. This issue is critical as this makes LLMs unreliable collaborators, e.g., people cannot trust their task-oriented bots when asking information-seeking questions. This work characterizes this ...	Rebuttal 1: Rebuttal: Thank you for your attention to our work and for highlighting the importance of the problem we focus on and the excitement of our pragmatics-aware approach. We will address the remaining comments below 1. Application of LACIE to knowledge-intensive tasks Like other work in training models to...	Rebuttal 1: Rebuttal: We would like to thank the reviewers for their attentive and detailed reviews, which highlight our work’s importance and excitement (Reviewers dvWw, NSP4, rP95), the effectiveness of our method (Reviewers dvWw, NSp4, rP95) and the strength of our experimental setup and analysis (Reviewers Aw31, rP...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
B'MOJO: Hybrid State Space Realizations of Foundation Models with Eidetic and Fading Memory	Accept (poster)	Summary: Introduces a class of models called B'MOJO which combines sliding window attention with SSMs. The main version of B'MOJO leverages an error function to decide what gets added to the long term sliding KV cache, inspired by ideas from Stochastic Realization Theory. Experiments are performed on multi-query associ...	Rebuttal 1: Rebuttal: Thank you for your feedback and your insightful comments. We've addressed the main concerns shared with other reviewers in the global comment, here we will address specific concerns. Related work References. We are grateful for the references and associated insights, we have revised our manus...	Summary: I appreciate the clarification about the method being specified for a single block as well as the new longer context results. I have decided to increase my score. --- This seems like a very interesting paper which proposes a new recurrent architecture that seeks to combine advantages of transformers and o...	Rebuttal 1: Rebuttal: Thank you for your feedback and your suggestions. We've addressed the main concerns shared with other reviewers in the global comment, here we will address specific concerns. Confused about the structure of the block, and the overall architecture. We have made revisions to improve the clarit...	Summary: The paper presents B'MOJO, a novel building block combining the strengths of Transformers and modern SSMs. The main motivation of the paper is to develop a system capable of combining an eidetic memory, responsible for performing transductive inference via in-context learning, and a fading memory capable of st...	Rebuttal 1: Rebuttal: Thank you for your feedback and your suggestions. We've addressed the main concerns shared with other reviewers in the global comment, here we will address specific concerns. Mistral 7B seems to perform comparably or even better. As we clarify in general comments, in a true apples-to-apples ...	Summary: This work presents a new module by combining eidetic memory, fading memory, and long-term eidetic memory (through an innovative selection operation). The proposed new module has strong sequence modeling capacity and high inference efficiency. The proposed new architecture achieves perplexity comparable to Tran...	Rebuttal 1: Rebuttal: Thank you for your feedback and your suggestions. We've addressed the main concerns shared with other reviewers in the global comment, here we will address specific concerns. Lack of sufficient ablation study. Throughout our work (see Table 1, 2, and Figures 2, 3, 4, and 5), we compare B’MOJ...	Rebuttal 1: Rebuttal: We thank the reviewers for their constructive feedback and the positive comments on the novelty of our method (MJ98, Plgm, Wgie) as well as its theoretical motivation and connection with Stochastic Realization Theory (MJ98, Wgie). Furthermore, we are glad that reviewers appreciated our efficient i...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Multi-modal brain encoding models for multi-modal stimuli	Reject	Summary: This paper introduces a novel approach using cross-modal and multi-modal models to align brain activity with naturalistic stimuli, evaluates several unimodal Transformer models, and examines the effects of removing unimodal features from multi-modal representations on brain alignment. Strengths: - (S1) The pa...	Rebuttal 1: Rebuttal: We thank the reviewer for their strong positive, insightful and valuable comments and suggestions which are crucial for further strengthening our manuscript. 1. How do the authors ensure the robustness of Pearson correlation metric in the context of brain alignment evaluation? Thank you fo...	Summary: The authors present a framework for applying brain encoding models with multimodal stimuli. They apply this to a series of video, audio, and mutlimodal models (cross-modal and jointly embedded models). They introduce a residual analysis to analyze the impact each particular feature had on the corresponding fit...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive, insightful and valuable comments and suggestions which are crucial for further strengthening our manuscript. 1. Clarification on feature removal * For the removal of information from the model representations, we use the previously published approac...	Summary: The manuscript investigated the process of multi-modal information in human brains through predicting neural responses based on semantic features extracted by existing models. Strengths: 1. The problem is interesting. 2. The results show insights into brain region's roles in processing multi-modal information...	Rebuttal 1: Rebuttal: We thank the reviewer for their valuable comments and suggestions which are crucial for further strengthening our manuscript. 1. Affect of performance by the choice of pre-trained models. Thank you for this question. * Impact of Pre-Trained Models: - To understand the relationship ...	Summary: This paper addresses an important question of how accurately multi-modal models can predict brain activity when participants are engaged in multi-modal stimuli. The key challenge is how to integrate or separate the information from different sensory modalities. This work explored two types of models, ie cross-...	Rebuttal 1: Rebuttal: We thank the reviewer for their strong positive, insightful and valuable comments and suggestions which are crucial for further strengthening our manuscript. 1. There exist `clock’ relationships in train-test settings lead to information leakage during inference. * We made sure to follow prop...	Rebuttal 1: Rebuttal: We thank the reviewers for their strong positive, insightful and valuable comments and suggestions which are crucial for further strengthening our manuscript. CQ1. Cross-modal setting using ImageBind: with and without alignment of video and audio? (reviewer 28FZ) * To investigate the cross...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
HuRef: HUman-REadable Fingerprint for Large Language Models	Accept (poster)	Summary: This paper investigated the problem of identifying the base model of a given large language model (LLM) using fingerprint. First, the authors found that the vector direction of the parameters of the LLM is unique to each LLM. Thus, the vector direction can be leveraged as a model fingerprint. Based on this fin...	Rebuttal 1: Rebuttal: Thank you for acknowledging the effectiveness of our method and the adequacy of our experiments. We appreciate your time in reviewing our paper and providing valuable suggestions. Below are our point-by-point responses to your comments. > 1. Motivation of human-readable fingerprints. The MPC sc...	Summary: This paper proposes a method to identify the original base model of a fine-tuned LLM via weight-based invariant terms, addressing the issue of a potential misuse or licensing violations with respect to the foundation models. Authors have identified a consistently low cosine distance between the parameter vect...	Rebuttal 1: Rebuttal: Thank you for recognizing the insights and contributions of our research. We appreciate your time and constructive feedback. Our point-to-point responses to your comments are given below. > 1. I'm not very comfortable with the Zero-Knowledge Proofs, and can be mistaken, but it seems to me that ZK...	Summary: This work aims at producing a human-readable watermark for LLMs as unique identifiers in a black-box setup, e.g., without exposing model parameters. Starting from an interesting observation that the model parameters become stable after convergence, esp in the post-training process, the authors proposed a creat...	Rebuttal 1: Rebuttal: Thank you for acknowledging that our method is creative and effective. We appreciate your time in reading the paper and providing helpful suggestions. Our point-to-point responses to your comments are given below. > 1. It is noteworthy that using the unique visual identifier to reveal the model i...	null	null	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Optimal-state Dynamics Estimation for Physics-based Human Motion Capture from Videos	Accept (poster)	Summary: This paper proposes a method to capture physically plausible human motions from monocular videos. The paper builds on top of NeuralPhys and introduces Kalman filtering to alleviate noise from kinematic motions. The framework combines Kalman filtering and physics simulation to be fully differentiable, thus enab...	Rebuttal 1: Rebuttal: We thank the reviewer for the thoughtful and constructive feedback. We address the concerns and questions from the reviewer in the section below. 2D overlay of estimated pose on the input image. Thank you for the suggestion. We plotted the overlay of 3D estimated poses from OSDCap onto the i...	Summary: The paper presents a novel physics-based human motion capture method that is physically explainable, conforming to the PD control theory and rigid body dynamics. The key designs of the method involve an integration of a kinematic Kalman filter and Newtonian equation-based physics simulation, and learnable Kalm...	Rebuttal 1: Rebuttal: We thank the reviewer for the highly positive evaluation and the constructive feedback to our work. For a better clarification, we provide the answers to the reviewer's questions below. Full-body contacts. We agree that full body contact is the next logical step towards a fully environment-a...	Summary: This work focuses on tackling the problem of single-person motion estimation from a monocular video. Current approaches produce temporal artifacts such as jittering. Most approaches are entirely kinematic while others that combine physics, do it by re-simulating the kinematic inputs by using automatic PD contr...	Rebuttal 1: Rebuttal: We thank the reviewer for the very positive assessment and the recognition of our method's novelty. We would like to provide answers and clarifications to the remaining questions of the reviewer. Presentation Improvement. We appreciate the suggestions of the reviewer. We will change the visu...	Summary: This paper introduces a novel method to estimate 3D human motion from a single camera, aiming for physical plausibility and accuracy. The authors use a differentiable Kalman-filtering approach in an online setting that balances kinematics estimated from an off-the-shelf algorithm (TRACE) and the physics simula...	Rebuttal 1: Rebuttal: We thank the reviewer for the feedback and constructive review. We would like to address questions and clarify the concerns below. Proxy character creation. For character construction, we used the Human 3.6M metadata skeleton as the initialization of our character. The bone lengths are treat...	Rebuttal 1: Rebuttal: We would like to express our gratitude towards the reviewers for their helpful reviews and assessment! We are happy that the reviewers recognize that the proposed method OSDCap for physics-based 3D human motion capture is novel (wqMZ, DQhK, BVYQ, QEqx), of high significance to the field (BVYQ), p...	NeurIPS_2024_submissions_huggingface	2,024	Summary: The kinematic motion estimation suffers from inconsistencies of frame-wise predictions while the physics-based method suffers from the gap between the simulator environment and the real-world ground truth. This paper proposes a method to taking advantages of both by connecting them by a learnable kalman filter...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive assessment to our proposed idea and writing quality. For a better interpretation of our work, we clarify and answer the questions of the reviewer below. Comparison to a classical Kalman Filter. Classical Kalman filters have been used traditionally for a...	null	null	null	null	null	null
EEG2Video: Towards Decoding Dynamic Visual Perception from EEG Signals	Accept (poster)	Summary: The manuscript proposes an EEG decoding model that aims to reconstruct the video stimuli presented to participants. To this end, a large dataset corresponding to twenty participants is collected. The dataset is annotated with respect to different features such as the dominant colour of the video or the presenc...	Rebuttal 1: Rebuttal: Thanks for your valuable comments. Below we have addressed your questions and concerns point-by-point. > W1. The decoding power ... We appreciate your careful reading. We can't agree more and believe that the decoding power of the recorded EEG signals w.r.t. some specific tasks is more than qu...	Summary: The authors present a novel annotated dataset of EEG-video pairs and an approach to reconstruct videos from EEG brain activity data. The dataset contains brain responses of 20 subjects watching 2-s videos from 40 general concepts. A total of 7 classification tasks are built based on the metadata available in t...	Rebuttal 1: Rebuttal: Thanks for your comments with expertise. Below we have addressed your questions and concerns point-by-point. > W1.1 ... "categorical leakage" between the two sets. We may be misunderstanding your concern, but we argue that "Categorical leakage" is a pseudo-concept and should not be considered a ...	Summary: The authors provide an EEG-video paired dataset, addressing the lack of data for decoding dynamic visual perception tasks from EEG signals. They also propose a dynamic noise-adding perception video reconstruction method for this dataset. Strengths: 1.The authors introduce a new EEG-video paired dataset, provi...	Rebuttal 1: Rebuttal: Thanks for your valuable comments, and we'd like to express our appreciation that our contributions of the dataset and the benchmarks are well recognized. Below we address your questions and concerns point-by-point. Weaknesses > Method innovation: ... We would like to emphasize that our cont...	Summary: This paper presents a novel framework named EEG2Video for video reconstruction from EEG signals based on Seq2Seq architecture to densely utilize the highly dynamic information in brain signals. It also developed a large EEG dataset named EEG-DV dataset collected from 20 subjects, offering 1400 EEG-video pairs ...	Rebuttal 1: Rebuttal: Thanks for your valuable comments. Below we have addressed your questions and concerns point-by-point. Weaknesses > The different steps constituting the proposed method in Section 4 are not well highlighted. Thanks for your constructive suggestion, we will add an algorithm to demonstrate th...	Rebuttal 1: Rebuttal: We thank all the reviewers for your proficient and valuable comments and suggestions. We are cheerful to find that all the reviewers have reached the consensus that our datasets are novel and valuable to the research community. Moreover, we're also glad to see that our contributions in building th...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Sample Efficient Bayesian Learning of Causal Graphs from Interventions	Accept (poster)	Summary: This paper introduces a way of doing causal discovery from interventional samples using bayesian inference. They prove some theoretical results of convergence of the task at hand. Finally, they test their approach against common benchmarks on synthetic data. Strengths: - Apart from the related work section (s...	Rebuttal 1: Rebuttal: We thank the reviewers for their thorough and constructive feedback. Below, we address each of the points raised. Weakness of Writing: We will rearrange the related work section in the revision to position the previous works and our study better. Questions of Overall Significance: The ...	Summary: This paper proposes a Bayesian approach for learning causal graphs with limited interventional samples. The proposed algorithm first constructs a separating system to design intervention targets and then enumerates the causal effects for all possible cutting edge configurations for each target, and tracks thei...	Rebuttal 1: Rebuttal: We thank the reviewers for their thorough and constructive feedback. Below, we address each of the points raised. Weakness of Major 1: Our approach differs from most of the Bayesian causal discovery paper in that we do not have a parametric representation of the structure. We do not directly...	Summary: The paper considers the problem of learning causal graphs using limited interventional samples through the a Bayesian perspective. An algorithm is proposed which returns the most probable causal graph given a limited set of samples. The approach is empirically evaluated and code is given. Strengths: I did not...	Rebuttal 1: Rebuttal: We thank the reviewers for their thorough and constructive feedback. Below, we address each of the points raised. Weakness of "small scale" experiments: In this work, we assume the access to the joint observational distribution. When the graph is large, it is intractable in practice to handl...	Summary: In this paper, the authors present a Bayesian method to learn the causal graph from observational data and limited interventional data. The authors assume that there are plenty of observational data, which can be used to learn a ground-truth CPDAG. Then, by using the efficient DAG enumeration method to sample ...	Rebuttal 1: Rebuttal: We thank the reviewers for their thorough and constructive feedback. Below, we address each of the points raised. Weakness of Objective: There are indeed plenty of studies in the literature that use Bayesian methods for causal discovery, but most of them have parametric assumptions like a li...	Rebuttal 1: Rebuttal: Extra Experiments We included the results of extra experiments in the pdf as required by the reviewers. Experiment for Scale-Free Graphs We generate 50 random scale-free graphs under 2 setting: $n=7, m=2$ and $n=7, m=4$, and compare with the baselines. The results are plotted in Figure...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Linear Mode Connectivity in Differentiable Tree Ensembles	Reject	Summary: This work extrapolates the concept of Linear Mode Connectivity (LMC) modulo model invariances to differentiable tree ensembles (DTE). The authors revealed that, in contrast to neural networks (NNs), permutation invariance is insufficient to provide LMC in DTE and propose two additional tree-specific invariance...	Rebuttal 1: Rebuttal: Thank you for your review. > The main weakness of this work is lack of theoretical support and practical implications. However, I acknowledge that these are the same limitations that are attributed to LMC in neural networks, which is a significantly more broad and well-studied field than LMC in t...	Summary: This paper provides an analysis of types of neural networks called soft trees from the linear mode connectivity point of view. The authors enumerate 3 types of invariances inherent to soft trees and study linear mode connectivity between different solutions (by solution they understand a trained ensemble of so...	Rebuttal 1: Rebuttal: Thank you for your review. Due to the 6000 character limit, we will address minor points during the discussion phase. Below in the rebuttal, we have included responses to the aspects we consider important. > In my opinion, the main contribution of this paper is a showcase that different archit...	Summary: This paper empirically shows that separately trained tree emsemble models can show Linear Mode Connectivity (LMC) when considering tree invariant operations. Strengths: - The exploration of LMC on tree emsemble models is interesting. - The computational process is clearly stated which makes this paper easy to...	Rebuttal 1: Rebuttal: Thank you for your review. > This paper does not provide any insights into the question of LMC in neural networks, as it is exploring a totally different model. Although it is always interesting to consider LMC in another senario, I find the contribution of this paper rather insignificant and inc...	Summary: This paper aims to achieve LMC for soft tree ensembles. Akin to achieve LMC for neural network after accounting for permutation invariance, the authors introduce three different kinds of invariance in soft tree ensembles: tree permutation invariance, subtree flip invariance, splitting order invariance. Additio...	Rebuttal 1: Rebuttal: Thank you for your review. > I am not familiar with differentiable tree ensembles, therefore, I would suggest the authors put more efforts on explaining tree ensembles and illustrating the invariances. Thank you for your comment. We will include an explanation with diagrams similar to Figure 1 i...	