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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
Geodesic Optimization for Predictive Shift Adaptation on EEG data	Accept (spotlight)	Summary: This paper proposes a novel method, Geodesic Optimization for Predictive Shift Adaptation (GOPSA), for predictive regression modeling with multi-source domain adaptation. The proposed method employs a domain-specific re-centering operator and a regression model using EEG data for age prediction. The method is ...	Rebuttal 1: Rebuttal: Dear Reviewer r2hf, We thank you for your detailed review. We took into account your comments and modified the experiments accordingly: Addressing Weaknesses: 1. Knowledge of the target label mean:\ We acknowledge your concern regarding potential information leakage due to the assumption...	Summary: This paper presents a method for tackling domain adaptation challenges in EEG data analysis, specifically addressing shifts in both the feature space (represented by SPD matrices) and outcome variables $y$. The proposed method is designed on top of Riemannian mixed-effects model and is tailored for regression ...	Rebuttal 1: Rebuttal: Dear Reviewer ucmS, We thank you for your detailed review of our paper. We considered your feedback and addressed the concerns and suggestions you raised to improve the clarity and impact of our research. Clarification of Motivation and Problem Setup: 1. **Motivation for domain adaptation on...	Summary: The authors proposed GOPSA a new approach for alignment of EEG datasets from multiple subjects and sites. The method respects the Riemannian manifold of the covariance matrices and learns the parallel transport length parameter simultaneously with the regression model used for solving the downstream task. Whi...	Rebuttal 1: Rebuttal: Dear Reviewer N9Jm, Thank you for your positive assessment of our submission. In the following, we detailed answers to the weaknesses and questions you raised. Addressing Weaknesses: 1. Evaluation of the methods on simulated scenarios:\ We agree with the reviewer and added numerical expe...	Summary: This study presents an approach to learning robust models under joint shifts in X and y (an important issue in healthcare). Focusing on EEG signals, a covariance-matrix-based learning framework is developed to address this challenge. Empirical results on EEG-specific benchmarks demonstrate its overall efficacy...	Rebuttal 1: Rebuttal: Dear Reviewer 85Rg, Thank you for your positive evaluation of our submission. We took into account your concerns, and answered your questions point by point in the following: Addressing Weaknesses and Questions: 1. Other similar problem settings and Combat harmonization algorithm: \ The ...	Rebuttal 1: Rebuttal: We thank the reviewers for the thorough and insightful reviews of our submission. We appreciate the acknowledgment of our method’s novelty, simplicity, and effectiveness in addressing domain adaptation challenges in EEG analysis. We are also glad that the presentation and technical soundness wer...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities	Accept (poster)	Summary: The paper presents an advanced vision model capable of handling a wide range of tasks and modalities, demonstrating the potential to train a single model on tens of diverse modalities without a loss in performance compared to specialized models. Specifically, the model is trained on a multitude of modalities, ...	Rebuttal 1: Rebuttal: We thank reviewer 66Uu for the positive feedback. We address the main concerns and questions below: > Quality of generated images > Please see the `PDF` for detailed caption-conditioned generation metrics on COCO, as well as Section 2 of the common response for a discussion. > Detailed configu...	Summary: The paper addresses the limitations of current multimodal and multitask foundation models, such as 4M and UnifiedIO, which are constrained by the limited number of modalities and tasks they can handle. The authors present a model trained on a wide variety of modalities and tasks using large-scale multimodal da...	Rebuttal 1: Rebuttal: We thank reviewer PHF3 for the positive feedback. We address the main concerns and questions in the following response: > Encoder-Decoder vs Decoder-only. Thank you for your question. **Both encoder-decoder and decoder-only architectures are valid design choices, depending on the specific use ca...	Summary: The authors present a new vision model that can generate tokens in all directions that represents multiple modalities like RGB images, depth map, segmentation maps, color palette, DINOv2 features etc. given a conditioning on a subspace of the modalities. They develop a new way to tokenize certain modalities an...	Rebuttal 1: Rebuttal: We thank reviewer b2ju for the constructive feedback. We address the main concerns and questions in the following response: > What are the performances of the trained tokenizers? Please see the following items as the response: 1) Figure 1 in the rebuttal `PDF` for a visual comparison of multimod...	null	null	Rebuttal 1: Rebuttal: ## Response to all reviewers We thank the reviewers for their insightful comments and constructive feedback. We are pleased that they commended our performance with remarks such as: “good performances on a lot of downstream tasks” (b2ju), **“The experiments and performance in the paper ar...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Empowering Visible-Infrared Person Re-Identification with Large Foundation Models	Accept (poster)	Summary: The authors aim to tackle the challenge of lacking detailed information in the infrared modality by employing foundation models. Their proposed method includes an Incremental Fine-tuning Strategy (IFS) and Modality Ensemble Retrieving (MER). These techniques enhance the representation of the infrared modality ...	Rebuttal 1: Rebuttal: We appreciate the positive feedback regarding the clear architecture figure, feasibility, and soundness of our method. We also acknowledge and appreciate the constructive criticisms for improving certain aspects of our paper writing. **Q1: Explain the discrepancy of the misaligned results of YYD...	Summary: This paper proposes a text-enhanced VI-ReID framework driven by Foundation Models (TVI-FM). VI-ReID often lags behind RGB-based ReID due to the inherent differences between modalities, particularly the absence of information in the infrared modality. This paper enriches the representation of the infrared modal...	Rebuttal 1: Rebuttal: We are grateful for the positive recognition of the soundness, clearly presentation of our framework and also appreciate your detailed comments aimed at improving our paper writing. We believe our revisions will address your suggestions. Thank you for the valuable feedback for guiding these improv...	Summary: This paper incorporates a pretrained multimodal language vision model (LVM) to extract textual features and incrementally fine-tune the text encoder to minimize the domain gap between generated texts and original visual images. Meanwhile, to enhance the infrared modality with text, this paper employs LLM to au...	Rebuttal 1: Rebuttal: We are grateful for your recognition of the adequate experiments, the soundness and competitive performance of our work. We also appreciate the constructive comments on the motivation, rationale, and content distribution balance, which is valuable for improving our paper writing. **Q1: Motivatio...	Summary: Visible-infrared person re-identification often underperforms due to the significant modality differences, primarily caused by the absence of detailed information in the infrared modality. This paper investigates a feasible solution to empower the VI-ReID performance with off-the-shelf foundation models by pro...	Rebuttal 1: Rebuttal: We are grateful for your positive recognition of the detailed tables, clear figures, and the soundness of our framework. We also appreciate your constructive comments and will revise and clarify the suggested points to improve the quality of the paper writing. **Q1: Some content in the appendix s...	Rebuttal 1: Rebuttal: We thank all reviewers for their positive feedback on clear diagrams ($\color{red}{R\\#88cp}$)($\color{red}{R\\#BZVu}$), methodology feasibility ($\color{red}{R\\#hNfL}$)($\color{red}{R\\#88cp}$)($\color{red}{R\\#EHC5}$), competitive performance ($\color{red}{R\\#EHC5}$)($\color{red}{R\\#368Y}$), ...	NeurIPS_2024_submissions_huggingface	2,024	Summary: To address the loss of performance in Visual_infrared re-identification wrt to visual , the authors propose a novel text-enhanced VI-ReID framework driven by Foundation Models (TVI-FM) , which enriches infrared representations with automatically generated textual descriptions. This framework incorporates a pre...	Rebuttal 1: Rebuttal: We appreciate your recognition of the soundness, competitive performance, and adequate experiments of our method. Our method is a novel exploration of applying foundation models to down-stream data-intensive multimodal tasks. It uses LVM-generated text to enrich infrared representations and employ...	null	null	null	null	null	null
Source Code Foundation Models are Transferable Binary Analysis Knowledge Bases	Accept (poster)	Summary: The paper focus on Human-Oriented Binary Reverse Engineering (HOBRE) task. The author propose a probe-and-recover framework that incorporates a binary-source encoder-decoder model and LLMs for binary analysis. The proposed approach leverages the pre-trained knowledge within SCFMs to synthesize relevant, symbol...	Rebuttal 1: Rebuttal: > ### Q1. Presentation We will improve the presentation for clarity and impact, such as the positioning of Table 1 and Table 2. > ### Q2. What is the purpose of the user study? Is it just for the statement that CHRF is consistent with human preference in line 216? The user study is a crucial co...	Summary: This paper presents a method for Human-Oriented Binary Reverse Engineering (HOBRE) tasks based on Large Language Models (LLMs). In summary, the authors instruct an LLM to generate the desired answer directly and augment their prompt with the idea of Chain-of-thought and few-shot examples. To get the few-shot e...	Rebuttal 1: Rebuttal: > ### Q1. Analyze the reason why prober helps Please refer to Q2 in global response. > ### Q2. Why are previous works ignored? We will cite and discuss the related work CP-BCS in our paper. We cited supervised methods that train end-to-end binary summarization models, such as BinT5 [1] and HexT...	Summary: Human-Oriented Binary Reverse Engineering (HOBRE) seeks to transform binary code into human-readable content that aligns closely with its original source code, effectively bridging the semantic gap between binary and source. While recent advancements in uni-modal code models, including generative Source Code F...	Rebuttal 1: Rebuttal: Thank you for reviewing our paper and for your kind feedback! We are delighted to hear that you consider our work in this important area to be both novel and effective. Please see our response below. > ### Q1. Missing example of RAG We show two examples of RAG in the uploaded PDF file. Both fig...	Summary: This paper presents a new framework, using an encoder-decoder architecture, call ProRec and an LLM black-box model for helping convert binary code in human readable format. The authors try multiple models in an effort to develop ProRec, and settle on using CODEART and Codellama. For black-box LLM they experime...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our paper and for your kind words! We are delighted to know that you enjoyed the well-grounded motivation and thorough experiments. > ### Q1. Security concerns about ProRec being used by malicious agents to understand better and exploit critical software i...	Rebuttal 1: Rebuttal: We appreciate all the reviewers for their insightful questions and suggestions! We are glad that the reviewers recognized our paper for studying an "interesting and important problem," being "well written," addressing a "critical and emerging area," presenting a "novel framework," and offering "pr...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Continual learning with the neural tangent ensemble	Accept (spotlight)	Summary: This is a theoretical paper that seeks to find an algorithm to train neural networks without forgetting in continual learning settings. The authors show that in the infinite width limit, a single classification network can be reformulated as a weighted ensemble of fixed classifiers (fixed experts). They prov...	Rebuttal 1: Rebuttal: >The claim that Bayesian ensembles of fixed functions are natural continual learners does not seem obvious to me.... We have added a Lemma that describes more precisely why weighing fixed functions according to their posterior probability is a learning strategy which does not depend on the order ...	Summary: This is a theoretical research on preventing forgetting by considering a single network trained with a lazy-regime as an ensemble model of multiple functions and adjusting their weights. Strengths: The discussion is based on solid theory. This paper obtained an insight that the posterior update rule for the N...	Rebuttal 1: Rebuttal: For Q1 and Q2, there are really two possible statements in question. The first is whether a particular network at any single moment in time can be seen as an ensemble of valid classifiers. Theorem 1 describes how this is always the case as long as the network is Lipschitz, as one could construct a...	Summary: The paper introduces a novel approach to mitigating catastrophic forgetting in neural networks by introducing the concept of Neural Tangent Ensemble (NTE), a formulation interpreting a single neural network as an ensemble of fixed classifiers, leveraging the Neural Tangent Kernel (NTK) framework. This interpre...	Rebuttal 1: Rebuttal: Thank you for appreciating the theoretical novelty and generality of the NTE idea. Following these suggestions, we have implemented several more empirical characterizations with modern CNN architectures and the more complicated CIFAR-100 incremental learning task. We would like to emphasize that ...	Summary: The paper suggested that linearized networks (under lazy learning regime) at the initialization can be understood as an ensemble model where each ensemble component is a function parameterized by a single parameter in the network (i.e. if the network has N parameters then the prediction is an ensemble over N f...	Rebuttal 1: Rebuttal: Thank you for your review and recognition of the novelty of our work. We would like to clarify some points raised under the weakness section before addressing specific questions. The first two bullet points raised the possibility that, although the infinite-width limit does not forget past tasks...	Rebuttal 1: Rebuttal: We were happy to see that all 4 reviewers found our primary contribution – that linearized classifier networks are ensembles of experts (Theorem 1) – to be interesting, novel, well-supported, and relevant to problems of importance. Likewise, the reviewers appreciated the surprising nature and impo...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Assessing the quality of information extraction	Reject	Summary: * This paper studies the evaluation of information extraction, particularly LLM-based IE, in scenarios where human-annotated data is unavailable. * The proposed evaluation framework relies on the `Needle in a haystack` evaluation. That is, an LLM is first used to generate a piece of information (needle) given ...	null	Summary: This paper focuses on the quality evaluation of information extraction (IE) performed by large language models (LLMs). It discusses the methods to handle the input/output size limitations of the LLMs and their performance in IE. It also introduces additional scores to evaluate the extraction quality and discus...	null	Summary: The paper introduces a framework to capture information extraction quality in the absence of humanly labelled and curated datasets. It explains how an approach on how to include the schema, and the role and limitations of LLM's (specifically gpt-4-1106-preview). Experiments are done (I guess), by "extracting ...	null	Summary: The paper proposes an automated framework for evaluating the quality of IE tasks using LLMs. The framework introduces a scoring method called MINEA, which creates evaluation criteria by injecting artificial data ("needles") into documents. The paper also discusses how to deal with the limitations of LLMs when ...	null	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Uncertainty-aware Fine-tuning of Segmentation Foundation Models	Accept (poster)	Summary: This paper introduces the Segmentation with Uncertainty Model (SUM) that combines high-quality annotated data with a large unlabeled dataset to improve performance without forgetting. First, the authors quantify the uncertainty in the SAM pseudo labels associated with the unlabeled data and leverage it to perf...	Rebuttal 1: Rebuttal: We thank the reviewer for the thoughtful feedback. We respond in detail below. ### Q1 Difference with previous uncertainty-aware segmentation methods Our approach **differs fundamentally from previous approaches in both 1) the generation of uncertainty maps and 2) the utilization of these u...	Summary: The paper introduces a novel framework for enhancing the accuracy of the Segment Anything Model (SAM) while maintaining its generalization capabilities. SAM is a foundational model for interactive binary segmentation, but it struggles with segmenting intricate structures accurately. Fine-tuning SAM with high-q...	Rebuttal 1: Rebuttal: We thank the reviewer for the thoughtful feedback. It will help us improve the manuscript. We respond in detail below. ### Q1 Training complexity and Q3 computational overhead We acknowledge the reviewer's concern about the additional training phase for obtaining uncertainty maps in the SUM ...	Summary: In this paper, the authors proposed the Segmentation with Uncertainty Model (SUM) which combines high-quality annotated data with a large unlabeled dataset. This novel framework improves the performance of the large-scale foundation model without forgetting. Strengths: Paper clarity. The paper is overall well...	Rebuttal 1: Rebuttal: We thank the reviewer for the thoughtful feedback. We respond in detail below. ### Q1 Clarification on the figures We appreciate the reviewer’s suggestion to make the figures clearer. We have included updated figures in the rebuttal PDF (see Figure 1, 2, 3 in the rebuttal PDF) and will ...	Summary: This paper proposes the Segment with Uncertainty Model (SUM), a fine-tuning framework for existing foundational segmentation models like SAM. Specifically, SUM consists of two main components: an uncertainty-aware training pipeline and the task prompt concept to reduce ambiguity. Technically, the uncertainty-...	Rebuttal 1: Rebuttal: We thank the reviewer for their encouraging feedback. We believe it will improve the manuscript. We respond in detail below. ### Q1 Extra training burden The reviewer is correct that the additional training phase for obtaining uncertainty maps in the SUM framework adds some computational ove...	Rebuttal 1: Rebuttal: ## Response for all the reviewers: We thank the reviewers for their thoughtful comments and are encouraged by their positive feedback. We appreciate the recognition of our paper's soundness, contributions, and presentation. Positive Feedback: - Soundness: Excellent (Reviewer NsqR), Good ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Generative Subspace Adversarial Active Learning for Outlier Detection in Multiple Views of High-dimensional Tabular Data	Reject	Summary: This paper proposed GSAAL to simultaneously address three changeling problems in outlier detection: inlier assumption (IA), curse of dimensionality (CD), and multiple views (MV). Strengths: The paper has a good flow. The paper proposed the first outlier detection method that explicitly addresses IA, CD, and M...	Rebuttal 1: Rebuttal: - There are a lot of abbreviations in this article. Before using them, they should be first defined. - Thanks, we went through each abbreviation in the article and explained it accordingly. - **In line 96, “Classical Methods” lacks recently published work, such as “Mean-shift outlier detecti...	Summary: This paper presents this generalization of GAAL Generative Subspace Adversarial Active Learning (GSAAL) for outlier detection to address the limitation of the previous work such as multi-view and the curse of dimensionality, where the theoretical convergence, the scalability of the algorithm are discussed. Exp...	Rebuttal 1: Rebuttal: - The novelty of the work appears to be small. Theoretically, the derivation of theorem 1 is very similar to GAN derivation. - We do not agree that this can be a straightforward derivation from the classical GAN result. In GSAAL, as in GAAL methods, the detectors are trained after the generat...	Summary: The main contribution of this paper is to improve existing work on Generative Adversarial Active Learning (GAAL) by using multiple discriminators for multiple views to detect outliers in tabular data. The training mechanism is similar to existing works. The paper also introduces a theoretical analysis on Multi...	Rebuttal 1: Rebuttal: - The empirical results are not strong (or at least unclear in the way the authors presented them in the main paper); most of the experiments are on synthetic datasets. - We run experiments on 22 datasets, which are more datasets than relevant and popular competitors in the field of OD [3],[4...	null	null	Rebuttal 1: Rebuttal: We thank all of the reviewers for their efforts and their time. Our detailed response is in each individual message. The attached pdf contains edits to Fig.3 and Fig.4 and new information in Fig.3 as requested in the reviews. The new information does not change any of our conclusions. The referenc...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Prism: A Framework for Decoupling and Assessing the Capabilities of VLMs	Accept (poster)	Summary: This paper introduces Prism, a framework to disentangle and evaluate the perception and reasoning capability of vision-language models (VLMs). They evaluate state-of-the-art VLMs, including proprietary ones and open-source ones, with varying model sizes. The evaluation results demonstrate that VLMs' perception...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable feedback. We are uncertain if there is some misunderstanding and would like to clarify that VLMs discussed in our paper specifically refer to large visual language models (LLaVA, GPT-4v, etc.) designed for solving general visual language tasks (as...	Summary: The paper introduces Prism, a framework designed to decouple and independently assess the perception and reasoning capabilities of VLMs. Prism operates in two stages: a perception stage that extracts visual information and converts it into text using a VLM, and a reasoning stage that generates answers based on...	Rebuttal 1: Rebuttal: We thank the reviewer for the encouraging comments and address the main concerns below. Limitations and broader impacts have been discussed in Appendix. D. We are inspired that the reviewer believes our work "provides some valuable insights" and "brings a new approach to solve the tasks that requi...	Summary: In this paper, the authors propose prism, a framework to decouple the VLMs' capabilities in two stages: perception stages and reasoning stages. This framework allows the breakdown analysis of VLM capabilities and can also serve as a framework to integrate any given VLM and LLM. Based on their explorations and ...	Rebuttal 1: Rebuttal: We express our sincere gratitude to the reviewer for the constructive feedback. We are glad that the reviewer appreciates the "good analysis, findings, and insights" presented in this work and recognizes the potential of the Prism framework in both evaluation and as a task solver. We address the c...	null	null	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Fantasy: Transformer Meets Transformer in Text-to-Image Generation	Reject	Summary: The paper proposes Fantasy, a T2I model based fully on transformers (except for the VQGAN for the latents encoding and decoder): * A __fine-tuned LLM__ (based on Phi-2) for the text encoding * A image generator based on the MIM (Masked Image Modelling) approach The training happens in two stages, a generic st...	Rebuttal 1: Rebuttal: We appreciate for the valuable feedback and address the concerns as follows. ### W1: Explanation for Chosen Benchmarks. This is a good question. Several articles have noted that FID often misaligns with human evaluations, has limitations in assessing model quality, and is affected by factors ...	Summary: This paper proposes an efficient text-to-image generation model that integrates LLM and MIM. It demonstrates that MIM can achieve comparable performance. Unlike commonly used text encoders like CLIP and T5, this study introduces an efficient decoder-only LLM, phi-3, achieving better semantic understanding. The...	Rebuttal 1: Rebuttal: We appreciate the valuable feedback and address the concerns as follows. ### W1: Discussion about the generated images. There are currently many powerful diffusion-based T2I methods (e.g., pixart-$\alpha$, SDXL) that generate images with excellent visual appeal and details. However, our goal is ...	Summary: This paper proposes a technique for training transformer based masked image modeling in an efficient way. Two main contributions include (1) use of a LLM decoder as text embeddings, and (2) Two-stage training strategy for MIM models. Experimental results show good generation quality. Strengths: - The use of L...	Rebuttal 1: Rebuttal: We appreciate the valuable feedback and address the concerns as follows. ### W1: Core Contributions of Fantasy. We propose a novel T2I framework by combining decoder-only LLM with transformer-based image generators to achieve the balance of effectiveness and efficiency. Our approach aims to empow...	Summary: To develop a resource-efficient, high-quality image generator for long instructions, the authors presented Fantasy, an efficient T2I generation model that integrates a lightweight decoder-only LLM and a transformer-based masked image modeling (MIM). They demonstrate that with appropriate training strategies ...	Rebuttal 1: Rebuttal: We appreciate the valuable feedback and address the concerns as follows. ### W1: Contributions Key Points. Our goal is to investigate whether combining LLM with transformer-based generators can enhance generative models by achieving a balance between effectiveness and efficiency. We also explore ...	Rebuttal 1: Rebuttal: We appreciate all the reviewers for their valuable feedback and will address several frequently mentioned issues below. ### Q1: Core Contributions of Fantasy. We would like to emphasize our core contributions again. Our goal is to investigate whether combining LLM with transformer-based generators...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Recognize Any Regions	Accept (poster)	Summary: The paper presents RegionSpot, a novel architecture designed for efficient open-world visual region recognition. The primary goal of RegionSpot is to leverage the strengths of powerful pretrained foundation models, specifically a localization model (SAM) and a vision-language model (CLIP), to improve the recog...	Rebuttal 1: Rebuttal: We thank the reviewer for your insightful comments. Q1: Dependency on Pretrained Models. R1: Many thanks for these great comments. It has been a trending research focus in AI towards integrating rich, pre-trained models to enhance a target task, particularly when these foundation models get ...	Summary: To address open-world object detection, this paper proposes RegionSpot, which combines the localization capabilities of SAM with the classification strengths of CLIP. RegionSpot integrates position-aware tokens from SAM with image-level feature maps extracted from CLIP, creating region-level semantic tokens. T...	Rebuttal 1: Rebuttal: We thank the reviewer for your insightful comments. Q1: While effective, the method by which RegionSpot uses position-aware tokens is somewhat implicit. It is not entirely clear how these localization features directly contribute to performance gains. R1: We summarize the reason for performa...	Summary: The paper proposed a method for open-world object detection, which utilises the segment anything model (SAM) to produce region priors and the CLIP model to extract image and language features. The region priors from SAM, which are implicitly encoded in the query tokens, are used in a learnable transformer deco...	Rebuttal 1: Rebuttal: We thank the reviewer for your insightful comments. **Q1: The main advantage of the proposed method seems to be the low training time, which is somewhat less important compared to inference speed. The proposed model employs two foundation models, which will most likely result in very slow inferen...	Summary: The paper introduces RegionSpot, a compute-efficient method that leverages localization foundation models (such as SAM) with semantic information from a ViL model (such as CLIP). RegionSpot is demonstrated on multiple scenarios and achieve better results than baseline methods while being much faster to train t...	Rebuttal 1: Rebuttal: We thank the reviewer for their insightful comments. Q1: RegionSpot use SAM. Effectively, RegionSpot could be used for object detection by optimizing SAM properly. RegionSpot is restricted to identifying regions given a regional proposal or a bounding box. R1: Many thanks for these great...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit	Accept (poster)	Summary: The paper studies the classical problem of the single index model over Gaussian inputs, i.e. $x\sim \mathcal{N}(0,I_d)$ and $f_(x)=\sigma_(\langle \theta_,x \rangle)$ for an unknown direction $\theta_$. Information theoretically, one needs $\Omega(d)$ samples to learn this function class. The paper shows t...	Rebuttal 1: Rebuttal: We thank the reviewer for the thoughtful comment and constructive feedback. We address the technical concerns below. --- "Can the use of momentum be avoided in the first layer of training?" We use an interpolation step to improve the signal-to-noise ratio in the gradient; this is crucial in ...	Summary: This paper studied the problem of learning single index models under Isotropic Gaussian distribution. The target model $f^*(x) = \sigma(\theta^\top x)$ is a polynomial function $\sigma$ composed with a one-dimensional structure $\theta^\top x$, where the polynomial $\sigma$ is of degree at most $q$ and has inf...	Rebuttal 1: Rebuttal: We thank the reviewer for the thoughtful comment and constructive feedback. We address the technical concerns below. --- Polynomial activation function We make the following clarifications. 1. Note that all square-integrable activation functions (ReLU, sigmoid, etc.) can be written as a lin...	Summary: This paper addresses the problem of learning single-index targets with polynomial link functions under Gaussian inputs. The authors demonstrate that using SGD on a two-layer fully connected network with a specific activation function can learn such targets with O(d poly(log(d))) samples. The analysis involves ...	Rebuttal 1: Rebuttal: We thank the reviewer for the thoughtful comment and constructive feedback. We address the technical concerns below. --- Standard SGD practices, relaxing assumptions We agree that Algorithm 1 deviates from the most standard training procedure in practice. Note that layer-wise training and fi...	Summary: This manuscript studies the learning properties of two-layer networks trained with SGD reusing the batch. The authors show that this simple modification allows SGD to surpass the limits of CSQ algorithms and learn single-index functions efficiently. The submission considers both recovery of the target features...	Rebuttal 1: Rebuttal: We thank the reviewer for the thoughtful comment and constructive feedback. We address the technical concerns below. --- "The idea of label transformation implemented by [CM20] could be reported" In the current manuscript, the difference between our label transformation and that in [CM20] i...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Persistence Homology Distillation for Semi-supervised Continual Learning	Accept (poster)	Summary: The paper proposes a new method PsHD to preserve intrinsic structural information in semi-supervised continual learning. The method proposes to uses distillation and cross-entropy loss on the continual learning samples. Strengths: 1. I think the paper presents quite comprehensive experiments with different se...	Rebuttal 1: Rebuttal: A1. We will make substantial revisions to enhance the overall clarity and readability: (1) Provide detailed results to verify the description of our advantages. As shown in Table 1. of attached PDF file, we provide additional comparison of the forgetting rate (BWT) among strong baseline...	Summary: The paper proposes a persistence homology knowledge distillation for continual learning (PsHD). PsHD loss is calculated using a ''memory buffer'' between a previous variant of a network and a new one. Experiments show some improvement w.r.t. baselines. Ablation studies are provided. The main issue of the paper...	Rebuttal 1: Rebuttal: A1. Traditional CL methods are not always effective for unlabeled data, as they assume the knowledge of previous models is accurate. This specific challenge of SSCL has been explored in previous SSCL methods (NNCSL, DSGD, etc). As suggested by the reviewer, our ablation experiments provide add...	Summary: This paper proposed to preserve intrinsic structural information with the use of persistent homology, so as to improve knowledge distillation and memory replay in semi-supervised continual learning. The authors provided an efficient acceleration algorithm to reduce computational overheads and theoretically dem...	Rebuttal 1: Rebuttal: A1. The limited replied 500 samples restrict the improvement space, while our method reduces the degree of forgetting (BWT) by a substantial and balanced margin compared to the newly strong methods. Furthermore, our methods demonstrate effective utilization of replayed unlabeled samples, as ev...	null	null	Rebuttal 1: Rebuttal: We thank the reviewers for recognizing novelty (AVV3, K7Tp, 2waA), well-organization (AVV3, K7Tp), significance and reproducibility (AVV3, K7Tp, 2waA), good performance (AVV3, K7Tp, 2waA), and comprehensive comparisons (AVV3, K7Tp, 2waA) of proposed PsHD. **Reviewer AVV3's Questions and Our Re...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