Rebuttal 1: Rebuttal: Thank you for your valuable comments. We would like to engage in discussions by replying to each of your comments. We provide a PDF of an additional figure to address a comment from Reviewer Varw. Pdf: /pdf/6557d230ed5bfd2d1c7d61cc17bc7292d569382c.pdf	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Mixture of In-Context Experts Enhance LLMs' Long Context Awareness	Accept (poster)	Summary: Large language models (LLMs) have shown promise in various NLP tasks but often fall short in tasks requiring deep contextual understanding, such as coherent long-text generation and Retrieval-Augmented Generation (RAG). Challenges like the "lost-in-middle" phenomenon, where LLMs struggle with middle context in...	Rebuttal 1: Rebuttal: We sincerely appreciate your valuable feedback and suggestions! We hope our response could address your concerns. ### 1. Mean and standard deviation of Table 1 Thanks for your valuable comment. We reported the t-test results of MoICE in Table 1: the p-values are both less than 0.02, which illust...	Summary: This paper presents an approach, Mixture of In-Context Experts (MoICE) for enhancing the long-context awareness of LLMs with RoPE. Specifically, the authors use a router to dynamically select multiple RoPE angles for each attention head and token. They also use a lightweight router-only training strategy and f...	Rebuttal 1: Rebuttal: We sincerely appreciate your valuable feedback and suggestions! We hope our response could address your concerns. --- ### 1. Performance on general open-ended tasks (Weakness 1) Thanks for your valuable suggestions. We have added an additional benchmark Longbench [1], which is a bilingual multit...	Summary: The paper introduces the "Mixture of In-Context Experts" (MoICE) method to address uneven context awareness in large language models (LLMs) using Rotary Position Embedding (RoPE). The central element of MoICE is a router that selects different RoPE angles. The authors propose a loss function that learns to sel...	Rebuttal 1: Rebuttal: We sincerely appreciate your valuable feedback and suggestions! We hope our response could address your concerns. --- ### 1. The auxiliary loss appears to be ad hoc (Weakness 1) Thanks for your valuable feedback. The auxiliary loss (Eq.8-10) is a widely adopted practice in MoE systems to address...	Summary: After rebuttal: raised score by 1 point after discussion. --- The paper proposes a new strategy Mixture of In-Context Experts (MoICE) to increase the input context length of LLMs while allowing the model to function effectively on longer context inputs. Their key idea is to introduce a routing mechanism at ea...	Rebuttal 1: Rebuttal: We sincerely appreciate your valuable feedback and suggestions! We hope our response could address your concerns. --- ### 1. To test MoICE with LLMs whose context length is greater than 8k. (Weakness 1 & Question 1) Thanks for your valuable suggestions. We have implemented MoICE on Qwen1.5-7B-Ch...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Incremental Learning of Retrievable Skills For Efficient Continual Task Adaptation	Accept (poster)	Summary: This paper tackles the problem of continual imitation learning, where an agent needs to continually adapt to new tasks through imitation learning. The paper proposed IsCiL, a method that utilizes prototype-based skill incremental learning to gradually grow a repository of skill prototypes that can be retrieved...	Rebuttal 1: Rebuttal: Thank you for your thorough and insightful review of our paper. Here, we respond to your comments and address the issues. > W1. The idea of tackling lifelong learning through skills have already been explored by previous works, and it is not clear what the key innovation of this work is. Our con...	Summary: The paper presents IsCiL, an approach to continual learning that addresses the limitations of knowledge sharing in traditional Continual Imitation Learning methods. IsCiL uses a prototype-based skill incremental method where each skill is represented by prototype embeddings and skill adapter parameters for LoR...	Rebuttal 1: Rebuttal: Thank you for your thorough and insightful review of our paper. We greatly appreciate your constructive feedback and for highlighting our contributions! Here, we respond to your comments and address the issues. > W1, Q1. The retrieval and adaptation processes at every time step might lead to incre...	Summary: The paper introduces a new adapter-based method for continual imitation learning that avoids episodic replay and exhibits better forward and backward transfer and overall performance as compared to prior work. The authors compare their method against baselines on a couple of simulation benchmarks and also prov...	Rebuttal 1: Rebuttal: Thank you for your thorough and insightful review of our paper. Here, we respond to your comments and address the issues. > W1. Figure 1 could be made clearer. We added revised version of Figure 1 in global rebuttal PDF. > W2. The method assumes access to datasets labeled with sub-goals. This ...	Summary: Learn a two-layer hierarchy from a sequence of datasets, where the low-level skills are represented by a discrete set of prototypes: vectors that can be mapped to repeated patterns of actions represented by basis functions. The basis function parameters are then passed into a decoder function which takes actio...	Rebuttal 1: Rebuttal: Thank you for your thorough and insightful review of our paper. Here, we respond to your comments and address the issues. > W1. How the skills might be entangled together post-hoc? since the reusability of a skill across tasks might make its unlearning impossible, the experimental results also see...	Rebuttal 1: Rebuttal: We sincerely thank all reviewers for their thoughtful reviews and greatly appreciate the insightful feedback on our work. In this section, we include experiments (with PDF) and references to address the comments provided. ## Experiment --- > Exp 1. Skill adapter rank ablation. [KNda Q2 \| kNto...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Scaling the Codebook Size of VQ-GAN to 100,000 with a Utilization Rate of 99%	Accept (poster)	Summary: This study introduces VQGAN-LC (Large Codebook), an innovative image quantization model that significantly extends the codebook size to 100,000, achieving a utilization rate of 99%. Unlike previous methods that optimize each codebook entry individually, VQGAN-LC initializes its codebook with 100,000 feature ce...	Rebuttal 1: Rebuttal: Dear Reviewer igUX, Thanks for your valuable comments. Q1: Dependence on Established Architectures VQGAN and VQVAE are foundational works that introduced the encoder-quantizer-decoder framework for image quantization. Subsequent works, such as RQ-VAE, SQ-VAE, Reg-VQ, ViT-VQGAN, and the two ...	Summary: The paper introduces VQGAN-LC (Large Codebook), a novel image quantization model that significantly extends the codebook size and enhances codebook utilization. Traditional models like VQGAN-FC are limited in codebook size and utilization rates, with a maximum size of 16,384 and utilization rates typically bel...	Rebuttal 1: Rebuttal: Dear Reviewer c7tj, Thanks for your valuable comments. Q1: Comparison of Smaller Codebook Sizes with Other Methods Table 1 in the main paper compares our VQGAN-LC with baseline models VQGAN-FC and VQGAN-EMA across various codebook sizes (1,024, 16,384, 50K, and 100K). The ev...	Summary: The VQGAN-LC (Large Codebook) model tackles the challenges of expanding codebook size and utilization in image quantization. Unlike its predecessors, which struggled with limited codebook sizes and low utilization rates, VQGAN-LC increases the codebook size to 100,000 and achieves over 99% utilization. This mo...	Rebuttal 1: Rebuttal: Dear Reviewer Kj4D, Thanks for your valuable comments. Q1: Reconstruction and Generation on Billion-Level Datasets - Firstly, we want to highlight that we adhere to the methodologies established in previous works such as VQGAN [1] and RQTransformer [2], conducting our experiments on the com...	null	null	Rebuttal 1: Rebuttal: We thank all reviewers for their constructive comments. In this global response, we provide the performance tables for Reviewer c7tj, and address some comments from Reviewer igUX. Reviewer-c7tj-Q1: Comparison of Smaller Codebook Sizes with Other Methods Reconstruction performance ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Iteratively Refined Behavior Regularization for Offline Reinforcement Learning	Accept (poster)	Summary: The authors introduce a modified version of prior behavior-regularized offline RL methods based on conservative policy iteration, where the current policy is regularized towards an older policy. Performance benefits are demonstrated in the D4RL benchmark. Strengths: - Easy to implement/add to TD3+BC or other ...	Rebuttal 1: Rebuttal: ## Whether the algorithm does what the authors claim it does. While Proposition 1 establishes that the exact solution of Eq 9 remains within the data support when the actor is initialized as $\pi_\omega=\pi_D$, practical implementations often rely on a limited number of gradient descent steps for ...	Summary: The authors propose a new offline RL algorithm based on the on conservative policy iteration. The main idea is that the reference policy used for behavior regularization is iteratively modified. The practical algorithm is implemented as a simple modification over TD3-BC, where an additional regularization term...	Rebuttal 1: Rebuttal: ### The algorithm is highly dependent on the value of $\tau$ In Section 5.3.4, we provide ablations of the effect of the two hyperparameters. We also summarize some empirical experiences in the section. The regularization parameter $\tau$ plays a crucial role in determining the weightage of t...	Summary: The paper introduces Conservative Policy Iteration (CPI), a new policy regularization algorithm for offline reinforcement learning. The core concept behind this approach is the iterative refinement of the reference policy for regularization. The algorithm guarantees policy improvement while avoiding out-of-sam...	Rebuttal 1: Rebuttal: ## more detailed discussion and hyperparameter study results In Section 5.3.4, we provide ablations of the effect of the two hyperparameters. We also summarize some empirical experiences in the section. - $\lambda$ : When $\lambda=0.1$, the early-stage performance excels, as the behavior policy ...	Summary: Policy constraint is a standard approach to offline RL. Research in this area often involves using different types of divergence to regulate the distance between the current (learned) policy and the behavior (reference) policy. This paper proposes a new perspective on policy constraint offline RL: why not upda...	Rebuttal 1: Rebuttal: ## The writing can be improved We sincerely appreciate your suggestions for improving the readability of our paper. We have modified our paper as your suggestion! ## question regarding Proposition 1. You’re right that $E_{a \\sim \\pi^*}\\left[Q^\\pi(s, a)\\right] \\geq E_{a \\sim \\pi}\\left[Q^...	Rebuttal 1: Rebuttal: In the PDF we provide more hyperparamerts ablations. Pdf: /pdf/faa46939045fb3b8e0fd9144827749a79365e203.pdf	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Feature-Level Adversarial Attacks and Ranking Disruption for Visible-Infrared Person Re-identification	Accept (poster)	Summary: There is currently a lack of research focused on the security of VIReID systems. In light of this, the authors are the first to propose a method to disrupt the output ranking of VIReID systems by leveraging feature-level adversarial attacks, considering the specific characteristics of VIReID. This paper introd...	Rebuttal 1: Rebuttal: w1 :Comparison with ReID As illustrated in Figure 2, the differences and shared characteristics between visible and infrared pedestrian images can be understood. Compared to ReID, VIReID can extract features from two different types of images. While VIReID leverages the complementary informat...	Summary: There is currently a lack of research on the security of VIReID systems. This paper proposes to explore the vulnerabilities of VIReID systems and prevent potential serious losses due to insecurity. To obtain adversarial features, this paper introduces Universal Adversarial Perturbations (UAP) to simulate commo...	Rebuttal 1: Rebuttal: w1 & limitation: Discussion of the definition of adversarial attacks and what they mean, and how they can be applied in the real world Adversarial attacks induce misclassification in classifiers by introducing subtle perturbations to inputs. In 2014, Goodfellow et al. [1] demonstrated this us...	Summary: This paper aims to explore the security of VIReID and introduces a Universal Adversarial Perturbations to simulate common disturbances in real-world environments. Additionally, a Frequency-Spatial Attention Module is proposed to integrate frequency information extraction and spatial focusing mechanisms. An Aux...	Rebuttal 1: Rebuttal: w1: The introduction of frequency domain and attention mechanism should be added in the related work. Frequency domain： In recent years, frequency domain information processing has gained significant attention in deep learning, proving effectiveness for tasks like face recognition and pe...	Summary: This paper addresses the security of visible-infrared person re-identification systems by introducing a method for feature-level adversarial attacks. The proposed approach integrates universal adversarial perturbations and a frequency-spatial attention module to disrupt the output ranking of VIReID systems. Th...	Rebuttal 1: Rebuttal: w1: Limitations of the proposed method Although the proposed method is effective in many scenarios, it has certain limitations and conditions under which it may fail. These include: 1. Extreme Imaging Conditions**: Our method relies on the alignment of adversarial features across different i...	Rebuttal 1: Rebuttal: We thank all reviewers for their valuable feedback, with three reviewers (sAfc, jhhd, and 78wy) strongly supporting our work. We are pleased to see that reviewers consider our paper: - The ideas presented are novel and interesting (Reviewer sAfc); - The theoretical proofs are solid (Reviewer 78wy...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Hamiltonian Mechanics of Feature Learning: Bottleneck Structure in Leaky ResNets	Reject	Summary: The paper introduces the so-called "Leaky ResNet" ordinary differential equation. Leaky ResNets are a variant of the NeuralODE with an additional vector field that attracts trajectories to the origin, the strength of which is governed by a parameter $\tilde{L}$ that is later shown to correspond to a separation...	Rebuttal 1: Comment: Thanks for the thoughtful review. Regarding the weaknesses (and also the questions) you raise: - We agree with the sentiment and we usually try to follow this approach for most paper, but for this paper most results require several definitions to be stated so it is difficult to summarize all resul...	Summary: The paper maps the dynamics of representations across layers of leaky ResNets to a Largrangian and Hamiltonian formulation, giving an intuitive picture of a balance between two terms: a kinetic energy term which favors small layer derivatives and a potential energy that favors low-dimensional representations. ...	Rebuttal 1: Comment: Thanks for your thoughtful review. Regarding the weaknesses you point to: 1. For Figures 1b and 2b the color have no meaning, we just assign different colors to different singular values for aesthetic purpose. The experimental setup and the synthetic data is described in the Appendix B, we will ad...	Summary: This paper studies feature learning in Leaky ResNets and shows the emergence of the previously studied Bottleneck structure under certain assumptions. In particular the paper provides a Hamiltonian formulation of the features and their dynamics to show that the ResNet will prefer low dimensional features (low ...	Rebuttal 1: Comment: Thanks for the thoughtful review. To answer to the weaknesses you mention: 1. Readability should be improved thanks to your and the other reviewers' remarks. Regarding $\tilde{L}$, our proofs only need $\tilde{L}\geq0$, and you seem to have misunderstood line 80, the range $[0,1]$ is the integrati...	Summary: This paper explores the feature learning dynamics in Leaky ResNets using Hamiltonian mechanics. By introducing the concept of 'representation geodesics', the authors analyze continuous paths in representation space that minimize the parameter norm of the network. The study provides a Lagrangian and Hamiltonia...	Rebuttal 1: Comment: Thanks for the thorough and thoughtful review. Responding to the weaknesses you point: 1. We will fix all the typos you have identified. 2. This line follows from the fact that $W_{p}$ is the minimal Frobenius norm solution of $\partial_{p}A_{p}=-\tilde{L}A_{p}+W_{p}\sigma(A_{p})$. We will add th...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
SSA-Seg: Semantic and Spatial Adaptive Pixel-level Classifier for Semantic Segmentation	Accept (poster)	Summary: In this paper, it analyzes that current pixel-level classifiers for semantic segmentation suffers limitations such as feature deviation in the semantic domain and information loss in the spatial domain. To this end, the authors propose a novel Semantic and Spatial Adaptive (SSA) classifier. Specifically, the a...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for their time and efforts in reviewing our work and providing valuable feedback that can further strengthen our manuscript. Below please find our detailed responses: #### Results on high resolution dataset. ------ Due to page limits, we chose three widely u...	Summary: This work primarily focuses on semantic segmentation. Specifically, a semantic and spatial adaptive (SSA) classifier is proposed to address the "feature deviation in the semantic domain and information loss in the spatial domain" issues. The proposed classifier mainly consists of - Semantic Prototype Adapta...	Rebuttal 1: Rebuttal: Code of SSA. We download the supplementary material and confirm that the code is included. Redundant expressions. We will streamline the expression in the revised version with your comments. Comparison with mask-level classification models and parameters comparison. please refer...	Summary: This paper proposes an adaptive method to improve the semantic segmentation quality. The main idea is to adaptively update the prototypes by using the coarse segmentation masks predicted by the baseline method. Both semantic and spatial prototypes are employed to achieve complementary improvement. Extensive ex...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for their time and efforts in reviewing our work and providing valuable feedback that can further strengthen our manuscript. Below please find our detailed responses: #### Ablation of loss ------ We retain by default the cross-entropy losses $L_{ce}$ and $L_{...	null	null	Rebuttal 1: Rebuttal: We are grateful to the reviewers for their thoughtful and constructive feedback. We are pleased that they recognized the novelty of the methodology, the completeness of the experiments and the excellent segmentation performance. In addition, each reviewer individually made some very valuable comme...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning	Accept (spotlight)	Summary: The work is interested in answering the following question: "In the RL setting, can we identify the best policy faster if we only estimate the difference between the value of individual policies?". The paper provides a positive answer in the contextual bandit setting and a negative, but more nuanced answer for...	Rebuttal 1: Rebuttal: Thank you for your efforts to review the paper and for the positive feedback. Please find answers inline to your questions below. Gains for simpler setting with two policies > In an A/B test scenario, we have only two policies that are compared. How does the PERP algorithm improve on naively ...	Summary: The author investigate if estimating the difference in policies is sufficient in determining the best policy for contexual bandits and tabular RL. A (somewhat) practical algorithm is proposed to determine the number of samples needed without any unknown quantities. Strengths: - The motivating example clearly...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our paper and for the positive feedback. Please find answers inline to your questions below. Intuition for terms > Some intuition could have been provided to describe certain value such as $U(\pi, \bar{\pi})$ or $\delta_h^\pi$ to make the resulting bou...	Summary: This paper studies the problem in tabular RL: finding an \epislon-optimal policy given a set of policy with high probability. The author proposed with a new lower-bound of this problem and explained why the previous one is not correct with an example. The author proposed one algorithm PERP that computes refere...