A Closer Look at the CLS Token for Cross-Domain Few-Shot Learning	Accept (poster)	Summary: In this paper, the authors found a new phenomenon that the CLS token used in Vision Transformer (ViT) absorbs the domain information in Cross-Domain Few-Shot Learning (CDFSL). On the basis of the findings, they proposed a novel CDFSL method that updates only the CLS token during the target training. A comprehe...	Rebuttal 1: Rebuttal: Thank you for your appreciation of our work! ## W1. Verification of other pre-trained model Due to time limitations, we did not fully tune the model in our appendix submission. Here we report the fully-tuned performance on the iBot and ViT-B model. \| iBot \| Crop. \| Euro. \| ISIC \|...	Summary: This paper explores an intriguing phenomenon in Cross-Domain Few-Shot Learning (CDFSL) using Vision Transformers (ViT). The authors observe that randomly initializing the CLS token, instead of using source-domain pre-trained parameters, consistently improves target-domain performance. They attribute this to th...	Rebuttal 1: Rebuttal: We truly appreciate your valuable comments. In the following, we respond to the concerns. ## W1. Handling few-shot learning by absorbing domain information We would like to point out that for the cross-domain few-shot learning (CDFSL) problem, one of the most important challenges is the domain...	Summary: Based on the observation that pretrained ViT models perform better on cross-domain few-shot tasks when the cls-token is re-initialized, the authors hypothesize that this is due to the absorption of domain-specific information and consequently propose a modified training and inference scheme to combat this. St...	Rebuttal 1: Rebuttal: We truly appreciate your valuable comments. In the following, we respond to the concerns. ## Q1. DINO pretraining The training paradigm of DINO pretraining follows current cross-domain few-shot learning (CDFSL) works [13,42]. 1. Why this setting? Current works [A] have shown that unsup...	Summary: This paper presents a novel approach to Cross-Domain Few-Shot Learning (CDFSL) by investigating the role of the CLS token in Vision Transformers (ViT) for knowledge transfer under great domain gaps. The authors identify an intriguing phenomenon where not loading the CLS token parameters improves target-domain ...	Rebuttal 1: Rebuttal: We truly appreciate your valuable comments. In the following, we respond to the concerns. ## 1. How could this method benefit other tasks Our method could also benefit other cross-domain tasks. To verify this, we conduct experiments on the domain generalization task on 4 datasets (Sketch, Cart...	Rebuttal 1: Rebuttal: We thank all the reviewers for their valuable input. ## Q1. One CLS token for each class? Since the source dataset (miniImageNet) is a general classification dataset, the difference between each class is larger (e.g., than fine-grained datasets where domain information is clear). Therefore, fo...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Watermarking Makes Language Models Radioactive	Accept (spotlight)	Summary: This paper considers the problem of detecting whether watermarked text was used as training data for a language model. It identifies two several different settings under which to study this question, and proposes detection methods for identifying language models trained on watermarked text. Experiments analyze...	Rebuttal 1: Rebuttal: We thank the reviewer for the feedback. We have addressed each point individually. While we understand the concern, we argue that the p-values are not “heuristic”. We add a detailed response to clarify the reliability of the p-values and refer to App. D.2.1 “More details on token scoring and d...	Summary: This paper proposes a method to detect whether a language model is trained on (a subset of) watermarked outputs from another victim model. Their method utilizes the fact that the watermarking schemes are shifting the output tokens' distributions, such that the model trained on the watermarked outputs will also...	Rebuttal 1: Rebuttal: We thank the reviewer for their feedback on our paper. We address each point specifically. Please note that Appendix A "Limitation" and Appendix C.3 "Does the radioactivity generalize to other watermarking schemes?" address some of the concerns. Importantly, we emphasize the reliability of our p-v...	Summary: The paper studies the "radioactivity" of watermarked texts, i.e. if using such texts in LLM finetuning leaves noticeable watermark signal that can be reliably detected in future outputs. The main case study considered is the common scenario of using LLM-generated data for IFT. Authors use off-the-shelf LLM wat...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive feedback on the paper, as well as valuable questions and comments. > 1. The last abstract sentence renders as an overclaim given that it applies only to the less realistic open case, while most readers would assume the more realistic closed case, where the...	Summary: The paper investigates the "radioactivity" of text generated by large language models (LLMs), focusing on the detectability of synthetic text used as training data. It introduces a novel method to reliably identify whether the outputs of a watermarked LLM have been employed to fine-tune another language model....	Rebuttal 1: Rebuttal: We thank the reviewer for the constructive comments and suggestions. > 1. The author's lack of proper consideration of the written rigor of the essay is evident here in many places where semantic or formatting errors are made. For example: (1) Uniformity of punctuation: [...] for example, "text g...	Rebuttal 1: Rebuttal: We thank all reviewers for their insightful comments and suggestions. We address two main weaknesses that emerged from the reviews: Radioactivity is only demonstrated for some LLM watermarking schemes. 1) We focus on LLM watermarking schemes designed for AI-generated text detection, which i...	NeurIPS_2024_submissions_huggingface	2,024	Summary: The paper investigates the detection problem of whether LLM-generated texts are used to train another LLM, a phenomenon referred to as 'radioactivity'. The paper finds that it is feasible to detect the radioactivity of LLM-generated text via LLM watermarking. Consequently, the authors design radioactivity dete...	Rebuttal 1: Rebuttal: We thank the reviewer for the feedback. We have addressed each point individually. We kindly invite the reviewer to refer to Appendix A ("Limitations") for details on weaknesses 1 and 2, and Appendix F ("Comparison to Active IP Protection Methods") for the comparison related to weakness 3. **We al...	null	null	null	null	null	null
HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning	Accept (oral)	Summary: This paper proposes two improvements to LoRA geared towards heterogeneous corpora on which LoRA underperforms full fine-tuning. First, it proposes training a number of smaller Lora heads (Ai,Bi) (Lora-Split) rather than a single head which improves performance while preserving the overall number of parameters....	Rebuttal 1: Rebuttal: Thank you for your thoughtful review and insightful comments. We hereby address your concerns below： > W1: HydraLoRA still underperforms full fine-tuning. - *HydraLoRA is more efficient*. HydraLoRA offers the advantage of low training overhead, allowing LLMs to adapt to specific domain tasks...	Summary: The paper presents HydraLoRA, an innovative and asymmetric Low-Rank Adaptation (LoRA) framework designed to enhance the efficiency of fine-tuning Large Language Models (LLMs) for specific tasks. The authors identify inefficiencies in the original LoRA approach, particularly its underperformance in complex doma...	Rebuttal 1: Rebuttal: Thank you for your thoughtful review and insightful comments. We hereby address your concerns below： > W1& Limitation2: HydraLoRA has multiple adapter copies. The reason for multiple "B" modules is that, in practice, downstream tasks are often complex and multi-task. Traditional PEFT method...	Summary: The paper introduces HydraLoRA, a PEFT (Parameter-Efficient Fine-Tuning) architecture designed to improve the efficiency and performance of fine-tuning large language models (LLMs). HydraLoRA's main contribution lies in its asymmetric structure, which employs a shared matrix (A) for commonalities across tasks ...	Rebuttal 1: Rebuttal: Thank you for your thoughtful review and insightful comments. We hereby address your concerns below： > W1：Clarify asymmetric structure and workflow. - *Asymmetric structure*: Figure 3 presents the post-fine-tuning characteristics of the LoRA module within Llama-7B across four different tas...	Summary: This paper tackles the challenge of efficiently adapting large language models to new tasks. The authors highlight the limitations of current techniques like LoRA, which, while parameter-efficient, struggle with diverse data. Through a series of experiments, they discover that using multiple, task-specific Lo...	Rebuttal 1: Rebuttal: Thank you for your thoughtful review and insightful comments. We hereby address your concerns below: > W1 & Q1: Exploring potential limitations of this design. Thanks for the insightful question. The limitations may primarily stem from the training data. Particularly, in multi-task, extreme cond...	Rebuttal 1: Rebuttal: Dear PCs, SAC, AC, and Reviewers: We sincerely appreciate your thoughtful review and insightful comments, we have tried our best to address your concerns one by one in the correspondence rebuttal sessions. If our responses address your concerns, we would be grateful if you could consider raising ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Differentially Private Equivalence Testing for Continuous Distributions and Applications	Accept (poster)	Summary: This paper studies closeness (equivalent) testing between two continous distributions under approximate differential privacy. In particular, they propose a private version of the equivalent testing algorithm in [Diakonikolas et al.], which discerns whether the two are identical or far-apart in terms of the $\m...	Rebuttal 1: Rebuttal: 1. The main challenge in transitioning a non-private algorithm to a private one in a continuous setting is using the \emph{data itself} to divide the domain. As far as we know, there is no known algorithm for the continuous case in distribution testing that does not in some way partition the domai...	Summary: This paper introduces a novel algorithm for equivalence testing between two continuous distributions under the framework of differential privacy. The proposed method adapts the algorithm by Diakonikolas et al. to a differentially private version, using various clever constructions and privacy mechanisms. The a...	Rebuttal 1: Rebuttal: Weakness question \#1: The constant $c_{dkn}$, which represents their list of inequality of the expectation with $\Omega$ notation in Diakonikolas et al, does not specify the exact value of this constant. In our second algorithm, we used $10^7$ because in their proof, Diakonikolas et al used the ...	Summary: This paper considers the sample complexity of the problem of equivalence testing for continuous distributions under approximate differential privacy. Mathematically, given two distributions $P$ and $Q$ how many samples is required to have an algorithm that outputs $\texttt{yes}$ or $\texttt{no}$ such that - i...	Rebuttal 1: Rebuttal: 1. We made no effort to minimize the polylog$(1/\delta)$ factor. The sample complexity is given in Line 1 of Part II of our algorithm: $N \gets 10^7\left(\frac{k^{1/3}}{\alpha^{4/3}\epsilon^{2/3}} + \frac {\sqrt k}{\alpha\epsilon}+\frac{\sqrt{k}}{\alpha^2}\right)\log^6(\frac k {\alpha\epsilon\delt...	Summary: This paper talks about differentially private mechanism for property testing -- testing if two continuous distributions are equivalent. The main contribution of this paper is to develop DP versions of the algorithm in [16], which does not support DP. The algorithms in [16] uses discretization and when two dist...	Rebuttal 1: Rebuttal: 1. We tried to describe our algorithm prior to presenting it formally in lines 50-84. We would highly appreciate suggestions as to improving said description. 2. See above discussion as to lower bound comparison. 3. Alpha is the distance parameter. In our case is used for bound below the $\math...	Rebuttal 1: Rebuttal: First we wish to thank all reviewers for their thoughtful remarks and some spot-on comments. In broad brushstrokes, all reviews agree the paper and the algorithm has merit, but the presentation is lacking. We ourselves agree with the reviewer's feedback. In our defense we can only say that (1) t...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Spec-Gaussian: Anisotropic View-Dependent Appearance for 3D Gaussian Splatting	Accept (poster)	Summary: This paper introduces an anisotropic spherical Gaussian (ASG) appearance field into 3D Gaussian splatting for modeling the view-dependent appearance of each 3D Gaussian, which increases the ability of 3D Gaussian in representing high-frequency information. The key idea of this paper is combining ASG and SH to ...	Rebuttal 1: Rebuttal: We thank you for the positive feedback and constructive suggestions. Our response to the your concerns are incorporated below: Q1: Training time of our method. This is a great question regarding the scalability of our Spec-Gaussian model. The training time of Spec-Gaussian does not signific...	Summary: Spherical harmonics-based 3D Gaussian splatting (3DGS) struggles with specular and anisotropic components. To address this problem, the paper proposes adopting anisotropic spherical Gaussians (ASG). However, directly adopting ASG does not demonstrate superior performance in representing specular and anisotropi...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive and detailed review as well as the suggestions for improvement. We will revise the mathematical notations in the paper based on these insightful suggestions. Our response to the reviewer’s comments is below: Q1: Color Separation. Great question. It's wo...	Summary: This paper proposes using Anisotropic Spherical Gaussians (ASGs) as view encoding to enhance the modeling of specular reflections in 3D Gaussian splatting. In addition to Spherical Harmonics (SH) encoded colors, the method additionally queries reflection direction with multiple ASGs to generate a view encoding...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive review as well as the insightful suggestions for improvement. Our response to the reviewer’s comments is below: Q1: Ablation on the Number of ASGs. That's an excellent question. During the implementation of our code, we explored the number of ASGs. In t...	Summary: This paper presents an approach for reconstruction and view synthesis of scenes that exhibit strong specular/view dependent appearance. In particular, the authors extend the framework of Gaussian Splatting [Kerbl et al. 2023] and Scaffold-GS [Lu et al. 2023], replacing spherical harmonics for parameterizing vi...	Rebuttal 1: Rebuttal: We are glad and appreciate that you recognizes that the results of Spec-Gaussian are comprehensive and compelling. Our response to your valuable comments is below: Q1: What makes Spec-Gaussian work: Evaluation of the different components. The key components that make Spec-Gaussian work inclu...	Rebuttal 1: Rebuttal: We thank all the reviewers for their valuable comments. We are glad and appreciate that the reviewers recognize that our proposed ASG appearance field and coarse-to-fine training are sound, efficient, and show significant performance improvements. We will polish our paper further and release our c...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Dynamic Neural Regeneration: Enhancing Deep Learning Generalization on Small Datasets	Accept (poster)	Summary: This paper proposes Dynamic Neural Regeneration (DNR), a framework to enhance the generalization of deep neural networks on small datasets. The method is inspired by neurogenesis and offers more flexibility in defining a parameter mask as compared to previous approaches such as Knowledge Evolution (KE). The re...	Rebuttal 1: Rebuttal: > Experiments on Medical Datasets: We have chosen to focus on widely-used benchmark datasets that are representative of various low-data regimes. We believe these benchmarks provide a robust and fair comparison of our method’s performance. While we understand the importance of validating our appr...	Summary: This paper presents a novel iterative training framework called Dynamic Neural Regeneration (DNR) designed to enhance the generalization of deep learning models on small datasets. The DNR approach utilizes a data-aware dynamic masking scheme inspired by neurogenesis to eliminate redundant connections, thereby ...	Rebuttal 1: Rebuttal: > Complexity of Implementation: We appreciate the reviewer's concern regarding the complexity of implementing the DNR framework. To address this, we have expanded the Appendix section in the revised manuscript that includes detailed implementation guidelines. This section provides step-by-step in...	Summary: This submission investigates efficient training and generalization of deep neural networks in the low-data regime. Drawing inspiration from neurogenesis in the brain, authors propose an iterative training framework termed Dynamic Neural Regeneration (DNR). The authors further investigate the efficacy of the pr...	Rebuttal 1: Rebuttal: > Experiments on Medical Datasets: We have chosen to focus on widely-used benchmark datasets that are representative of various low-data regimes. We believe these benchmarks provide a robust and fair comparison of our method’s performance. While we understand the importance of validating our app...	Summary: This manuscript introduces the Dynamic Neural Regeneration (DNR) framework, which improves the generalization of deep neural networks on small datasets. DNR uses the SNIP method to reinitialize less important neural connections for the current generation selectively. Experimental results show that DNR outperfo...	Rebuttal 1: Rebuttal: > Empirical Validation Thank you for highlighting the concern. We have performed the empirical validation of our method by including results on five small datasets and three large datasets—CIFAR-10, CIFAR-100, and Tiny ImageNet. **Each experiment is conducted three times, and the mean and standar...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
FouRA: Fourier Low-Rank Adaptation	Accept (poster)	Summary: This paper presents a PEFT method (mainly for text-to-image tasks) called FouRA. FouRA learns the LoRA projection in the frequency domain. This idea helps solve the problems of data copying and distribution collapse and thus improves the generated image quality. The effectiveness of FouRA is verified on both C...	Rebuttal 1: Rebuttal: We appreciate reviewer 4zX8 for their detailed feedback and an in-depth review to helped us improve our work. Training Time: We provide detailed analysis of training time per epoch in Table R.1. One training epoch takes 24.5s (for FouRA with inference adaptive masking) compared with 22s (for...	Summary: This paper address a fundemental diversity limitation of any LoRA fine-tuned diffusion model. More specifically, we can observe distribution collapse with these fine-tuned models in the setting of limited data. The authors propose to address this problem by applying LoRA in the frequency domain. The fourier t...	Rebuttal 1: Rebuttal: We appreciate reviewer yqCK for their meticulous review and insightful feedback, helping us improve our work. Memory Overhead/Scaling with batch size: Thank you for raising the point on memory. We provide details including memory overhead in Table R.1 of the rebuttal pdf. The reported numbers...	Summary: The authors propose FouRA, a novel low-rank adaptation for pretrained diffusion models that can successfully handle data copying and distribution collapse problems observed in previous works. FouRA performs low-rank adaptation in the frequency domain and incorporates input-dependent adaptive rank selection dur...	Rebuttal 1: Rebuttal: We thank reviewer AmEw for their constructive feedback and acknowledgement of our motivation/novelty. Inference time: Thanks for suggesting the inference time analysis. As requested, we show the inference latency along with other compute analysis in Table R.1 of the provided pdf file. We obse...	Summary: This paper proposes a new parameter-efficient fine-tuning method that operates in the frequency domain, termed FouRA. Specifically, The method operates in the frequency domain, learning low-rank adapter transforms to Fourier-transformed input features. It also incorporates an adaptive rank selection strategy ...	Rebuttal 1: Rebuttal: We appreciate reviewer R4Sn for their insightful feedback to help us improve our work. Quantitative Results: Thank you for the suggestion. Based on your recommendation, we provide more quantitative analysis in the Rebuttal pdf. We have trained FouRA adapters over a LLaMA3-8B model and tested...	Rebuttal 1: Rebuttal: We appreciate all the reviewers for providing insightful reviews, which has truly helped us improve our work. We provide a single-page PDF including tables and figures to supplement our response to reviewers’ comments. Reviewers largely acknowledged multiple aspects of the paper such as “paper is...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Stealth edits to large language models	Accept (poster)	Summary: Introduces a new computationally efficient technique to edit facts in a LM. The proposed technique has nice theoretical properties that ensures the selectivity of the edits. The paper also discusses how the ability to edit a specific LM can be measured by its intrinsic dimension, which can be approximated with...	Rebuttal 1: Rebuttal: We would like to thank the Reviewer for taking the time to review our paper and for providing valuable comments and feedback. We have responded to these in detail below. Generalisability. We refer the reviewer to the discussion on generalising edits in our 'Global Rebuttal'. Here, we focus on...	Summary: This paper studies the problem of, when given a particular prompt, whether it is possible to surgically and efficiently edit a model’s parameters to produce a certain response in a way that does not otherwise change the behavior of the model. The paper provides an efficient technique for this in the form of bo...	Rebuttal 1: Rebuttal: We would like to thank the Reviewer for their thorough reading of the paper and detailed comments. We have responded to these individually below. Weaknesses 1. Here, we focus on stealth in the sense that the architecture is unchanged and/or performance on a large unknown validation set is un...	Summary: This paper proposes a new algorithm and studies a family of methods it refers to as stealth edits, which modify a large language model to selectively correct a set of known hallucinations without otherwise affecting the responses. It also proposes intrinsic dimension, a pairwise separability-based metric t...	Rebuttal 1: Rebuttal: We would like to thank the Reviewer for their thoughtful comments on the paper. We have responded these individually to below. Weaknesses 1. Thank you for pointing this out. It is of course to be expected that the worst-case theoretical guarantees of Theorems 2 and 3 underestimate practical ...	Summary: This paper focuses on stealth editing in large language models, presenting methodologies for making targeted, subtle changes to these models without retraining. The techniques, called "stealth edits," aim to correct specific issues like factual inaccuracies by directly updating the model's weights. The researc...	Rebuttal 1: Rebuttal: We would like to thank the Reviewer for their comments and careful reading of the paper. We have responded to each comment below. Weaknesses Many thanks for the suggestion. In the submitted version, the Appendix contains details of the algorithmic steps, the experimental protocols, and the r...	Rebuttal 1: Rebuttal: We would like to thank all of the reviewers for their detailed comments on the paper, which we have responded to individually. Two of the reviewers also raised an interesting philosophical discussion, regarding model edits which aim to generalise beyond the original target prompt. We discuss this ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Small steps no more: Global convergence of stochastic gradient bandits for arbitrary learning rates	Accept (poster)	Summary: This paper studies stochastic gradient bandits with arbitrarily large learning rates. The authors prove an interesting result -- the learning rate of the gradient bandit algorithm (in particular, REINFORCE without baseline) can be arbitrarily large. Some numerical simulations are also provided to validate the ...	Rebuttal 1: Rebuttal: We appreciate that the reviewer recognized the contribution of the work. We answer the questions as follows. >include some literature review on deep learning with large learning rates Thank you for pointing the "edge of stability" paper to us. We will cite this line of work in the related wo...	Summary: This work studies the asymptotic global convergence rate of the stochastic gradient bandit algorithm with an arbitrary constant learning rate and proves that this algorithm asymptotically converges to the global optimal. This work reveals how this algorithm balances exploitation and exploration and proves the ...	Rebuttal 1: Rebuttal: We appreciate that the reviewer understood and recognized the contribution of the work. We answer the questions as follows. >$\eta = 100$ and $\eta = 1000$ do not converge. Can you discuss this phenomenon in detail? We ran more iterations for $\eta = 100$ and $\eta = 1000$, and eventually al...	Summary: The paper presents a novel theoretical analysis of the stochastic gradient bandit algorithm, showing that it converges to a globally optimal policy almost surely using any constant learning rate. This result is significant as it extends the understanding of stochastic gradient methods in bandit settings, even ...	Rebuttal 1: Rebuttal: We appreciate that the reviewer understood and recognized the contribution of the work, and we thank the reviewer for carefully reading and checking the results. The main concerns are addressed as follows. >detailed explanations for Lemma 1 and Lemma 2 According to Eq. (1), the sampled rewar...	Summary: The paper reveals that the stochastic gradient bandit algorithm converges to a globally optimal policy almost surely using any constant learning rate. This result stands even when traditional smoothness and noise control assumptions are not met, showing the algorithm’s balance between exploration and exploitat...	Rebuttal 1: Rebuttal: We appreciate that the reviewer understood and recognized the contribution of the work. We answer the questions as follows. Please refer to the common rebuttal for questions regarding Assumption 1, rate of convergence, and comparison with [23]. >**...maximizing the cumulative reward (or minimizi...	Rebuttal 1: Rebuttal: We thank the reviewers for their valuable comments and recognition of the contributions. This common feedback answers questions raised by multiple reviewers. >Comparison to [23] (Reviewers RHGg, jZkA, qHxg, 4TYZ) First, we would like to emphasize that the asymptotic convergence arguments...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper proved that stochastic gradient bandits converge to a globally optimal policy almost surely for arbitrary constant learning rates if true mean reward has no ties. Strengths: This work extends the previous convergence results of stochastic gradient bandits by generalizing from a specific constant le...	Rebuttal 1: Rebuttal: We thank the reviewer for taking time to review our work. We hope the following can help clarify matters. >heavily relies on prior work [23] This is simply incorrect. Our analysis is significantly different from [23], which is built on smoothness and growth condition, while our results do no...	null	null	null	null	null	null
FlexPlanner: Flexible 3D Floorplanning via Deep Reinforcement Learning in Hybrid Action Space with Multi-Modality Representation	Accept (poster)	Summary: In this paper, the authors have proposed FlexPlanner, a flexible 3D floorplanning method with deep reinforcement learning. Existing learning methods mainly focus on the 2D scenarios. However, it suffers from overlooking alignment requirements and multi-die property. To address these, FlexPlanner learns a hybri...	Rebuttal 1: Rebuttal: ## Response to Reviewer jFwz (5: Borderline accept) Thank you for your time and valuable feedback. Our replies to the concerns and questions are as follows. > W1: Better to include a teaser showing the problems when directly applying 2D FP method to 3D scenario We sincerely appreciate your c...	Summary: This paper proposes the FlexPlanner, a reinforcement learning-based method utilizing multi-modality representation, including vision, graph, and sequence, to handle different challenging scenarios. Additionally, the design of the action space to uniformly handle constraints represents a new and innovative meth...	Rebuttal 1: Rebuttal: ## Response to Reviewer 3CLd (7: Accept) Thank you for your time and valuable feedback. Our replies to the questions are as follows. > Q1: Defend RL on Floorplanning Task Thanks for your insightful comment. Compared to other methods, our RL-based approach offers the following advantages in 3...	Summary: This paper presents a new learning-based method for IC design that simultaneously handles the position, aspect ratios, and alignment of blocks. The method achieves significant improvements compared to baselines by leveraging reinforcement learning with a hybrid action space and multi-modality representation t...	Rebuttal 1: Rebuttal: ## Response to Reviewer pH9C (5: Borderline accept) Thank you for your time and valuable feedback. Our replies to the concerns and questions are as follows. > W1: Clarity and Writing. According to your suggestions, we have polished the paper in the following aspects: - We will provide **more ...	Summary: The paper proposes a learning-based method called FlexPlanner in hybrid action space with multi-modality representation to simultaneously handle position, aspect ratio, and alignment of blocks. FlexPlanner models 3D FP based on multi-modalities, includ15 ing vision, graph, and sequence. The work designs a p...	Rebuttal 1: Rebuttal: ## Response to Reviewer 5TLE (5: Borderline accept) Thank you for your time and valuable feedback. Our replies to the concerns and questions are as follows. > W1: Clarity In the final version, we will conduct more ablations studies in terms of different hyperparameters and the impact of diff...	Rebuttal 1: Rebuttal: ## Global Response Dear Area Chairs and Reviewers, We appreciate your time, valuable comments, and constructive suggestions. From an overall perspective, we are happy to see that all reviews are positive and the reviewers approve of the novelty (`3CLd`, `pH9C`, `5TLE`), **notable improvem...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