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our paper and for the positive feedback. Please find answers to your questions below. Computational Complexity > The paper didn't provide the computational complexity of the algorithm Thank you for pointing this out, and we will include additional des...	null	null	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Optical Diffusion Models for Image Generation	Accept (poster)	Summary: This work presents an image generation framework using denoising diffusion models implemented though optical computing. The method uses passive diffractive optical layers that are trained to manipulate light propagation through the system — peforming effective denoising. Using the physical properties of light,...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and attention to our work. We would like to provide some replies to the issues raised, and they will be incorporated into the main text in the next revision. Scalability of the Proposed Model and Comparison with GPU-based Denoising Frameworks Thank you fo...	Summary: This paper proposes an opto-electronic realization of a toy diffusion model for image generation. The system consists of a DMD for optical image projection, SLMs for phase modulation, and a CMOS photosensor for signal intensity recording. These physical components, together with a digital processor (for noise ...	Rebuttal 1: Rebuttal: We thank the reviewer for the in-depth analysis of our study, and numerous crucial suggestions for improving the quality of the manuscript significantly. We hope to address the points raised with the following responses and the common author rebuttal. Online training algorithm Re: "...Al...	Summary: Summary: This paper has trained free-space diffractive optical neural networks as an implementation of diffusion model for image denoising application. On-chip learning and hybrid training have been used to improve the noise and variation robustness. Strengths: The application of optical computing hardware is...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and efforts. We reply to the comments under four main sections: Limitations - 1 This paper shows diffusion-based image generation with the advantages of the optical modality for the first time. We use an architecture that incorporates off-the-shelf hardw...	Summary: The authors present a hardware-based implementation of a denoising diffusion model. Instead of using a neural network, the authors propose to use an optical setup to perform the denoising steps during image generation. The system is trained using a digital twin simulating the optical setup. The authors evaluat...	Rebuttal 1: Rebuttal: We thank the reviewer for their overall positive appreciation of our manuscript. We start with point-by-point replies to the mentioned weaknesses (W1-4), questions (Q), and limitations (L) respectively. We will incorporate the additional results we present here and revisions in the main text. **W...	Rebuttal 1: Rebuttal: We sincerely thank the reviewers for their time and efforts in providing their insightful comments. They recognized our work as an “innovative demonstration of an opto-electronic implementation of a toy diffusion model (5uf9)”, agreed with its potential impact; “efficient hardware-based diffusion ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
DDR: Exploiting Deep Degradation Response as Flexible Image Descriptor	Accept (poster)	Summary: This paper proposed a low-level visual descriptor with text embeddings and explored its application on many low-level vision tasks. Strengths: 1. The experiment is sufficient and detailed. 2. The paper is well-written. Weaknesses: Weaknesses of the methods. 1. This paper uses a text-based model to descr...	Rebuttal 1: Rebuttal: We thank the reviewer for the thoughtful response to our paper. We address specific points below: Q1. Comparison with Q-bench: Thank you for the suggestion. There are key differences in target and methodology between the proposed DDR and Q-bench [r1] (in this response letter): - **T...	Summary: The paper introduces Deep Degradation Response (DDR), a method to quantify changes in image deep features under varying degradation conditions. DDR facilitates flexible and adaptive degradation through text-driven prompts. It reports to excel in blind image quality assessment and image restoration tasks like d...	Rebuttal 1: Rebuttal: Thank you for your thoughtful feedback. We address specific points below: Q1. Comparison with other BIQA metrics: Thank you for the suggestion. The proposed DDR is an Opinion-Unaware Blind Image Quality Assessment (OU-BIQA) metric, which does not require training with human-labeled Mean Opin...	Summary: In this paper, the authors propose a feature descriptor to assess low-level image quality degradations. Based on CLIP, the proposed method first encodes input image and its degraded version to features in CLIP space; the input image is encoded by CLIP image encoder, and the degraded image feature is generated ...	Rebuttal 1: Rebuttal: We thank the reviewer for the thoughtful response to our paper. We address specific points below: Q1. Clarifying of some technical details: - Explanation of Figure 2: We measure the amount of images with different Degradation Response (DDR) values to represent the distribution of DDR. Sp...	null	null	Rebuttal 1: Rebuttal: We thank the reviewers for their insightful feedback, which has significantly improved our paper. We are delighted that they appreciate the following: “This paper introduces a novel approach using textual features, …, the results look encouraging.” (Reviewer Y3EC) “*The adaptability to diffe...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
DDN: Dual-domain Dynamic Normalization for Non-stationary Time Series Forecasting	Accept (poster)	Summary: This paper proposes a novel Dual-domain Dynamic Normalization (DDN) method to dynamically capture distribution variations in both time and frequency domains, leveraging wavelet transform to handle time-varying periods. DDN eliminates non-stationarity in time series through normalization within a sliding window...	Rebuttal 1: Rebuttal: Thanks for your valuable comments of our work, we will carefully review and revise our manuscript to correct any inappropriate expressions or symbol errors. Here are responses to your concerns and questions: Question 1: The implementation and core idea of SlideNorm bear significant resemblanc...	Summary: This paper proposes a novel Dual-Domain Dynamic Normalization (DDN) method to address the non-stationary variations in real-world time series by operating in both the time and frequency domains. In the frequency domain, wavelet transform is employed to decompose the time series into high and low-frequency comp...	Rebuttal 1: Rebuttal: Thanks for your valuable comments on our work, we will carefully review and revise our manuscript to correct any inappropriate expressions or symbol errors. Here are responses to your concerns and questions: Question 1: What is effects of window size for low and high-frequency components? **...	Summary: The authors consider the data distribution variations for real-world data and then propose a novel dual-domain dynamic normalization. Unlike the previous methods work in time domain, the proposed method decompose time series into a linear combination of different frequencies, and dynamically capture distributi...	Rebuttal 1: Rebuttal: Thanks for your valuable comments of our work. Here are responses to your concerns and questions: Question 1: What is effects of the way of decomposition methods? Response 1: As highlighted by the work [1] of reviewer G9hB, for long-term distribution changes, a larger sliding window is r...	Summary: The paper introduces a approach to improve the accuracy of time series forecasting by addressing the challenge of non-stationary data, where data distributions change rapidly over time. The authors propose a Dual-domain Dynamic Normalization (DDN) framework that captures distribution variations dynamically in ...	Rebuttal 1: Rebuttal: Thanks for your valuable comments of our work. Here are responses to your concerns and questions: Question 1: Lack of Hyperparameter Sensitivity Analysis, such as the length of the sliding window. Response 1: As suggested, we conduct experiments to evaluate the impact of sliding window s...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Outlier-Robust Phase Retrieval in Nearly-Linear Time	Reject	Summary: This paper addresses the challenge of achieving outlier robustness in phase retrieval, specifically focusing on the recovery of real-valued signals from intensity measurements that have been corrupted by adversarial outliers. The contribution of this work is the development of a nearly-linear time algorithm th...	Rebuttal 1: Rebuttal: We would like to clarify that many of the reviewer's comments have been addressed in our revisions. Several "quotes" provided by the reviewer no longer appear in our current draft. --- R: The paper's theoretical results assume the corruption level $\epsilon$ as a constant in Theorem 3.1, des...	Summary: This paper focuses on the problem of outlier robust phase retrieval, whose goal is to recover a vector $x \in \mathbb{R}^d$ from $n$ intensity measurements $y_i = (a_i^\top x)^2$ when a small fraction of the samples are adversarially corrupted. The authors propose and study this problem, providing a nearly sam...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments and feedback. --- R: Why in Theorem 1.4 (or its formal version in Theorem 3.1), there is no dependence on $\epsilon$ in both the sample complexity and the upper bound on the error? A: Our proofs are formally correct and show that there is no dep...	Summary: This paper studies a classical problem called phase retrieval. The goal is to obtain unknown $d$-dimensional vector $x$ from $n$ datapoints $(a_i, \langle a_i, x \rangle^2)$. This work assumes that $a_i$ are iid Gaussian vectors, but also that a small $\varepsilon$ fraction of the data is corrupted. The author...	Rebuttal 1: Rebuttal: We appreciate the reviewer's careful review and feedback. --- R: The method for RME that is used assumes that variance $\sigma$ is known? But in the way it is used here, it depends on the distance between the current solution and the true vector. The authors do not comment on this issue. **...	Summary: The authors study the phase retrieval problem for retrieving a real signal under the influence of arbitrary corruption. The corruption is allowed to be present in labels or features. They propose a two-step solution. First, they ensure that the initialization is robust to the corruption and second, they show t...	Rebuttal 1: Rebuttal: We thank the reviewer for their close reading of our work and their feedback. --- R: The authors claim that corruption levels up to some universal constant $\epsilon’$ can be handled through their method. Although, to the best of my understanding, this quantity is not characterized in the ma...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Conditional Controllable Image Fusion	Accept (poster)	Summary: The paper proposes a controllable conditional image fusion method. This method enables dynamically controllable fusion for each image pairs. The core idea is to empirically construct a conditional bank and dynamically select different control conditions during the diffusion fusion process. The method is suitab...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for your valuable comments and appreciate your recognition for our novelty on controllable conditional image fusion and generalization on different fusion tasks. We believe the constructive feedback will improve the paper and increase its potential impact on...	Summary: This paper proposes a novel Controllable Condition Fusion (CCF) framework that utilizes a pre-trained diffusion model to achieve dynamic and adaptive condition selection without requiring specific training for general image fusion tasks. The authors presented a conditional bank conducted by various conditions ...	Rebuttal 1: Rebuttal: We'd like to thank the reviewer for the valuable comments, and acknowledgment of our novel, ingenious integration and over current state-of-the-art framework. We appreciate your support and constructive suggestions and address your concerns as follows. - W1: This paper introduces a co...	Summary: This paper proposes a diffusion-based image fusion method with adaptive fusion conditions. It aims to solve the drawback of existing method, i.e., the application of distinct constraint designs tailored to specific scenes. This method builds a condition bank with basic, enhanced, and task-specific conditions. ...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for recognizing our method as applicable to multiple fusion tasks, rich experiments and competitive results. We will also make an effort to increase clarity throughout. - W1&L1: More explanations for contributions and the "gate" of conditions. Thanks f...	Summary: This paper proposes a Conditional Controllable Fusion framework called CCF, effectively addressing the issue that existing data-driven fusion methods struggle to adapt to all scenarios. The authors conducted extensive experiments to demonstrate the effectiveness of the CCF. This manuscript is standardized and ...	Rebuttal 1: Rebuttal: We'd like to thank the reviewer for the valuable comments and appreciate your recognition of the novel idea, effective method and fluent writing. We provide detailed responses to the constructive comments. - W1&Q1: How was the Selection frequency map in Figure 1 obtained? Is it based ...	Rebuttal 1: Rebuttal: Dear PCs, SACs, ACs, and Reviewers, We would like to thank you for your valuable feedback and insightful reviews, which have greatly contributed to improving the paper. This is a fluent and well-structured (Reviewer spMU, y2va) manuscript with a novel idea (Reviewer spMU, y2va), we ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Bridging the Divide: Reconsidering Softmax and Linear Attention	Accept (poster)	Summary: The paper addresses the computational inefficiency of Softmax attention in Vision Transformers, particularly when handling high-resolution inputs. The authors provide a theoretical analysis showing that the injectivity and local modeling capabilities of attention mechanisms significantly impact performance. Th...	Rebuttal 1: Rebuttal: We would first like to express our appreciation for your time and insightful comments. Please find our response to your concerns in the following: --- 1. Novelty and contribution. Thanks for your valuable comment. The novelty and contribution of our work can be summarized as follows: - We ...	Summary: This paper invetigate the linear attention in the Vision task Strengths: 1. The paper is well written, the motivation is clear. 2. The findings is this work is 1) inear attention is not injective, which is prone to assign identical attention weights to different query vector. 2) effective local modeling is e...	Rebuttal 1: Rebuttal: We would first like to express our appreciation for your time and insightful comments. Please find our response to your concerns in the following: --- 1. The source code. - *Firstly, we provide the Pytorch-style pseudo code of our InLine attention module below.* It can be seen that the ...	Summary: This paper aims to solve the computational challenges of Softmax attention in vision tasks due to its quadratic complexity with respect to sequence length. Linear attention as an alternative, reduces complexity to linear time by altering the similarity function from Softmax to kernel functions. However, the au...	Rebuttal 1: Rebuttal: We would first like to express our appreciation for your time and insightful comments. Please find our response to your concerns in the following: --- 1. The performance of InLine models and comparison with related works. Thanks for the valuable comment. ***Firstly, under fair comparison,...	Summary: While linear attention reduces the quadratic complexity of softmax attention, it often suffers from inferior performance. The authors analysed the reason behind it and identified two crucial properties which linear attention lacks: 1) injectivity where different queries in linear attention may have the same at...	Rebuttal 1: Rebuttal: We would first like to express our appreciation for your time and insightful comments. Please find our response to your concerns in the following: --- 1. The locality problem and related work. Thanks for pointing out this important related work [1]. Here, we offer clarification on the relat...	Rebuttal 1: Rebuttal: We thank all the reviewers for their insightful and valuable comments. We have carefully considered the reviewers' comments and provided additional clarification to address each concern. Here, we offer general responses to all reviewers on two key issues. --- **1. Discussion with related works...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
CoSW: Conditional Sample Weighting for Smoke Segmentation with Label Noise	Accept (poster)	Summary: This paper tackles the problem of noisy label in smoke segmentation by introducing uncertainty measure in the some area, especially in the boundary of smoke/not smoke. Noisy label can be problematic for training stability. Entropy is used to measure uncertainty. Highly uncertain prototypes and pixels should no...	Rebuttal 1: Rebuttal: We thank the reviewer for the constructive comments which helped us improve the quality of our work. In the following, we have provided a point-by-point response to the comments. We adopt different letters to represent the different parts of the question raised. The "W" represents "weakness" and t...	Summary: In order to solve the problems of complex and blurred edges of non-grid smoke in smoke segmentation, as well as the existence of noisy labels in large-scale pixel-level smoke datasets, this paper proposes a conditional sample weighting (CoSW) method. CoSW uses a multi-prototype framework, in which prototypes a...	Rebuttal 1: Rebuttal: We thank the reviewer for the constructive comments. Below, we have provided a point-by-point response. The "W" for weakness, "Q" for question, and "L" for limitation. >W1. Relationship between the two smoke challenges and CoSW. The two analyses are to explain **why smoke tends to produce noisy ...	Summary: Smoke segmentation is an important problem as it can be directly tied to health and safety. That being said, it is also a difficult problem as annotations for smoke segmentation datasets are noisy, sometimes leading to inconsistent or even poor segmentation performance. The authors address this issue by propos...	Rebuttal 1: Rebuttal: We thank the reviewer for our paper's positive feedback and constructive suggestions. Here are our responses to the reviewer's comments. We adopt different letters to represent the different parts of the question raised. Where "W" represents weakness and "Q" represents question. >W1(1). L104: Wha...	Summary: This work proposes a method for Smoke Segmentation with Label Noise, addressing the issue of noisy labels commonly found in non-grid smoke annotations. This idea is meaningful and reasonable. The main contributions of the paper are the conditional sample weighting (CoSW) and regularized within-prototype entrop...	Rebuttal 1: Rebuttal: We understand the reviewer's confusion, but this paper is different from DSA. We would like to clarify the differences between our proposed method and the DSA point-by-point below. 1. The key difference is that CoSW is to construct conditional sample weighting to address the issue of **n...	Rebuttal 1: Rebuttal: We thank the reviewers for their careful reading of our paper and help with improving our manuscript. We sincerely appreciate that you find our work: - It is the first paper to tackle the noisy label problem in smoke segmentation (Reviewer 1bVm). - Create a synthetic smoke noise dataset, NS-1K (...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Collaborative Refining for Learning from Inaccurate Labels	Accept (poster)	Summary: In common practical scenarios, autonomous annotators are used to create labeled datasets, reducing the dependence on manual labeling, which can be costly and time-consuming. Learning methods leverage multiple weak labels to annotate large amounts of data, though these weak labels are often noisy and imperfect....	Rebuttal 1: Rebuttal: Thank you for your feedback. We recognize that there may be some misunderstandings regarding our paper and would like to take this opportunity to clarify these points and address your concerns. **Main issue 1: "My main issue with this paper is that the problem is not well explained, there is a l...	Summary: This paper proposes a collaborative refining approach for learning with inaccurate labels provided by low-cost annotators, such as rule-based systems. It introduces strategies based on annotator agreement to filter out noise and enhance data quality. The method includes comparative filtering for conflicting la...	Rebuttal 1: Rebuttal: Thank you for your insightful comments and questions. We hope our response can satisfactorily address your questions. Weakness 1: The proposed method of this paper seems similar to some research methods explored in unreliable partial label learning. Is there any relations between the two? In...	Summary: This paper considers binary classfication from multiple sets of noisy labels, focusing on data refinement to generate clean labels. At each step of training, It proposes to first separate the dataset by whether label disagreement exists, and then tackle each subset using different methods. For the subset with ...	Rebuttal 1: Rebuttal: Thank you for the time and effort you have dedicated to reviewing our work. We appreciate your insightful comments and questions, which are valuable for improving the quality of our research. **Weakness 1: Limitations such as binary classification and class-conditional noise assumption for LRD c...	Summary: This paper introduces a framework for learning from inaccurate labels obtained from multiple annotators. It utilizes annotator agreement to assess label reliability and applies two strategies: one for samples with annotator disagreements (LRD) and another for samples where all annotators agree (RUS). In both c...	Rebuttal 1: Rebuttal: We would like to express our sincere gratitude to you for your insightful comments and questions. Weakness 1: The absence of multiple runs or statistical measures such as standard deviation limits the robustness and reliability of the reported results. Thank you for pointing out the necessit...	null	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper studied learning from multiple noisy labels via data refinement. It first uses the annotator agreement as an instrument to divide all samples into the samples where some annotators disagree and the samples where all annotators agree. Then, a comparative strategy is proposed to filter noise in the sa...	Rebuttal 1: Rebuttal: We appreciate your insightful feedback, which is valuable for improving the quality of our research. We hope our response can satisfactorily address your questions. **Weakness 1: The novelty of robust union selection is limited, since it is very similar to robust mean estimation in [1]. Could the...	null	null	null	null	null	null
Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning	Accept (spotlight)	Summary: This paper focuses on Uncertainty Quantification (UQ) in high-dimensional regression. The authors develop a new data-driven approach that applies both to classical optimization methods, such as the LASSO (which imposes an l1l1 penalty on the weights), and to neural networks. They address the limitations of tr...	Rebuttal 1: Rebuttal: We sincerely thank you for your thorough and constructive feedback. Your insights have highlighted important areas for improvement in our paper's clarity and presentation. We would like to emphasize our novel contribution (see general rebuttal) and we kindly ask you to check the other reviews to i...	Summary: This work develops an uncertainty quantification technique based on the debiased LASSO. The error is decomposed into noise and bias terms, which allows non-asymptotic confidence intervals to be derived. An empirical version of Chebyshev's inequality allows for their construction when the bias term is only assu...	Rebuttal 1: Rebuttal: We are particularly grateful to the reviewer for raising the insightful point regarding conformal prediction and we will add some clarification about the difference between this technique and our work. While the two methods address aspects of total uncertainty, including both epistemic and aleator...	Summary: Improve debiasing technique for better estimation/inference for high-dim models. Strengths: Non-asymptotic result which helps in better numerical performance compared to asymptotic CIs. General idea which can be extended to other statistical models. Weaknesses: NA Technical Quality: 3 Clarity: 3 Question...	Rebuttal 1: Rebuttal: Thank you for your review and question about generalizing our result to other distributions. We carefully address it below. We would like to re-emphasize that our method is general enough and allows, for the first time, to quantify uncertainty when we do not have access to the ground truth and est...	Summary: The paper presents a framework for constructing non-asymptotic confidence intervals around the debiased LASSO estimator. It derives a data-driven adjustment whereby the means and variances of the bias term of the debiased LASSO are estimated from the data and used to correct the confidence intervals. The frame...	Rebuttal 1: Rebuttal: We thank you for your meticulous examination of our paper and for offering valuable feedback and criticism. We are particularly thankful for suggestions to improve clarity and for acknowledging that our work is likely to benefit a variety of high-dimensional regression applications. We address the...	Rebuttal 1: Rebuttal: We sincerely thank the reviewers for their thoughtful and constructive feedback. We appreciate the time and effort invested in evaluating our work. We will increase the size of the figures (and expand their discussion in the appendix) and add individual labels. We would like to emphasize the three...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
VideoTetris: Towards Compositional Text-to-Video Generation	Accept (poster)	Summary: This paper proposes VideoTetris, a novel framework for compositional text-to-video generation. It addresses the limitations of existing methods in handling complex scenes with multiple objects and dynamic changes. VideoTetris achieves this through several key innovations: I) Spatio-Temporal Compositional Diffu...	Rebuttal 1: Rebuttal: We sincerely thank you for your time and efforts in reviewing our paper and your valuable feedback. We are glad to see that the paper is easy to follow, the approach is novel and the experiment are comprehensive. Please see below for our responses to your comments. **Q1: Concerns about the comp...	Summary: The paper presents "VideoTetris," a new framework designed to improve text-to-video generation in complex scenarios with dynamic changes and multiple objects. It introduces spatio-temporal compositional diffusion techniques for better alignment with textual semantics and integrates a dynamic-aware data process...	Rebuttal 1: Rebuttal: We sincerely thank you for your time and efforts in reviewing our paper and your valuable feedback. We are glad to see that the paper is well written the motivations are clearly explained, and the experiments are sufficiently thorough. Please see below for our responses to your comments. **Q1: ...	Summary: The paper proposes VideoTetris, a novel framework for compositional T2V generation. It introduces Spatio-Temporal Compositional Diffusion method for handling scenes with multiple objects and by manipulating and composing the attention maps of denoising networks spatially and temporally. Moreover, authors propo...	Rebuttal 1: Rebuttal: We sincerely thank you for your time and efforts in reviewing our paper and your valuable feedback. We are grateful for your acknowledgment of the clarity, methodological strengths, and resource efficiency of our paper. Please see below for our responses. **Q1: Novelty Clarification & Capariso...	Summary: The paper presents a novel framework designed to improve text-to-video (T2V) generation, especially for complex scenarios involving multiple objects and the composition of different objects. The proposed VideoTetris introduces spatio-temporal compositional diffusion, a dynamic-aware data processing pipeline, a...	Rebuttal 1: Rebuttal: *We sincerely appreciate the time and effort you have dedicated to reviewing our paper and providing valuable feedback. We are pleased that the spatial-temporal decomposing method, visual quality, and the adequacy of our quantitative and ablation experiments were well-received. Below, we address y...	Rebuttal 1: Rebuttal: We sincerely thank all the reviewers for their thorough reviews and valuable feedback. We are pleased to hear that our method offers significant advancements in the field (Reviewer pWwG), the paper is well-written and easy to follow (Reviewers pWwG, uyep, and 9Z5K), the visual quality is satisfyin...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Lisa: Lazy Safety Alignment for Large Language Models against Harmful Fine-tuning Attack	Accept (poster)	Summary: This paper demonstrates that baseline mixtures of samples from an alignment dataset into a potentially contaminated dataset isn’t enough to protect against this type of mixed data fine tuning attack. Similarly bi-state optimization of alternating objectives on alignment and fine-tuning datasets are not enough....	Rebuttal 1: Rebuttal: This is a very long and informative review (more than one page). We are more than grateful for all the good suggestions and efforts being made to improve our paper. **W1+Q3: several issues with lack of details. For example, the initial Jail-broken effect by harmful fine-tuning experiment is unc...	Summary: In this paper, the authors proposed a data-driven method for mitigating the decay of safety standard of LLMs during fine-tuning. To this end, the authors put forward a BSO method alternating between alignment and fine-tuning. However, excess drift is observed in the BSO method. Hence, the authors further lever...	Rebuttal 1: Rebuttal: We thank the reviewer for the informative and helpful review comments. Below we try to address your concern. W1: Safety alignment data are unavailable to users It is true that safety alignment data are not available to users. However, the proposed Lisa method is used by the service provider...	Summary: The authors propose a new method to mitigate the risk of fine-tuning breaking safety. Lisa works by introducing a proximal term and balancing the goal of optimizing for the alignment dataset and the user dataset. The authors provide strong empirical evidence in support of the method and also include theoretica...	Rebuttal 1: Rebuttal: We thank the reviewer for raising all these constructive review comments. Below we try to address them. W1: It is unclear how to find the best mixture parameter $\rho$ for different datasets/ models The proximity penalty needs to be carefully tuned to find the best value. Here are the resul...	Summary: The paper proposes an approach to make adversarial fine-tuning on harmful datasets ineffective and preserve alignment. The baseline proposed, called BSO, in the paper is to alternate between alignment fine-tuning and task-dependent fine-tuning. The main contribution of the paper, called Lisa is to add a proxim...	Rebuttal 1: Rebuttal: W1: Choice of fine-tuning dataset. In the paper we use general task like SST2, AGNEWS, GSM8K, and AlpacaEval because they are all well-established dataset and are commonly used for benchmark. We generally believe the method can be generalized to more complicated scenario, e.g., such as socia...	Rebuttal 1: Rebuttal: We sincerely thank Reviewer MRGF, Reviewer 1udp, and Reviewer ytGJ for the very constructive review comments. All of these comments significantly help us increase the quality of the paper, and we would like to address their concerns in individual comments to them. Specially, for reviewer ytGJ, we ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
On the Mode-Seeking Properties of Langevin Dynamics	Reject	Summary: The authors consider the Langevin process for sampling from a target distribution $\pi$. This process is known to be slow-converging for multimodal targets: in practice, it has been observed that the process gets "stuck" in some modes of the target, and do not "reach" other modes of the target. The authors pro...	Rebuttal 1: Rebuttal: We would like to thank Reviewer nEdB for his/her time and constructive feedback on our work. Below is our response to the questions and comments in the review. 1- Guarantees in terms of standard distance measures between probability models Re: Please refer to global rebuttal #1. **2...	Summary: The authors study Langevin dynamics (as well as its annealed counterpart) for gaussian mixtures and sub-gaussian mixtures. In Sec. 4, they prove that Langevin remains stuck in the "dominant mode" for an at least exponential time, a claim that is often made in the ML literature but which is never formally prove...	Rebuttal 1: Rebuttal: We thank Reviewer Ave7 for his/her time and constructive feedback on our work. Below is our response to the questions and comments in the review. 1- Insights behind Theorems 1,2 and the role of mode $P^{(0)}$ Re: Please note that in Theorem 1, the mode $P^{(0)}$ plays the role of a la...	Summary: A new algorithm is proposed, called Chained Langevin Dynamics, to improve on the mode-seeking properties of Langevin Dynamics, after annleade Langevin Dynamics had been proposed but did not give significant improvements. Results about the mode-seeking properties of the three algorithms are obtained. The result...	Rebuttal 1: Rebuttal: We would like to thank Reviewer 7G8Z for his/her time and constructive feedback and suggestions on our work. Below is our response to the questions and comments in the review. 1- The order of patches in Chained Langevin Dynamics Re: In our analysis, the convergence rate of chained Lang...	Summary: This paper studies Langevin-based algorithms for sampling from multimodal distributions, motivated by generative modeling. The main content of the paper are lower bounds on the convergence of both Langevin and annealed Langevin for mixtures of Gaussian and sub-Gaussian distributions, as well as a proposed modi...	Rebuttal 1: Rebuttal: We thank Reviewer 3VKE for his/her time and detailed feedback on our work. Below is our response to the questions and comments in the review. 1- Guarantees in terms of standard distance measures between probability models Re: Please refer to global rebuttal #1. **2- Insights of the...	Rebuttal 1: Rebuttal: We would like to thank the reviewers for their constructive feedback. Here we respond to the common question of Reviewers 3VKE and nEdB. We provide our response to the other comments and questions under each review textbox. **1- Guarantees in terms of standard distance measures between probabili...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for Time Series Forecasting	Accept (poster)	Summary: This paper presents an adaptive hypergraph learning module for modeling group-wise multi-scale interactions to improve transformer-based model for time series data. Given a time series data, Multi-Scale Feature Extraction (MFE) Module first converts it to a hypergraph. Then, intra-scale and inter-scale learnin...	Rebuttal 1: Rebuttal: Comment: Many thanks to Reviewer VVbq for providing the insightful reviews and comments. Q1: In long-range, short-range, and ultra-long-range time series forecasting, does Ada-MSHyper perform better on one type compared to the others? What might be the reasons for this? Thanks for your valu...	Summary: (1) Design an AHL module to model the abundant and implicit group-wise node interactions and a multi-scale interaction module to model group-wise pattern interactions at different scales. (2)Introduce a NHC mechanism to cluster nodes with similar semantic information and differentiate the temporal variations w...	Rebuttal 1: Rebuttal: Comment: Many thanks to Reviewer HJMp for providing the insightful reviews and comments. Q1: The pipeline can be improved and some expressions in the paper can be more formal. Thanks for your valuable suggestions and scientific rigor. We have improved the color of the pipeline, see **Figure...	Summary: This paper presents a Time Series Forecasting method Ada-MSHyper that uses a hyper graph to capture the group-wise interactions at different time scales rather than Point-Wise interaction. Experiments are performed on 8 data sets and the proposed method is compared with SOTA methods Strengths: 1. The use of H...	Rebuttal 1: Rebuttal: Comment: Many thanks to Reviewer HQ9E for providing the insightful reviews and comments. Q1: Graph-Transformer methods should be included in Related Works and used for comparisons. Thanks for your valuable suggestions and scientific rigor. We have added two latest Graph-Transformer methods,...	Summary: This paper introduces a hypergraph-based multi-scale time series forecasting model. By treating multi-scale feature representations as nodes, the proposed AHL module automatically generates incidence matrices to model implicit group-wise node interactions at different scales. Node constraints and hyperedge con...	Rebuttal 1: Rebuttal: Comment: Many thanks to Reviewer Y3pA for providing the insightful reviews and comments. Q1: About some different long-range prediction results from those in the DLinear and iTransformer paper. Thanks for your careful check and scientific rigor. As for the long-range time series forecasting...	Rebuttal 1: Rebuttal: We sincerely thank all the reviewers for their insightful reviews and valuable comments, which are instructive for us to improve our paper further. The reviewers generally hold positive opinions of our paper, in that they perceive our approach as interesting, detailed, and clear. The ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Image Understanding Makes for A Good Tokenizer for Image Generation	Accept (poster)	Summary: This paper shows that image understanding models can be helpful in image generation and that a stronger IU model can result in better IG performance. To demonstrate it, this paper sets multiple experiments from many perspectives (e.g. different datasets and codebook sizes) and gives out reasonable analysis. S...	Rebuttal 1: Rebuttal: Thank you for your detailed review and valuable comments. Please note our top-level comment. Below we address specific questions. # Qualitative results Please see our top-level comment. We present more qualitative results in the attached pdf file. Note that image reconstruction capabilities and ...	Summary: This work focuses on the connection between image understanding (IU) and image generation (IG). The authors introduce a token-based IG framework and a novel feature reconstruction objective for tokenizer training. They introduce an extra feature reconstruction loss to distill semantic knowledge from pretrained...	Rebuttal 1: Rebuttal: Thank you for your detailed review and thoughtful comments. Please note our top-level comment. Below we address specific questions. # Related works Our main conclusion is that IU models can aid IG tasks, which have not been explored before. While LaViT adopts a pretrained ViT encoder in the toke...	Summary: This paper introduces a novel framework that leverages the rich semantic capabilities of Image Understanding models for Image Generation tasks. By employing a token-based generation framework and a feature reconstruction objective, the paper trains tokenizers capable of mapping images into token sequences. Com...	Rebuttal 1: Rebuttal: Thank you for your constructive comments. Please note our top-level comment. Below we address specific questions. # Theoretical Support While it is hard to theoretically prove the superiority of the VQ-KD tokenizer, we hypothesize that superiority is because each token generated by VQ-KD contain...	Summary: This paper explores using image understanding (IU) models to aid image generation (IG) performance. To verify the hypothesis, the authors focus on the different tokenizers and introduce feature reconstruction (VQ-KD) as a training objective for image tokenizers, distilling knowledge from pre-trained IU encoder...	Rebuttal 1: Rebuttal: Thank you for your thoughtful feedback. Please note our top-level comment. Below we address specific questions. # Generalizability Please see our top-level comment. We examine VQ-KD ConvNext and observe good image generation abilities. More types of IU encoders will be added to our revised manus...	Rebuttal 1: Rebuttal: Dear reviewers, We would like to thank you all for providing constructive feedback that helps us improve the paper. We are encouraged by the reviews: - "The paper is the first to demonstrate that image understanding models can substantially enhance image generation." (Reviewer cC74) - "The clar...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