N-agent Ad Hoc Teamwork	Accept (poster)	Summary: The paper introduces a novel problem setting within cooperative multi-agent systems, where a dynamically varying number of autonomous agents must cooperate with a set of uncontrolled teammates to achieve a common goal. This setting generalizes existing paradigms of cooperative multi-agent reinforcement learnin...	Rebuttal 1: Rebuttal: Novelty - Encoder/Decoder-based Agent Modelling While we understand the reviewer’s reservations on the prevalent use of the encoder-decoder architectures for agent modeling, we believe it should not be the sole basis for assessing the novelty of our work. Despite using encoder-decoders for ...	Summary: This paper proposes a generalization to the ad hoc teamwork (AHT) setting where N agents follow a trained policy instead of just 1 agent or all agents. Within this NAHT framework, the authors describe a technique for modeling the other agents, accelerating the ability to learn in this setting relative to basel...	Rebuttal 1: Rebuttal: Out-of-Distribution Evaluation - Environment Selection Hanabi represents a challenging scenario for AHT and requires a large amount of computational resources. Prior papers have used billions of training steps to train agents, in contrast with the tens of millions used in our work [1,2]. Unfo...	Summary: The paper proposes a MARL algorithm for the N-agent ad hoc teamwork setting. The algorithm specifically includes policy optimisation by modelling the other agents. The agent modelling uses and encoder-decoder architecture. In the actor-critic framework, the critic uses data from both controlled and uncontrolle...	Rebuttal 1: Rebuttal: Points addressed in common rebuttal - Additional agent modeling baselines and suggested references - Number of test domains On Off-policy data for training PPO critic > (R 9xa8) The decision to use data from uncontrolled agents is not well justified in the paper. The value function is ...	Summary: This work is motivated by the realistic limitations of current multi-agent studies, specifically the assumption that either all agents are controllable or that only a single agent is controlled in the multi-agent system. To address this challenge, the authors introduce the N-agent ad hoc teamwork (NAHT) approa...	Rebuttal 1: Rebuttal: Points Addressed in Common Rebuttal - Only one evaluation domain - Question about on/off-policy algorithms for POAM - Requested baselines: in-distribution performance for Fig. 6; performance of uncontrolled teams Accuracy of Inferred Actions and Observations in the Dec-POMDP setting We...	Rebuttal 1: Rebuttal: We thank the reviewers for lending their expertise in reviewing our paper, as well as for their thoughtful and helpful feedback. Here we address questions and points brought up by multiple reviewers. We respond to the other questions individually below. Our contributions include: 1. Proposing an...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Enhancing LLM’s Cognition via Structurization	Accept (poster)	Summary: The paper presents a method to improve the cognitive capabilities of large language models (LLMs) by organizing input context into a hierarchical structure. The method is called context structurization, and it involves transforming unordered contextual sentences into hierarchically structured elements to mimic...	Rebuttal 1: Rebuttal: Dear reviewer eQho: We thank the reviewer for the valuable time and constructive suggestions, and our point-to-point responses are presented below: > W1: The performance improvements heavily depend on the quality of the structurization process. Poor structurization can lead to suboptimal mod...	Summary: This paper proposes a new technique for prompting Large Language Models (LLMs) called StruXGPT. The basic idea is to transform the original prompt into a more structured description of the request which contains three levels of information: Scope, Aspects, and Descriptions. While the Scope provides an outl...	Rebuttal 1: Rebuttal: Dear reviewer yxZb: We thank the reviewer for the valuable time and constructive suggestions, and our point-to-point responses are presented below: > W1: In the abstract and introduction of the paper, there are claims about how human cognition works that are poorly backed up. This is an area...	Summary: The paper presents a novel approach to improve the cognitive capabilities of large language models (LLMs) without inferring the model by structuring contextual information hierarchically. The authors propose transforming plain, sequential text into a structured format, enabling LLMs to process and understand c...	Rebuttal 1: Rebuttal: Dear reviewer 8sM9: We thank the reviewer for the valuable time and constructive suggestions, and our point-to-point responses are presented below: > W1/Q1: Lack of parameter-efficiency analysis: authors provide a 7B model for structurization process, but it is not clear whether smaller or b...	Summary: This paper introduces the concept of context structurization to enhance the comprehension capabilities of large language models (LLMs) for long texts. The authors propose summarizing the input text into a three-layer structure of Scope-Aspect-Description using LLMs, and then inputting this three-layer structur...	Rebuttal 1: Rebuttal: Dear reviewer 7Sjn: We thank the reviewer for the valuable time and constructive suggestions, and our point-to-point responses are presented below: > W1: Many tables only compare the scenarios with and without StruXGPT (except for Table A2 and A5), without comparing against other advanced pr...	Rebuttal 1: Rebuttal: We thank all reviewers for their valuable time and constructive suggestions when evaluating our manuscript. We are really encouraged to see ALL reviewers find our method technically solid, extensively validated, and well-presented. We have provided point-to-point responses to rev...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Self-Play Fine-tuning of Diffusion Models for Text-to-image Generation	Accept (poster)	Summary: The paper introduces a novel method called SPIN-Diffusion for fine-tuning text-to-image diffusion models. SPIN-Diffusion uses a self-play mechanism where the model competes against its earlier versions to iteratively improve its performance. This approach eliminates the need for human preference data, which is...	Rebuttal 1: Rebuttal: Q1: The paper lacks a comparison with traditional fine-tuning methods for diffusion models, e.g., LoRA A1: While LoRA is a parameter-efficient fine-tuning method that focuses on reducing trainable parameters under resource constraints, it is orthogonal to SPIN-Diffusion, which utilizes a ...	Summary: This paper introduces a method called self-play fine-tuning for diffusion models (SPIN-Diffusion), where the model engages in a competitive process with its earlier versions, driving iterative self-improvement. This method presents an alternative to conventional supervised fine-tuning and RL strategies. Experi...	Rebuttal 1: Rebuttal: Q1: The main contribution of this work is an approximate SPIN loss compared to the previously proposed exact SPIN loss. The major modification is moving the average over sampling steps outside the loss function, resulting in an upper bound. I kindly argue this improvement is straightforward w...	Summary: - This paper introduces SPIN-Diffusion, a new self-play fine-tuning technique for diffusion models that improves iteratively by competing with previous versions. - They show that SPIN-Diffusion outperforms existing supervised and reinforcement learning fine-tuning methods in aligning with human preferences and...	Rebuttal 1: Rebuttal: Q1: The sampling overhead is significant, requiring 5-10 times more training time. A1: Thank you for highlighting this concern. Since this is a common concern among all reviewers, during the rebuttal period, we have worked on practical solutions addressing the sampling overhead problem, a...	null	null	Rebuttal 1: Rebuttal: We sincerely thank the reviewers for the constructive feedback! To address some common concerns, we summarize the improvements on sampling overhead that we have done during rebuttal period as follows: Sampling Overhead: By using batching, torch precompiling, and DPMSolver, we reduced the samp...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
NeuralClothSim: Neural Deformation Fields Meet the Thin Shell Theory	Accept (poster)	Summary: The paper uses Physics-Informed Neural Networks (PINNs) to solve cloth quasistatics. The cloth is represented by a neural implicit function, which provides infinite resolution. The cloth elasticity is modeled using Kirchhoff-Love thin shell theory. The equilibrated displacement field is obtained by minimizing ...	Rebuttal 1: Rebuttal: We thank the reviewer Gekm for the detailed comments. The reviewer notes that our cloth modelling “offers infinite resolution”, and the method “does not suffer from numerical locking issues” like classical mesh-based methods. We now address the remaining concerns: ### **Distinction from PINN-app...	Summary: The paper proposes a cloth simulation model based on the Kirchoff-Love thin shell theory, using a neural network (SIREN activations) to parameterize a deformation field (NDF) from a base parameterization. The model can handle periodic and Dirichlet boundary conditions, and uses the network to calculate the nec...	Rebuttal 1: Rebuttal: We thank the reviewer BLQv for their comments. The reviewer nicely summarizes our paper and notes that our method "leverages a principled and sophisticated thin shell theory", "may spur further work in neural cloth simulation", and that our presentation is "pretty thorough". We now address the poi...	Summary: The paper proposes to model the cloth as a fixed parameter domain embedded via a function encoded as a neural network. The network weights are then optimized to minimize a Kirchoff-Love free energy, thus implementing a quasistatic cloth deformation model without a mesh discretization. Strengths: The method is...	Rebuttal 1: Rebuttal: We thank the reviewer bD8m, for their comments. The reviewer notes that our "method is technically sound", and the "results are compelling". We will update the draft to include minor comments, such as copy editing. We now address the points raised in the review. ### **Discretisation-independenc...	null	null	Rebuttal 1: Rebuttal: We thank all reviewers for their valuable feedback, which will help us improve our work further. The reviewers note that our "results are compelling"(bD8m), our proposed method "may spur further works" in neural cloth simulation (BLQv), and, in contrast to classical mesh-based simulators, ours "do...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Pseudo-Private Data Guided Model Inversion Attacks	Accept (poster)	Summary: The paper introduces a novel method to enhancing model inversion attacks (MIAs), which aim to reconstruct class characteristics from a trained classifier. Typically, MIAs rely on training image priors, such as GANs, on public data that differ in distribution from the target model's training data. This distribu...	Rebuttal 1: Rebuttal: Sincerely thank you for your constructive comments and generous supports! Please see our detailed responses to your comments and suggestions below. > W1, W2: Missing evaluations on PLG-MI and state-of-the-art model inversion defenses. Regarding these additional evaluations, please refer to the g...	Summary: This work introduces a novel application of a generative model inversion attack utilising dynamic (pseudo-private) priors, improving the existing results of MI. Strengths: The work is very clear, the idea itself makes sense and the results are well-presented. Authors explicitly target a specific problem and m...	Rebuttal 1: Rebuttal: Thank you for your time in reviewing our work and for your constructive comments. Please see our detailed responses to your comments and suggestions below. > W1. So while the idea is straightforward and makes a lot of sense ... in the context of MIs. First, we would like to clarify that the prob...	Summary: It is well known that deep learning models are susceptible to model-inversion attacks, which is to say that they can be probed to reveal their training data. The authors design a more powerful method of attack by increasing the density of their prior using “pseudo-private data”, they can increase the probabili...	Rebuttal 1: Rebuttal: Thank you for your constructive comments and generous supports! Please see our detailed responses to your comments and suggestions below, where we use references in our manuscript due to the token limit. > W1. There are a number of typographic errors (e.g. “vallina”) Thank you for your detailed ...	Summary: The paper proposes a novel plug-and-play method for current state-of-the-art MI methods to enhance their performance and mitigate the challenge of distribution discrepancy. This method first conducts a round of MI attack to acquire pseudo-private data and then utilizes the data to fine-tune the generative prio...	Rebuttal 1: Rebuttal: Thank you for your time and careful review of our work. Please see our detailed responses to your comments and suggestions below. > W1, W2: Missing evaluations on PLG-MI and state-of-the-art model inversion defenses. Regarding these additional evaluations, please refer to the general response. T...	Rebuttal 1: Rebuttal: We sincerely thank all reviewers for their thoughtful and insightful suggestions on our submission. We address a few common points in this response. All other questions are addressed in reviewer specific responses. > Re: The evaluation of PLG-MI [r1]. We have included the experimental comparison ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Apathetic or Empathetic? Evaluating LLMs' Emotional Alignments with Humans	Accept (poster)	Summary: This paper investigates how LLMs respond to diverse emotional situations (i.e., empathy ability of LLMs). They collected 428 distinct situations for evaluation. They then utilize their collected dataset to investigate five LLMs, including GPT models and LLaMA models. They also compared the response with human ...	Rebuttal 1: Rebuttal: Thank you for your hard work of reviewing! We appreciate that you highlight our comprehensiveness. We will address your concerns one by one. > Missing hyperparameters: I wonder what are the hyperparameters used for decoding? Like temperature, top-K or top-P values? > What are the hyperparameters...	Summary: This paper proposes a dataset covering a wide range of human emotion situations for evaluating empathy behaviors in large language models. The evaluations are based on related psychological studies and performed via a self-report questionnaire format. Comparison with a collected large-scale human baseline reve...	Rebuttal 1: Rebuttal: Thank you for your hard work of reviewing! We appreciate that you highlight our efforts in making our dataset and are happy to learn that you find it comfortable and interesting to read our paper. We will address your concerns one by one. > The application of self-report questionnaires to study L...	Summary: The paper evaluates LLM’s emotional alignment with humans. The authors introduce a comprehensive survey in the emotion appraisal theory of psychology and evaluate five LLMs with it. The experimental results demonstrate that current LLMs still have considerable room for improvement. Strengths: 1. A comprehensi...	Rebuttal 1: Rebuttal: Thank you for your hard work of reviewing! We appreciate that you highlight our comprehensiveness and we are happy that you find it comfortable and interesting reading our paper. We will address your concerns one by one. > Overall, the paper is well-written and easy to follow. The evaluation is r...	Summary: the paper assesses the emotional alignment of Large Language Models (LLMs) with human emotions. Towards this goal, a dataset is constructed and a testing framework is designed. For the dataset, over 400 scenarios elicit eight emotions: anger, anxiety, depression, frustration, jealousy, guilt, fear, and embarra...	Rebuttal 1: Rebuttal: Thank you for your hard work of reviewing! We will address your concerns one by one. > W1: evaluation seems weak since two LLM families are kind of limited. For closed LLMs, there are Claude-3, Gemini, et al; For open-sourced (open weights) LLMs, there are mistral, falcon, phi-3, flan-t5, vicuna,...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Train-Attention: Meta-Learning Where to Focus in Continual Knowledge Learning	Accept (poster)	Summary: This paper proposes a meta-learning framework to dynamically adjust token perplexity weights based on their usefulness to achieve good continuous knowledge learning performance and also provides a new benchmark for CKL. Strengths: 1. It is inspiring to use meta-learning technique for adjusting token weight. ...	Rebuttal 1: Rebuttal: [W1] The idea of adjusting token weights is not fresh enough and shares some similarity with “RHO-1”. However, training a meta-learner to evaluate token importance is still a good try. We truly appreciate your recognition of our effort, as well as your deep understanding of our paper. As you...	Summary: This paper studies the continual knowledge learning (CKL) problem in large language models. The authors notice that the existing methods either apply equal weights to all tokens or re-weight tokens with a trivial consideration that tokens with low confidence are important. They propose a new definition of toke...	Rebuttal 1: Rebuttal: [W1] Naming token importance as "usefulness" is too broad and does not accurately reflect the motivation of the paper. Thank you for the constructive feedback on the clarity of expression. We consider "task-utility" as it more clearly implies usefulness in relation to the task. * [W2] M...	Summary: The article introduces Train-Attention Augmented Language Model (TAALM), a novel approach for continual knowledge learning in large language models. TAALM dynamically assigns weights to tokens based on their usefulness, optimizing learning efficiency and minimizing forgetting. Strengths: - This paper introduc...	Rebuttal 1: Rebuttal: First of all, we appreciate your constructive feedback on our work. However, it seems there might be some misunderstandings about our work. Most of the queries raised are addressed in Section 2; we recommend reexamining this section for clarification. Before addressing the issues raised by the rev...	Summary: The paper introduces Train-Attention-Augmented Language Model (TAALM), a novel approach to continual knowledge learning (CKL) in large language models (LLMs). Unlike traditional methods that uniformly apply weight across all tokens, TAALM dynamically predicts and applies weights to tokens based on their import...	Rebuttal 1: Rebuttal: We truly appreciate your deep understanding of our paper. We address the key issue raised by the reviewer in the comments below: * [W1+Q1] Can the authors provide more details on the potential applicability of TAALM to other types of continual learning tasks outside of language models?** Than...	Rebuttal 1: Rebuttal: # Global Rebuttal We first thank all reviewers for their thoughtful feedback on our work. We would like to address a suggestion commonly raised by reviewers, and introduce our progress in significantly reducing the GPU resource required. We believe that constructive suggestions from all reviewe...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Abstracted Shapes as Tokens - A Generalizable and Interpretable Model for Time-series Classification	Accept (poster)	Summary: This work aims to provide an interpretable and generalizable model, called VQshape, for learning on time-series. The key idea of the work is to learn representation based on shapelets, or shape-level features of timeseries as quantized vectors. By learning such a codebook, VQshape can learn in a dataset-agnost...	Rebuttal 1: Rebuttal: Thank you very much for taking the time to carefully read our paper. Below, we address your questions. > Regarding "why and how could you guarantee the produced attributes are accurate?" It is important to note that our proposed method is a self-supervised pre-training model, and there are no la...	Summary: The paper introduces VQShape, an interpretable pretaining method for time series (TS) classification. VQShape uses transformer-based TS encoding combined with a VQ-VAE style codebook representation. The latter enables the representation of a TS as a set of shapelets. The VQShape encodes shapelets in a generali...	Rebuttal 1: Rebuttal: Thank you very much for taking the time to carefully read our paper and writing a detailed review. We appreciate your comments and suggestion on giving more comprehensive statistical comparisons with the baseline. Below, we address your main concerns and your questions. > Regarding "standard devi...	Summary: Authors propose a self-supervised model which can be used for classification. Their method learns abstracted shapes which serves as interpretable tokens and an information bottleneck in their modeling architecture. They compare with state-of-the-art methods on standard classification datasets and show promisin...	Rebuttal 1: Rebuttal: Thank you very much for taking the time to carefully read our paper. We are glad you found our method provides interesting insights. Below, we address your main concerns and your questions. > Regarding "why excluding the InsectWingBeats dataset" We discussed the reason at Line 393 in Appendix A...	Summary: The paper introduces VQShape, a model designed for time-series (TS) data representation learning and classification. VQShape provides interpretable and generalizable representations by utilizing vector quantization to create a codebook of abstracted shapes. These shapes represent low-dimensional codes that des...	Rebuttal 1: Rebuttal: Thank you very much for taking the time to carefully read our paper, recognizing our contributions and giving valuable feedback. Below, we address your questions. > Regarding "Generalization experiment of MOMENT and TST" The authors thank the reviewer for this suggestion. However, as the pre-tra...	Rebuttal 1: Rebuttal: The authors would like to thank the reviewers for taking the time to review our manuscript and for providing constructive feedback. Here, we aim to address and clarify several key points raised by multiple reviewers, and explain added results and figures in the Rebuttal attachment. > Regarding "c...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Achieving $\tilde{O}(1/\epsilon)$ Sample Complexity for Constrained Markov Decision Process	Accept (poster)	Summary: The paper studies the linear program formulation of constrained MDPs. The paper first characterizes the instance hardness of the underlying LP. Then, by proposing an algorithm that operates in the primal space and resolves the primal LP in an online manner, the paper derives an overall sample complexity of O(1...	Rebuttal 1: Rebuttal: We would like to thank you for your insight review! Please find below our response to the weakness and your questions! $\textbf{Response to weakness 1}$: Thank you for mentioning the important references to us! We will refine our literature review part to incorporate a better comparison with exis...	Summary: The paper proposes a new algorithm that solves the CMDP problem in $O(1/\epsilon)$ sample complexity, which improves the best-known $O(1/\epsilon^2)$ sample complexity in the literature. To achieve this, this paper made three contributions: (1) New characterizations of the problem instance hardness for CMDP pr...	Rebuttal 1: Rebuttal: We appreciate your insightful comments, which allow us to provide further clarifications. Please find below our response to the weakness and the questions. We hope that our response would clarify your concerns about the paper and we are happy to provide further clarifications if needed. $\textbf{...	Summary: This paper addresses the reinforcement learning problem for CMDPs. The authors derived a problem-dependent sample complexity bound that is $\tilde O(1/\epsilon)$, improving upon the state-of-the-art. They introduce a novel way to characterize the hardness of CMDP instances using the LP basis, enabling proble...	Rebuttal 1: Rebuttal: We would like to thank you for your positive review and insightful comments! Please find below our response to your comments and questions! $\textbf{Response to weakness}$: Thank you so much for the comment! Indeed, the method developed in this paper is mainly for the tabular setting. However, th...	Summary: The strength of this paper is that it provides strong sample complexity results for the constrained MDP, enhancing the existing analysis in the literature by developing a new algorithm. However, despite presenting a promising method, it lacks thorough comparisons with existing methods in the literature. Stren...	Rebuttal 1: Rebuttal: We would like to thank you for your insightful comments! Please find below our response to each of the weakness points and the questions you posted. We hope that our response would clarify your concerns about the paper. $\textbf{Response to weakness}$: Thank you for the comment! We will for sure ...	Rebuttal 1: Rebuttal: We implement our algorithm to study the numerical performance. We consider a CMDP problem with the state space $\|\mathcal{S}\|=10$ and the action space $\|\mathcal{A}\|=10$. We set the discount factor $\gamma=0.7$. We then randomly generate the probability transition kernel $P$. To be specific, for e...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper studies reinforcement learning problem under Constrained Markov Decision Processes (CMDPs). It formulates the problem using linear programming and designs a novel algorithm to solve it. Using the newly designed algorithm, the authors prove a sample complexity of $\tilde{O}(1/\epsilon)$, albeit at th...	Rebuttal 1: Rebuttal: We would like to thank you for your positive review and insightful comments! Please find below our response to the weakness and your question. We hope that our response would clarify your concerns regarding our work. $\textbf{Response to weakness 1}$: Thank you for the comment! You are right that...	null	null	null	null	null	null
Efficient Combinatorial Optimization via Heat Diffusion	Accept (poster)	Summary: This work solves combinatorial optimization problems using the gradient method by transforming the discrete problem into a continuous problem. Under the invariant of the optimal solution, the authors transformed the hard continuous problem into an easier problem by changing the objective function using a heati...	Rebuttal 1: Rebuttal: > 1. The motivation of this paper seems to improve the scope of the search-based combinatorial optimization solver, but the proposed method is to change the objective function to improve the efficiency of the gradient method. The method seems a little bit irrelevant to the original motivation of t...	Summary: This paper proposes the Heat Diffusion Optimization (HeO) method, which leverages thermodynamic principles to enhance combinatorial optimization (CO) problems. Specifically, it integrates heat diffusion equations into gradient-based optimization to improve efficiency and help escape local minima. Strengths: 1...	Rebuttal 1: Rebuttal: > 1. The paper could benefit from a deeper theoretical and empirical analysis of the HeO algorithm. For example, a detailed analysis of the convergence properties and computational complexity of the algorithm is needed. Also, summarizing and providing recommendations on parameter sensitivity and s...	Summary: This paper aims to improve the efficiency of existing combinatorial optimization methods via heat diffusion. The author have made a thorough analysis over the existing problems and propose the heat diffusion method HOE for general combinatorial optimization problems. The empirical evaluation verifies its advan...	Rebuttal 1: Rebuttal: > In section 2, the analyses are only focused on the methods doing the gradient descent over the relaxed variables. It is unclear to me how the conclusions are generalized to the method like large neighborhood search, variable neighborhood search and path auxiliary sampling. Good question. We ack...	Summary: The paper presents a novel framework for solving combinatorial optimization problems using a concept termed "Heat diffusion optimization (HeO)." The approach diverges from traditional methods by utilizing heat diffusion to enhance information propagation within the solution space, allowing for more efficient p...	Rebuttal 1: Rebuttal: > 1. Could you elaborate on how K (Page 4, Line 129) is selected in different scenarios? Thank you for your valuable feedback. We will clarify this point after revision. The value of K is directly determined by the target function $f(\mathbf{s})$ to be optimized. For example: For minimum vertex...	Rebuttal 1: Rebuttal: We sincerely thank all the reviewers for their valuable feedback and suggestions. In addition to responding to each reviewer individually, we have included two figures in the *PDF file* of this global author rebuttal. Furthermore, to address concerns about theoretical soundness raised by Revie...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Stochastic Kernel Regularisation Improves Generalisation in Deep Kernel Machines	Accept (poster)	Summary: The authors present an improved convolutional deep kernel machine (CDKM) that achieves state-of-the-art performance for kernel methods on the CIFAR-10 image classification task. They introduce a novel regularization technique where the learned inducing Gram matrices are randomly sampled from a Wishart distribu...	Rebuttal 1: Rebuttal: Thank you for your review. > "In Table 1, the test-log likelihood improvements over previous work and traditional neural networks (without weight decay) is compelling as a result. But also surprised to see models with weight decay outperforming the other methods, if I understand correctly, CDKMs ...	Summary: The paper explores how to improve the convolution deep nuclear machines (DKMs)to improve their generalization ability, especially on the CIFAR-10 dataset. The authors introduced several modifications, especially random kernel regularization (SKR), which involves adding noise to the learned Gram matrix during t...	Rebuttal 1: Rebuttal: Thank you for your review. > "Modifications involve complex changes to the DKM framework, which may be difficult to implement and understand for developers unfamiliar with these methods." We agree that the methods themselves are very novel and hence somewhat unfamiliar. To address these issues,...	Summary: This paper proposes a new method for Deep Kernel Machine, which achieves state-of-the-art results on kernel methods by using methods including regularization. Strengths: Compared to previous kernel methods, the proposed method achieves better results in CIFAR-10 test accuracy. The use of stochastic regulariz...	Rebuttal 1: Rebuttal: Thanks for your review. > "Though achieving state-of-the-art results for kernel methods, the proposed method is still relative time consuming and requires much resource... sensitive to hyper-parameters" We agree that the method is time-consuming and sensitive to hyperparameters relative to SOTA ...	Summary: The paper reports numerical results in which the authors have achieved state-of-the-art performances for an image classification task, namely the CIFAR-10 dataset, with a ''convolutional deep kernel machine''. The performance of this kernel-based model is close to the ones of state-of-the-art neural network ar...	Rebuttal 1: Rebuttal: Thank you for your detailed review. > "...main weakness of the paper is to only provide results for the CIFAR10 dataset" We have managed to secure enough compute resources to also benchmark our method on the CIFAR-100 dataset. We improve the CIFAR-100 test accuracy from 72.1\% in earlier CDKM li...	Rebuttal 1: Rebuttal: We thank all the reviewers for their helpful comments. All reviewers recognised that our work presents state-of-the-art results for kernel methods on CIFAR-10. > "the improvement in generalization with respect to previous works is significant and certainly contributes to closing a gap between ke...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Hydra: Bidirectional State Space Models Through Generalized Matrix Mixers	Accept (poster)	Summary: The paper introduces a matrix mixer framework for sequence models which linearly applies an LxL matrix M to a sequence representation X of length L. Popular sequence models can be framed within this context e.g. softmax self-attention or SSMs, according to different properties of M. The authors use their fram...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for recognizing the novelty of our framework, its potential to motivate performant sequence mixers, and superior empirical performance enjoyed by Hydra. --- The reviewer's feedback highlights the following key concerns:\ Q1. Ablating away the shift and the diagona...	Summary: This paper presents Hydra, an innovative framework that builds upon the Mamba model by introducing bidirectional capabilities. Hydra's approach centers on a matrix mixer perspective, which allows it to consolidate various sequence models, including Transformers and structured state space models, into a unified...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the thorough evaluation and for recognizing the novel contributions of our framework. We are pleased that the reviewer appreciated the bidirectional capabilities, the valuable insights provided by the matrix mixer perspective, and the **extensive experim...	Summary: The paper introduces the concept of matrix mixer and sequence alignment for explaining recent sequence models including Transformer, linear transformer, and Mamba. It also proposes a quasiseparable matrix mixer (Hydra) as an alternative to the bidirectional SSM model. The experiments show that the quasiseparab...	Rebuttal 1: Rebuttal: We thank the reviewer for their insightful comments and we are glad that they found our Matrix Mixer framework informative and the Hydra model simple and effective. The review focuses on the following concerns:\ Q1. The main comparison table (Table 4) does not include any recent transformers ...	Summary: Most of sequence models include the token mixers and the channel mixers, and this paper provides a detailed summary. They also identify the matrix parameterization is crucial for recent SSMs. Therefore, they extend the Mamba model by adding bidirectional quasiseparable matrix mixer. The experiments on GLUE ben...	Rebuttal 1: Rebuttal: We thank the reviewer for recognizing the detailed categorization of prior methods using the Matrix Mixer framework and for appreciating Hydra’s simple yet effective design. --- We now address the reviewer’s concerns as follows: ### A1. Ablations on extendability: The extendable property of...	Rebuttal 1: Rebuttal: # Global Response --- We express our sincere gratitude to the reviewers for their valuable feedback and constructive suggestions. We are glad that they found our Matrix Mixer framework insightful and informative, and that they appreciated Hydra's simplicity and strong empirical performance. In ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Frieren: Efficient Video-to-Audio Generation Network with Rectified Flow Matching	Accept (poster)	Summary: They propose FRIEREN, an efficient video-to-audio generation model based on rectified flow matching that obtains state-of-the-art results. Strengths: - They successfully propose rectified flow matching for video-to-audio, that is a problem that is important in current generative AI setup where most video gene...	Rebuttal 1: Rebuttal: We highly appreciate your positive appraisal of our work and would like to discuss the issues you raised here. [Computing alignment accuracy] As stated in the Metrics part, Section 4.1, we adopt the alignment classifier provided in Diff-Foley[1] for calculating alignment accuracy. Specif...	Summary: This paper presents a new model for video-to-audio generation. The proposed model is based on rectified flow formulation and adopts Transformer-based architecture. A conditional video is fed into the model via channel-level concatenation to the audio tokens after processed by a length regulator. After training...	Rebuttal 1: Rebuttal: We are highly grateful for your positive appraisal of our work, and we'd like to discuss the issue you raised here. [Generization experiments on landscape dataset] Following your advice, we conduct experiments on the landscape dataset to investigate the generalization capability of our model...	Summary: The following work proposes a video to audio generation model. The model architecture closely follows that of prior work diff-foley, which operates on 4 frames per second, fits a temporally aligned latent space between audio and video content, and then a latent diffusion model to map from this latent space to ...	Rebuttal 1: Rebuttal: Thanks for your valuable comments. We'd like to make some clarification and discussion about the issues you raised. [Difference with Diff-Foley in architecture] We'd like to clarify that our model significantly differs from Diff-Foley in both architecture and alignment mechanisms...	Summary: This paper proposes a diffusion model based on rectified flow matching. Besides, to generate better audio quality, the authors propose re-weighting objective. The method achieves the state-of-the-art results on V2A benchmark. Strengths: * The proposed method is the first to leverage rectified flow matching o...	Rebuttal 1: Rebuttal: Thank you for your valuable comments on our work. We would like to discuss the issues you raised here. [Effect of transformer architecture and rectified flow] We believe that both the transformer architecture and the rectified flow (RF) modeling method contribute to the model performance. We...	Rebuttal 1: Rebuttal: To all reviewers, ACs, and PCs: We thank all reviewers for their valuable suggestions with their effort and time. Your comments have improved our work. We have individually responded to the comments and concerns of each reviewer. Please refer to each response for details. In order to better illu...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Hallo3D: Multi-Modal Hallucination Detection and Mitigation for Consistent 3D Content Generation	Accept (poster)	Summary: The authors point out that generating 3D content with Score Distillation Sampling leads to multi-view inconsistencies. To address this challenge, they propose a novel, tuning-free method called Hallo3D. Specifically, they utilize large multi-modal models (e.g., GPT-4V) to detect and correct these inconsistenci...	Rebuttal 1: Rebuttal: Thank you for your feedback. We appreciate your recognition of our method's innovation and effectiveness. Here are our responses to your comments. W1. "Fig.2 seems to understate the performance of current baselines." A1. We appreciate your attention to the details in Fig.2. Actually, this il...	Summary: Hallo3D presents a tuning-free method, empowering 3D content generation frameworks via multi-modal LLM. The paper aims to solve the hallucination and inconsistent problems in SDS-based 3D content generation pipelines. With the design of multi-view appearance alignment and enhanced negative prompts, Hallo3D is ...	Rebuttal 1: Rebuttal: Thank you for your feedback. We appreciate your recognition of our method's innovation and effectiveness. Here are our responses to your comments. **W1. "In lines 148-150, the paper claims defining a new denoising strategy, but there's no further details. Lines 184-186 provide some description, b...	Summary: This paper aims to alleviate the multi-Janus problem in SDS-based 3D generation tasks. Inspired by the spatial structure inference capability of large multimodality models (LMMs), they propose a novel automatic negative prompt strategy. Specially, they input rendered images and 3D-aware inquiry prompts to LMM ...	Rebuttal 1: Rebuttal: Thank you for your feedback. We appreciate your recognition of our method's innovation and applicability. Here are our responses to your comments. W1. "I doubt the reproduction of this work." Thanks for pointing this out. We'll share our code upon publication to help with reproducing our wor...	null	null	Rebuttal 1: Rebuttal: We thank the reviewers for their constructive feedback and valuable insights, which have significantly contributed to the improvement of our research. We are grateful for your thoughtful suggestions. Our work has been recognized for *the innovative introduction of the LMMs* (gwDu, bBV6, isRS...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Spectral Editing of Activations for Large Language Model Alignment	Accept (poster)	Summary: This paper focuses on the problem of editing undesirable behaviours at inference time that does not require any training. To that end, they present SEA, a method based on spectral editing of activations. To find the editing projections, the method requires keeping track of LLM activations over several neutral,...	Rebuttal 1: Rebuttal: 1. Comment: There is no significance testing for benchmark scores. Response: On TruthfulQA, we did the pair-wise t-test on SEA vs ICL baseline (in Table 1). We also confirm that SEA significantly outperforms LoRA-FT. We did not add more significant tests over other baselines as some of the ...	Summary: This paper introduces Spectral Editing of Activations (SEA), which adjusts the internal activations of LLMs to enhance alignment with truthful and unbiased content. This technique involves projecting input representations to maximize correlation with positive examples (truthful content) while minimizing correl...	Rebuttal 1: Rebuttal: 1. Comment: I suggest that the author compare SEA with TrFr and TruthX to highlight the novelty of the proposed method. Response: We will add them to the related work and provide a comparison. We agree the general motivations for SEA and TruthX are similar, but there are many differences: ...	Summary: The paper introduces a novel method called Spectral Editing of Activations (SEA) to improve the alignment of LLMs by enhancing truthfulness and reducing bias. SEA operates at inference time, projecting input representations in ways that maximize correlation with positive demonstrations (truthful content) and m...	Rebuttal 1: Rebuttal: *1. Comment: The paper could benefit from experiments on a broader array of tasks to further validate SEA's effectiveness across different contexts. This would help generalize the findings beyond the current benchmarks. How does SEA perform on other important NLP tasks not covered in this study? E...	Summary: ### Summary - This paper presents an inference time alignment algorithm based on activation editing. - Their technique named as spectral editing of activations (SEA) projects the input representations onto directions with maximal covariance with positive demonstrations (truthful) and minimum covariance with ...	Rebuttal 1: Rebuttal: 1. Comment: Line 294 is a strong claim. As shown in Figure 4 BBQ, the performance plateaus. Consider re-phrasing. Response: Thank you for your feedback. We agree that when comparing with results on TruthfulQA, the performance plateaus in Figure 4 for BBQ which suggests that the benefits of ...	Rebuttal 1: Rebuttal: This additional PDF page contains two figures asked by the reviewers: 1. Figure1: Visualisation for the spectrum of covariances. 2. Figure2: Visualisation for editing the activations. Pdf: /pdf/b9d1a5465f6125c98d5891cbd828597d8f718955.pdf	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
SuperDeepFool: a new fast and accurate minimal adversarial attack	Accept (poster)	Summary: The paper introduces SuperDeepFool (SDF), a new family of adversarial attacks aimed at testing the robustness of deep neural networks against minimal L2 perturbations. The proposed attacks generalize the DeepFool (DF) attack, improving both computational efficiency and effectiveness. The authors show that SDF ...	Rebuttal 1: Rebuttal: ## $\textbf{General comment:}$ We sincerely thank the reviewer for their insightful and comprehensive assessment. We are particularly pleased that the reviewer recognized SuperDeepFool's core strengths: its computational efficiency, rigorous theoretical foundation, and robust empirical results dem...	Summary: The paper introduces SuperDeepFool (SDF), a new adversarial attack algorithm designed to evaluate the robustness of deep neural networks against L2-norm adversarial attacks. SDF generalizes the DeepFool (DF) attack by incorporating a projection step to find smaller perturbations while maintaining computational...	Rebuttal 1: Rebuttal: ## $\textbf{General comment:}$ We are grateful to the reviewer for recognizing SuperDeepFool's efficiency, theoretical justification, and strong empirical performance across various datasets and tasks. We also appreciate the acknowledgment of its potential to improve adversarial robustness through...	Summary: The paper presents a novel, parameter-free and computationally efficient minimal-$L_2$ adversarial attack. Building on the DeepFool attack and incorporating a novel geometric perspective, the SuperDeepFool attack achieves state-of-the-art success rates on selected MNIST, CIFAR10, and ImageNet classifiers. The ...	Rebuttal 1: Rebuttal: ## $\textbf{General comment:}$ We really appreciate the reviewer’s enthusiasm and acknowledgment of the significance of our work. We address the reviewer's concerns below. ### $\textbf{Optimality Measurements:}$ Firstly, it is important to note that while avoiding overly perturbed perturbation is ...	Summary: The paper introduces a new family of adversarial attacks called SuperDeepFool (SDF) attack, extending the well-known DeepFool (DF) attack. This novel approach strikes an proper balance between effectiveness and efficiency, and consistently outperforms existing methods in terms of both. Additionally, the method...	Rebuttal 1: Rebuttal: ## $\textbf{General comment}$: We sincerely thank the reviewer for his/her positive assessment of our work, recognizing the novelty of our approach, the soundness of our theoretical analysis, and the strength of our empirical results. We are also pleased that the potential of SuperDeepFool for adv...	Rebuttal 1: Rebuttal: We attached a pdf file containing the desired results. Pdf: /pdf/cbcdd494fb56beace37dea6895312527341177e7.pdf	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
SnapKV: LLM Knows What You are Looking for Before Generation	Accept (poster)	Summary: This paper analyzes the issue of high storage pressure in KV caches within long-context scenarios and proposes a method for KV cache compression. Specifically, it calculates the attention weights between the last 16 Q tokens and K, and utilizes pooling and topK to determine the KV pairs to retain. The paper te...	Rebuttal 1: Rebuttal: *Q1. The paper uses a pooling method in the approximate stage and claims it is for more efficient pooling. However, Fig. 8 shows significant performance improvements with pooling, leading to doubts about whether the important Keys identified by the observation Query remain unchanged during the dec...	Summary: This paper presents a training-free KV cache compression approach. This approach builds off of the observation that the model consistently focuses on particular features during generation, and that these features can be detected when the prompt is passed in. Their approach uses a small “observation window” of ...	Rebuttal 1: Rebuttal: Q1. The paper lacks benchmarking experiments to justify that their approach doesn’t add any overheads to the prompt processing step. Answer: Thanks for the valuable feedback. To address your comment, we evaluate the prefilling time and memory usage on Mistral-7B with input sequence lengths ...	Summary: The paper introduces a method for minimizing KV cache size in LLMs. The authors offer the insight that attention heads focus on specific prompt attention features (tokens and their feature representation) during generation. This pattern can be discovered via an observation window at the end of the prompt. The ...	Rebuttal 1: Rebuttal: Q1. How sensitive is the method to the observation window size, or the pooling kernel size? It would be useful to include some more details about the performance measures used in Table 1. Answer: Thanks for the valuable feedback. To address your comment, we experiment with SnapKV on various...	null	null	Rebuttal 1: Rebuttal: Thank you for all the questions and suggestions. The PDF file contains all the tables and figures mentioned in the rebuttal. Pdf: /pdf/fb0ff7875d4e4703ba9b68950f33d9ad41487a5a.pdf	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
CAT: Coordinating Anatomical-Textual Prompts for Multi-Organ and Tumor Segmentation	Accept (poster)	Summary: This paper proposes a prompt-based deep model for organ and tumor segmentation. The authors leverage two types of prompts—cropped target volumes and textual descriptions—to perform the segmentation. The proposed model demonstrates good performance across three datasets for the segmentation task. Strengths: 1....	Rebuttal 1: Rebuttal: Thank you very much for your constructive suggestions. Below is our detailed response to answer your concerns. # W1&W5: Motivation and Details of textual prompts The motivation for using textual descriptions was to provide the model with the general concept of each category. While descriptive tex...	Summary: The paper "CAT: Coordinating Anatomical-Textual Prompts for Multi-Organ and Tumor Segmentation" introduces a novel dual-prompt schema that leverages both anatomical and textual prompts for medical image segmentation. The proposed CAT model coordinates 3D anatomical prompts with enriched textual prompts to impr...	Rebuttal 1: Rebuttal: Thank you for your thorough review. Below, we will address your concerns on each point. # W1&W3: Details of textual descriptions and Reasons for superior performance As detailed in Section 3.4, for each category, we curate long descriptions. As highlighted in the motivation section, learning the a...	Summary: This paper proposed CAT, a promptable segmentation model that utilizes the strengths of both visual and textual prompts without human interaction, aiming at a fully automatic model for medical professionals. Extensive experiments demonstrate the benefits of coordinating anatomical prompts and textual within on...	Rebuttal 1: Rebuttal: Thank you for your constructive comments. We will address your concerns in the following parts. # W1: Details of Experiments Sorry for the confusion caused by the omission of certain experimental details. In our experiments, we followed Universal's experimental settings [1]. We also trained the co...	null	null	Rebuttal 1: Rebuttal: We sincerely appreciate all reviewers’ time and efforts in reviewing our paper. We are glad that reviewers are generally interested in our proposed method of combining anatomical and textual prompts for medical image segmentation. We also thank all reviewers for their insightful and constructive s...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Algorithmic Capabilities of Random Transformers	Accept (poster)	Summary: This paper explores the inherent algorithmic capabilities of randomly initialized transformer models, particularly focusing on the functions that can be learned when only the embedding layers are optimized. It demonstrates that even without training the internal transformer layers, these models can perform com...	Rebuttal 1: Rebuttal: Thanks for the insightful review! The following are our responses. > Need for Improved Writing: The paper's writing style and organization need enhancement. For instance, there is a missing reference in line 19 of the introduction's first paragraph. Additionally, a footnote on page 4 is left ...	Summary: The aim of the paper is to understand how much of the effectiveness of Transformer models depends on the architecture itself rather than the possibility to train its internal parameters. To this aim, the authors study causal Transformers where only the embedding matrix, the positional encodings and the final p...	Rebuttal 1: Rebuttal: Thanks for the detailed and insightful review! The following are our responses. > Misuse of term "algorithmic capabilities" Let us humbly disagree. While a good algorithm should work for all possible data, in practice implementations often cannot extrapolate well, which does not disprove that ...	Summary: This paper studies transformers with freezed random intermediate layers, and embedding-only trainable layers. The authors show that wide enough random transformers are capable of performing simple algorithmic tasks such as addition and parenthesis balancing. This study further investigates the reason behind su...	Rebuttal 1: Rebuttal: Thanks for the detailed and insightful review! The following are our responses. > It is not clear how this observation about random transformers is helpful and useful. Especially, given that the (1) studied tasks are very simple (2) random transformer needs to be very wide to compete with a norma...	Summary: Update after rebuttal: My main concerns have been addressed and/or clarified, and some interesting results have been added. I therefore raise my score from 6 to 7, and vote and argue for accepting the paper. The paper investigates capabilities of randomly initialized, untrained transformers, where only a ...	Rebuttal 1: Rebuttal: Thanks for the detailed and throughout review! The following are our responses. > Compare against random LSTM In this paper, we are trying to narrow our already-pretty-broad discussion to random transformers instead of random neural architecture in general and we have shown that random transform...	Rebuttal 1: Rebuttal: We would like to thank all the reviewers for their detailed, thorough, insightful, and warm-hearted reviews. Your suggestions and criticisms definitely shaped the paper for better. We are especially pleased to see that most reviewers are generally satisfied with our presentation. During the rebut...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Cost-efficient Knowledge-based Question Answering with Large Language Models	Accept (poster)	Summary: 1. This paper proposes a cost-efficient strategy named "Coke" to automatically assign the most promising model for particular questions. 2. Experiments show the effectiveness of their method in Knowledge-based question answering (KBQA). Strengths: 1. The problem definition and mathematical explanation is clea...	Rebuttal 1: Rebuttal: We gratefully thank you for your constructive comments which we believe will absolutely improve the quality of our paper. We also wish to invite you to check our new results with a more comprehensive consideration of evaluation metrics, cost of KGMs and generalizability. > Response to weaknesses ...	Summary: The paper introduces a method for deciding whether to use a Large Language Model (LLM) or a Knowledge Graph-based Model (KGM) to solve various Knowledge-based QA tasks in an episodic manner, based on historical data. The main goal is to achieve better performance at a lower cost throughout the entire QA proces...	Rebuttal 1: Rebuttal: We would like to sincerely express our gratitude for your encouraging support and strong recognition, especially for your emphasis of our contribution to the community in the weakness again. We will carefully revise the final version following your suggestions. > Response to weaknesses - **W1: Cla...	Summary: This manuscript presents a novel cost-efficient strategy to leverage LLMs for knowledge-based question answering. It could balance both inferential accuracy and cost saving. Several SOTA methods, inlcuding both traditional KGQA methods and LLMs are combined since KGQA models are small and knowledgeable but les...	Rebuttal 1: Rebuttal: We would like to gratefully thank you for your strong support! We also cherish your expertise for plenty of professional suggestions. It is encouraging to be acknowledged by experts from the community. We believe your insightful comments will greatly help us to enrich the experiments and further d...	Summary: The authors proposed a strategy to switch between LLMs and KGMs when performing Question Answering, aiming to optimize both cost and accuracy. The evaluation is performed on three datasets: CommonsenseQA, OpenBookQA, and MedQA. Strengths: The authors address an important and practical problem, which is cost-s...	Rebuttal 1: Rebuttal: Thanks for your recognition of our contributions to a very important and practical problem. We value your insightful comments and will carefully revise the final version following your suggestions. > Response to weaknesses * W1: Implementation details. Thanks for raising these points. In the...	Rebuttal 1: Rebuttal: General Response We would like to sincerely thank all the reviewers for their valuable comments. We are also very excited to be highly acknowledged for our `contributions` and `significance` to future studies in a variety of communities. To address the concerns raised by reviewers, we have...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
HumanVLA: Towards Vision-Language Directed Object Rearrangement by Physical Humanoid	Accept (poster)	Summary: This work aims at building a vision-language-action (VLA) model that can control a humanoid to interact with dynamic objects without ground-truth state information. The proposed method first trains a teacher policy with ground-truth information. It then distills the policy to a VLA model via imitation learning...	Rebuttal 1: Rebuttal: W1: Border class of synthesis. One-agent multi-object: Our framework does not lose generalizability in muti-object interaction, which can be achieved by task reward design. However, from a model mechanism perspective, it will be more suitable with two dexterous hands. Multi-agent one objec...	Summary: In this paper, the authors address the task of room arrangement with a humanoid agent and propose a teacher-student framework to achieve vision-language guided object rearrangement. The teacher policy utilizes privileged state information, including object states, goal states, and waypoints derived from A* pla...	Rebuttal 1: Rebuttal: W1: Widely-used methodologies We agree some methodologies like AMP, RL, and teacher-student distillation are widely used in applications. But we also investigate new innovative insights like style reward clipping, carry curriculum, active rendering, and more to apply these techniques in the ...	Summary: This paper proposes HumanVLA, a framework for training humanoid controllers powered by vision and language. First, a teacher control policy is trained to control a simulated humanoid to carry objects to specific positions. Then, this policy is distilled into a vision-language-action model that uses vision to g...	Rebuttal 1: Rebuttal: W1: Qualitative results Thanks for your suggestions. The corresponding text prompts for demos align with Fig. 4 and Fig. 12. We provide an extra qualitative result with text and egocentric rendering in the rebuttal PDF. Due to the unavailability of demo submission in the rebuttal window, we ...	null	null	Rebuttal 1: Rebuttal: ## General Response We sincerely thank all reviewers for dedicating their time to review our work. And we highly appreciate their positive ratings and recognition of our work: - Our work addresses fundamental challenges for humanoid and points out a very promising and potential research ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Wasserstein Distributionally Robust Optimization through the Lens of Structural Causal Models and Individual Fairness	Accept (poster)	Summary: The authors propose a novel framework to enhance individual fairness guarantees under a Wasserstein distributionally robust optimization strategy. For such purposes, they employ counterfactuals based on the underlying causal structure of the model at hand. They further propose an alternative with theoretical g...	Rebuttal 1: Rebuttal: First of all, thank you for your detailed comments. We will address each of them in detail. W1. In the global response, we explained that introducing a new DRO framework requires considering key theorems such as strong duality, closed-form worst-case loss, regularizer estimation, and finite s...	Summary: This paper uses wasserstein distributionally robust optimization to address individual fairness concerns with causal structures and sensitive attributes. Strengths: The problem is well-motivated and novel to my knowledge. The formulation is clear. The solution is novel. Weaknesses: It does not seem easy to s...	Rebuttal 1: Rebuttal: W1. Thank you for pointing out this issue. We would like to address your question from two different perspectives: 1. Regularization and Scalability: In our work, we demonstrate the strong duality theorem (Theorem 1), which shows that the DRO learning problem can be transformed into an em...	Summary: This submission studies the connection between Wasserstein Distributionally Robust Optimization (DRO) and individual fairness in certain Structural Causal Models (SCMs). Namely, it is first shown that, in the case that the SCM at hand is an Additive Noise Model (ANM) with known structural equations, one may de...	Rebuttal 1: Rebuttal: First of all, I would like to thank you for your insightful comments. We appreciate the attention given to our work. In the following, I respond to the mentioned weaknesses and questions in detail. W1. In the global response, we explained the intuition behind our assumptions. Assumption 2 se...	Summary: This paper proposes a novel framework called Causally Fair Distributionally Robust Optimization (CDRO) to address individual fairness in machine learning. It combines causal modeling with distributionally robust optimization, using a causally fair dissimilarity function (CFDF) to measure individual similarity ...	Rebuttal 1: Rebuttal: Thank you for your helpful comments. We will address each point of weakness and questions, labeled Wi and Qi respectively, in order. W1. We will add the Structural Causal Model (SCM) in line 49. W2. We appreciate your comment and agree that the numerical section could be improved...	Rebuttal 1: Rebuttal: We thank the reviewers for their valuable feedback and constructive comments. We are honored to have received your attention. Motivation: Distributionally Robust Optimization (DRO) is a data-driven framework addressing out-of-sample challenges, such as distribution overfitting or shifts, usin...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Near-Optimal Distributed Minimax Optimization under the Second-Order Similarity	Accept (poster)	Summary: The paper proposes SVOGS, and improved algorithm for distributed minimax optimization by client mini-batch sampling and gradient variane reduction. Theoretical rates on communication complexity and gradient computations are provided, along with their lower bounds. The analysis shows that SVOGS achieves the cor...	Rebuttal 1: Rebuttal: The implementation details We tune the step-size $\eta$ of SVOGS from $\\{0.01,0.1,1\\}$. The probability $p$ is tuned from $\\{p_0,5p_0,10p_0\\}$, where $p_0=1/\min\\{\sqrt{n}+\delta/\mu\\}$. The batch size $b$ is determined from $\\{\lfloor b_0/10\rfloor,\lfloor b_0/5\rfloor,\lfloor b_0\rfl...	Summary: The paper studies distributed min-max optimization under the assumption of second-order data similarity, i.e. the hessians of the objectives at different nodes are close enough. For the classes of (strongly)-convex-(strongly)-concave functions with Lipschitz gradient, lower complexity bounds are proposed. More...	Rebuttal 1: Rebuttal: Why is the communication complexity different from the number of communication rounds? Recall that the communication complexity in our paper refers to the overall volume of information exchanged among the nodes. We take the convex and concave case as an example (Theorem 1) to explain why the ...	Summary: This manuscript considers solving (strongly) convex (strongly) concave distributed minimax optimization problem. The authors proposed a stochastic variance-reduced optimistic gradient sliding method with node sampling, named SVOGS, which achieves complexity nearly matches the obtained lower bound. Strengths: ...	Rebuttal 1: Rebuttal: Results for minimization in Ref. [25] Ref. [25] studies both minimization and minimax problems. We only compare their results for the minimax problem (Table 1-3). The minimax problem is indeed more difficult than the minimization problem. For example, Section 3 of Ref. [25] considers the str...	null	null	Rebuttal 1: Rebuttal: We thank the reviewers for their appreciation of our work. Both Reviewer Vpvu and Reviewer DkHm have raised questions about experiments. We provide the response as follows. The implementation details (hyperparameters) We tune the step-size $\eta$ of SVOGS from $\\{0.01,0.1,1\\}$. The proba...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
AdaptiveISP: Learning an Adaptive Image Signal Processor for Object Detection	Accept (poster)	Summary: Image Signal Processors (ISP) are software pipelines that aim to improve images for their visual quality or application-dependent downstream tasks. This work presents AdaptiveISP, a method to simultaneously optimize an ISP pipeline, consisting of individual functions such as image sharpening or color correctio...	Rebuttal 1: Rebuttal: Thank you for your feedback, we provide the results for a range of values for 𝜆𝑐, as shown in Table 3 on the global PDF, the efficiency-oriented method significantly reduces the average running time for each sample, with only a slight decrease in performance. As 𝜆𝑐 increases, our method tends ...	Summary: This paper proposes AdaptiveISP, a task-driven and scene-adaptive ISP, which uses deep reinforcement learning to automatically generate an optimal ISP pipeline and associated ISP parameters, aiming to maximize the detection performance. Experimental results show that AdaptiveISP outperforms prior state-of-the-...	Rebuttal 1: Rebuttal: 1. Performance on a newer detector. Please refer to part 1 of the Author Rebuttal. 2. Generalization ability. We utilized a model trained on the LOD dataset and conducted testing on the OnePlus dataset. As shown in Table 1 of the main paper, our method demonstrates the best generalizatio...	Summary: This paper proposes a novel approach to image signal processing (ISP) specifically tailored for object detection tasks, leveraging deep reinforcement learning to optimize both ISP structures and parameters. This method dynamically adjusts the ISP pipeline in response to different scene requirements, which enha...	Rebuttal 1: Rebuttal: 1. The performance of different detectors. Please refer to part 2 of the Author Rebuttal. 2. Briefly highlight the paper's novelty. We aim to design the first adaptive ISP tailored for detection. There are two main challenges: complexity and efficiency. First, optimizing ISP mod...	Summary: A new perspective of designing ISP pipeline. Good results with some problems should be addressed. Strengths: 1/ One method for raw detection which is still a new subarea waiting more discovery. 2/ Good performance compared with some methods. 3/ Discuss some orders in ISP pipeline. Weaknesses: 1/ Discussion o...	Rebuttal 1: Rebuttal: 1. Related works, especially for ISP pipelines, are not sufficient. In this paper, we primarily discuss how to design an ISP tailored for a specific high-level computer vision task. In most ISP tuning or design topics, the ISP consists of multiple modules with distinct roles. Using a network t...	Rebuttal 1: Rebuttal: We thank the reviewers for their feedback. We will revise the manuscript as suggested. Below are responses to common questions. We hope this can address your concerns. If you have other concerns, we will reply as soon as possible. 1. **Additional experiments on different detectors and comparison ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