On Giant's Shoulders: Effortless Weak to Strong by Dynamic Logits Fusion	Accept (poster)	Summary: The article discusses existing weak-to-strong methods, noting that current approaches typically use a static knowledge transfer ratio and a single small model to convey complex knowledge, which results in suboptimal performance. Consequently, the article proposes a dynamic logit fusion method that employs a se...	Rebuttal 1: Rebuttal: > Q1: In the multi-task setting, which 7B model is used as the expert to implement your algorithm on the 13B model? I am very confused. If you are using different sets of experts to operate on the 13B model, isn't this weak-to-strong? Because the parameters of multiple 7B models exceed 13B In the...	Summary: They tackle the problem of merging the logits from multiple models. To do so, they propose an objective that minimizes the squared loss of the KL between the two pairs of (student, teacher) models. This is solved via a random search. Strengths: - Paper well written and easy to follow - Nice ablations on alph...	Rebuttal 1: Rebuttal: > Q1:Using the squared error between two KL's is not theoretically motivated (at least that I am aware of) The goal of our motivation is to enhance the constraints using KL divergence, aiming for the shift of the fine-tuned large model to be equal to the shift of the fine-tuned small models in ea...	Summary: The paper studies the problem of adapting large general language models via smaller expert language models fine-tuned on specific tasks. Prior work proposed the idea of mixing logits between a large model and the differencei in logits pre- and post- finetuning of a small model. The authors take this idea a ste...	Rebuttal 1: Rebuttal: > Q1: It is not intuitively obvious to me why matching the KL divergence is the right objective. Could the authors please provide some intuition? I imagine it is something like this: when the small model updates significantly for some token, we want the large model to also udpate significantly. Th...	Summary: This paper focuses on the weak-to-strong generalization paradigm where the goal is to transfer knowledge from a small language model to larger one. The method they study is the one proposed by Mitchell et al. [1]: they use log probability algebra to combine the logits of the large model, the ones of a small mo...	Rebuttal 1: Rebuttal: > Q1:Solving the optimization problem at every decoding step is expensive? As we analyzed in the "Complementary to Efficiency Analysis" section of the Global Rebuttal, our method only adds the term "$nBV$" compared to the static method. Optimizing $n$ times ($n \le 20$) during each forward pass ...	Rebuttal 1: Rebuttal: ## Global Rebuttal Dear reviewers, We much appreciate for your acknowledgment of our work and helpful, insightful comments. Following the reviewers' suggestions, we have carefully revised the paper and conducted a series of new experiments to address the reviewers' concerns. The below contains ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Coarse-to-Fine Concept Bottleneck Models	Accept (poster)	Summary: This paper presents a type of label free concept bottleneck model for ante-hoc interpretability that incorporates a hierarchichal concept representation. The concepts are represented in a two-level hierarchy with high-level concepts denoting scenes/objects and lower level concepts denoting more specific attrib...	Rebuttal 1: Rebuttal: We thank the reviewer for highlighting the strengths of our work and for raising some interesting questions. - Weakness 2: Comparison to supervised baseline. > Following the reviewer’s suggestion, and given the limited time in the rebuttal, we compare the concept prediction accuracy of our...	Summary: The author introduced a novel Concept Bottleneck Model (CBM) that facilitates hierarchical concept learning. Specifically, the proposed Concept Discovery Block (CDB) plays a pivotal role in uncovering concepts from preprocessed image-text similarity embeddings by employing a variational Bayesian framework to l...	Rebuttal 1: Rebuttal: We thank the reviewer for their time, consideration and their helpful insights that will help improve the manuscript. - Weakness 1: The class/label designated at the high level and its attributes at the low level are strong hierarchical constraints. > It is true that such a hierarchical re...	Summary: This work introduces a novel framework that leverages recent advances in vision-language models and a Bayesian approach for coarse-to-fine concept selection. It introduces the notion of concept hierarchy, allowing high-level concepts to be characterized by lower-level attributes and exploiting granular informa...	Rebuttal 1: Rebuttal: We thank the reviewer for recognizing the qualities of our approach concerning the originality, the significance, the quality and the clarity. - Weakness 1: Over-reliance on the vision-language backbone's capability might result in poor performance for images from uncommon datasets. > Thi...	Summary: The authors propose coarse-to-fine concept selection in Concept Bottleneck Models (CBMs). They introduce a concept hierarchy that identifies low-level concepts in local patches of input images, as well as high-level concepts in the overall images. Additionally, the authors enhance interpretability by consider...	Rebuttal 1: Rebuttal: We thank the reviewer for their thoughtful feedback and suggestions. - Weakness 1: The use of Jaccard similarity. > We thank the reviewer for raising this point that can help clarify the importance of using a different metric compared to the standard binary accuracy typically considered i...	Rebuttal 1: Rebuttal: We thank all the reviewers for taking the time to review our manuscript and for their insightful comments. We carefully considered all the comments that the reviewers raised and addressed them diligently. To this end, we respond to each question individually and we also include a PDF with some n...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Gradients of Functions of Large Matrices	Accept (spotlight)	Summary: This paper introduces a new matrix free method for automatically differentiating functions of matrices. The computational problem discussed in this paper is of interest because the matrix dimension scales with respect to the size of the dataset (e.g. Gaussian process regression, etc.). The authors' algorithm y...	Rebuttal 1: Rebuttal: Thank you so much for your positive evaluation! To answer your questions: 1. We agree that these directions are interesting for future research! 2. We use 10 Rademacher samples to match GPyTorch's default settings. Appendix H explains all Gaussian-process-related parameters. 3. The errors are ...	Summary: There are some useful iterative methods for calculating important matrix products, to wit, Lanczos and Arnoldi iterations, which apparently did not have known derivatives, until now. The paper provides a generic framework for calculating the derivatives of such iterative linear operator approximations. The cor...	Rebuttal 1: Rebuttal: We greatly appreciate your positive assessment! We are happy that you acknowledge how our work "provide(s) immediate quality-of-life improvements to linear algebra users in high-value problems" because this was precisely our goal! We hope that you continue to fight for this paper's acceptance. --...	Summary: The paper proposes an adjoint method for functions of matrices that utilize Arnoldi/Lanczos iterations to compute gradients with respect to large dimensional variables and demonstrates their approach's utility on a variety of common compute/memory intensive tasks encountered in machine learning. They derive th...	Rebuttal 1: Rebuttal: Thanks for the positive evaluation! Before we answer your questions, we would like to reply to your points listed under "Weaknesses" briefly: Matvecs, sparse/dense complexity: The complexity of our adjoint mirrors that of the forward pass of Lanczos/Arnoldi and depends almost entirely on th...	Summary: This paper proposes a new approach to perform automatic differentiation for function of large matrices. Specifically, the paper outlines the backward computation of the matrix-vector product f(A(\theta)) * v where A(\theta) is the jacobian of a large NN that will not fit into memory. The proposed approach uses...	Rebuttal 1: Rebuttal: Thanks for the review and the positive assessment! We would like to briefly reply to the points you list as weaknesses: Motivation: You are correct that the Lanczos and Arnoldi iterations yield approximate matrix-function-vector products. When we write "exact gradient of the forward pass",...	Rebuttal 1: Rebuttal: We thank all reviewers for their reviews and for assessing the paper so positively! We are grateful that all reviewers praised the clarity of the contribution. Reviewers FpA9 and X6Aj did not find any particular weaknesses, and we believe that the weaknesses listed by RkAM and 3YyQ might be easy t...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Improving Generalization of Dynamic Graph Learning via Environment Prompt	Accept (poster)	Summary: This work investigates the issue of spatio-temporal data distribution shift, which is a long-standing challenge in dynamic graph learning. First, the authors systematically analyze the limitations of existing works over OoD challenge, and then propose a comprehensive solution to address their limitations. Sp...	Rebuttal 1: Rebuttal: Dear Reviewer YNjr, We would greatly appreciate your positive comments on our work. We have carefully considered your questions and have provided detailed answers as follows. W1. The design of our self-prompted mechanism. There are some readily accessible environment factors in the original...	Summary: This paper provides a novel dynamic graph learning framework EpoD to tackle the temporal distribution shift issue by exploiting prompt learning. EpoD addresses two limitations of existing works regarding inference and exploitation of unseen environments. The EpoD includes two modules, i.e., self-prompted envir...	Rebuttal 1: Rebuttal: Dear Reviewer 5ZgN, Thank you for taking the time to review our work. We greatly appreciate your positive feedback and will address your comments with careful consideration. W1. Advantages of self-prompted learning mechanism. Our self-prompted mechanism has two key advantages that disting...	Summary: The paper is about dynamic graph learning, which is an interesting topic. The authors propose a novel dynamic graph learning model named EpoD based on prompt learning and structural causal model to comprehensively enhance both environment inference and utilization. The paper is well written and well organized....	Rebuttal 1: Rebuttal: Dear Reviewer XPEX, Thank you very much for your thoughtful review! We have carefully considered your comments, and we will provide detailed responses below. We hope these details can address your concerns. W1. The ability to counter structural distribution shifts of EpoD. Addressing node-s...	Summary: The paper introduces EpoD, a novel dynamic graph learning model that leverages prompt learning and structural causal models to address out-of-distribution (OOD) generalization challenges. EpoD features a self-prompted learning mechanism for inferring environment variables and a node-centered subgraph extractor...	Rebuttal 1: Rebuttal: Dear Reviewer Zz1E, Thank you for your valuable time in reviewing our manuscript. We greatly appreciate your positive comments on our work, and your insights are invaluable to us. We will provide detailed replies to your questions next. W1. More extensive prompts design. One of the purposes...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Neural Collapse To Multiple Centers For Imbalanced Data	Accept (poster)	Summary: This paper explores Neural Collapse (NC) in the context of imbalanced data, proposing the concept of Neural Collapse to Multiple Centers (NCMC). It establishes that aligning features from minor classes with more directions improves classification accuracy, introducing the Generalized Classification Rule (GCR)....	Rebuttal 1: Rebuttal: We appreciate the reviewer's recognition of our contribution to the field of deep learning and classification. To convey the meaning of our work more fluently, we answer the questions in the following order. $\textbf{Response to weaknesses 1 and 5 }:$ We will enlarge the Fig.1 for a better ill...	Summary: This paper addresses the issue of minority collapse in imbalanced learning, finding an optimal structure to represent a better classification rule. The authors induce a new definition called NCMC and design an MSE-type loss to alleviate the minority collapse phenomenon. Strengths: This paper is well-written a...	Rebuttal 1: Rebuttal: $\textbf{Response to weakness 1}$: The main purpose of this paper is to assign a novel classification rule to the imbalanced classification. The classification rule focuses on the hard-to-predict subpopulation, which is different from the popular margin theory. In order to justify the usefulnes...	Summary: This paper studies the Neural Collapse (NC) phenomenon in imbalanced learning. Specifically, the authors find that the minor classes should align with more directions to achieve better classification results. Such finding yields the Generalized Classification Rule (GCR). The authors study NC under UFM. They fi...	Rebuttal 1: Rebuttal: Thank you for the recognition of our contribution. $\textbf{Response to the weakness}$: 1. thank you for your advice. We think it is a good idea to move proposition B.1 to the Main Result section and add more explanations of $\tilde{w}_j^{(k)}$. In particular, the set of $\tilde{w}_j^{(k)}$’s fo...	Summary: This paper studies the Neural Collapse phenomenon under the imbalanced training data. The authors extend the optimal structure of neural collapse classification to a multiple center setting to enhance the model performance. Specifically, the authors propose to leverage the Generalized Classification Rule to ma...	Rebuttal 1: Rebuttal: Thank you for your review. $\textbf{Response to weakness 1}$: As far as we see, the proposed CAL has marginal improvement on cifar10 compared to ARBloss, but clearly outperforms other classical methods in comparison; it also has non-negligible improvements on cifar100. The experiment shows that ...	Rebuttal 1: Rebuttal: Thanks to the reviewers for their patience and time. According to the questions from the reviewers, we add a few experiments w.r.t loss P and the generalized classification rule. The attached contains three figures: $\textbf{Figure 1}$: The 3D illustration of multi-center frame; $\textbf{Figure...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Online Adaptation of Language Models with a Memory of Amortized Contexts	Accept (poster)	Summary: This paper focuses on how to adapt static language models (LMs) with streaming documents during inference time. There are two high-level challenges here: 1) how to store new domain/task relevant information, 2) how to utilize the stored information for downstream task-solving, i.e., doing question answering ...	Rebuttal 1: Rebuttal: Dear reviewer pwuf, We sincerely appreciate your efforts and insightful comments to improve the manuscript.\ We respond to each of your comments one-by-one in what follows. --- [W1] Comparison with memory-augmented networks by combining context compression [1] with RAGs. Thank you for the ...	Summary: This paper proposes Memory of Amortized Contexts (MAC) which can encode the documents into compact modulations stored in a memory bank, which can later be retrieved to answer questions. Strengths: 1. The proposed method is efficient compared to the baselines. 2. The paper is well-written and easy to follow. ...	Rebuttal 1: Rebuttal: Dear reviewer QtU4, We sincerely appreciate your efforts and insightful comments to improve the manuscript.\ We respond to each of your comments one-by-one in what follows. --- [W1] Unfair comparison: Online learning distils information into parameter vector where MAC stores the modulation....	Summary: The paper proposes an online learning framework called MAC (Memory of Amortized Contexts) designed to efficiently adapt large language models (LLMs) online. By using feature extraction and memory augmentation methods, MAC compresses and stores new document information in a memory bank, retrieving relevant know...	Rebuttal 1: Rebuttal: Dear reviewer j5pf, We sincerely appreciate your efforts and insightful comments to improve the manuscript. We respond to each of your comments one-by-one in what follows. --- [W1] Possible implementation difficulty due to a somewhat complex method. We respectfully argue that MAC is a si...	Summary: This paper presents a novel online adaptation framework (Memory of Amortised Contexts, MAC), which effectively solves the problem of rapid updating of large language models (LLMs). MAC successfully preserves the knowledge learned by the model during the original training phase and the new data streams through ...	Rebuttal 1: Rebuttal: Dear reviewer kPwd, We sincerely appreciate your efforts and insightful comments to improve the manuscript.\ We respond to each of your comments one-by-one in what follows. --- [W1] Memory bank growth issues. It is true that one possible limitation of MAC can be the growing size of the mem...	Rebuttal 1: Rebuttal: Dear reviewers and AC, We sincerely appreciate your valuable time and effort spent reviewing our manuscript. As reviewers highlighted, we believe our paper provides a novel (kPwd,j5pf), efficitent (all reviewers) yet effective (kPwd, pwuf) framework for online adaptation of LLMs followed by a c...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models	Accept (poster)	Summary: The authors propose an alternative optimization method for the Cox proportional hazards model. They derive quadratic and cubic upper bounds on the loss and minimize these upper bounds with respect to a single model parameter at a time (similar to coordinate descent) via explicit formulas. They test their metho...	Rebuttal 1: Rebuttal: Thank you very much for reviewing our paper. Please see below for our answers to your questions. 1. Runtime reduction results for computing derivatives are trivial. Please look at Line 146. The result is surprising especially for computing the exact 2nd order partial derivatives. Even fo...	Summary: This paper explores the optimisation of the Cox model. Through careful mathematical analysis, the authors identified efficient ways to calculate the exact derivatives and surrogate loss functions necessary for efficient optimisation, addressing existing strategies' imprecision and time limitations. Strengths:...	Rebuttal 1: Rebuttal: Thank you very much for reviewing our paper. Please see below for our answers to your questions. 1. Proof of convexity of negative log likelihood (nll) The essential part of the nll is the logSumExp function, which is defined as $f(\boldsymbol{x}) = \log(\sum_{i=1}^m \exp(x_m))$. It is well...	Summary: This paper presents a new optimization algorithm for the Cox model, which is a classical algorithm for survival analysis presented in 1972. Strengths: This paper is well-written. I am not an expert on optimization algorithms for convex functions, but I think I could understand the proposed algorithm and I en...	Rebuttal 1: Rebuttal: Thank you very much for reviewing our paper. Please see below for our answers to your questions. 1. Utility of Cox Model We want to emphasize that we agree with what the reviewer has commented and simply want to continue this conversation. We think there are three aspects that make the Cox ...	Summary: The authors propose an optimization of the Cox proportional hazards model based on minimizing surrogate functions obtained from exploiting the Lipschitz continuity property of the first and second order partial derivatives of the loss wrt coefficients. The authors show that the optimization works for sparse pe...	Rebuttal 1: Rebuttal: Thank you very much for reviewing our paper. Please see below for our answers to your questions. 1. Comparison with fastCPH and BigSurvSGD on computational efficiency Thank you for pointing out these two baselines. We will cite them during revision. Regarding BigSurvSGD, below is a comparis...	Rebuttal 1: Rebuttal: We would like to thank the reviewers for their detailed reviews. We will use this general response to address some common questions and concerns. 1. Is sorting needed during optimization? Thanks to both Reviewer EDwF and Reviewer GBdX for asking this question. We never perform sorting during...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
YouDream: Generating Anatomically Controllable Consistent Text-to-3D Animals	Accept (poster)	Summary: 1. This paper proposes to generate animals with pose control. 2. The pose control is achieved by controlnet trained on pose-image pair data. 3. The pose can be generated with LLM during inference. Strengths: 1. The pipeline consists many parts and some efforts are put to implement the whole pipeline. 2. Exp...	Rebuttal 1: Rebuttal: _We appreciate the reviewer for acknowledging the novelty of our work and the robustness of our method. We sincerely thank the reviewer for pointing out two very important points that should be included in the manuscript. We will make sure to update the same in the final version of the manuscript....	Summary: The paper presents YouDream, a framework for text-to-3D animal generation. The two keys to their methods are (1) a tetra-pose ControlNet that synthesizes animals given a text prompt and 2D tetrapod poses, and (2) a multi-agent LLM system that modifies 3D keypoint templates to generate different animal poses. U...	Rebuttal 1: Rebuttal: _We sincerely thank the reviewer for their in-depth analysis of our work and for providing extremely helpful comments and questions which will greatly help us to improve our manuscript. We deeply appreciate the reviewer's acknowledgment of the impact of our work and their recognition of YouDream’s...	Summary: This work proposes a method to generate 3D models with pose control. It mainly contains two steps: 3D pose generation with LLM agents and text-to-3D generation with pose-conditioned ControlNet and viewpoint sampling. From the experiments, the method can generate novel 3D animals with controlled poses, while pr...	Rebuttal 1: Rebuttal: _We thank the reviewer for their detailed questions which will help us improve our manuscript. If our response has adequately addressed their concerns and provided new insights, we would be grateful if they consider revising their score._ __Q. Prior art can achieve explicit pose control using onl...	Summary: The paper proposes a novel technique called YouDream for text-guided animal generation. Specifically, the authors first propose a multi-agent LLM that's capable of generating a 3D pose of the text-described animal. Secondly, YouDream includes a TetraPose ControlNet to generate the images based on the projectio...	Rebuttal 1: Rebuttal: _We appreciate the reviewers' thorough evaluation of our work, including recognition of our novel contributions and results. We thank them for their insightful suggestions to enhance our paper. If our response has adequately addressed their concerns and provided new insights, we would be grateful ...	Rebuttal 1: Rebuttal: _We sincerely thank the reviewers for their comments. Reviewers __127z__, __rnqi__, and __4gaH__ appreciated the __novelty__ of YouDream and the improvement in performance in terms of __geometric consistency__ and __robustness__, while reviewer __PBQU__ identified __YouDream’s out-of-domain genera...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