On Causal Discovery in the Presence of Deterministic Relations	Accept (poster)	Summary: The paper delves into the challenges of causal discovery from observational data, with a particular focus on deterministic relationships often found in real-world scenarios. Firstly, the paper demonstrates that exact score-based methods can effectively handle deterministic relationships under mild assumptions...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewer’s time and constructive suggestions. With the help of such valuable feedback, we believe that our manuscript could be improved significantly. Please find the point-by-point responses below. Q1: "The algorithm presented in this paper guarantees a partial i...	Summary: This paper addresses the challenge of causal discovery in the presence of deterministic relationships by developing a novel framework called Determinism-aware Greedy Equivalent Search (DGES). DGES improves efficiency and scalability in detecting deterministic relations through a three-phase process and is vali...	Rebuttal 1: Rebuttal: We greatly appreciate the reviewer’s time and constructive comments. With the help of such valuable feedback, we believe that our manuscript could be improved significantly. Please find the point-by-point responses below. Q1: "Even if real-world scenarios frequently encounter deterministic re...	Summary: This paper focuses on the problem of deterministic dependencies in causal structure learning. Many algorithms for causal structure learning assume faithfulness between the conditional independencies present in the data and those implied by the graph, and this assumption can be violated when deterministic depe...	Rebuttal 1: Rebuttal: We deeply appreciate the reviewer for your time dedicated to reviewing our paper, encouraging words and constructive suggestions. In light of your valuable feedbacks, we have carefully modified the structure and narrative of our manuscript. Please find the responses to all your comments point-by-p...	Summary: Summary In this paper, the authors develop an approach to causal discovery with deterministic casual relations. They adapt the GES algorithm to deal with common faithfulness violations due to spurious conditional independences. Strengths: Strengths - The paper is well-written and the ideas are clearly present...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewer’s time and constructive suggestions. With the help of such valuable feedback, we believe that our manuscript could be improved a lot. Please find the point-by-point responses below. Q1-Q9 correspond to the points in "Questions", while Q10-Q12 correspond to the ...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms	Accept (poster)	Summary: This paper analyse overoptimisation in the context of direct alignment algorithms (DAA). They show that even when no explicit reward model is being optimised against, a similar phenomena as Gao et al. is shown, where as KL budget increases "gold" reward increases and then decreases. This phenomena is shown acr...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for the kind words! All analysis is only performed on the TL;DR summarisation dataset, which is somewhat different from Gao et al. and the setting these preference learning algorithms are generaly used in (dialogue and instruction-following)... We are work...	Summary: This paper studies the reward over-optimization issue for offline alignment algorithms (e.g., DPO series) with massive experiment trials and discussions on why this phenomenon happens. Strengths: 1. This is the first paper that studies the over-optimization issue for DPO-like algorithms systematically. The ob...	Rebuttal 1: Rebuttal: Why do you choose DPO, IPO, and SLiC-HF? Why not some other variants, say ORPO? Although I do agree that the current selections can be sufficiently representative, some discussions on why they are prioritized can be appreciated. We chose these versions of the DAAs as they were the most extens...	Summary: This paper studies the scaling laws of DAAs for RLHF. The authors conducted extensive empirical studies and the discoveries are reported and discussed. Strengths: The paper is well-written and easy to follow. The authors conducted extensive empirical studies. The current results are useful to the community to...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for the useful feedback! Specifically, in Figure 1, the results do not seem to be a good fit, and more experimental results might be helpful to draw the conclusion. We used win rates as computed by GPT-4 for evaluation, which is now well-established in the...	Summary: Reinforcement Learning from Human Feedback (RLHF) is a popular paradigm for aligning Large Language Models (LLMs) to human preferences. Direct Alignment Algorithms (DAA) are an alternative to traditional RLHF methods, which reduce the need to learn a reward and policy model separately. It has been shown that t...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for the informative review! The paper only performs experiments on a single model class and task, so it is not clear if these results generalize… We are working on replicating our main experiment with the Gemma-2 2B model on the Anthropic Helpful and Harml...	Rebuttal 1: Rebuttal: We would like to thank all the reviewers for the useful comments! 1. We are working to expand the experiments in our work using the Gemma 2 2B model on the Anthropic Helpful and Homeless Dataset. We have attached our preliminary results here, which show the same general effects as our Pythia TL;D...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Sharing Key Semantics in Transformer Makes Efficient Image Restoration	Accept (poster)	Summary: This paper proposes a dictionary-based image restoration method that leverages the most relevant information to recover images with low computational costs. Specifically, the method constructs a key-semantic dictionary that stores the top-k semantically related regions for each patch and performs attention onl...	Rebuttal 1: Rebuttal: ## Response to Reviewer YUtD: ### Q1: Comparison to KiT[1] A: Please refer to our answer to the 1st question in the shared "Author Rebuttal". [1] KNN Local Attention for Image Restoration. CVPR'22 ### Q2: Fig.2 needs to improve regarding the dimension A: Thanks for this very important...	Summary: This paper propose SemanIR, a novel Transformer architecture for image restoration. The paper propose a new attention mechanism for better efficiency and efficacy, based on that within a degraded image, patches semantically close to the target patch to restore provide major information in the restoration proce...	Rebuttal 1: Rebuttal: ## Response to Reviewer aTgi: ### Q1: Top-k selection discussion? A: See the answer to the 2nd question in the shared "Author Rebuttal". ### Q2: Visualization: A: We set a query region in the input and provided a detailed comparison from the attention-based activation map together with t...	Summary: The paper proposes an efficiency-first modification to the self-attention mechanism in ViTs. The main premise of the work is to construct a key-semantic dictionary which relates each key to its semantically-relevant patches, and then share the dictionary across Transformer blocks in the same stage for computat...	Rebuttal 1: Rebuttal: ## Response to Reviewer hRwb: ### Q1: Have the authors considered window-size ablations? A: The windows indeed contain mixed information from different semantic parts. Yet, it is precisely this semantic distinction that motivates us to develop a selection mechanism for semantic information u...	Summary: Unlike traditional transformers where the multi-head self-attention layer calculates the correlation between one patch and all patches, the method proposed by the authors computes the correlation among the top k semantically similar patches, allowing image restoration with lower computational cost. Additiona...	Rebuttal 1: Rebuttal: ## Response to Reviewer xhpu: ### Q1: What's the difference between SemanIR and other methods like KiT or DRSformer? A: Please refer to our 1st answer in the shared "Author Rebuttal". ### Q2: What is the difference between SemanIR and token merging and pruning methods? A: The key focus ...	Rebuttal 1: Rebuttal: Dear All, We appreciate the dedicated efforts each of you has invested in evaluating our work and providing invaluable suggestions the positive feedback (i.e., logically explain, decent alternative to dense self-attention, numerous and various experiments, and strong experimental results). We hav...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
LoTLIP: Improving Language-Image Pre-training for Long Text Understanding	Accept (poster)	Summary: This paper introduces LotCLIP, which enhances CLIP’s capability to understand long texts. It highlights that merely increasing the length of texts (i.e., context length) is not beneficial as it adversely impacts the understanding of short texts (i.e., image classification). To mitigate this trade-off, addition...	Rebuttal 1: Rebuttal: ``Q1: Contributions are unclear.`` Sorry for confusion. We reaffirm our core contribution: - We are the first to explore how to improve understanding long texts in contrastive language-image pre-training, and also firstly design learnable text [CLS] tokens (corner tokens) for this purpose. M...	Summary: To improve the ability of vision-language models (VLMs) for long-text understanding, the paper proposes to relabel the data with long captions, however, direct learning may lead to performance degradation in understanding short text (e.g., in the image classification task). Then, corner tokens are introduced t...	Rebuttal 1: Rebuttal: Thank you for your positive comments and valuable feedback on our work! We are excited and encouraged by your support! Bellow we address your concern separately. ``Q1: Lack of details for addressing the limitation for the token length limitation of the text encoder in Sec. 3.4.`` Sorry for confu...	Summary: The paper addresses a significant gap in current language-image pre-training models, which are typically trained on datasets with short captions. This limitation hinders the models' ability to effectively understand and process long texts. The proposed solution, LotCLIP, introduces methods to enhance long-text...	Rebuttal 1: Rebuttal: Thank you for your positive comments and valuable feedback on our work! We are excited and encouraged by your support! Bellow we address your concern separately. ``Q1: LotCLIP on other similar algorithms, e.g. ALIGN, CoCa.`` *Our LotCLIP can be applied on other similar algorithms as well, e....	Summary: The paper describes a framework to adapt language-image pre-training models to longer captions. For that, first a new dataset is created with longer captions and second, training is modified to adapt to longer captions. New corner tokens are introduced that are supposed to capture longer dependencies in the te...	Rebuttal 1: Rebuttal: Thank you for your positive comments and valuable feedback on our work! We are excited and encouraged by your support! Bellow we address your concern separately. ``Q1: Difference between Dora dataset and DreamLIP.`` The main difference between Dora dataset and the dataset proposed in DreamLIP l...	Rebuttal 1: Rebuttal: Dear reviewers, We thank all reviewers for their time and efforts in reviewing our paper. These constructive reviews can bring the improvements for our manuscript. We are encouraged that the reviewers appreciate our method, including * Problem definition and analysis (Reviewer sjvn, NJUx) * Effe...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
End-to-End Video Semantic Segmentation in Adverse Weather using Fusion Blocks and Temporal-Spatial Teacher-Student Learning	Accept (poster)	Summary: This paper provides a practical solution for video-based semantic segmentation under adverse weather conditions. Existing methods mainly focus on domain adaptation from synthetic to real data (Viper/Synthia to Cityscapes), but this is the first paper to address videos under adverse weather conditions. Current...	Rebuttal 1: Rebuttal: We thank the reviewer for recognizing that our task is important, the idea is novel and contributes to the community, and with a good performance. __Weakness-1:__ The white area is not involved in computing the spatial loss, we will adjust the arrow to make it correctly pointing to the right-bott...	Summary: This paper proposes a video segmentation method for adverse weather conditions by using the unsupervised domain adaptation paradigm. Its general idea is to introduce the temporal information from adjacent video frames by the proposed spatial-temporal teacher-student learning scheme. Notably, the proposed m...	Rebuttal 1: Rebuttal: We thank Reviewer 1Ffg for recognizing that our work can benefit the community, novel, effective, and with a significant performance gain. __Q1:__ The MVSS dataset consists of a total of 52,735 RGB images, with 3,545 of these images annotated. The dataset includes a variety of adverse conditions ...	Summary: In this paper, a end-to-end domain-adaptive video semantic segmentation method without optical flow estimation is proposed to address the problem of video frame quality degradation under adverse weather conditions. The proposed method uses the temporal information of adjacent frames through fusion blocks and s...	Rebuttal 1: Rebuttal: We thank Reviewer L8p7 for recognizing the importance and performance of our work. Here is our response to the feedback: __Weakness-1:__ Thank you for the feedback. Our model has an inference time of 0.11 seconds, which is faster than the state-of-the-art baselines: DA-VSN's 0.35 seconds and TP...	Summary: This paper studies an important task of video semantic segmentation. Specifically, it focuses on adverse weather scenes and proposes an end-to-end, optical-flow-free, and domain-adaptive algorithm by using fusion blocks and temporal-spatial teachers. Extensive experiments are conducted on VIPER, Synthia and MV...	Rebuttal 1: Rebuttal: We thank Reviewer G5E9 for recognizing the importance of our task, our good results, and the clarity of our paper. __Weakness-1 and Weakness-2__ Thank you for your valuable feedback. We will address both Weakness-1 and Weakness-2 together, as they are closely related. Additionally, the novelty o...	Rebuttal 1: Rebuttal: We thank all the reviewers for their insightful feedback. We are encouraged by their recognition of the importance of our task (G5E9, L8p7, dY49) and its potential benefit to the community (1Ffg, dY49). We are also pleased that they acknowledged the novelty of our method (1Ffg, dY49). Additionally...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Generation	Accept (poster)	Summary: The authors proposed FOLDFLOW++, which is built on top of FOLDFLOW [ICLR 2024]. It adds a joint structure and sequence representation and a transformer-based geometric decoder, enabling folding and inpainting applications. Strengths: The tasks the authors are attempting to solve seem very interesting and impo...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and effort in reviewing our paper. We are heartened to hear that the reviewer views that with FoldFlow++ we are tackling a problem that is “very interesting and important for drug discovery” which was our primary aim with this new state-of-the-art protein struc...	Summary: The paper proposes a new model FOLDFLOW++ for Conditional Protein Backbone Generation. It incorporates several techniques including sequence model, finetuning strategies, and high-quality synthetic structures to improve its performance on various tasks. The experimental results suggest the method achieves SOTA...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their detailed feedback and constructive comments, which allowed us to significantly improve our submission with new experiments and results which can be found in our global response. In addition, we appreciate that the reviewer found our paper to be “well-w...	Summary: The paper presents a protein generative model FoldFlow++ augmented with protein language model embeddings upon FoldFlow. The model is trained with sequence and structure information to learn embedding projections in SE3 space. Experiments on unconditional generation show a favorable performance of FoldFlow++ o...	Rebuttal 1: Rebuttal: We thank the reviewer for their enthusiastic review and positive appraisal of our work! We are heartened to hear that the reviewer found our paper to be an “excellent piece of work” with the architecture being “novel” and the overall model to show “wonderful protein generative model potential”. We...	Summary: The paper introduces FoldFlow++, a sequence-conditioned SE(3)-equivariant flow matching model designed for protein structure generation. FoldFlow++ builds upon previous FoldFlowmodels by incorporating a protein language model to encode sequences, a multi-modal fusion trunk to integrate structure and sequence r...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and detailed feedback which gave us an opportunity to strengthen our manuscript with additional ablation and results. We are encouraged that the reviewer felt that our paper includes a “detailed ablation” study of the different architectural components which en...	Rebuttal 1: Rebuttal: We thank all reviewers for their time and thorough reviews. We are glad that the reviewers found that Foldflow++ has high potential impact as a new SOTA with important applications in real-world scenarios (R VRjh, R ffQt), such as being important for drug discovery (R pz4y). We are also grateful t...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Multi-Stage Predict+Optimize for (Mixed Integer) Linear Programs	Accept (poster)	Summary: This paper addresses optimization problems with parameter values unknown at solving time. Specifically, the work takes a supervised learning approach to predicting these parameters, which are revealed over multiple “stages.” The authors extend the Predict+Optimize framework, which uses an optimization-inspired...	Rebuttal 1: Rebuttal: Thank you for your positive review of our work. We respond to your questions and comments below. Venue fit: While Predict+Optimize does have some operations research flavor, it is fundamentally a machine learning problem nonetheless. We also note that the NeurIPS+ICML community has shown inte...	Summary: The paper proposes a Multi-Stage Predict+Optimize framework that addresses optimization problems where parameters are revealed in sequential stages. It introduces three neural network training algorithms tailored for mixed integer linear programs (MILPs) under this framework. The methodologies are empirically ...	Rebuttal 1: Rebuttal: Thank you for your review and your appreciation of the significance of our work. We address your concerns in this individual response. Assumption on constraints: As the reviewer pointed out, we make an assumption that the optimization problems are always feasible, which seems like a strong as...	Summary: The authors present an approach for learning hidden parameters for multi-stage optimization problems where parameters are gradually revealed at each stage. In this setting, latent parameters are predicted, then soft committed decisions are made based on those predictions, that stage’s parameters are revealed, ...	Rebuttal 1: Rebuttal: Thank you for your detailed review of our work. We respond to your concerns below. Datasets: Please see overall response. Furthermore, thank you for your suggestion of stock data. However, the datasets from the cited works are not suitable for our purposes either. This is because the cited wo...	Summary: The paper proposes a new framework called Multi-Stage Predict+Optimize for tackling optimization problems with parameters revealed in multiple stages, rather than simultaneously. The authors develop three training algorithms for neural networks within this framework, particularly for mixed integer linear progr...	Rebuttal 1: Rebuttal: Thank you for your review and your appreciation of the rigor in our work. Here, we address your concerns in the "Weaknesses" part of the review. Comparison with existing Two-Stage framework: We want to emphasize that the multi-stage framework does cover a much wider range of applications. T...	Rebuttal 1: Rebuttal: We thank the reviewers for the detailed, in-depth and constructive reviews. We are glad that reviewers recognize and appreciate that our work tackles an important problem, gives robust empirical evaluations, and that our paper is well-written. In this overall response, we address some of the comm...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Data Free Backdoor Attacks	Accept (poster)	Summary: This work introduces data free backdoor attacks (DFBA). The idea behind this attack is to introduce a backdoor into already trained neural nets by manually modifying parameter weights without requiring fine-tuning or any initial clean data. The backdoor is implemented by manually defining a path from the input...	Rebuttal 1: Rebuttal: Thanks for the constructive comments! Q1: Potential adaptive defense methods A1: Thank you for your question. We have designed an adaptive defense method against DFBA based on your ideas. Please refer to CQ2 for details. Given your concerns, we will further explore this issue in the Limi...	Summary: In this paper, the authors propose DFBA, a novel approach for injecting backdoors into pre-trained classifiers without the need for retraining or access to clean data. This method stands out by not altering the model's architecture, which enhances its stealthiness and efficiency. The authors claim that DFBA's ...	Rebuttal 1: Rebuttal: Thanks for the constructive comments! Q1: Further clarification on CNN structure A1: We apologize for any confusion. For CNNs like VGG and ResNet, DFBA follows the same core principles as FCN, but with some adjustments: First, for convolutional layers, we select one convolutional filter...	Summary: In this work, the authors design DFBA, a novel retraining-free and data-free backdoor attack that does not alter the architecture of a pre-trained classifier. They theoretically prove that DFBA can evade multiple state-of-the-art defenses under mild assumptions. Their evaluation on various datasets demonstrate...	Rebuttal 1: Rebuttal: Thanks for the constructive comments! Q1: Further clarification on CNN structure A1: We apologize for any confusion. For CNNs like VGG and ResNet, DFBA follows the same core principles as FCN, but with some adjustments: First, for convolutional layers, we select one convolutional filter...	Summary: This paper proposes a backdoor attack that directly modifies the model parameters and does not rely on any data. By designing a backdoor switch in the first layer, optimizing the trigger, and amplifying outputs in the following layers, the method creates a backdoor path that can be activated by backdoored inpu...	Rebuttal 1: Rebuttal: We greatly appreciate the reviewer's constructive suggestions! Q1: Writing issues A1: Thank you very much for your valuable comments! We will make the following modifications based on your suggestions: a): We will summarize our method's experimental results against various defense metho...	Rebuttal 1: Rebuttal: We thank all the reviewers for your valuable comments! Here we address some common questions for all reviewers: CQ1: Maintaining method stability and Backdoor Accuracy. CA1: We discuss DFBA's performance in two aspects: Attack Success Rate (ASR) and Backdoor Accuracy (BA). Regarding A...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper proposes a strategy for injecting backdoors into a DNN without the attacker requiring access to the training data of the model or having to change the architecture of the model. The attack is executed by directly manipulating the parameters of the neural network. Concretely, this is achieved by sele...	Rebuttal 1: Rebuttal: We greatly appreciate the reviewer's positive feedback and constructive suggestions for this research work! Q1: How to include multiple triggers, and how would this affect accuracy? A1: In brief, for additional triggers with the same target class $y_{tc}$, we only need to modify one extr...	null	null	null	null	null	null
E-Motion: Future Motion Simulation via Event Sequence Diffusion	Accept (poster)	Summary: The paper proposes a novel approach to integrate event-sequences with a video diffusion model for event-based future motion prediction. The authors integrate the learning capacity of video diffusion models with the rich motion information of event cameras to create a motion simulation framework and propose to ...	Rebuttal 1: Rebuttal: ## Q1: Event V.S. High-Speed Camera There are basically three reasons for event data outperforming high-speed cameras: (1) Data characteristics. Event cameras record only dynamic intensity changes and have extremely high-temporal resolution, which means they capture a wealth of motion in...	Summary: The paper explores video diffusion models on the modality of information captured by event cameras. Stable Video Diffusion model is fine-tuned on an event stream dataset. On top of traditional diffusion set up, additional training using FVD and SSIM losses as rewards in a PPO is done. A method to inject motion...	Rebuttal 1: Rebuttal: ## Q1: Event-specific Design The authors want to note that the proposed method indeed makes event-based designs. Specifically, during the high temporal resolution guided sampling stage in Section 4.1 of the main text, our method fully leverages the high temporal resolution and flexible sa...	Summary: This work focuses on the task of future motion estimation, where the goal is to leverage event-based vision sensors (an alternate modality, compared to traditional vanilla RGB inputs) to predict motion flow in settings useful for robotics and autonomous vehicles. The authors propose a method that leverages sta...	Rebuttal 1: Rebuttal: ## Q1: Additional Analysis w.r.t. Baselines There are indeed some conflicts between different metrics, e.g., FID, IOU, and FVD, because only FVD can comprehensively evaluate the both spatial and temporal distribution alignment between the generated samples and GTs. FID and IOU can only measur...	Summary: The paper introduces a novel framework that leverages the high temporal resolution of event-based sensors to predict future motion trajectories with unprecedented detail and precision. The authors propose an integration of video diffusion models with event camera data, resulting in an Event-Sequence Diffusion ...	Rebuttal 1: Rebuttal: ## Q1: Performance in Challenging Visibility Benefiting from the unique characteristics of event cameras, our event-based video diffusion framework can handle future motion estimation issues to some extent. To further address your concern about algorithm performance on challenging visibili...	Rebuttal 1: Rebuttal: ## General Response We thank all reviewers for your time, constructive feedback, and acknowledgment of our work. We believe all concerns have been clearly and directly addressed. Here, we also want to summarize a few key clarifications concerning the contributions of our work. Our major ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
ReMoDetect: Reward Models Recognize Aligned LLM's Generations	Accept (poster)	Summary: The authors demonstrate that reward models inherently possess the capability to distinguish between human-written and machine-generated text. They propose a method for continuous pairwise fine-tuning of existing RMs, which achieves excellent results on several LLM-generated text (LGT) detection datasets and ex...	Rebuttal 1: Rebuttal: Dear reviewer 3CSf,\ We sincerely appreciate your efforts and comments to improve the manuscript. We respond to your comment in what follows. --- [W1] Further clarification regarding the mechanisms behind the improvements is needed. We clarify that ReMoDetect is effective due to the followi...	Summary: This paper proposes a method named ReMoDetect to use a reward model for model-generated text detection. Firstly, The authors find that the existing reward model can easily distinguish human-written text from language-model-generated responses. Then, the authors propose two techniques, 1) continual preference t...	Rebuttal 1: Rebuttal: Dear reviewer dzQj, We sincerely appreciate your efforts and comments to improve the manuscript. We respond to your comment in what follows. --- **[W1] How the corresponding prompts are determined when evaluating the proposed models. RM works for the prompt $x$ given. What about prompt ungiven ...	Summary: The paper is about a novel and effective approach for LLM-generated text (LGT) detection by making use of the reward model score. The authors observe that LGT often has higher reward model score compared to human-written texts. They then further increase the separating by fine-tuning the reward model to score ...	Rebuttal 1: Rebuttal: Dear reviewer KLmr, We sincerely appreciate your efforts and comments to improve the manuscript. We respond to your comment in what follows. --- [W1] Lack of qualitative analysis on examples Thank you for your constructive comment. While we have a portion of qualitative examples in Appendix...	Summary: The paper finds that reward models used in RLHF can detect texts generated by LLMs. Based on this, the paper presents ReMoDetect, a novel method that further trains the reward model using continual preference fine-tuning and a challenging text corpus rephrased by LLMs. ReMoDetect achieves new SOTA on various L...	Rebuttal 1: Rebuttal: Dear reviewer CEwj, We sincerely appreciate your efforts and comments to improve the manuscript. We respond to your comment in what follows. --- [W1] The method relies on the quality and initialization of the RM. First, we would like to clarify that we have only trained a single reward mod...	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 tackles an interesting and important problem (CEwj) and provides an effective (all reviewers) framework for detecting LGT, which is motivated...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Learning to Merge Tokens via Decoupled Embedding for Efficient Vision Transformers	Accept (poster)	Summary: This article provides a novel way to enhance the efficiency of token merging within ViTs. DTEM, the proposed method, introduces a lightweight embedding module that operates independently from the ViT's forward pass, overcoming the constraints imposed by utilizing intermediate features. DTEM can be integrate wi...	Rebuttal 1: Rebuttal: > W1. Will the determination of these hyperparameters bring complexity to the model implementation? There is no ablation study on parameter m in soft merging. → We report further analysis related to hyper parameters, regarding (1) the number of steps in soft grouping and (2) temperature scal...	Summary: This paper proposes DETM, which calculates similarity through additional embeddings instead of the original intermediate features. Additionally, it further introduces soft grouping and soft merging to make the merging process differentiable. Strengths: 1. The paper is well-written and easy to follow. 2. The m...	Rebuttal 1: Rebuttal: > W1. There is a lack of discussion and comparisons with recent related works, such as [DiffRate] and [METR]. → We appreciate your feedback regarding the need for discussions and comparisons with recent related works, specifically [DiffRate] and [METR]. DiffRate focuses on determining the ...	Summary: This paper works on the topic of visual token merging to improve the efficiency of ViT. Specifically, this work introduces decoupled token embedding for merging (DETM) which learns decoupled embedding via an additional module. Bu introducing the soft grouping and soft merging scheme, the proposed method is dif...	Rebuttal 1: Rebuttal: > W1. It is concerned that if the capacity of the proposed decoupled embedding module is able to learn different aspects compared to its input--the original visual features. If so, how to regulate the decoupled feature is still representative of the original feature. → We clarify the main pu...	Summary: In this paper, decoupled token embedding for merging (DTEM) is proposed for more efficient and effective token merging. It employs a lightweight embedding module to obtain a feature vector which is solely used for token merging process. To train this embedding module, DTEM uses the relaxed merging method base...	Rebuttal 1: Rebuttal: > W1. Why is [BAT, 18] not compared in Table 1? → Table 1 showcases the results for methods applied to a pretrained, frozen ViT with modular training, whereas BAT was originally proposed and evaluated in an end-to-end training setting without results from modular training. While it seems pos...	Rebuttal 1: Rebuttal: We thank the reviewers for their time and effort in providing constructive reviews. We appreciate the encouraging remarks about the paper's novelty (oGzP), technical soundness (PcYu), promising experimental results (oGzP), and applicability (oGzP, wYAw, FV5y). We are happy to respond to the weakne...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Causal Dependence Plots	Accept (poster)	Summary: The paper introduces causal dependence plots to visualize how (black-box) model predictions are affected by changes in input variable distributions. The explanations are generated based on an underlying explanatory causal model and can be generated for different types of causal quantities such as direct or ind...	Rebuttal 1: Rebuttal: We are glad you agree our method embeds the causal inference literature in a general framework for model explanations and that you found our paper relevant and well-supported. Thank you for pointing out the minor comments and typos. We will be careful to address each of these in the revision. W...	Summary: The authors present a novel set of attribution methods called Causal Dependence Plots (CDPs) that extend partial dependence plots (PDP) while respecting possible causal dependencies in the method's inputs. The approach aims to obtain truthful and reliable insights for black box model analyses. In detail, a giv...	Rebuttal 1: Rebuttal: Thanks for your review. It motivates us to make some small but key additions that should improve the paper. We take the claimed weaknesses and questions seriously and will do our best now to respond to each. A: Our main goal is not to contribute novel methods for causal learning or inference, but...	Summary: The authors propose a new approach to visualize the impact of a change in a variable on the outcome of interest. They argue that if some of the other variables in the model are mediators, and that they should not be held constant as is currently done in variable importance measures, as it may lead to bias (pos...	Rebuttal 1: Rebuttal: Thanks for your assessment. We are happy you found the soundness and contribution of our paper good and the presentation excellent. We agree that most explainable ML has a serious flaw and that an approach based on causal graphs and visualizations is intuitive, useful, and very informative. We al...	Summary: The authors tackle the problem of evaluating the dependence between the inputs and output of black box machine learning models. Generally, analysis of this type of dependence is done in a univariate manner, holding constant all but one variable and visualizing how the outcome changes as we vary that one varia...	Rebuttal 1: Rebuttal: Thanks very much for your insightful and thorough review. You’ve given us useful feedback that will help clarify and improve the paper. You’re right that we were pressed for space- the CDP framework is quite general and there are many things we would like to demonstrate with it (e.g. other interes...	Rebuttal 1: Rebuttal: We are really grateful for these high quality reviews. We condense reviews/rebuttals below and invite reviewers to correct us if we changed the meaning or missed important points (full reviews and responses are of course available separately). # Summary ## Reviewer 6pua Positives: "I really...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Learning Partitions from Context	Accept (poster)	Summary: This paper studies a learning problem where we are given sequences of tokens and the task is to predict a label. At each step of the sequence, there is a different clustering of the tokens into classes and the output label only depends on the sequence of classes corresponding to the tokens. The task is then to...	Rebuttal 1: Rebuttal: We thank the reviewer for their helpful review. Below, we comment on the weaknesses pointed out in the review and reply to the questions raised. > The usage of the word "interact" in the abstract is rather vague. - We will make the abstract a bit more verbose and clarify that the interaction i...	Summary: This article studies the properties of tokens, i.e word embeddings in NLP, that are grouped in a small number of clusters. It proposes and analyzes a relatively simple model which is basically a composition of a clustering and a real function, but that shares many similarities with real world complex models. T...	Rebuttal 1: Rebuttal: We thank the reviewer for their insightful review. We would like to briefly comment on the weaknesses and the question raised. > The authors provide no algorithm - This is a valid point, and we will clarify this in the paper: It does not seem too easy to design a problem-specific algorithm. Ho...	Summary: The paper proposes a new learning problem: learning the partitions of tokens given sample sequences of tokens. The authors first study the problem from an information-theoretical perspective, where $\tilde{O}(N)$ samples are sufficient to recover the partition for an alphabet of $N$ tokens. Then, they investig...	Rebuttal 1: Rebuttal: We thank the reviewer for their insightful review. Let us first answer the reviewer's questions and then briefly comment on the criticism raised in the review. > What is $N_0$? - We denote by $N\_0(I,K,\eta)$ a constant depending on $I$, $K$, and $\eta$, i.e., we will change the statement to 'T...	Summary: This paper defines a learning problem for functions that depend only on a set of unknown partitions of the data. It establishes sample complexity, computational hardness and guarantees for GD-based algorithms under additional assumptions. Strengths: The model is simple and clean, the presentation is clear wit...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive review. Regarding your question about the testability of the assumptions: The assumptions on the model used for learning are (in principle) testable. For the dataset, we can test whether samples follow a uniform distribution. What is difficult to test is ...	Rebuttal 1: Rebuttal: We thank all reviewers for their careful and insightful reviews. All reviewers agree that the problem formulation is interesting and relevant ('simple and clean' R. `Rx9k`, 'novel and clear' R. `NSnU`, 'important insights' R `8unk`, and 'interesting and novel' R. `w8Q8`), the results are acknowle...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Transformers as Game Players: Provable In-context Game-playing Capabilities of Pre-trained Models	Accept (poster)	Summary: This paper investigates the in-context learning capabilities of pre-trained transformer models in competitive multi-agent games, i.e., in-context game-playing (ICGP). The authors provide extensive theoretical guarantees to validate that pre-trained transformers can approximate NE in an in-context manner for bo...	Rebuttal 1: Rebuttal: Thank you for reviewing this work! Please find a point-by-point response provided in the following, with the reviews compressed due to the length limit. --- >Weakness 1. The paper ... the analysis of in-context learning in game theory ... why this research problem is important. **Response 1...	Summary: This paper explores the in-context learning capabilities of pre-trained transformer models in two-player zero-sum games. The authors provide the theoretical guarantees that pre-trained transformers can learn the approximate Nash equilibrium in an in-context manner, both in decentralized and centralized setting...	Rebuttal 1: Rebuttal: We would like to first express our appreciation to the reviewer for reviewing this work. A point-by-point response is provided in the following, which hopefully can answer and clarify the raised questions and concerns. --- >Weakness 1. In this work, the game instances are assumed to have the...	Summary: The authors built on the recent work of Lin et. al 2023, extending their ICLR framework so that instead of being for one agent, it is for multi-agent systems; at the same time, they also analyze and provide evidence of "ICGP" (in-context game-playing) capabilities in transformers. The paper analyzes zero-sum ...	Rebuttal 1: Rebuttal: Thank you for reviewing this paper! The following responses are provided with the reviews compressed due to the length limit. --- >W1. (Lines 157-170) If the data collected ... a limited range of strategic interactions ... might lack the necessary diversity ... >Q1. Given ... the diversi...	Summary: The paper proposes a framework for pre-trained transformers to approximate Nash equilibrium in two-player zero-sum normal-form games and provides theoretical guarantees to show that these pre-trained transformers can learn to approximate Nash equilibrium in an in-context manner. This is shown for both the dece...	Rebuttal 1: Rebuttal: Thank you for reviewing this work! We are happy to hear your recognition of the new insights and contributions of this work. The following point-by-point response is provided, which hopefully can address the raised questions and concerns. --- >Weakness 1. I have concerns that since the pre-t...	Rebuttal 1: Rebuttal: Dear Reviewers, We would like to express our gratitude for all your time and effort in reviewing this work. Point-by-point responses have been provided, which hopefully can address the raised questions and comments. It would be our pleasure to engage in further discussions and incorporate any sug...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
$\boldsymbol{\mu}\mathbf{P^2}$: Effective Sharpness Aware Minimization Requires Layerwise Perturbation Scaling	Accept (poster)	Summary: This paper proposed $\mu P^2$ which is an effective way to scale the perturbation radius of SAM for each layer so that the optimal hyperparameter (learning rate $\eta$ and perturbation radius $\rho$) transfers across different widths. Experiments show that this approach indeed allows the transfer of optimal $\...	Rebuttal 1: Rebuttal: Thank you for carefully reading our paper, providing detailed feedback and your help in improving the presentation of our results. We take your concerns about clarity and presentation seriously as we would like that a large audience is able to appreciate our results. If we are able to address some...	Summary: This paper analyzes Sharpness-Aware Minimization (SAM) in the infinite-width limit using tensor program theory. The authors identify issues with standard SAM implementations in wide networks and propose a new parameterization called μP^2 to address these problems. They provide theoretical analysis and conduct ...	Rebuttal 1: Rebuttal: Thank you for carefully reading our paper and providing detailed feedback. We are delighted about your overall positive evaluation of our work. If we are able to address some of your concerns, we kindly ask you to consider updating your score as you positively alluded to both our rigorous theoreti...	Summary: The authors extend muP based learning rate transfer to the extra gradient ascent step involved in the SAM algorithm. The authors present "tensor programs" theory to derive this scaling, and present convincing experiments that in their method, both step sizes in SAM transfer across width (learning rate and pert...	Rebuttal 1: Rebuttal: We sincerely appreciate your thoughtful review of our paper. We are delighted about your positive evaluation of our work and are grateful for your insights which have been invaluable in enhancing the accessibility and clarity of our work. Improving the presentation. In the main paper, after ...	Summary: Sharpness Aware Minimization (SAM) improves performance across various neural architectures and datasets, but understanding its scaling behavior as models grow is crucial. This study examines the infinite-width limit of neural networks trained with SAM using the Tensor Programs framework. Findings show that in...	Rebuttal 1: Rebuttal: Thank you for carefully reading our paper. We are delighted about your overall positive evaluation of our work. As other reviewers have pointed out, this paper is already quite dense and contains extensive experiments. Hence we would like to defer experiments on further SAM variants to future work...	Rebuttal 1: Rebuttal: We are thankful for all of the thoughtful comments and constructive feedback to improve the clarity of our paper’s presentation. We are delighted to have received overwhelmingly positive feedback about our content and results, and we will do our best to improve the accessibility of the paper — in ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Leveraging Catastrophic Forgetting to Develop Safe Diffusion Models against Malicious Finetuning	Accept (spotlight)	Summary: This paper studies the possibility of preventing the T2I models from malicious fine-tuning attacks. The authors draw their inspiration from contrastive learning and propose two ways of separating the safe distribution from the harmful distribution in the latent space of the T2I models (LT, NG). The authors hav...	Rebuttal 1: Rebuttal: Thank you for your valuable review. Q1. Achieving a Universally Safe Model To address this concern, we have designed a new experiment with a harmful dataset including types such as sexual and violence content. We conduct experiments on four metrics on this new dataset, and the results are sh...	Summary: The paper addresses the problem of ensuring the safety of generative models (here text-to-image diffusion models) against malicious fine-tuning as well as the erasure of undesired concepts and capabilities. To this end, the proposed approach leverages catastrophic forgetting through contrastive learning The au...	Rebuttal 1: Rebuttal: Thank you for your detailed feedback on our paper. Q1. Discussion on more Technique Details - Equation 4 $\hat{z}= \frac{1}{\sqrt{\bar{\alpha_t}}}(x_t-\sqrt{1-\bar{\alpha_t}}\hat{\epsilon})$ derives from the forward process of DDPM, which is described by the function $x_t=\sqrt{\bar{\alpha}_...	Summary: This paper, inspired by the phenomenon of catastrophic forgetting, proposes a training policy using contrastive learning to increase the latent space distance between clean and harmful data distribution, thereby protecting models from being fine-tuned to generate harmful images due to forgetting. Two main step...	Rebuttal 1: Rebuttal: Thank you for your constructive feedback and suggestions on our paper. Q1. More Discussion on Performance Trade-off of Our Safety Models Our method tries to resist malicious finetuning by manipulating the latents of Stable Diffusion models to prevent generating harmful images. We introduce t...	Summary: This paper considers a scenario where malicious entities want to train a diffusion model for harmful content generation. To prevent the model from being finetuned to generate harmful content, this paper proposes to leverage the catastrophic forgetting mechanism to counteract the harmful finetuning. To trigger ...	Rebuttal 1: Rebuttal: Thank you for your time and valuable feedback on our work. Q1. Adding FID-30k metric to verify the performance degradation of clean images after harmful finetuning We acknowledge the importance of demonstrating the performance degradation of clean images after harmful finetuning. We have con...	Rebuttal 1: Rebuttal: We thank all reviewers for their time and detailed reading of our paper. Reviewers remark that our paper extends the concept of safety models beyond basic safety alignment, where the resistance to malicious finetuning is introduced as a potential criterion for evaluating model safety performance. ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-Training	Accept (poster)	Summary: The paper proposes MSAGPT, a novel method for generating MSAs. Utilizing 2D evolutionary positional encoding, MSAGPT reformalizes MSA generation as a one-dimensional sequence generation task optimized with a simple GPT objective. The model incorporates feedback from AlphaFold2 to reduce hallucinations during M...	Rebuttal 1: Rebuttal: About Question-1: the explanation of 2D RoPE and the novelty clarification. 2D RoPE Explanations. Rotary Positional Embeddings (RoPE) encode position information of tokens with a rotation matrix that naturally incorporates explicit relative position dependency. First, consider the 1D Rotar...	Summary: Protein structure prediction tools such as alpha fold take a query protein sequence, expand it to a multiple sequence alignment (MSA) of related natural sequences, and then feed this alignment into the model. The first expansion step isn't possible, however, for proteins that don't have many natural relatives,...	Rebuttal 1: Rebuttal: Thanks for your insightful feedback and constructive suggestions for our work. We addressed your questions as follows: About Question-1: add more evaluations. We have adopted both suggestions to confirm the superiority of MSAGPT: + Statistical Significance of Metrics: We conducted a paired St...	Summary: This paper proposes a method to generate multiple sequence alignments for a given protein sequence. To model the co-evolutionary information, the paper proposes 2d evolutionary positional encoding. After pretraining on the alignment sequences, the models are fine-tuned with AlphaFold2 annotations to avoid hall...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback and careful assessment of our work. We address your concerns below, About Weakness-1: the missing important experimental details. The training details and experimental settings, including the processes for Pre-training, Rejective Finetuning, and DPO, as we...	null	null	Rebuttal 1: Rebuttal: # Global Response on Newly-Added Comprehensive Evaluations and Claims Dear Reviewers, Thank you for your insightful feedback and constructive suggestions. We have incorporated additional experimental results, detailed in the attached PDF, and provided thoughtful discussions to address your c...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Smoothie: Label Free Language Model Routing	Accept (poster)	Summary: This paper proposes a label-free routing method, Smoothie, to route an ensemble of LLMs without annotated data. Smoothie constructs a latent variable graphical model over semantic embedding representations of observable LLM outputs and the unknown ground truth, estimates the sample-independent quality scores o...	Rebuttal 1: Rebuttal: We thank the reviewer for their thoughtful comments and for engaging with our work. We are grateful they appreciated (1) the generality of Smoothie’s algorithm beyond supervised methods, and (2) the breadth of experimental results. We refer the reviewer to our general response for more informati...	Summary: This paper proposes a method for selecting LLMs' responses for generative tasks. It can essentially be viewed as a "truth inference" problem in the research community of "weak supervision" and "crowdsourcing"; unlike ordinary truth inference methods, this paper takes into account unstructured textual informat...	Rebuttal 1: Rebuttal: We thank the reviewer for their detailed feedback and are glad that they found the problem interesting and practical. Below, we address the reviewer’s concerns around related work and baselines, differences with Shin et. al., and writing clarifications. Related work: Thank you for your sugges...	Summary: - This work proposes a method called SMOOTHIE, which can route label-free test examples to LLMs. Specifically, - it employs a latent variable model and Gaussian distribution for efficient quality score estimation and uses LLM outputs to estimate generator quality. - it estimates specific to each test sam...	Rebuttal 1: Rebuttal: We thank the reviewer for their feedback and are happy that they found the topic interesting. In our global response, we discuss the limitations of Smoothie, which the reviewer pointed out. Below, we address the reviewer’s comments on writing clarifications as well as the Minimum Bayes method. **...	Summary: This paper presents a model for routing an input to an LLM from a pool of LLMs. The aim is to estimate which LLM will produce highest quality generation without using any labeled training data in estimating the routing model. Instead, the approach relies on weak supervision to learn the parameters of a Gaussia...	Rebuttal 1: Rebuttal: We thank the reviewer for their thoughtful comments and for engaging with our work. We are glad to hear they appreciated our approach and the comprehensiveness of our experiments. We refer the reviewer to our general response for more information on (1) Smoothie’s routing behavior, and (2) Smoothi...	Rebuttal 1: Rebuttal: We thank the reviewers for their valuable feedback. We are glad that reviewers found the Smoothie algorithm to be elegant (RLiV, Cr26, gxgv), recognized the practical applications of this work (r3vp, Cr26), and appreciated the breadth of our evaluation (RLiV). Our global response (1) discusses re...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
What Variables Affect Out-of-Distribution Generalization in Pretrained Models?	Accept (poster)	Summary: The paper sets out to evaluate empirically the generality (referred to as "universality") of the "tunnel hypothesis", the idea that compression at later layers may hinder OOD performance. Strengths: Work on OOD learning, network probing and explainability is very relevant to the conference. Weaknesses: How e...	Rebuttal 1: Rebuttal: Thank you for your insightful reviews and feedback. We have carefully considered your concerns. Below, we have responded to your comments. # Weaknesses W1. How do empirical evaluations support the generality of a hypothesis? - We addressed this by considering a diverse range of experimental se...	Summary: This paper investigates the "tunnel effect" in NNs introduced in a NeurIPS 2023 paper. According to the “tunnel effect” the deeper layers compress representations, limiting OOD generalization. The authors challenge the prior assumption that the tunnel effect is universal. They imply that it's heavily influence...	Rebuttal 1: Rebuttal: Thank you for your insightful review, comments, and feedback. We have carefully considered your concerns and tried to address your concerns below. # Weaknesses W1. Increased resolution mitigates the tunnel effect, but doesn’t increasing DNN depth in that setting bring the tunnel effect back? -...	Summary: This paper studies how well self-supervised models transfer after self-supervised pretraining. Using linear probe experiments on top of frozen encoders, and by varying the depth in the model being fed to the linear probe, they study the effect of network depth on ID performance and OOD (transfer learning) per...	Rebuttal 1: Rebuttal: Thank you for your insightful review, comments, and feedback. We have carefully considered your concerns. Below, we have responded to your comments, and we hope these will address your concerns. # Weaknesses W1. The scope of results seems narrow, reflecting known findings - Our work is much ...	Summary: This paper studies the factors influencing out-of-distribution (OOD) generalization of pre-trained DNN embeddings through the lens of the tunnel effect hypothesis, which suggests deeper DNN layers compress representations and hinder OOD performance. Strengths: - The paper includes a sufficient amount of exper...	Rebuttal 1: Rebuttal: Thank you for your thorough reviews and insightful feedback. We have carefully considered your concerns and tried to address them. Below, we have provided detailed responses to each review separately. # Weaknesses W1. Limited Technical Contributions - We believe our work presents substantia...	Rebuttal 1: Rebuttal: We thank the reviewers for their constructive feedback, valuable insights, and thoughtful questions. We have carefully considered all comments and provided detailed responses to each review separately. We have revised our paper as we should. We hope our responses have addressed the reviewers' con...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
KV Cache is 1 Bit Per Channel: Efficient Large Language Model Inference with Coupled Quantization	Accept (poster)	Summary: This paper presents a novel approach to KV Cache compression in Large Language Models (LLMs) called Coupled Quantization (CQ). The authors analyze the correlation between different channels in the KV Cache from an information entropy perspective, revealing significant interdependencies. Leveraging this insight...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the insightful review and invaluable feedback. We address the reviewer's concerns as follows. [W1] Limited Evaluation Tasks: KV Cache compression is most relevant in long-text scenarios. - We appreciate the reviewer's suggestion to test KV cache quantizatio...	Summary: This paper identifies a significant interdependency among distinct channels of key/value activation tensors in Transformer models. By quantizing multiple key/value channels together using joint entropy, the authors achieve high inference throughput while maintaining model quality. In extreme cases, the KV cach...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the support of our paper and the insightful suggestions. We address the reviewer's concerns as follows. [W1, L1] Include latency experiments. - In the table below, we present additional latency measurements of CQ with comparison to FP16 cache and KIVI. We ...	Summary: The paper explores the idea of compressing the KV cache in Transformer models through quantization; specifically, the authors propose Coupled Quantization, which quantizes multiple KV channels together in order to exploit their interdependency. The gains are guaranteed by the fact that the joint entropy is sma...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for their careful review of our paper and the insightful suggestions. We address your concerns as follows. [W1] Focused on the Llama family of models. -- We have added Mistral model results! - We would like to draw the reviewer's attention to the results on Mi...	Summary: The authors have addressed the KV-cache compression problem by providing a finer quantization level. The KV-cache can pose a significant barrier to the inference of most autoregressive language models, a challenge that has been well studied in recent publications at ICML and NeurIPS. This paper introduces a no...	Rebuttal 1: Rebuttal: We express our sincere gratitude to the reviewer for their thoughtful comments and suggestions. We address the reviewer's concerns as follows. [W1] The code is not available. - We will open source our code during the camera-ready phase. To provide additional context, we have included expande...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewers' careful evaluation of our paper and their valuable feedback. In the following section, we address some common concerns raised by multiple reviewers. We are happy to provide further clarification during the discussion period. **1. Evaluations with long-contex...	NeurIPS_2024_submissions_huggingface	2,024	Summary: LLM inference typically involves Key-Value (KV) caching to avoid recomputation. However, the KV cache size grows with batch and context length, imposing bottlenecks in memory footprint and inference speed. Quantization can be employed to reduce the size of the KV cache. Instead of channel-wise independent quan...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for their careful consideration of our work and for providing valuable feedback. We have addressed their comments in detail below. [W1] Table 2 shows that CQ only outperforms the baselines in the 1bit regime, which already suffers great accuracy loss. - It is ...	null	null	null	null	null	null
CVPT: Cross-Attention help Visual Prompt Tuning adapt visual task	Reject	Summary: In this paper, in order to break the dominance of adapter-based methods, the authors first analyze the weakness of the previously widely-used prompt-based method, Visual Prompt Tuning (VPT). Firstly, the prompt mechanism is inherited from NLP where each token/prompt represents an actual word with rich semantic...	Rebuttal 1: Rebuttal: Firstly, we would like to show our gratitude. The paper you cited (Ref1) has greatly inspired us, supporting many of our ideas and significantly helping our subsequent work. W1&W2: (1)Lack of representative information. The lack of representative information we refer to is relative to ...	Summary: This paper focuses on prompt learning of pre-trained ViT in downstream tasks, and improves the widely used visual prompt tuning (VPT) by employing cross-attention techniques and weight-sharing mechanisms. Strengths: The paper's research topic on vision model prompting technology is highly significant in the e...	Rebuttal 1: Rebuttal: W1 (An unnecessarily large number of prompts): For VTAB (comprising 19 datasets), 10 datasets achieved the best performance using 50 or more prompts. For FGVC (comprising 5 datasets), 3 datasets performed best with 50 or more prompts. Additionally, complex downstream tasks such as semantic seg...	Summary: This paper proposes a variant of visual prompt tuning (VPT) where the authors suggest applying cross-attention instead of self-attention in the Transformer layers to reduce training complexity. The authors analyze several drawbacks of existing VPT approaches and claim to address them using cross-attention. St...	Rebuttal 1: Rebuttal: W1 (Limited novelty): In fact, our contribution lies in optimizing the prompt insertion of VPT by decoupling the prompt from self-attention and linking the prompt with embedded tokens using cross-attention. CVPT doesn't modify the self-attention mechanism in ViT, nor does it involve the combin...	Summary: This paper furthers the research on Parameter Efficient Fine Tuning on the visual tasks. PEFT optimizes a large scale model by selecting a small set of parameters. This work refines the Visual Prompt Tuning by leveraging the cross attention between the prompt and embedded tokens. Further the model uses weight ...	Rebuttal 1: Rebuttal: W1(Conclusion seems to be more of an abstract): Below is our modified conclusion, and this will be introduced in our revised version. In the current field of visual fine-tuning, many researchers overlook prompts in favor of adapters due to their strong performance. The few prompt-based derive...	Rebuttal 1: Rebuttal: We thank all reviewers for their thoughtful feedback. We are encouraged that they found our experiments are detailed, the structure of the writing is complete, and the methods are straightforward (R3). Moreover, R1, R2, and R4 think our works explore the weakness of VPT. R1, an...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning	Accept (poster)	Summary: The authors introduce two methods for learning agents to trade-off imitation and exploration across generations, by incorporating the behavior of noisy oracles and/or the best-performing agents in prior generations into the observations of the next generation of agents. Two settings are studied: in-context lea...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their clear and focused review. We are pleased that the reviewer finds that “the approach is novel and interesting” and that “the work is well situated among related prior work in generational methods and social learning.” The reviewer seems to have graspe...	Summary: This paper introduces the problem of modelling cultural accumulation in populations of deep RL agents, with evolution happening through non-communicative social learning. The paper introduces 2 setups for cultural accumulation: one where the agents learn in-context from other agents within an episode composed ...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their extensive and detailed review. We are pleased that the reviewer finds that “the questions being studied are worthwhile and timely”, “the positioning with respect to the existing literature is good”, “the background section does a good job of refreshing...	Summary: The paper "Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning" introduces the concept of cultural accumulation in reinforcement learning (RL), where agents benefit not only from their own experiences but also from the knowledge passed down from previous generations, akin to h...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their positive and informative review. We are pleased that the reviewer finds that the paper exhibits an “innovative concept”, “robust experimental design” and “comprehensive analysis”. Whilst we appreciate the reviewer’s acknowledgement of these strengths, ...	Summary: The paper studies cultural accumulation within the context of RL agents. The techniques involve social learning based on in-context learning and in-weights learning. Each agent is modelled as a POMDP within a larger POSG. The techniques are applied to memory sequence, goal sequence, and TSPs, where they are co...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their thorough and extensive review. We are pleased that the reviewer finds that “the concept of transmitting information across generations other than the genotype is underexplored” and that “the results show a positive performance trend.” # On related wor...	Rebuttal 1: Rebuttal: We are grateful to the reviewers for their insightful feedback. We appreciate the consensus that our work is exploring an important, understudied area and that our results indicate positive progress by demonstrating that cultural accumulation can outperform single-lifetime baselines. This is the k...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
The Implicit Bias of Heterogeneity towards Invariance: A Study of Multi-Environment Matrix Sensing	Accept (poster)	Summary: This paper studies "implicit invariance learning" within a simplified but meaningful setting---multi-environment low-rank matrix sensing problem. Authors show the implicit bias of SGD over heterogeneous data drives the model learning towards an invariant solution. The key insight is, through simply employing t...	Rebuttal 1: Rebuttal: We would like to thank the reviewers for the valuable feedback and insightful comments. We have carefully considered your comments and questions and have addressed them as below: > This finding is interesting and potentially quite helpful to invariant learning. From my own experience with many DG...	Summary: The authors show that in a matrix sensing context, and under a data distribution that includes invariant and environment-dependent components, SGD with successive batches from different environments lead to invariant features being provably learned. The authors show that SGD with mixed batches provably does no...	Rebuttal 1: Rebuttal: We would like to thank the reviewers for the valuable feedback and insightful comments. We have carefully considered your comments and questions and have addressed them as below: > About discussing the adoption of matrix sensing problem. A: We fully understand your concerns regarding our model. ...	Summary: The paper studies the implicit bias of (Hetero) SGD towards learning invariant representations and discarding non-invariant features in a matrix sensing setup. The applicability of the setup to the 2-layer NN with quadratic activations is demonstrated. Finally, the authors demonstrate that pooledGD fails to re...	Rebuttal 1: Rebuttal: We would like to thank the reviewers for the valuable feedback and insightful comments. We have carefully considered your comments and questions and have addressed them as below: > About the findings on ``HeteroSGD`` verses ``PooledGD``? Does the result in theorem 3 extend to pooledSGD as well? ...	Summary: This paper studies the difference between the solutions to multi-environment matrix-sensing obtained by gradient descent and "heterogenous" stochastic gradient descent. The authors show through analytical results and simulations that HeteroSGD helps discover invariant solutions while gradient descent converges...	Rebuttal 1: Rebuttal: We would like to thank the reviewers for the valuable feedback and insightful comments. We have carefully considered your comments and questions and have addressed them as below: > The problem of multi-environment matrix sensing seems under-motivated. Instances of problems and reasonability. A:...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