UniEdit: A Unified Tuning-Free Framework for Video Motion and Appearance Editing	Reject	Summary: The paper proposes UniEdit, a framework that allows the editing of videos. More specifically, UniEdit allows manipulation via text prompts to change the visual style or the motion pattern that is visible in the video. Moreover, it also targeted steering, e.g. via segmentation masks. They achieve this by introd...	Rebuttal 1: Rebuttal: We thank the reviewer for providing encouraging comments on our paper! We provide clarifications to the concerns below:   > The implementation may not be entirely reconstructable. Hopefully, this issue will be fixed when they publish the code as promised. We will definitely release the code...	Summary: This paper suggests UniEdit, a tuning-free method for editing the motion of a given video. The authors use a pre-trained text-to-video diffusion model and utilize its motion prior, to performing motion editing on a video while keeping the appearance of the original video. During the denoising process, they app...	Rebuttal 1: Rebuttal: Thanks for your constructive comments! We address the concerns below:   > Feature injection is a known technique in image editing. As you mentioned, similar feature injection techniques have been explored previously. However, our approach differs in several key ways: 1. We address the nove...	Summary: This paper focuses on developing a tuning-free framework capable of editing both the motion and appearance of videos. They introduce UniEdit, an approach designed for text-guided motion editing that maintains the original content of the source video. By utilizing two branches—an auxiliary reconstruction branch...	Rebuttal 1: Rebuttal: Thanks for the elaborate review! We will address your concerns below:   > The number of the participants in the user study might not be representative enough. Thanks for the advice! We additionally recruited 20 participants to make their evaluations on the synthesized videos of the proposed...	null	null	Rebuttal 1: Rebuttal: # General Response to All Reviewers   Dear Reviewers:   We would like to thank you for the constructive comments and the time you dedicate to the paper! We are encouraged to see that UniEdit is acknowledged to address an important problem (Reviewer rXMa) and presents an effectiv...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
What Factors Affect Multi-Modal In-Context Learning? An In-Depth Exploration	Accept (poster)	Summary: The paper studies what influences multimodal ICL and finds that using multimodal retrievers to find demonstrations, ordering of modalities in the context and introductory instructions help to improve the few-shot performance. The study spans 6 models and several multimodal datasets. Strengths: - The paper add...	Rebuttal 1: Rebuttal: We sincerely appreciate your thorough and insightful comments on our work. In the following, we will clarify your concerns and we would greatly appreciate it if you can reconsider the work in light of our clarification. --- Q1: The difference between our work with previous studies [1,2]. ...	Summary: This work explores an interesting research question: “What factors affect the performance of MM-ICL?” To this end, they conduct comprehensive experiments on the three fundamental steps of MM-ICL: demonstration retrieval, demonstration ordering, and prompt construction. In addition, they explore 20 strategies a...	Rebuttal 1: Rebuttal: Thanks for your acknowledgment and interest in our work! We sincerely appreciate your thorough and insightful comments on our work, and we will address each of your main concerns below: --- Q1: The 'Exploration of MM-ICL Prompt Construction' section can be explained more clearly. R1: Tha...	Summary: The work presents an in-depth analysis of the factors influencing the performance of Multi-modal In-Context Learning (MM-ICL). The authors systematically investigate the core steps of MM-ICL, including demonstration retrieval, ordering, and prompt construction. Utilizing six vision large language models and a ...	Rebuttal 1: Rebuttal: Thanks for your acknowledgment and interest in our work! We sincerely appreciate your thorough and insightful comments on our work, and we will address each of your main concerns below: --- Q1: The captions in the paper can be enriched to help readers gain a better understanding. R1: Tha...	Summary: The paper investigates the underlying factors that influence the effectiveness of Multi-Modal In-Context Learning (MM-ICL). The authors conducted extensive experiments on three core steps of MM-ICL: demonstration retrieval, demonstration ordering, and prompt construction using six vision large language models ...	Rebuttal 1: Rebuttal: Thanks for your acknowledgment and interest in our work! We sincerely appreciate your thorough and insightful comments on our work, and we will address each of your main concerns below: --- Q1: The results of the experiments seem apparent and intuitive. R1: Thanks for your constructive f...	Rebuttal 1: Rebuttal: We thank all reviewers for your insightful and thoughtful feedback. 1. We are greatly encouraged that all reviewers observe that our work addresses an important research topic by conducting a thorough exploration of the factors affecting MM-ICL (Reviewer #Pm2y, Reviewer #Wrbn, Reviewer #jbhA,...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Sample-efficient Bayesian Optimisation Using Known Invariances	Accept (poster)	Summary: This paper provides sample complexity bounds for Bayesian optimization under settings with invariant kernels. Invariant kernels are able to model functions which are invariant under transformation families. Such models allow us to carry out Bayesian optimization efficiently due to having to being able to obtai...	Rebuttal 1: Rebuttal: We thank the reviewer for the close attention paid to our methods and results and for the relevant comments. Below we provide answers to the questions and the remark on the weaknesses identified. ### Response to weaknesses and questions > "The modeling comes from previous literature, and the ban...	Summary: The authors proposed a new setting where the invariance is either known or partially known. They theoretically examined the upper and lower bounds of convergence rates concerning sample complexity. Their findings demonstrated both theoretical and empirical superior performance over the standard UCB approach in...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments, and for their close reading that brought to our attention minor typographical errors. ## Response to weaknesses ### Limited applicability We respectfully disagree with the reviewer's comment on the limited applicability of our work. Some examples: - Mole...	Summary: The paper introduces an Bayesian Optimisation method which is able to take into account known invariances of the objective function. Specifically, it is assumed that the objective function remains invariant under a finite group action $G$. The approach is straightforward, one does standard Bayesian opti...	Rebuttal 1: Rebuttal: We thank the reviewer for their engagement with our paper and recognition of its "novel approach" to a popular problem. ## Response to weaknesses ### Critique of literature review We cannot hope to provide an exhaustive survey of the literature in this setting. We acknowledge, however, that the r...	Summary: The paper targets the Bayesian optimization problem for a class of invariant functions, which is useful in many fields including machine learning and physics. Specifically, the paper proposes to incorporate the invariances into the kernel of the GP to produce invariance-aware algorithms, either fully or partia...	Rebuttal 1: Rebuttal: We thank the reviewer for their appreciation of our work, and their positive comments regarding its importance, thoroughness and empirical performance. ### Response to weaknesses and questions Concerning the plots in Figure 3: Currently, we plot the cumulative regret for UCB and the simple ...	Rebuttal 1: Rebuttal: # Global rebuttal The authors thank the reviewers collectively for engaging with the work, and providing both positive feedback and actionable critique of our manuscript. We believe we have provided satisfactory answers, and produced a further body of evidence that strengthens the cause for our ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Trading Place for Space: Increasing Location Resolution Reduces Contextual Capacity in Hippocampal Codes	Accept (oral)	Summary: This paper develops an analytical framework for understanding the coding of space and context in the hippocampus. The novel contributions include a characterization of how tuning width contributes to these functions and the trade-off between them. In particular, smaller tuning widths improve spatial localizati...	Rebuttal 1: Rebuttal: We are grateful for the positive response, as well as the valuable suggestions and pointers to existing literature. In the initial submission, there was a typo that propagated throughout our text, exchanging dorsal and ventral, and hence also the predicted scaling along the dorso-ventral axis. W...	Summary: This paper offers a computational investigation on the problem of encoding environmental information using population codes based on place cells, which are known to play a key role in hippocampal encoding of context, experience / goals and spatial locations. The authors propose to analyze the geometry of hippo...	Rebuttal 1: Rebuttal: We appreciate the time taken by the reviewer to review our submission, and the suggestions provided. We believe that our submission is relevant to the NeurIPS community, and in particular, to the neural coding section of the Neuroscience topic, which is listed in the call for submissions. As cont...	Summary: In this paper, the authors take a geometric approach of analysing context-encoding capacities admitted by place cells population firing. Specifically, through examining the manifolds underlying neural activities within different environments, the authors propose to quantify the separability of context encoding...	Rebuttal 1: Rebuttal: Thank you for the comments and for taking the time to review our paper. Indeed, we chose to focus our paper on global remapping, with only brief discussion of rate and partial remapping. This is because global remapping has a more dramatic and constraining effect than rate/partial remapping in our...	null	null	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Your Diffusion Model is Secretly a Noise Classifier and Benefits from Contrastive Training	Accept (poster)	Summary: The paper presents a method to improve the parallel sampling of the diffusion model by improving the denoising network for out-of-distribution evaluation. The paper proposes to finetune a trained model using the log-likelihood ratio of a sample at two different noise scales. The log-likelihood ratio is obtaine...	Rebuttal 1: Rebuttal: Please see our top-level comments for clarification on FID scores, where we show that we consistently produce SOTA results across a wide variety of settings on this metric. We address the two weaknesses pointed out by the reviewer below. 1. Training cost The numerical integration in our loss...	Summary: The paper establishes a connection between the diffusion model denoiser and noise classifier through an examination of log-likelihood ratio estimation. The authors introduce a novel loss function, termed Contrastive Diffusion Loss (CDL), designed to encourage diffusion models to explore OOD regions in noise le...	Rebuttal 1: Rebuttal: Thank you so much for taking the time to read and review our paper. We’re glad that you are positive about our paper. Please see our top-level comments for clarification on FID scores, where we show that we consistently produce SOTA results across a wide variety of settings on this metric. We ad...	Summary: Building on the denoising score matching loss, the paper introduces a novel contrastive learning loss. This loss estimates the log-likelihood ratio between mixture densities with varying noise levels. The contrastive learning loss is then employed to fine-tune the diffusion model, enhancing its ability to esti...	Rebuttal 1: Rebuttal: Thank you so much for taking the time to read and review our paper. Please see our top-level comments for clarification on FID scores, where we show that we consistently produce SOTA results across a wide variety of settings on this metric. We address the two weaknesses pointed out by the review...	null	null	Rebuttal 1: Rebuttal: We are grateful for the time reviewers have invested in reviewing our paper and for the insightful feedback provided. In this top-level comment, we will explain concerns about FID results and baseline comparisons, as this was the subject of discussion among the reviewers. In particular, our choice...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Qualitative Mechanism Independence	Accept (poster)	Summary: The paper considers the framework of directed hypergraphs and demonstrates how it can be used to represent the structural properties of probability distributions. Inspired by the successes of Bayesian networks (which are essentially DAGs), they generalize the findings from the perspective of directed hypergrap...	Rebuttal 1: Rebuttal: Thank you for your review! A few responses to your questions: 1. The division between hypergraphs (defn 1) and original contributions. Definition 1 is an old (if not particularly common) idea; we cite Gallo et. al. [4] as a reference, although surely they were not the first to study directed ...	Summary: The paper presents a formalism that is claimed to extend the qualitative structure of probabilistic dependency graphs. Strengths: I think it might be original, but it is difficult to tell. It might be potentially significant, but it isn't clear what the significance might be. Weaknesses: It is not clear what...	Rebuttal 1: Rebuttal: Thank you for your review! QUESTIONS 1. You start with a very important question: > what is the problem that this is a solution to? What can it do (or do better) that other proposals cannot do?” Until now, there has not been a satisfying generalization of qualitative Bayesian netwo...	Summary: The paper studies notions of "compatibility" between probability distributions and directed hypergraphs with causal mechanisms. In such a hypergraph, we have (roughly) hyperedges T ---> S annotated by a latent/exogenous variable U where the variables S are functionally determined by the variables T and U. Ba...	Rebuttal 1: Rebuttal: Thank you for the reference to Druzdzel and Simon’s 1993 paper! You are right to point out a similarity between their Theorem 1 and ours; we both provide causal interpretations of Bayesian Networks. We will certainly discuss this in the full paper! However, upon close examination, we believe that...	Summary: This paper establishes a notion of "QIM compatibility" between the functional dependences and the joint distribution through the directed hypergraph. The functional dependence is a general notion of dependences containing conditional independences. Strengths: I want to state that my understanding of this pape...	Rebuttal 1: Rebuttal: Thank you for your review! RESPONSES TO QUESTIONS 1. While your understanding is not far off at a high level, there is an important wrinkle in your restatement of Definition 2: what exactly do you mean by “the causal Markov condition (for a hypergraph) with respect to a distribution?” There...	Rebuttal 1: Rebuttal: Thank you all for your careful reading and useful comments! First and foremost, we want to emphasize that our work has focused purely on qualitative aspects of a model (those properties that can be described with a graphical structure, without needing to know, for instance, the specific values t...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner	Accept (poster)	Summary: The paper provides the orthogonal estimator for the distributional treatment effect (the conditional CDF of $Y[1]-Y[0]$). Strengths: This paper is technically strong, demonstrating a high level of mathematic rigor and diligence. It presents an in-depth description of the proposed estimator. I think the propos...	Rebuttal 1: Rebuttal: First of all, thank you for the detailed and positive review of our paper. Below, we respond to the mentioned weaknesses and questions. Importantly, all the issues will be easily fixed for the camera-ready version of the manuscript. ### Response to weaknesses 1. We want to stress that there are s...	Summary: The authors propose a partial identification of quantiles of the individual treatment effect, which are not point-identifiable in general. The authors justifiably argue that characterizing the distribution of individual treatment effects gives a better idea of the aleatoric uncertainty in a causal-inference pr...	Rebuttal 1: Rebuttal: Thank you for your positive review! It is great to hear that you found our contribution significant. ### Response to weaknesses Benefit of the CA-learner. We introduce the CA-learner only as an interim step to develop the full AU-learner. We follow the classical hierarchy of learners, which...	Summary: In this paper, the authors propose a method to quantify the aleatoric uncertainty of the treatment effect. For this, authors estimated Makarov bounds on the CDF and quantiles of the CDTE, and then showed, how one can build a learner, which has properties of Neyman-orthogonality and double robustness. The autho...	Rebuttal 1: Rebuttal: Thank you for the positive review. Below, we respond to your questions. ### Response to weaknesses Thank you for the suggestions on how to improve the paper. We are more than happy to implement in the final version of the paper. Action: We will improve our paper as follows: - We realized t...	Summary: The authors introduce AU-learner, a method to estimate the conditional distribution of treatment effects (CDTE) and hence capture the variability in the treatment effect. They use Makarov bounds for partial identification and use conditional normalizing flows for estimation. Further, they show that AU-learner...	Rebuttal 1: Rebuttal: Thank you for your positive review and the interesting questions. It’s great that you found our paper well-written and that you appreciate the theoretical contributions. ### Response to weaknesses (W1) This is an interesting question. In our paper, we adopted the Makarov bounds, which were sh...	Rebuttal 1: Rebuttal: We are grateful for the insightful and high-quality reviews. We appreciate seeing that the reviewers found our paper to be “well-written”, “theoretically grounded”, “demonstrating a high level of mathematic rigour and diligence”, containing numerous informative diagrams and explanations, and with ...	NeurIPS_2024_submissions_huggingface	2,024	Summary: The authors study the distribution over the individual treatment effect for binary treatments, continuous outcomes, and observed, potentially high-dimensional confounders. The authors build up on prior work on Makarov bounds, to develop a new method to lower/upper-bound the conditional CDF and the quantile fun...	Rebuttal 1: Rebuttal: Thank you for the positive review. In the following, we respond to the weaknesses and questions. ### Response to weaknesses Thank you. We will follow your suggestion and expand our explanation around the CA-learner. Here, we would like to give more intuition as to why we think that the AU-learne...	null	null	null	null	null	null
ImOV3D: Learning Open Vocabulary Point Clouds 3D Object Detection from Only 2D Images	Accept (poster)	Summary: This paper addresses the challenge in Open-vocabulary 3D object detection (OV-3Det), specifically the modality gap between training images and testing point clouds, which hinders effective integration of 2D knowledge into OV-3Det. The main contribution of the paper is a novel method to generate pseudo multimod...	Rebuttal 1: Rebuttal: Q1：\ 【*Pre-training Stage*】While our method, ImOV3D, has been primarily designed and evaluated for indoor scenes, we recognize the importance of assessing its performance on real-world 3D data beyond the benchmark datasets mentioned in our paper. To address this, we conducted experiments o...	Summary: The paper aims to develop an open-vocabulary 3D detector with the help of 2D data. The key idea is to lift 2D images to 3D point clouds by using metric monocular depth estimation models, combined with estimating the extrinsics and intrinsics. The 2D bounding boxes are lifted to 3D as well, and filtered based o...	Rebuttal 1: Rebuttal: Q1：\ We gladly accept the suggestion and are considering modifying it to “How far can 2D images drive 3D open-vocabulary object detection? ” We appreciate your advice, but our motivation is to explore how can we fully exploit information from 2D images and how far they can drive the performa...	Summary: This paper presents a novel LiDAR-based open-vocabulary 3D detection model that relies solely on 2D images for training, without using any 3D annotations. During the training phase, a pre-trained monocular depth estimation model generates depth maps from 2D images, which are then projected into pseudo point cl...	Rebuttal 1: Rebuttal: Q1：\ We focus on 2D images because real-world 3D point cloud data is not only limited in quantity but also has relatively few annotations. At the same time, we have observed that 2D image data is not only abundant in quantity but also rich in annotation information. Based on these observations...	Summary: The paper introduces ImOV3D, a novel framework for open-vocabulary 3D object detection (OV-3Det) that learns exclusively from 2D images. The method addresses the scarcity of annotated 3D data by leveraging the wealth of annotations in 2D images. ImOV3D employs a pseudo-multimodal representation that bridges th...	Rebuttal 1: Rebuttal: Q1：\ While our method, ImOV3D, has been primarily designed and evaluated for indoor scenes, we recognize the importance of assessing its performance on real-world 3D data beyond the benchmark datasets mentioned in our paper. To address this, we conducted experiments on the KITTI dataset, which...	Rebuttal 1: Rebuttal: We are deeply grateful for the professional reviews and valuable feedback from all the reviewers. Reviewer MLwT acknowledged the motivation behind our research and its contribution to addressing the 3D data issue; Reviewer teog praised the innovation of our approach and the robust method for integ...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This work addresses the problem of the lack of 3D data and attempts to train open-vocabulary point-based 3D detection models solely with 2D images to avoid using 3D data and annotations. It proposes a pseudo data generation pipeline for this purpose, which generally follows a paradigm of estimating depth, proj...	Rebuttal 1: Rebuttal: Q1：\ In depth estimation research, using fixed camera intrinsics is common practice [1][2]. While we've followed this approach, we recognize it may lead to inaccurate bounding box sizes. To address this, we've implemented a scale filter using GPT-4, adjusting bounding boxes based on empiricall...	null	null	null	null	null	null
Parseval Regularization for Continual Reinforcement Learning	Accept (poster)	Summary: The paper addresses the challenge of selecting and designing embedding methods by proposing a unified framework that treats these methods as RL problems. This framework encompasses various embedding techniques, including VAEs, UMAP, and t-SNE, providing insights into their relationships and enabling the creati...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their comprehensive assessment, also including positive aspects. For the concern about computational complexity, we point the reviewer to the shared rebuttal statement, where we discuss this in detail. We address the other points individually below: - _“H...	Summary: The paper addresses challenges in continual reinforcement learning settings by introducing an additional term, called Parseval regularization. This regularization ensures that the weight update direction remains somewhat orthogonal to the current weight, thereby preserving beneficial optimization properties. E...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for the specific and concise points. For the first point concerning the computational complexity, we have discussed this in detail in the shared rebuttal statement and would direct the reviewer’s attention there. Concerning the memory requirement, Parseval r...	Summary: This work studies the problems of plasticity loss in continual reinforcement learning. Parseval regularization is proposed as a solution to plasticity loss. Parseval regularization encourages the weight matrices in all layers to remain orthogonal, which ensures that useful learning properties are preserved. Em...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for the clear feedback and for recognizing the qualities of the paper. As the main concern was centered around the computational complexity of Parseval regularization, we would like to point the reviewer to the shared rebuttal statement where we have discussed...	null	null	Rebuttal 1: Rebuttal: We thank every reviewer for their time and valuable feedback. First, we would like to highlight some positive qualities the reviewers have identified, including: - The simplicity and effectiveness of the algorithm ( “The proposed solution is well-justified, found to be useful in many cases, and...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Navigating Extremes: Dynamic Sparsity in Large Output Spaces	Accept (poster)	Summary: [Edited: My overall score has increased from 6 to 7 (Accept) after the authors' rebuttal.] Dynamic Sparse Training (DST) holds the promise of more efficient training and model robustness, but remains largely impractical due to the lack of inherent structure and severe amplification of training steps neede...	Rebuttal 1: Rebuttal: > The authors claim, in line 142, that 2:4 structured sparsity results in deteriorated model accuracy, but there was no reference (other than the 2:4 whitepaper showing only high model qualities), and I couldn't find evidence in the submission that 2:4 sparsity behaved poorly for XMC tasks. We sh...	Summary: This paper investigates the application of Dynamic Sparse Training (DST) methods to the domain of extreme multi-label classification (XMC), where the label space can be very large, on the order of millions. The authors propose several enhancements to standard DST approaches to address the challenges posed by t...	Rebuttal 1: Rebuttal: > Limited novelty in core techniques: The main components (DST, semi-structured sparsity, auxiliary objectives) are existing methods, though their combination and application to XMC is novel. More discussion and intuition on how these components interact and complement each other in an overview wo...	Summary: The paper proposes the application of DST(Dynamic Sparse Training) to XMC(Extreme Multi-label classification) by employing an intermediate layer and adding an auxiliary training objective to enable end-to-end training of XMC problems with millions of labels on commodity hardware. Strengths: 1. The paper is we...	Rebuttal 1: Rebuttal: > It would be good to add training time estimates in addition to memory usage. The training times of the proposed dynamic sparse approach is close to LightXML, and about 1.5x compared to using a dense last layer (corresponding to the Renee architecture). In order to have a fair timing comparison,...	null	null	Rebuttal 1: Rebuttal: We express our gratitude to all reviewers for their insights and will endeavor to address each comment separately. We performed rigorous ablation studies on key hyperparameters: fan-in (sparsity level) and auxiliary loss. The results for the Amazon-670K dataset are presented in the tables below...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Bayes-optimal learning of an extensive-width neural network from quadratically many samples	Accept (poster)	Summary: Studies a teacher-student setting with very specific configurations of input data, teach weight distribution, noise distribution and teacher/student architecture. The minimum MSE achievable, Eqn (3), is subjected to a particular limit, Eqn (4), and shown in closed-form to be Eqn (14). From Eqn 14 and assuming ...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and dedication. Please refer to the common answer for the recurring questions. - The asymptotic limit (which we generically denote by $\lim_{d \to \infty}$) is achieved by defining $m(d) = \kappa d$, $n(d) = \alpha d^2$, and then taking the limit of $d\to\inft...	Summary: This paper explores the performance of an optimal (in the Bayes-Optimal sense) estimator in the teacher-student setting with aligned architectures being one-hidden layer neural networks with quadratic activations. The number of neurons is extensive in the dimension. Given previous works where the "proportional...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and dedication. Please refer to the common answer for the recurring questions. - (W1) -- It is indeed possible to consider that while the teacher has $m^\star$ hidden units, the student has $m$ with $m > m^\star$ ("overparametrized" regime) or $m < m^\star$ (`...	Summary: The paper presents some learning theory for learning the large-dimension, large width perceptrons with a quadratic activation functions. The results seem to be twofold: Claim 1, and its specialisation in eq 14, which gives us the MSE test error. In addition they note that empirically the that the SGD solution...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and dedication. Please refer to the common answer for the questions. --- Rebuttal Comment 1.1: Comment: I am embarrassed at the quality of my review. I had intended to return and expand upon it before the deadline, but did not. I apologies to the authors and ...	Summary: The paper explores Bayes-optimal learning of a neural network with extensive input dimensions and a single hidden layer using quadratic activations. It presents a closed-form expression for the optimal test error when the sample complexity is quadratic. This work connects to matrix denoising and ellipsoid fitt...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and dedication. Please refer to the common answer for most of their questions. - (Q2) -- $P_{prior}({\bf W})$ is the distribution from which the teacher weights ${\bf W}^\star$ are sampled, which we denote ${\bf W}^\star \sim P_{prior}$. Since this distributio...	Rebuttal 1: Rebuttal: We thank all referees for their interest in our work and their comments that will help to clarify our paper. We will answer here to the most common questions of the reviewers. - Why are square-activated networks relevant? (Q1 of bzi8. Question of xzmx. W3 of NWbd. W3 of XJ6b). We want to stre...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
RealCompo: Balancing Realism and Compositionality Improves Text-to-Image Diffusion Models	Accept (poster)	Summary: This paper proposes to balance the realism and compositionality of the generated images by means of different diffusion models, such as pre-trained T2I models and models based on spatial perceptual control. The authors develop a balancer that optimizes the two coefficients through mask-based attention manipula...	Rebuttal 1: Rebuttal: *We sincerely thank you for your time and efforts in reviewing our paper, and your valuable feekback. We are glad to see that the proposed method can be generalized to different models, achieve SOTA generation results, and expand to various application scenarios in a training-free manner. Please s...	Summary: This paper presents a method, named RealCompo, for combining multiple diffusion models, such as a text-to-image model and a spatial-aware one, to achieve the best of both worlds: superior image realism and compositionality. It merges predicted noise from both models during each denoising step, and balances the...	Rebuttal 1: Rebuttal: *We sincerely thank you for your time and efforts in reviewing our paper, and your valuable feekback. We are glad to see that the proposed method is straightforward and flexible, the whole paper is well written, the idea is interesting and promising, and the experiments are extensive. Please see b...	Summary: The paper introduces a training-free and flexible text-to-image generation framework called RealCompo, which enhances compositional text-to-image generation by balancing the realism and compositionality of generated images. It features a novel balancer that dynamically combines the predicted noise from T2I mod...	Rebuttal 1: Rebuttal: *We sincerely thank you for your time and efforts in reviewing our paper, and your valuable feekback. We are glad to see that the proposed method is flexible, the experimental results are promising, and the explanation of the important concepts is reasonable. Please see below for our responses to ...	null	null	Rebuttal 1: Rebuttal: We sincerely thank all the reviewers for the thorough reviews and valuable feedback. We are glad to hear that the idea is interesting, promising and flexible (Reviewer juPc), the paper is well written and easy to follow (Reviewer XVVX, juPc), the experiments are extensive and performance improvem...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Exploitation of a Latent Mechanism in Graph Contrastive Learning: Representation Scattering	Accept (oral)	Summary: The authors focus on exploring an essential mechanism existing in different contrastive strategies. They first define a concept in embedding space, which contains a center $\mathbf{c}$, a subspace $\mathbb{S}$ and two constraints, called representation scattering. They then investigate the relationships betwee...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable suggestions and comments. We respond below. > The authors demonstrate that the representation scattering mechanism exists in several graph contrastive frameworks and argue that these methods do not fully utilize this mechanism. But the reviewer only observes...	Summary: In this paper, the authors provide an interesting discovery: the successes with mainstream GCL paradigms essentially come from implicitly scattering representations. They point out that the bottleneck of current GCLs lies in ignoring this, and they provide detailed theoretical proofs. Furthermore, they propose...	Rebuttal 1: Rebuttal: Thank you for taking the time to read and review our submission. We have provided our responses below. > As I mentioned in the summary, the authors have designed an asymmetric contrastive framework with two opposing types of losses, which may lead to misunderstandings of the training process. Alt...	Summary: This paper provides an insightful perspective of representation scattering to unify various GCL frameworks, and proposes an effective framework called SGRL. Specifically, the contributions are as follows: 1) Theoretically, with a well-defined representation scattering concept, the authors provide a universal t...	Rebuttal 1: Rebuttal: Thanks for your time in reading and reviewing our submission. We respond below. > Although the results in Figure 5 clearly demonstrate how model performance varies with different strengths of topological constraints, I believe it would be beneficial for the authors to present more results from ad...	Summary: The authors attempt to propose a universal theory of graph contrastive learning which may benefit this field. Most existing GCLs directly inherit from other fields. While existing GCLs have achieved similar success, there are intuitive differences and even conflicts in the operations. By analyzing three repres...	Rebuttal 1: Rebuttal: We thank the reviewer for the time in reading our paper and giving valuable suggestions. To address your concerns, we respond below. > The proposed SGRL is an augmentation-free framework, avoiding manual bias and reducing training overhead in augmentation. To my knowledge, there are also some aug...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Parameter Efficient Adaptation for Image Restoration with Heterogeneous Mixture-of-Experts	Accept (poster)	Summary: This paper introduces the mixture-of-expert approach to the image restoration community, enabling rapid adaptation of pre-trained models to various image restoration tasks. The proposed AdaptIR framework comprises three parallel branches: local interaction, channel gating, and frequency affine modules, which t...	Rebuttal 1: Rebuttal: ### [Q1: Analysis of feature response intensity] > The proposed AdaptIR combines three experts in local spatial, global spatial, and channel representations and adaptively weights them for adapting to downstream tasks. The authors are suggested to analyze the distribution of feature response inte...	Summary: The paper presents a novel approach to image restoration tasks, which leverages a heterogeneous Mixture-of-Experts (MoE) architecture. The proposed method aims to address the limitations of existing PETL (Patch-Exemplar-based Texture Learning) techniques for image restoration. The key contributions of the pape...	Rebuttal 1: Rebuttal: ### [Q1-Comparison with other SOTA all-in-one methods] > lack comprehensive comparison with other SOTA all-in-one methods like IDR(cvpr23) and DyNet(arxiv) Thanks for your kind advice, the suggested comparison are are follows. TableA Effectiveness and efficiency comparison on Light-deraining...	Summary: This work introduces AdaptIR, a novel heterogeneous Mixture of Experts (MoE) structure, to adapt pre-trained restoration models to various downstream tasks. The proposed method achieves performance comparable to full fine-tuning while only training 0.6% within 8 hours, demonstrating the high efficiency. Extens...	Rebuttal 1: Rebuttal: ### [Q1: Differences on low-rank strategy] > Both the proposed AdaptIR method and LoRA use low-rank matrices for efficiency, clarifying the differences between these two methods would be beneficial. In fact, not just LoRA, the low-rank strategy is a common practice in existing PETL arts, e.g. Ad...	Summary: The paper proposes a Parameter Efficient Transfer Learning (PETL) method for image restoration, which utilizes local, global, and channel-related modules and adaptively combines them to obtain heterogeneous representation for different degradations. Experiments are conducted on multiple degradations and the re...	Rebuttal 1: Rebuttal: ### [Q1-1: Precise definition of the Heterogeneous Representation] > A more detailed and precise definition of Heterogeneous Representation is needed. The Heterogeneous Representation in this paper represents the learning of discriminative features across different degradation types. The term ...	Rebuttal 1: Rebuttal: ## [Global Author Rebuttal] We would like to express our sincere gratitude to all the reviewers for taking their time reviewing our work and providing fruitful reviews that have definitely improved the paper. And we are encouraged that reviewers find - "exploring PETL for image restoration is me...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Parallelizing Model-based Reinforcement Learning Over the Sequence Length	Accept (poster)	Summary: This paper introduces a new model-based reinforcement learning method, called PaMoRL, which parallelize the world model training (PWM) and the eligibility trace estimation (PETE). Specifically, the parallelization is achieved by leveraging efficient parallel scan operations. On the commonly used Atari 100K and...	Rebuttal 1: Rebuttal: Thank you very much for reviewing our paper and for your many detailed comments. The following are responses to the weaknesses and questions you listed: ### W1: More discussion of the connection to Mamba[1]. R1: Both our PaMoRL method and Mamba use parallel scanning algorithms for accelerati...	Summary: This paper proposes a new framework named Parallelized Model-based Reinforcement Learning (PaMoRL) to improve the training speed of MBRL methods. PaMoRL employs a parallel scan technique to parallelize world model learning and eligibility trace estimation. With experiments on the Atari and DMC domain, the pape...	Rebuttal 1: Rebuttal: Thank you very much for reviewing our paper and for your many detailed comments. The following are responses to the weaknesses and issues you listed: ### W1: Parallelized World Model and Parallelizable Eligibility Trace Estimation use only old methods and are more like programming tricks than nov...	Summary: The paper proposes a novel framework to parallelize model-based RL, including two improvements parallelizing the world model and parallelizing eligibility traces. They demonstrate the dramatic speed-up of training speed without sacrificing inference efficiency. The proposed method achieves state-of-the-art sco...	