OT4P: Unlocking Effective Orthogonal Group Path for Permutation Relaxation	Accept (poster)	Summary: This paper proposes a new method for relaxing permutation matrices onto the group of orthogonal matrices. A temperature controlled differentiable transform maps the permutations onto O(n) and this allows for adjusting the strength of regularity vs the problem difficulty. With this relaxation, the paper employs...	Rebuttal 1: Rebuttal: We sincerely thank Reviewer k6w2 for your insightful and constructive comments. We strongly recommend reviewing our global rebuttal first to clarify common concerns. Following are our responses to each individual comment. ## Q1: how do the authors implement rounding To implement rounding from...	Summary: The paper proposes a parameterization of $n\times n$ permutation matrices by $n(n-1)/2$ unconstrained numbers. This parameterization eases the training of neural networks and is applicable to tasks involving optimization over permutations. Strengths: - The paper is well-written and easy to follow. - The initi...	Rebuttal 1: Rebuttal: We extend our heartfelt thanks to Reviewer FuEB for your thorough and thoughtful review of our manuscript. We strongly recommend reviewing our global rebuttal first to clarify common concerns. We have carefully considered your feedback and responded to it below. ## **Q1: what's the role of the te...	Summary: This paper proposes OT4P, a differentiable transformation that relaxes permutations to the orthogonal group. Based on OT4P, the authors propose novel frameworks for deterministic and stochastic optimization on permutation matrices. Numerical experiments demonstrate its efficiency and scalability in permutation...	Rebuttal 1: Rebuttal: We are deeply grateful to Reviewer 3CCZ for the detailed and constructive feedback on our work. We strongly recommend reviewing our global rebuttal first to clarify common concerns. We address your specific questions below. ## Q1: the experiments in the main text are both synthetic We recogni...	null	null	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewers for dedicating their valuable time and effort to thoroughly reviewing our manuscript. Here, we address the common concerns raised and introduce the additional experiments conducted. # Common concerns ## **Q1: What is the intuition behind Step II of the propos...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Unleashing Multispectral Video's Potential in Semantic Segmentation: A Semi-supervised Viewpoint and New UAV-View Benchmark	Accept (poster)	Summary: The paper proposes advancements in multispectral video semantic segmentation (MVSS) through two key contributions: the creation of a new benchmark dataset, MVUAV, captured via UAVs, and the development of SemiMV, a semi-supervised learning baseline designed to optimize sparse annotations using Cross-collaborat...	Rebuttal 1: Rebuttal: Dear Reviewer Mx7S, we sincerely appreciate the time and effort you spent reviewing our paper and your positive feedback. Your comments are insightful, and we look forward to addressing each of your concerns point-by-point. --- *W1: "The paper presents a new dataset for semantic segment...	Summary: This paper introduces a new multi-spectral aerial-view semantic segmentation dataset called MVUAV, which consists of 413 video sequences and 53K frames with sparsely annotated pixel-level segmentation labels. To provide a way of using this sparsely annotated dataset, it also introduces a new semi-supervised se...	Rebuttal 1: Rebuttal: Dear Reviewer XBRU, thank you for your recognition that our MVUAV dataset is valuable and our SemiMV method shows better performance. We hope to address each of your questions point-by-point and clarify some misunderstandings. --- *Q1-a: "(a1) What is the memory feature* $f_i^$, ..." a...	Summary: The paper addresses multispectral video semantic segmentation (MVSS) and proposes a new semi-supervised learning approach. It introduces the SemiMV framework, which utilizes a Cross-collaborative Consistency Learning (C3L) module and denoised temporal aggregation strategy. Additionally, the paper establishes t...	Rebuttal 1: Rebuttal: Dear Reviewer w3ua, we greatly appreciate the time and effort you have dedicated to providing constructive suggestions on ways to strengthen our paper. We are also grateful for the positive comments and recognition. Below, we make a point-by-point response to all the comments. --- *W1: "Comp...	null	null	Rebuttal 1: Rebuttal: Dear Reviewers and Area Chairs, We would like to thank you for your valuable time and efforts in providing these insightful questions and suggestions for improving our paper. We are also pleased that the reviewers have generously appreciated our new MVUAV dataset and the semi-supervised MVSS bas...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
The Limits of Transfer Reinforcement Learning with Latent Low-rank Structure	Accept (poster)	Summary: The paper investigates transfer in reinforcement learning, where one tries to exploit latent structure common across several MDPs. Specifically the paper consider M "source" episodic MDPs sharing a latent low-rank structure of the transition matrix and one "target" episodic MDP whose transition matrix is also-...	Rebuttal 1: Rebuttal: We thank reviewer LmTe for the suggestions on how to improve the presentation of our paper and have made the modifications for the final version. We have corrected the typos addressed by points 1a, 1c, 1g, 1j, 3, 4, 5, 6, 7, 8, 9, 13, 15, 16, 17, and 20. Regarding Theorem 1, the key idea is that ...	Summary: This paper addresses the computational and data inefficiencies of reinforcement learning (RL) algorithms due to large state and action spaces. It introduces a transfer learning approach that utilizes latent low-rank structures in the transition kernels of source and target Markov Decision Processes (MDPs). The...	Rebuttal 1: Rebuttal: We appreciate reviewer nw9e’s feedback and suggestions on how to improve our paper. We completely agree that our paper would benefit from numerical experiments and will look into running simulations to illustrate the benefits of our algorithm. Q2&4: Regarding estimating $\alpha$, $\alpha$ is...	Summary: This work considers transfer RL, where the source and target MDPs admit low Tucker rank. An information-theoretic lower bound is derived for the source sample complexity, and the proposed algorithm is minimax optimal respecting the transfer-ability coefficient $\alpha$ (in the case of $(d,S,A)$). The results d...	Rebuttal 1: Rebuttal: We appreciate reviewer Stu9’s feedback and suggestions on how to improve our paper. We completely agree that our paper would benefit from adding a column listing the target regret bounds in Table 1 and will add it to the final version. Weakness 2: We realize that our terminology is misleading...	null	null	Rebuttal 1: Rebuttal: We appreciate the reviewer’s feedback and suggestions. We discuss the primary shared concerns of the reviewers below, and have deferred addressing clarification questions to the individual reviewer rebuttals. We will incorporate the below discussion into the final paper. **Intellectual novelty (n...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Scalable Constrained Policy Optimization for Safe Multi-agent Reinforcement Learning	Accept (poster)	Summary: The paper studies the problem of constrained MARL in a cooperative setting and focus on the decentralized learning settings without global observability. The paper proposes a constrained policy optimization method and its practical version, Scal-MAPPO-L. Theoretical results are established for the dynamics/pol...	Rebuttal 1: Rebuttal: We sincerely appreciate the valuable comments from the reviewer. We hope our responses below provide further clarity. Remark: Without further specification, we use "[number]" to refer to the corresponding reference in our paper. > W1: My major concern …… correlation assumption. A: We re-cla...	Summary: the work proposes a scalable version of MAPPO-L for constrained policy optimization, taking into account that decays in the inter-dependence between agents in a Markov Game projects into bounded errors while limiting the information sharing between agents. The theoretical results transfer nicely from the two s...	Rebuttal 1: Rebuttal: We sincerely appreciate the valuable comments from the reviewer. We hope our responses below provide further clarity. Remark: Without further specification, we use "[number]" to refer to the corresponding reference in our paper. > W1(Major): In the related …… cited as ……. A: We re-clarify ...	Summary: The paper proposed a scalable multi-agent constrained policy optimization for safe reinforcement learning. It is an extension of two previous work on safe reinforcement learning and scalable multi-agent reinforcement learning. The trust region policy updates and truncated policy/advantage function are combined...	Rebuttal 1: Rebuttal: We sincerely appreciate the valuable comments from the reviewer. We hope our responses below provide further clarity. > W1: The author should …… different formulations. > W2:The experiments …… algorithm to handle it). > Q2: The performance …… a bit? A: We thank the reviewer for appreciating ou...	null	null	Rebuttal 1: Rebuttal: # General Response We would like to express our sincere gratitude to the reviewers for reading our paper and providing valuable feedback. Below, we answer some common questions raised by the reviewers, including the technical contributions, the assumptions about spatial correlation decay,...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Make-An-Agent: A Generalizable Policy Network Generator with Behavior-Prompted Diffusion	Accept (poster)	Summary: Make-An-Agent is a novel policy network generator that uses conditional diffusion models to create control policies based on a single demonstration of desired behaviors. By encoding behavior trajectories into embeddings, Make-An-Agent generates latent policy parameter representations that are decoded into func...	Rebuttal 1: Rebuttal: We sincerely appreciate Reviewer YLrf for the insightful feedback and the acknowledgment of our work's novelty and empirical effectiveness. Below, we provide a detailed response to address your concerns: - W1 and L3: Limited evaluation: Our experiments cover 3 domains and 23 tasks, includ...	Summary: This paper proposes a novel approach to generate policy parameters based on the behavior through diffusion models. The paper leverages the autoencoder to map the parameters of the policy to latent representations. The model demonstrates remarkable generalization abilities on unseen tasks with few-shot demonstr...	Rebuttal 1: Rebuttal: We are grateful to Reviewer 8Q7F's acknowledgment of the novelty, empirical results, and presentation of our paper. Your feedback is very helpful in improving the quality of our work. Below are our detailed responses to each of your questions: > The generated policies seem unstable. The generatio...	Summary: This paper proposes the Make-An-Agent architecture, which synthesizes a policy neural network from an input trajectory. Make-An-Agent utilizes a parameter and behavior embedding. The behavior embedding is trained with the mutual information between the trajectory and the successful part of the trajectory. The ...	Rebuttal 1: Rebuttal: We sincerely appreciate the positive feedback from Reviewer Pxd2 on the originality, overall quality, and significance of our work. The valuable comments and suggestions from Reviewer Pxd2 are of great help to improve the quality of our work. Detailed responses regarding each problem are listed be...	Summary: In this work, the authors present Make-An-Agent, which is a method to generate policy parameters given a few intended trajectories from a task. The proposed method is straightforward, which makes it better that it seems to work in the experiments in the work. The method first generates a large dataset of polic...	Rebuttal 1: Rebuttal: We sincerely appreciate Reviewer 957J for your acknowledgment of our idea's novelty and method's applicability. Thank you for your valuable comments and suggestions, which are of great help to improve the quality of our work. We carefully answer each of your concerns as below. > The reported metri...	Rebuttal 1: Rebuttal: ## General Response ### Summary of Review and Highlights We sincerely thank all reviewers for their insightful comments, valuable questions, and helpful suggestions. We appreciate the positive feedback from all reviewers regarding our paper's presentation (Reviewers U6yw, 8Q7F), **idea no...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper presents Make-An-Agent, a conditional diffusion model that generates policy parameters based on demonstration of target behaviors. The authors propose an autoencoder to encode policy network parameters into compact latent representations. The behavior embeddings are learned using contrastive learnin...	Rebuttal 1: Rebuttal: We thank Reviewer U6yw for the positive comments to our writing and experiment thoroughness. Your questions are instrumental in helping us improve the quality of our paper. We have provided detailed responses point by point below. > Is there any way to roughly predict the performance of the gener...	null	null	null	null	null	null
Refusal in Language Models Is Mediated by a Single Direction	Accept (poster)	Summary: This paper studies the mechanisms behind refusal behavior in LLMs via the lens of internal representation. The authors demonstrate that refusal behavior is mediated by a one-dimensional subspace across several open-source chat models. They identify a single direction in the model's residual stream activations ...	Rebuttal 1: Rebuttal: We thank reviewer ZAwD for their thorough review. Addressing weaknesses: > The mediation of refusal behavior by directions in a one-dimensional subspace does not seem surprising to me…. It was not obvious a priori that refusal would be mediated by a single direction across all models. One co...	Summary: This work examines the specific direction within the internal activations of large language models (LLMs) that govern their refusal behavior. Using the difference-in-means technique, the researchers identify this direction and subsequently utilize it to manipulate model behavior in two ways: bypassing refusals...	Rebuttal 1: Rebuttal: We thank reviewer dLQd for their extremely thoughtful review. Addressing weaknesses: > 1 .The main contribution of this study is the identification of the "refusal vector."... We agree that our study does not disentangle whether the vector encodes "refusal" behavior or the model’s concept of...	Summary: The authors present a method to determine a single direction that mediates refusal in LLMs. Erasing this direction provides an effective jailbreak for the various open-source LLMs examined in the paper, while strenghtening it makes the models refuse even non-harmful instructions. The algorithm is easy to imple...	Rebuttal 1: Rebuttal: We thank reviewer DgJh for their thorough review. We’ll respond inline to specific concerns. Addressing weaknesses: > I don't see why choosing a mean activation vector corresponding to one of the token positions makes sense…. Each LLM family has a specific chat template - see Table 1 in sup...	Summary: This paper identifies a direction in the LLM activation space that can control refusal behavior, subsequently proposing a new jailbreaking method that does not require harmful responses. Additionally, the authors analyze the relationship between adversarial suffixes and the refusal direction. Strengths: 1. Th...	Rebuttal 1: Rebuttal: We thank reviewer zJsi for their review. We appreciate the positive comments, particularly those about the thoroughness of our experiments. We’ll now respond inline to specific concerns. Addressing weaknesses: > 1. The novelty of this paper is relatively limited, as it primarily extends the...	Rebuttal 1: Rebuttal: We’d like to sincerely thank all four of our reviewers for their engagement. We were very happy to read that reviewers characterized our work as a "nice application of interpretability research" (dLQd), with it "[standing] out as one of the rigorous studies in the field of MI, which addresses a p...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Speculative Decoding with CTC-based Draft Model for LLM Inference Acceleration	Accept (poster)	Summary: The paper proposes a novel architecture and training technique for LLM speculative decoding, aiming to improve the reliability and acceptance rate of generation candidates. Unlike Medusa, the proposed method replaces the draft module with a Transformer and utilizes CTC-based loss instead of CE loss. For CTC tr...	Rebuttal 1: Rebuttal: Many thanks for the insightful comments and constructive suggestions. $\textbf{R1.Ablation study of draft model structure. (W3)}$ Thank you for the constructive suggestions. We add ablation experiments with modified draft model structure (Transformer Layer + CE loss) and (Linear layer + CTC los...	Summary: The authors study the setup of speculative decoding where multiple tokens are being generated at parallel. In this setup the authors use the idea of connectionist temporal classification to train a draft model which generates multiple tokens in parallel. Strengths: - The method shows significant improvement ...	Rebuttal 1: Rebuttal: Many thanks for the insightful comments and constructive suggestions. $\textbf{R1. Evaluate the speedup on a real system(W1). }$ We measured the speedup not only using the token acceptance rate(denoted as $\gamma$), but also the inference speedup which is conducted on a real system(denoted as $...	Summary: The paper proposes a novel framework, CTC-drafter, to accelerate speculative decoding in large language models (LLMs). The authors introduce the use of Connectionist Temporal Classification (CTC) as a training objective, replacing the traditional cross-entropy loss. This method aims to improve context modeling...	Rebuttal 1: Rebuttal: Many thanks for the insightful comments and constructive suggestions. $\textbf{R1. More details on the hyperparameter settings and training configurations used in the experiments (Q1). }$ The main training configurations are listed in Section 4.1 including learning rate, gradient clipping thres...	null	null	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Learning on Large Graphs using Intersecting Communities	Accept (poster)	Summary: This paper introduces a novel approach for graph machine learning on large graphs using a concept called Intersecting Community Graphs (ICG). Traditional MPNNs face memory and computational challenges when dealing with large graphs due to their reliance on graph edges. The proposed method approximates the inpu...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments and for acknowledging the theoretical significance of our constructive version of the Weak Graph Regularity Lemma. We address each of their questions/concerns below. > W1: The proposed method's speed advantage over MPNNs hinges on the assumption that the c...	Summary: The paper proposes a novel method to improve the GNN efficiency for large and dense graphs by approximating them as Intersecting Community Graphs (ICGs). This approach significantly reduces time and memory complexity depending on the number of nodes rather than edges. The authors theoretically show the approxi...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments and for finding our work theoretically significant. > W1: ICG+GNN is similar to summarizin… Answer: In Comparison of ICG-NN to graph coarsening in the common response, we highlight the main differences to pooling methods. > … The examples of rece...	Summary: This paper proposes the Intersecting Communities Graph (ICG), which enables efficient learning on very large non-sparse graphs by representing the graph as a linear combination of intersecting communities such as cliques. Unlike Message Passing Neural Networks (MPNNs), the proposed method operates with memory ...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments and for highlighting the novelty of our approach and the overall quality of the paper. We address each of their questions/concerns below. > W1: …it is necessary to have a straightforward way to determine how dense a graph needs to be for the proposed metho...	Summary: The main idea of this paper is to break a graph into communities and approximate the graph with a limited number of these communities. The main idea of the work is rooted in the cut metric, which is defined for signals on the graphs and includes both graph structure and node features. They approximate the cut ...	Rebuttal 1: Rebuttal: > W1: Their analysis on the time complexity… >L1: The method seems to be more efficient for very dense graphs… Answer: A general message passing layer applies a function on the concatenated pair of node features of each edge. We refer to this general formulation of MPNN, which takes $O(ED^2)...	Rebuttal 1: Rebuttal: We thank the reviewers for their time and insightful comments. We respond to each concern in detail in our individual responses to the Reviewers, and provide here a summary of our rebuttal: 1. Significance of contribution: We want to stress that, while the numerical results are competitive an...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Mixture of Scales: Memory-Efficient Token-Adaptive Binarization for Large Language Models	Accept (poster)	Summary: This paper studies the problem of post-training binarization of large language models. Built upon OneBit, this work propose BinaryMoS, to use a mixture of scaling weights for the linear layer of binarized LLMs. Instead of static scales in OneBit, BinaryMoS employs a set of scaling weight experts and adaptively...	Rebuttal 1: Rebuttal: We appreciate the constructive reviews, and here we address your comments in detail: W1. High training cost Though training three epochs over the selected dataset may seem costly, the datasets used for fine-tuning LLMs for quantization, particularly C4 and Wikitext2, are generally very small...	Summary: This paper proposes using a mixture of scales for binarizing continuous latent weights. It includes a router implemented by a linear layer, outputting a softmax score which is then used as the combining matrix of the scale basis (called scale experts). The method uses two score basses, one for input $S_{in}$ a...	Rebuttal 1: Rebuttal: We appreciate the reviews, and hope our rebuttal could convince you and change your stance on our paper. W1. Weak significance and low originality In this work, we propose a new binary LLM architecture to increase the representational power of binary models with negligible inference overhead...	Summary: This paper proposes a binarization technique for LLMs, inspired by the mixture-of-expert (MOE) model. In the proposed approach, multiple scaling factors for the binarized matrices are available, each treated as an expert just like in MOE. The model infers a weight combination of these scaling factors adaptivel...	Rebuttal 1: Rebuttal: We appreciate the constructive reviews, and here we address your comments in detail: Q1. Importance of scale experts As you correctly pointed out, the choice of static experts $S_{in}$ and $S_{out}$ significantly influences the accuracy of BinaryMoS, so it is important to find proper $S_{in}...	Summary: This paper introduces a method to compress large language models (LLMs) by quantizing weight values to 1-bit. The goal is to mitigate performance degradation seen in previous quantization methods applied to LLMs. They utilize ideas from Mixture-of-Experts and introduce token-adaptive scaling factors that help ...	Rebuttal 1: Rebuttal: We appreciate the constructive reviews, and here we address your comments in detail: W1. Motivation of BinaryMoS The motivation stems from the fact that previous binarization methods, including OneBit, still have low representational power. To push the limits of binarized weights, we propose...	Rebuttal 1: Rebuttal: Dear reviewers, Thank you all for your valuable feedback on our work. We appreciate all the insightful questions and comments provided, and we have responded to each reviewer's comments as thoroughly as possible. Moreover, an additional one page PDF file is attached to provide supplementary fig...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Span-Based Optimal Sample Complexity for Weakly Communicating and General Average Reward MDPs	Accept (oral)	Summary: This paper presents a model-based reinforcement learning algorithm for tabular MDPs with the average reward criteria. The authors assume a generative model (each state-action pairs can be simulated) and studies learning algorithm that sample each state-action pairs n times. In the weakly communicating setting...	Rebuttal 1: Rebuttal: - Regarding algorithmic novelty, you are correct that several of our results are established with novel analyses of existing algorithms. We believe that this is a strength of our results, for several reasons. Simple algorithms are preferable in practice, and our analyses demonstrate that several o...	Summary: This work obtains the first minimax optimal sample complexity bound of weakly communicating and general average reward MDPs, without uniform mixing assumption, by introducing new transient time parameter and obtaining tighter minimax optimal sample complexity bound for discounted MDP. Strengths: This theoreti...	Rebuttal 1: Rebuttal: 1. Thank you for making us aware of the updated citation. We will fix this and the other citations which lack locations. 2. Our definition of Blackwell-optimal policies is standard and matches that of Puterman [14, Chapter 10]. We must use $\gamma \in [\overline{\gamma}, 1)$ since the discount fac...	Summary: This paper presents an algorithm with optimal sample complexity in general average reward MDPs. Strengths: The algorithm proposed is sample optimal for the general class of MDPs (possibly multichain) that are much harder to learn than uni-chain or ergodic MDPs. It also introduces a new parameter, the transi...	Rebuttal 1: Rebuttal: - We would like to call attention to our results for weakly-communicating MDPs, which we believe represent a major strength of our work as they resolve the longstanding problem of the sample complexity of average-reward MDPs (Theorem 2) with an interesting insight on the complexity of discounted M...	Summary: The paper resolves the open problem of designing an algorithm for the generative tabular average reward setting for weakly communicating MDPs that achieves optimal span-dependent sample complexity with known span. This is done by an original observation that is concerned with discounted MDPs: Existing sample c...	Rebuttal 1: Rebuttal: Thank you for your positive review.	Rebuttal 1: Rebuttal: We thank all reviewers for their time and positive feedback. We will respond to each reviewer directly via individual rebuttals.	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Faster Accelerated First-order Methods for Convex Optimization with Strongly Convex Function Constraints	Accept (poster)	Summary: The authors introduce faster accelerated primal-dual algorithms for minimizing a convex function subject to strongly convex function constraints. Strengths: 1. The authors address the theoretical questions about strongly convex-constrained optimization and the application of sparse optimization. 2. The autho...	Rebuttal 1: Rebuttal: Thanks for the careful reading and valuable suggestions! Q1:What is $l _2$ norm and $\perp$? We apologize for any confusion caused by the lack of clear definitions. $l _2$ norm for a vector x is defined as $(\sum _{i=1}^{n}\|x _{(i)}\|^2)^{1/2}$, where $x _{(i)}$ is the $i$-th element of x. The...	Summary: This method proposes new acceleration methods to solve the convex optimization problem with convex constraints. To do this, the authors propose to iteratively improve the lower bounds of the strong convexity parameter of the associated Lagrangian. In turn, this lower bound is used to create cutting planes for ...	Rebuttal 1: Rebuttal: We greatly appreciate your comments. We provide the following clarifications to address your concerns. We include Table1 summarizing the time required for the optimal gap and infeasibility to decrease to $10^{-3}$. Additionally, as per your suggestion, we will compare our results with APD+restart....	Summary: This paper introduces accelerated primal-dual algorithms for minimizing a convex function subject to strongly convex constraints. Currently, the best complextiy bound for these problems is $\mathcal{O}(1/\epsilon)$, even when the constraints are strongly convex. However, this work develops a technique to progr...	Rebuttal 1: Rebuttal: Thanks for the careful reading and valuable feedback! We hope that the following can resolve your concerns and questions. Firstly, you are correct that we can not accelerate our algorithm if $\tilde{\rho} _{K}$ is at order $\mathcal{O}(\epsilon)$. Indeed, when $\tilde{\rho} _{K}$ is of the order ...	Summary: Overall, this paper introduces a new idea and new result which is that we can accelerate constraint optimization as soon as the constraint sets are strongly convex. This result is very interesting to the community, but the paper’s writing is really bad as it is and needs a huge improvement to be publishable in...	Rebuttal 1: Rebuttal: We sincerely appreciate your feedback and are grateful for the thorough reading and valuable suggestions for our paper. Due to space limits, we respond to the main technical questions. l87: We establish two upper bounds for two error measures. Since new point $y^+$ is needed for optimality an...	Rebuttal 1: Rebuttal: We sincerely thank the PC, SAC, AC, and all the reviewers, especially the four reviewers. Their feedback has been invaluable, and we will carefully revise our manuscript to meet their standards. We have responded to each comment in the author rebuttal, aiming to resolve their concerns. Due to spac...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Constrained Latent Action Policies for Model-Based Offline Reinforcement Learning	Accept (poster)	Summary: This paper proposes C-LAP, a model-based offline RL method that mitigates the distributional shift problem without using uncertainty penalties or modifying the Bellman update. It learns a joint distribution of latent states and latent actions and constrains the latent action policy to the dataset distribution ...	Rebuttal 1: Rebuttal: 1a) Improvements regarding Figure 1 We updated Figure 1 (see Rebuttal Figure 1) to include the latent action prior and highlight the support constraint. **1b) During the policy training phase, are the imagined trajectories rolled out from the latent state prior or the latent state posterio...	Summary: The paper approaches the problem of model-based policy learning from static datasets. It firstly identifies that this offline MBRL setup inherits two major concerns from its components: the problem of value overestimation from out-of-distribution actions (common in offline RL) and the bias originated by learni...	Rebuttal 1: Rebuttal: The claim in L200 regarding a “significant speed up in policy learning” is questionable, ... After discussing pros and cons, we revise the claim and keep gradient steps as we would need to rerun all experiments again to compare computation time (not comparable because of variations in cluster...	Summary: The work introduce C-LAP (Constrained Latent Action Policies), a novel approach to model-based offline reinforcement learning in POMDPs. Notably, C-LAP does not employ explicit reward penalty terms regarding the action space. The paper proposes a methodology for learning latent variables for both states and a...	Rebuttal 1: Rebuttal: **The paper does not clearly address why constraining with respect to the latent action space is advantageous over the actual action space. Specifically, it remains unclear whether C-LAP offers any benefits compared to SSMs that include a support constraint term directly on the actual action space...	null	null	Rebuttal 1: Rebuttal: First and foremost, we want to thank all reviewers for their time, effort, and insightful feedback! This clearly helped us to improve our paper! We conducted further experiments and added the following figures: - Updated version of Figure 1 to include the latent action prior and highlight th...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof	Accept (poster)	Summary: This paper develops two types of modifications to neural networks to remove permutation symmetries: fixing certain weights, or using a non-elementwise activation function. The resulting "asymmetric" neural networks are found to improve on certain metrics that are observed in networks with permutation symmetrie...	Rebuttal 1: Rebuttal: We thank the reviewer for their appreciation of the novelty of our methods, thoroughness of our discussion of previous work, organization of the paper and methodology, and comprehensiveness of our proofs. Also, we thank the reviewer for putting effort into understanding the empirical setup of our ...	Summary: The paper suggested asymmetric neural networks in terms of an asymmetric nonlinearity and weight. It demonstrated some tasks including linear mode connectivity without permutation alignment, Bayesian neural networks, training metanetworks, and monotonic linear interpolation to show the role of permutation symm...	Rebuttal 1: Rebuttal: We thank the reviewer for appreciating the novelty and evaluations behind our work, and for their comments, which we now address one at at time: > “The number of learnable parameters of the standard NN and the asymmetric NN is reported only for the metanetwork task. It should also be reported for...	Summary: This paper proposes to study the effect of parameter symmetries on the neural networks' training and final properties by analyzing the behavior of networks without such symmetries (or with fewer of them). To do so, the authors develop two methods of parameterizing neural network architectures without parameter...	Rebuttal 1: Rebuttal: We thank the reviewer for appreciating the novelty of our ideas, the effectiveness of our asymmetric parameterizations, the wide range of problems that we consider, and our writing. We think that we have improved our work through your suggested clarifications and ablations. > “Even though I find ...	Summary: This paper studies how removing parameter symmetry of neural networks affects the loss landscape, Bayesian Neural Networks and meta-networks. The authors proposed two ways to remove the parameter symmetry: one is similar to pruning that making some parameters untrainable, the other is to adopt non-elementwise ...	Rebuttal 1: Rebuttal: We are glad that the reviewer appreciates the writing, topic, and experiments of the paper, and we appreciate the reviewer’s comments. Below, we address them one-by-one. > “A major issue about this paper is that some findings are not new. Especially, some studies have already demonstrated that as...	Rebuttal 1: Rebuttal: We thank the reviewers for their comments and suggestions. We have added new experiments and readied changes for the manuscript, which we think will improve the paper significantly. See our 1-page results PDF for more. Also, we have sent our code for reproducing experiments to the area chair, whic...