Rebuttal 1: Rebuttal: We greatly appreciate your high evaluation of our paper and your detailed feedback on its strengths. We are very pleased to see that you have recognized the innovation and contribution of our proposed PaMoRL method, and we will further improve and enhance our paper. Please feel free to let us know...	Summary: Model-based RL algorithms are popular due to their strong data efficiency compared to model-free alternatives. However, most MBRL algorithms learn a recurrent world model that scales linearly in time complexity wrt the input sequence length. This paper proposes several changes to the DreamerV3 algorithm that a...	Rebuttal 1: Rebuttal: Thank you very much for reviewing our paper and for your many detailed comments. The following are responses to the weaknesses and questions you listed: ### W1: Limited evidence of the key selling point of parallelism in our method. R1: Thank you very much for your suggestions on the experime...	Rebuttal 1: Rebuttal: ## Common Response We thank all reviewers for their valuable feedback, reviewers (MVJ1, wbEe, earm) for recognizing the efficiency and novelty of PaMoRL, and reviewers (MVJ1, 5drW, earm) for the promising results and comprehensiveness of the paper's experiments. We summarize the main ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Understanding Model Selection for Learning in Strategic Environments	Accept (poster)	Summary: The paper studies (non-)monotonicity of equilibrium payoff in certain classes of two-player games, which has implications for strategic machine learning. Under structural assumptions, the main results are: (1) if the unique equilibrium is not Pareto-optimal, then a player can unilaterally restrict the action ...	Rebuttal 1: Rebuttal: Firstly, we would like to thank the reviewer for the time and effort spent looking through our work. We are delighted to see how the reviewer recognizes this research direction as interesting and points to it as a realistic phenomenon that has potential interest to practitioners. Additionally, we ...	Summary: They study the trade-off between model expressivity and performance at equilibrium in presence of strategic interactions. They show that strategic interactions can cause non-monotone performance at equilibrium when the model gets more expressive. They show Braess'-paradox like examples where reverse scaling o...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for the time and effort spent reviewing our work. We are delighted to see that the reviewer recognizes the importance of the findings discussed. Please find below our response to your questions and concerns. Questions Thank you for pointing out places wher...	Summary: This paper studies the relationship between model class expressivity and equilibrium performance when there are strategic interactions between agents in MARL settings. In contrast with the conventional scaling laws in machine learning, where task performance typically improves with larger or more expressive mo...	Rebuttal 1: Rebuttal: We firstly would like to thank the reviewer for the time and effort spent in looking through our work. We are delighted to see how the reviewer recognizes how this work challenges conventional wisdom with respect to model selection in machine learning. Additionally, we appreciate the comment on th...	Summary: The authors study a strategic learning setting formalized as a game involving a player whose action space is some function class that the player optimizes over. The paper focuses on theoretically demonstrating that, in such games, a learning agent may have an incentive to unilaterally commit to a restricted ac...	Rebuttal 1: Rebuttal: Firstly, we would like to thank the reviewer for the time and effort spent looking through our work. We are delighted to see how the reviewer recognizes the novelty of the paper’s study as well as the unintuitive, surprising, yet impactful consequences of the notions investigated. Additionally, we...	Rebuttal 1: Rebuttal: We would like to thank all the reviewers for their input to our paper as well as their comments. We appreciate the broad consensus and recognition of the importance of this research avenue within the Machine Learning community, particularly its salience in ensuring the development of robust models...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Virtual Scanning: Unsupervised Non-line-of-sight Imaging from Irregularly Undersampled Transients	Accept (poster)	Summary: The paper introduces a unique unsupervised learning framework for non-line-of-sight imaging from irregularly undersampled transients. The proposed method overcomes the dependency on paired data and achieves higher fidelity, greater robustness, and remarkably faster inference times. Strengths: 1. The paper pro...	Rebuttal 1: Rebuttal: We are highly encouraged by the positive recommendation and comments from the reviewer on our experiment, method and presentation. Furthermore, the comments and suggestions are inspiring, helpful, and valuable. We address the main issues as follows. Q1: Irregularity of scanning patterns. **R...	Summary: To address the challenges of slow inference speed and poor generalization to irregularly relay surfaces in non-line-of-sight (NLOS) imaging, this paper proposes a learnable unsupervised training framework with the excellent novelty, as well as a Virtual Scanning Reconstruction Network (VSRnet). Furthermore, t...	Rebuttal 1: Rebuttal: We are highly encouraged by the positive recommendation and comments from the reviewer. We address the main questions as follows. Q1: The lack of explanations for some key symbols and schematics. Reply: Explanations: As stated in line 90-92, the forward operator $H$ is highly related to ...	Summary: This paper proposes a non-line-of-sight (NLOS) imaging method for scenarios where transients are irregularly undersampled on the relay surface. The proposed method includes a SURE-based denoising technique to handle noisy transient data, specifically addressing Poisson noise. Additionally, a novel unsupervised...	Rebuttal 1: Rebuttal: We thank the reviewer for valuable comments. We address the main questions as follows. Q1: Connection and novelty of our SURE-based denoiser. Reply: In the irregularly undersampling, the quality and stability of reconstruction could be severely affected by noise, necessitating a robust d...	Summary: The authors attempt the problem of NLOS imaging in the irregularly undersampled transients data case i.e. where the scan pattern on the relay wall isn’t dense or regular. To tackle the problem the authors introduce two main components (both trained unsupervised): 1) A SURE-based denoiser, which denoises the i...	Rebuttal 1: Rebuttal: We are highly encouraged by the positive recommendation and comments from the reviewer on our experiment, method and presentation. Furthermore, the raised questions are both central and valuable. We address the main questions as follows. **Q1: The significance of NLOS imaging from irregularly und...	Rebuttal 1: Rebuttal: Figure 1: As suggested by reviewers WCZG, 8wFw and wGeV who concern about the effects of different relay patterns, we address this issue by analysing the NLOS imaging model and corresponding experiments. Figure 1(a) shows the top view of a typical confocal imaging system. Different colored l...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Localizing Memorization in SSL Vision Encoders	Accept (poster)	Summary: This work focuses on memorization in self-supervised learning. This paper seems to extent the work from [1] on SSLMem, that leverage the fact that a given training data and its corresponding handcrafted image augmentations should have a lower distance in the SSL embedding space than with a model in which this ...	Rebuttal 1: Rebuttal: >W1: Novelty vs. extension of [1] The paper targets an orthogonal research question to [1]. While [1] is concerned with the question of quantifying memorization and identifying memorized samples, we answer the question of where inside the SSL encoders is the information stored. ...	Summary: This paper introduces two novel metrics, LayerMem and UnitMem, to measure where memorization occurs within self-supervised neural networks. Through extensive experiments, this paper finds that memorization increases with layer depth, highly memorizing units are distributed throughout the encoder, atypical data...	Rebuttal 1: Rebuttal: >W1a: In depth analysis and practical insights We thank the reviewer for their suggestion. We went through the paper and identified multiple sections for adding practical insights. We summarized them in the [first reply to W1 for Reviewer BVAM](https://openreview.net/forum?id=R46HGlIjcG&note...	Summary: This paper identifies where memorization occurs in SSLs, noting that memorization increases in deeper network layers, though high-memorizing units are distributed throughout the network. For the first time, they introduce a metric to measure memorization in SSLs and provide justification for its validity. They...	Rebuttal 1: Rebuttal: We thank the reviewer for their insightful comments. >**W1: It is not clear why the defined metric is a good measure of memorization, especially for comparing memorization across different layers. The magnitude of the distance between representations of different augmentations is highly dependent ...	Summary: This paper investigates memorization in self-supervised learning encoders. It introduces two metrics, LayerMem and UnitMem, to locate memorization on a layer-wise and unit-wise basis. These metrics provide insights into the distribution of memorization within neural networks. The study reveals that memorizatio...	Rebuttal 1: Rebuttal: >W1: Adding insights and explanations We thank the reviewer for their suggestion. We went through the paper and identified the following sections for adding explanations and insightful remarks within the additional page that could be added to the paper in case of acceptance: - **Section 4, d...	Rebuttal 1: Rebuttal: We would like to thank all the reviewers for their insightful comments and questions. We are happy that the reviewers recognize our work: “presents several interesting and novel findings” (Reviewer GSFs). We are also glad the reviewers appreciate our new metrics to be “computationally efficient an...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
The Reliability of OKRidge Method in Solving Sparse Ridge Regression Problems	Accept (poster)	Summary: This paper analyzes the estimation error of the Scalable Optimal K-Sparse Ridge Regression (OKRidge) method proposed by [1]. Specifically, they reframe the estimation error of OKRidge as a Primary Optimization (PO) problem and use the Convex Gaussian Min-Max Theorem (CGMT) to simplify the PO problem into an Au...	Rebuttal 1: Rebuttal: ## Answer to Reviewer 8imU Dear Reviewer 8imU, Thank you for your job in reviewing our paper. We are very sorry for the inconvenience caused by our presentations. We extend our heartfelt gratitude for your patience and meticulous guidance. Your insightful comments is valuable for us and we appre...	Summary: An error analysis of a lower bound technique for solving sparse ridge regression problems is presented. Sparse ridge regression is ridge regression with the constraint that the parameter vector has at most k non-zero entries, where k is a parameter. It has been proposed to solve the sparse ridge regression pro...	Rebuttal 1: Rebuttal: ## Answer to Reviewer mBVh Dear Reviewer mBVh, We truly appreciate the patience and effort you've dedicated to providing valuable feedback. Your meticulous guidance is greatly valuable for us to enhance the overall quality of our research. We appreciate the opportunity to address your questions ...	Summary: The authors provide a theoretical error analysis for the OKRidge method, which is both faster and more accurate than existing approaches for solving sparse ridge regression. The experimental results are in excellent agreement with the theoretical findings. Strengths: 1. The authors analyze the estimation erro...	Rebuttal 1: Rebuttal: ## Answer to Reviewer uvCX Dear Reviewer uvCX, Thank you very much for your detailed and thorough review of our paper. We sincerely appreciate the time and effort you have dedicated to providing insightful comments and bringing these issues to our attention. ### In regards to your Weaknesses: ...	Summary: OKRidge proposed in [1] shows promising results and becomes the SOTA sparse ridge regression solvers. This paper conducts the first error analysis for OKRidge. Convex Gaussian min-max theorem (CGMT) is well introduced in this paper. Based on CGMT, they develop the asymptotic theory for OKRidge. The experiments...	Rebuttal 1: Rebuttal: ## Answer to Reviewer mn2F Dear Reviewer mn2F, Thank you for your job in reviewing our paper. We are very sorry for the inconvenience caused by our presentations. To this end, following your comments, we will correct our work in the revision. ### In regards to your Weaknesses: __Weakness 1.__...	Rebuttal 1: Rebuttal: Additional experiments on the variations about the sparsity level and noise distribution can be seen in following pdf. Pdf: /pdf/cd1691deceaa9105f976827e6d2cd01a0d6b8ae5.pdf	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper improves the theoretical reliability of OKRidge method for the sparse ridge regression by introducing a theoretical error analysis. OKRidge is reframed in this paper as a Primary Optimization problem. Then this paper use the Convex Gaussian Min-max Theorem (CGMT) to simplify it to an Auxiliary Optim...	Rebuttal 1: Rebuttal: Dear Reviewer 553k, we truly appreciate the patience and effort you've dedicated to providing valuable feedback. We appreciate the opportunity to address your concerns. ### For Weaknesses: __W1:__ Error analysis of algorithms is a very crucial and popular topic in the field of machine learning, ...	null	null	null	null	null	null
EZ-HOI: VLM Adaptation via Guided Prompt Learning for Zero-Shot HOI Detection	Accept (poster)	Summary: In order to solve the HOI detection problem in a zero-shot setting, the authors propose the Unseen-class Text Prompt Learning module. Using learnable visual prompts and textual prompts, it effectively utilizes the knowledge from large language models and Vision-Language Models and performs well in unseen class...	Rebuttal 1: Rebuttal: We appreciate the positive feedback and detailed reviews. The following is our response for your questions. Note all references are from the main paper’s citations. ## 1. Illustration for Fig. 4 We provide more illustration for Fig. 4 in the following. Fig. 4 shows the qualitative results of both...	Summary: This work presents EZ-HOI, an innovative framework that addresses the zero-shot HOI detection challenge by employing prompt learning. It integrates LLM and VLM guidance to enrich prompts and adapt to HOI tasks effectively. By learning from related seen classes, EZ-HOI overcomes the limitation of lacking labels...	Rebuttal 1: Rebuttal: We appreciate the positive feedback. The following is our response for your questions. Note that references maintain their original numbering from the main paper, with new references denoted by letters and listed at the end. ## 1. Pre-definition of all HOI classes Regarding the comparison with ...	Summary: This paper investigates the problem of human-object interaction (HOI) detection. This paper introduces EZ-HOI, a method for efficient zero-shot HOI detection in an open-world setting. EZ-HOI also explores the use of LLM and VLM guidance for learnable prompts to enhance prompt knowledge and aid in adapting to H...	Rebuttal 1: Rebuttal: We appreciate the insightful feedback. Here is our response. Note all references are from the main paper’s citations. ## Weakness-1. Our novelty and technical contribution __(1) Novelty__ We would like to clarify that our innovation lies in proposing a novel framework, rather than a new approach...	Summary: The paper proposes a novel prompt learning framework for zero-shot Human-Object Interaction (HOI) detection, which enhances the generalizability of Vision-Language Models (VLMs) by interacting with Large Language Models (LLMs) to obtain descriptions. Strengths: 1. The proposed method appears logical and achie...	Rebuttal 1: Rebuttal: Thank you for your detailed, helpful feedback. We address each of your concerns in the following. ## 1. Task-specific configurations Our method is specifically designed for HOI settings with the following task-specific configurations. First, our method is configured for HOI detection by extra...	Rebuttal 1: Rebuttal: We thank all reviewers for their thoughtful and constructive feedback. We appreciate that the reviewers found our work to be "innovative" (Reviewer f78r), "tackling an important issue in zero-shot learning and generalization to unseen classes during VLMs adaptation" (Reviewer UfLd, kbXP), and "wel...	NeurIPS_2024_submissions_huggingface	2,024	Summary: The paper studies the challenges behind successful adaption of pre-trained vision language models (VLMs), i.e., CLIP, to the problem of zero-shot Human Object Interaction (HOI) detection. Specifically, during finetuning, VLMs overfit to seen HOI classes observed during the training on HOI training data, preven...	Rebuttal 1: Rebuttal: We appreciate the positive feedback and detailed reviews. The following is our response for your questions. Note that we use the same reference number for related papers as the main paper. ## Weakness-1. Structure of the paper We agree with the suggestion to introduce the "encoder layer" in Secti...	Summary: This paper tackles zero-shot HOI detection via prompt tuning. To address the challenge posed by the absence of novel classes, the authors first incorporate LLM and VLM guidance to enrich learnable prompt tokens. Further, the authors utilize LLM to provide nuanced differentiation between unseen classes and thei...	Rebuttal 1: Rebuttal: We appreciate the positive feedback and insightful comments. The following is a detailed response to each of your questions. Note that references maintain their original numbering from the main paper, with new references denoted by letters and listed at the end. ## Weakness-1. Use of unseen cla...	null	null	null	null
CoSy: Evaluating Textual Explanations of Neurons	Accept (poster)	Summary: The authors propose a framework for evaluating Neuron Annotation methods which label a neuron in a given vision model, via a textual description. This framework is based on generating a set of images using a text2image model, given the predicted textual description of the neuron which acts as the textual input...	Rebuttal 1: Rebuttal: We appreciate Reviewer 4 (R4) for their time taken and their great attention to detail shown in the review. We are honored by your positive feedback, and thankful that you consider our work as important. [A1] Comparison to other evaluation scores: We thank the reviewer for asking these import...	Summary: The authors present a new framework designed to evaluate the quality of textual explanations for neurons in deep neural networks (DNN). To this end, the paper introduces CoSY, which aims to provide a quantitative, architecture-agnostic evaluation method for these explanations, addressing the challenge of the l...	Rebuttal 1: Rebuttal: We thank Reviewer 3 for the constructive and insightful remarks and appreciate the positive feedback regarding the presentation quality. Below, we address each of their points in detail. [A1] Prompt Bias: We appreciate and strongly agree with the reviewer's insight regarding the dataset depen...	Summary: The paper presents a novel, architecture-agnostic framework called COSY for quantitatively evaluating textual explanations of neurons in deep neural networks. The framework utilizes generative models to create synthetic images based on textual explanations, allowing for a standardized comparison of neuron resp...	Rebuttal 1: Rebuttal: We thank Reviewer 2 (R2) for the detailed comments and in-depth remarks. We are pleased that our work was found to be a valuable contribution. Below, we address all individual comments. [A1] Text-to-image models: We agree that the effectiveness of CoSy depends up on the performance of the gen...	Summary: This paper proposes an automatic evaluation for textual explanations of neurons in vision models. The evaluation works by using a text-to-image model to generate images based on the explanation of a neuron. Then, these images are passed through the vision model and that neuron’s activations are recorded. These...	Rebuttal 1: Rebuttal: We appreciate Reviewer 1 (R1) for their time taken and their great attention to detail shown in the review. We are honored by the positive feedback, that our work was found to be highly relevant and impactful. In the following, we address the reviewer's comments in detail. **[A1] Meta-Evaluation:...	Rebuttal 1: Rebuttal: First, we would like to deeply thank all the reviewers for the time they spent reviewing our manuscript. We express our gratitude for the valuable comments and advice. We are honored by their detailed feedback and are strongly encouraged by the generally positive feedback. We are particularly gra...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null

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