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Hamba: Single-view 3D Hand Reconstruction with Graph-guided Bi-Scanning Mamba	Accept (poster)	Summary: The paper introduces Hamba, a novel technique for reconstructing 3D hand models from a single RGB image. This technique addresses the limitations of previous transformer-based methods, which struggle with occlusion, truncation, and capturing the intricate spatial relationships between hand joints. Hamba combin...	Rebuttal 1: Rebuttal: > Q1. Efficiency of the model R: We did not claim the "efficiency" of the model in terms of Inference time or GPU memory in our manuscript. From the line "GSS block uses 88.5% less tokens", we implied that compared to transformer-based models that utilize a large number of tokens for 3D h...	Summary: This paper proposes Hamba, a Mamba-based framework for single-view 3D hand reconstruction. Its main contribution is to introduce a graph-guided bidirectional scanning mechanism to fully exploit the joint relations and spatial sequences for accurate hand reconstruction. It additionally fuses global spatial toke...	Rebuttal 1: Rebuttal: > Q1. Ablation study with Transformer + GCN. R: As requested by the reviewer, we replaced the state space block with GCN + Attention (from Graformer [R2]) in our Hamba model and evaluated it on the Freihand benchmark dataset. Further we compared it with our GCN + SS2D (see Table below). B...	Summary: This paper presents an approach for 3D hand reconstruction from a single view. The main idea is to introduce a graph-guided Mamba framework in the model for hand reconstruction, by bridging graph learning and state space modeling. Building on top of the recent Hamer approach, the final proposed model, Hamba is...	Rebuttal 1: Rebuttal: > Q1.Clarification of motivation? R: Our main motivation is to improve SOTA methods (e.g., HaMeR [57]) by modeling the structural relation in the hand skeleton that leads to the model's performance improvement. HaMeR [57] designed a ViT-based model, using ViTPose weights and large datase...	null	null	Rebuttal 1: Rebuttal: Dear Reviewers and ACs, We appreciate the insightful review and constructive feedback that has helped us enhance our manuscript. Through our comments, we have tried to clarify the confusion and effectively address all questions asked by the reviewers. - It is the first work to demonstrate th...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Unlocking the Capabilities of Thought: A Reasoning Boundary Framework to Quantify and Optimize Chain-of-Thought	Accept (oral)	Summary: The paper presents a Reasoning Granularity (RG) framework designed to quantify and optimize the Chain-of-Thought (CoT) reasoning capabilities of large language models (LLMs). The framework introduces a new metric, RG, to measure the complexity of reasoning tasks that LLMs can handle. It also establishes a comb...	Rebuttal 1: Rebuttal: We express our sincere appreciation for your comprehensive feedback. We value the opportunity to address the concerns identified. Our responses to the enumerated points are as follows: --- Q1: Can you provide more detailed examples of tasks that fall into each of the three RG categories? How ...	Summary: The paper introduces a Reasoning Granularity (RG) framework that quantifies and optimizes Chain-of-Thought reasoning in large language models. Through extensive experiments, the authors validate the RG framework's effectiveness across various tasks and models, providing new insights into enhancing reasoning ca...	Rebuttal 1: Rebuttal: We extend our gratitude for your insightful feedback. We appreciate the opportunity to address the concerns presented. Below, we provide our detailed responses to each of the points raised: --- Q1: Lack of Theoretical Analysis: While the paper provides an empirical framework and experimen...	Summary: The article introduced a novel framework for quantifying and optimizing the reasoning capabilities of large language models (LLMs). The concept of Reasoning Granularity (RG) is innovative and may have the potential to significantly impact the field of natural language processing with LLMs. Strengths: 1. The R...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback. We appreciate the opportunity to address the concerns you have raised. Our responses to the specific points mentioned are as follows: --- Q1: Although the paper has demonstrated the effectiveness of the RG framework across several models and tasks, it cou...	Summary: This paper proposed a novel reasoning granularities (RG) methodological framework to quantitatively assess CoT capabilities and provide guidance on optimizing CoT performance. The experiement results show an upper bound of CoT, and the authors have proposed three catergories of RG to optimize CoT with combinat...	Rebuttal 1: Rebuttal: Thank you very much for your careful review and affirmation of our paper. Q1: Check the writing and grammar. Some occasional typos or misused commas. R1: Thank you for your constructive suggestions. We will correct these issues one by one in the next version. --- Rebuttal Comment 1.1: ...	Rebuttal 1: Rebuttal: We extend our gratitude to all reviewers for their insightful and thoughtful feedback. 1. We are greatly encouraged that all reviewers observe that our work introduces an innovative Reasoning Granularity framework targeting further optimization of CoT (Reviewer #fHpa, Reviewer #oUcy, Revi...	NeurIPS_2024_submissions_huggingface	2,024	Summary: The paper introduces a novel reasoning granularity (RG) framework to quantify and optimize CoT capabilities in LLMs. The authors define RG to measure the upper bounds of CoT and establish a combination law for RG, enabling a practical quantitative approach. They categorize tasks into three categories based on ...	Rebuttal 1: Rebuttal: Thank you for your insightful feedback. We appreciate the opportunity to address the concerns raised. Below are our responses to the points mentioned: --- Q1: When evaluating RG on multi-hop question answering, the difficulty of sub-questions in each hop is measured by the number of entities,...	null	null	null	null	null	null
Zipfian Whitening	Accept (poster)	Summary: This paper proposes new Zipfian whitening for static word embeddings inspired by Zipf's law. The main idea is to use empirical word frequencies as a prior rather than using a uniform prior. The authors show the superiority of their method compared to previous widely-used baselines. The paper also presents the ...	Rebuttal 1: Rebuttal: Thank you for your positive review! We're delighted to hear it. We especially appreciate your constructive feedback on the various aspects of our experimental setup. Below, we provide our responses. ### 1. Embedding models > While it's a minor point, I'm curious why you haven't explored token-le...	Summary: This paper considers the problem of post-processing static word embedding spaces based on the observation that the distribution is spatially skewed based on the occurrence frequency of corresponding words. The authors propose Zipfian whitening, an approach to symmetrize the embedding space using the empirical ...	Rebuttal 1: Rebuttal: Thank you for your positive evaluation of our paper, including its potential impact on future research! We also appreciate your many insightful and constructive questions. We'll do our best to provide honest answers below. If any remaining discrepancies in our understanding or points need clarific...	Summary: Prior work in natural language processing has shown that word embeddings are sometimes concentrated in a small cone of the embedding space. Prior work has also shown that correcting this can lead to better performance in some downstream tasks. These prior work, however, do not typically consider a word’s frequ...	Rebuttal 1: Rebuttal: Thank you for your thorough reading and your critical and constructive comments. We especially appreciate your feedback on the clarity and self-contained nature. ### 1. How to compute symmetry scores in Table 2 We made a typo! Thanks for pointing it out. The labels along the x-axis in Table 2 re...	Summary: This paper proposes "Zipfian whitening" of word vectors, that is, taking word probability in consideration when taking averages for whitening them. In addition to presenting the proposed simple algorithm and experimentally evaluating in suitable NLP tasks, this paper also introduces measure of isotropy of word...	Rebuttal 1: Rebuttal: We're pleased to receive your positive evaluation! We intend to address all the points you've raised. ### 1. Qualitative demo of the unnaturalness of uniform word distribution > I would like the authors to include some actual words uniformly sampled from the vocabulary, which clearly shows avera...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
A Globally Optimal Portfolio for m-Sparse Sharpe Ratio Maximization	Accept (poster)	Summary: This study addresses the problem of Sharpe ratio maximization under a cardinality constraint, referred to in the paper as an m-sparse constraint. Adding a cardinality constraint typically makes optimization problems NP-hard. Existing studies usually approach this problem using heuristic methods or relaxations....	Rebuttal 1: Rebuttal: Answer for Weakness 1: In modern portfolio management, it is widely-recognized that the number of selected assets should be restricted to a manageable size, in order to keep simplicity and save time and financial costs. Managerial strategies provide an approach to achieve this objective. Howe...	Summary: In summary, this paper studies Sharpe ratio optimization in portfolio management and contributes the achievement of sparse distribution iterates converging to a local optimum by converting the fractional optimization problem into a quadratic programming. Strengths: Originality: The task of optimizing SR with ...	Rebuttal 1: Rebuttal: Answer for Weakness 1 (Originality): The crucial contribution of our work is maximizing SR under two constraint simultaneously: the m-sparse (cardinality) constraint and the simplex constraint. While there are indeed a bunch of works elaborating SR maximization under various constraints, few ...	Summary: This paper studies the optimization of an m-sparse portfolio, which has an additional sparsity constraint on the portfolio compared to traditional portfolio optimization. Instead of the mean-variance approach, this work proposed to directly optimize the fractional objective which is the Sharpe ratio. The pape...	Rebuttal 1: Rebuttal: Answer for Weakness 1 In fact, under certain conditions, our method can directly converge to a globally optimal solution. Based on Theorem 2 $(i)$ in the original manuscript, we have now further proven more intuitive sufficient conditions for convergence to global optimum. For a detailed proo...	Summary: This paper studies Sharpe ratio optimization with sparsity constraints. The paper transforms the original m-sparse fractional optimization problem into an m-sparse quadratic programming problem and develops a proximal-gradient algorithm to solve it. Numerical experiments show that the proposed method improves ...	Rebuttal 1: Rebuttal: In fact, our method guarantees a locally optimal solution to the non-convex optimization in a general case, which is consistent with standard optimization theory. Only under certain conditions (Theorem 2 $(i)$), the locally optimal solution become a globally optimal solution. Additionally, based ...	Rebuttal 1: Rebuttal: We greatly appreciate the reviewers' professional feedback, which has significantly improved this paper. Based on their suggestions, we have added three main components: 1. sufficient conditions for PGA's convergence to a global optimum; 2. analysis of the convergence rate of PGA; 3. validation of...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Dual Risk Minimization: Towards Next-Level Robustness in Fine-tuning Zero-Shot Models	Accept (poster)	Summary: To address the robustness of foundational models under distribution shift conditions, this paper proposes a dual risk minimization approach. Specifically, the authors combine Empirical Risk Minimization (ERM) and Worst-Case Risk Minimization (WCRM) to optimize the model fine-tuning process. To achieve accurate...	Rebuttal 1: Rebuttal: We appreciate the reviewer's time and valuable insights. It is encouraging that the reviewer found our DRM approach is supported by "rigorous mathematical proofs" and it “effectively enhances the robustness of the model”. We appreciate that the reviewer has thoroughly engaged with our work and ack...	Summary: This paper proposes a method for robust fine-tuning by combining empirical risk minimization with worst-case risk minimization to better preserve core features. The approach uses descriptions of core features obtained from large language models (LLMs) like GPT-4 and employs these descriptions to estimate worst...	Rebuttal 1: Rebuttal: We appreciate the reviewer's time and valuable insights. It is encouraging that the reviewer found our DRM approach "novel", the use of LLMs to obtain core-feature descriptions is "innovative", and the method is "well-supported by theoretical foundations and empirical results". We appreciate that ...	Summary: This paper introduces dual risk minimization (DRM), a novel approach that combines empirical risk minimization (ERM) with worst-case risk minimization (WRM) to enhance the robustness of fine-tuning zero-shot foundation models. The authors address the limitations of existing methods that fail to effectively pre...	Rebuttal 1: Rebuttal: We are grateful for the reviewer's time and insightful feedback. It is encouraging to know that the reviewer found our DRM “novel and interesting” and “achieves state-of-the-art results on various benchmarks”. We appreciate that the reviewer has thoroughly engaged with our work and acknowledged it...	Summary: This paper presents Dual Risk Minimization (DRM), a novel approach that combines empirical risk minimization (ERM) and worst-case risk minimization (WRM) for fine-tuning the CLIP model while maintaining its out-of-distribution robustness. The idea is to create a classifier that utilizes concept descriptions of...	Rebuttal 1: Rebuttal: We are grateful for the reviewer's time and insightful feedback. Regarding the concerns and questions the reviewer raised, we provide detailed clarifications below. If not specified otherwise, all experiments below are conducted with CLIP ViT-B/16. --- ### 1. Reliability of the core features obt...	Rebuttal 1: Rebuttal: We thank all reviewers for their meticulous reviews of our work. In this global response, we address two main concerns shared by some reviewers. ## A. Quality and Robustness of LLM-Generated Concept Descriptions Reviewers D5j1 and zSx8 stressed the importance of thoroughly analyzing concept des...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
From Dictionary to Tensor: A Scalable Multi-View Subspace Clustering Framework with Triple Information Enhancement	Accept (poster)	Summary: This paper proposes a scalable tensor-based multi-view subspace clustering model by using triple information enhancement, which aims to reduce the computational complexity and the bias from the real rank minimization. Strengths: （1）This paper has provided significant proof of the algorithm's convergence. （2）C...	Rebuttal 1: Rebuttal: We appreciate your careful review and insightful feedback on our manuscript. We have thoroughly considered each of your comments and have addressed them in detail below: Weakness 1: Why does LatLRR use the nuclear norm for P, but the proposed method uses the weighted Frobenius norm? A1: ...	Summary: The manuscript introduces a novel tensor-based multi-view clustering algorithm designed to address three critical limitations of existing approaches: high computational complexity arising from reliance on complete dictionaries, inaccurate subspace representation due to disregarding local geometric information,...	Rebuttal 1: Rebuttal: Thank you for your thoughtful and detailed review of our manuscript. We have carefully considered each of your comments and addressed them point by point below: Weakness 1: The introduction of Enhanced Anchor Dictionary (EAD) lacks clarity. Could the authors clarify the fundamental difference...	Summary: The paper presents a novel framework, STONE (Scalable TMSC framework with Triple information Enhancement), addressing significant limitations in current Tensor-based Multi-view Subspace Clustering (TMSC) methods. The proposed approach aims to reduce computational complexity, improve subspace representation acc...	Rebuttal 1: Rebuttal: We sincerely appreciate your detailed review of our manuscript. We have carefully considered all your comments and provide our responses to each point below: Weakness 1: In Figure 5, the authors should carefully check the name of each subfigure. A1: We have carefully checked the names of...	Summary: The authors introduce the STONE framework, a Tensor-based Multi-view Subspace Clustering (TMSC) approach, designed to surmount the paramount limitations inherent in contemporary methodologies. By augmenting anchor dictionary learning, they adeptly reconstruct low-rank structures, resulting in a reduction in co...	Rebuttal 1: Rebuttal: Thank you for your thoughtful review of our manuscript. We have carefully considered each of your comments and addressed them point by point below: Weakness 1: The authors clarify that their proposed method employs an advanced dictionary learning mechanism to delve into and uncover latent da...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Learning to Solve Quadratic Unconstrained Binary Optimization in a Classification Way	Accept (spotlight)	Summary: This article introduces the Value Classification Model (VCM), a neural solver for the quadratic unconstrained binary optimization (QUBO) problem. VCM utilizes a Depth Value Network (DVN) and a Value Classification Network (VCN) to efficiently generate solutions without optimal labels. It outperforms existing m...	Rebuttal 1: Rebuttal: We sincerely appreciate your thorough review and constructive feedback on our manuscript. We are grateful for your insights and will address your concerns comprehensively in our revision. To better illustrate our work and to improve the manuscript based on your comments and suggestions, we have a...	Summary: The article introduces a novel neural solver named Value Classification Model (VCM) for solving the Quadratic Unconstrained Binary Optimization (QUBO) problem. Leveraging a Depth Value Network (DVN) that exploits the symmetry of the problem's matrix, VCM captures value features effectively. The solver uses the...	Rebuttal 1: Rebuttal: Thank you for your thorough review and valuable feedback on our paper. Your comments are crucial for improving the quality of our work. We would like to address each of your points and suggestions. 1. Comparison We appreciate your suggestion to discuss NeuralQP [1], a nice work targeting Qua...	Summary: This paper presents a novel approach, the Value Classification Model (VCM), for tackling the challenging Quadratic Unconstrained Binary Optimization (QUBO) problem. VCM improves by offering a classification-based solution, addressing limitations faced by existing deep reinforcement learning (DRL) methods. It d...	Rebuttal 1: Rebuttal: We sincerely appreciate your thorough review and the opportunity to clarify our work regarding the datasets and specific problems solved. We are grateful for the chance to provide a more comprehensive description. 1. Datasets Our study utilized datasets described in the format "dataset+insta...	null	null	Rebuttal 1: Rebuttal: Overall Response Dear Reviewers, We sincerely appreciate your thorough reviews and constructive feedback on our manuscript. Your insights have been invaluable in improving the quality and impact of our work. Below, we address your comments and outline the improvements made in response to you...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data	Accept (poster)	Summary: This paper aims to address the temporal distribution shifts in Tabular data based on TabPFN. Concretely, the proposed method use two structural causal models (SCM) to model the prior for TabPFN, one for the gradual shift of inductive bias over time and the other is to model the shift of the first SCM. Empirica...	Rebuttal 1: Rebuttal: Dear Reviewer m2Lp, Thank you for dedicating your time to review our work and providing valuable feedback. We really appreciate your recognition that our intuitive approach could address a useful setting in practice. > As far as I am concerned, CatBoost, XGboost, and LightGBM do not explicitly c...	Summary: The authors propose an extension of the TabPFN framework such that the model can be resilient to distribution shifts during the inference phase by observing the shifted samples in-context. Akin to TapPFN, the pipeline involves pretraining based on an SCM, and the authors introduce a temporal aspect to the SCM ...	Rebuttal 1: Rebuttal: Dear Reviewer 84bT, Thank you very much for your thorough review and in-depth questions, which have sparked ideas for further analyses and follow-up work. We highly appreciate your positive feedback on the structure and clarity of our paper. We are glad that our approach was communicated comprehe...	Summary: The paper presents a modification of TabPFN that incorporates a novel prior to incorporate temporal shifts. In particular, the proposed method introduces an additional SCM that, through a temporal representation (Time2Vec), learns to modify the model parameters in response to shifts. The authors train a versio...	Rebuttal 1: Rebuttal: Dear Reviewer LELq, Thank you for your thorough review and constructive feedback. We appreciate your recognition of our work's potential and your openness to raising your score. We have worked very hard during the rebuttal to answer your concerns and made major progress in rewriting the “related ...	Summary: This paper studies the setting of non-iid train / test distribution shift in the tabular machine learning. The authors extend the previous TabPFN sota tabular model to deal with domain shift cases. TabPFN uses SCM to model data prior, and in this paper, the core idea is to update the SCM prior graph by hyperne...	Rebuttal 1: Rebuttal: Dear Reviewer aHHC, Thank you for your insightful review. We appreciate the time and effort you've invested. Your comments have given us insights to clarify and strengthen several aspects of our paper. We are especially encouraged by your recognition of the strong practical value of our approach...	Rebuttal 1: Rebuttal: We sincerely appreciate all reviewers for their constructive feedback and insightful comments. We have addressed all the key criticisms raised in the reviews and made corresponding adjustments to our paper. We are delighted that the reviewers recognize our approach of modeling temporal distributio...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
A Surprisingly Simple Approach to Generalized Few-Shot Semantic Segmentation	Accept (poster)	Summary: The authors propose a simple yet effective method termed base-class mining (BCM) for GFSS that does not employ the techniques mentioned earlier in the existing method. Experiments show some improvements on COCO-20i, PASCAL-5i and PASCAL-10i. Strengths: 1. The article is well-written. 2. The proposed method is...	Rebuttal 1: Rebuttal: Thank you for your review. Please find our answers to your questions below. > The overall novelty is relatively limited, as the idea that "a novel class is classified as the background or a similar base class by the base-class model" has already been explored in continual semantic segmentation [...	Summary: This work introduces an interesting method for generalized few-shot segmentation. Unlike previous methods that mainly focus on meta-learning, the proposed method maintains the performance of base classes while achieving decent performance for novel classes. Feature pre-processing and model ensembling technique...	Rebuttal 1: Rebuttal: Thank you for your feedback. Please find our answers to your questions below. > It should be clarified whether the co-occurrences matrix and BNM are calculated per-dataset or per-batch. The current implementation computes BNM per dataset. We will clarify this point in the final version. > Sinc...	Summary: The paper presents a new and efficient BCM technique aimed at tackling the issue of generalized few-shot semantic segmentation. It identifies how base and novel classes relate to each other by examining the overlap between the base model's predictions and the true labels of the novel images. Utilizing these in...	Rebuttal 1: Rebuttal: Thank you for your feedback. It appears you have listed some of the strengths in the weaknesses section. If you forgot to copy and paste questions from your memo, e.g., questions about explanations in our paper, please let us know during the discussion. We would like to improve the presentation o...	null	null	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
A Walsh Hadamard Derived Linear Vector Symbolic Architecture	Accept (poster)	Summary: The paper introduces a new Vector Symbolic Architecture (VSA) termed Hadamard-derived Linear Binding (HLB), aimed at enhancing computational efficiency and performance in both classical VSA tasks and deep learning applications. VSAs involve binding two vectors to create a new vector in the same space, supporti...	Rebuttal 1: Rebuttal: We will add the explanations below to the manuscript to provide motivating reasons for each approach. 4.2.1: > When running on low-power computing environments, it is often desirable to offload the computation to a third-party cloud environment to get the answer faster and use fewer local re...	Summary: This work describes a novel vector-symbolic architecture with linear-complexity binding/unbinding operations which includes the following components: - A novel binding/unbinding scheme based on the Walsh-Hadamard transform, which reduces to element-wise multiplication and division due to the self-inverse prope...	Rebuttal 1: Rebuttal: W1: The noted work is indeed valuable, though we can not implement it's experiments in the short rebuttal time. We will add the following paragraph to the manuscript. > As noted in (Steinberg and Sompolinsky), most VSAs can be viewed as a linear operation where $\mathcal{B}(a,b) = a^\top G b...	Summary: The authors propose a new form of Vector Symbolic Architecture (VSA), which leverages the Walsh Hadamard transform for vector binding. The new binding is named Hadamard-derived linear binding (HLB), and it achieves comparable or better performance than existing VSAs when performing classic VSA tasks and combin...	Rebuttal 1: Rebuttal: W1: We will add the below explanation motivating the choice of the WHT to the manuscript: > Our motivation for using the WHT comes from its parallels to the FFT used to derive the HRR and the HRR's relatively high performance. The Hadamard matrix has a simple recursive structure, making analy...	Summary: The paper proposed HLB, a vector symbolic architecture derived from the Hadamard transform, reminiscent of holographic reduced representation, to mitigate the challenges that classical VSAs face in deep learning tasks such as numerical stability. Results show comparable memorization capability from alternative...	Rebuttal 1: Rebuttal: W1: Thank you for the typo catches, they have been fixed! W2: For CSPS each network has four (convolutional) U-Net rounds in every experiment and doubles from 64 filters after each round, halving in size for the decode. The local prediction network has 4 convolutional layers, with max-po...	Rebuttal 1: Rebuttal: We are pleased all reviewers are interested in the paper and found it novel and significant in its results. All feedback was valuable and has been incorporated into a revised manuscript, with responses inline to each reviewer's individual questions. xpT1 please note your answers can be found in th...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Particle Semi-Implicit Variational Inference	Accept (spotlight)	Summary: This paper introduces Particle Variational Inference (PVI), a novel method for semi-implicit variational inference (SIVI) that employs empirical measures to optimize the mixing distribution without making parametric assumptions. Unlike existing SIVI methods that face challenges with intractable variational den...	Rebuttal 1: Rebuttal: Thank you for your efforts in reviewing our work and for the helpful feedback and suggestions. All typographic errors will be fixed. > Figure 1: The current caption makes it difficult to interpret the figure. It would be helpful to explicitly mention that lighter shades represent smaller μ\muμ va...	Summary: The authors introduce a novel algorithmic approach to fitting semi-implicit variational approximations. This method is based on discretizing a suitable gradient flow, and the paper provides a comprehensive theoretical analysis to support it. The approach, called Particle Variational Inference (PVI), directly o...	Rebuttal 1: Rebuttal: Thank you for your hard work on our submission, and for recognizing the value of our work. > How sensitive is PVI to the choice of kernel and number of particles? We found that PVI the choice of kernel is an important one. In Section 3, we discuss the implications of the kernel choice more expli...	Summary: The authors propose a method for SIVI called Particle Variational Inference (PVI) which employs a particle approximation of an Euclidean–Wasserstein gradient flow. PVI directly optimizes the ELBO, and it makes no parametric assumption about the mixing distribution. Their empirical results demonstrate that PVI...	Rebuttal 1: Rebuttal: Thank you for your time spent reviewing our work. > what's the meaning of 'Here the ± denotes the average ...'? Thank you for pointing out this mistake. The comment referred to the notation $\mu_{\pm \sigma}$ where $\mu$ is the average and $\sigma$ is the standard deviation computed from $10$ in...	Summary: The paper proposes PVI as a new method to conduct variational inference using the semi-implicit distribution. The method is to construct a gradient flow to minimize a regularized ELBO, which is practically implemented as the particle propagations. The empirical studies show the accuracy over density estimation...	Rebuttal 1: Rebuttal: Thank you for the effort you spent on our work. > However, the current paper does not provide details of how the SVI, UVI, and SM are implemented. We perhaps didn't make this clear enough (and can address that in subsequent versions), but line 281 of the main text points out that all hyperparam...	Rebuttal 1: Rebuttal: We thank all of the referees for their helpful and thoughtful comments; we were pleased that three of the four reviewers reacted positively to the initial submission and hope that we can address the points that were raised during the reviewing process here. The main area of concern overall appear...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Exact, Tractable Gauss-Newton Optimization in Deep Reversible Architectures Reveal Poor Generalization	Accept (poster)	Summary: The paper address the problem of efficient computation of Gauss-Newton (GN) updates in deep neural networks and in particular the question whether GN results in better generalization behavior than SGD. For this, the authors devise a neural architecture based on reversible NNs that incorporates additional linea...	Rebuttal 1: Rebuttal: We thank the reviewer for the thorough and detailed comments. We provide answers to questions and weaknesses below. > I am wondering how transferable the results actually are to architecture that are typically used and do not include random feature projections such as those used in the proposed w...	Summary: In this work the authors use reversible neural networks to explore the benefits of exact Gauss-Newton optimization. They provide a theoretical framework for efficient Jacobian pseudoinverses in reversible networks. The authors then provide experiments on MNIST and CIFAR10 comparing SGD, ADAM, and SGD-GN traini...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive appraisal of our work, and for the constructive feedback. Below, we provide answers to their questions and address the weaknesses they have identified. > The experiments section could be more full; for example, exploring the batch size dependence more ful...	Summary: Even though the Gauss-Newton method is known as an effective second-order optimization, it suffers from intractability of Jacobian pseudoinverse computation. This paper proposes a fast and efficient optimization method which solves the intractability issue of the Jacobian pseudoinverse in Gauss-Newton optimiza...	Rebuttal 1: Rebuttal: We thank the reviewer for their time reviewing our paper -- we are glad they found it clear to follow. The reviewer wondered what is gained by replacing the Moore-Penrose pseudoinverse by a generalized inverse. To the best of our knowledge, there is no known tractable way of computing the Moore-P...	null	null	Rebuttal 1: Rebuttal: We thank all reviewers for their time reviewing our paper; we are glad the reviewers appreciated our paper's main strengths, found it “clear to follow” with a “very clean explanation of Gauss Newton” and “thorough and well executed” analysis, opening “a path towards future studies into GN training...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Constructive Universal Approximation Theorems for Deep Joint-Equivariant Networks by Schur's Lemma	Reject	Summary: The paper presents a unified approach to universal approximation theorems for neural networks using group representation theory. It extends to vector-valued joint-group-equivariant feature maps, providing a systematic method for both shallow and deep neural networks with nonlinear activation functions. By leve...	Rebuttal 1: Rebuttal: We appreciate your taking the time and detailed comments and suggestions. - Q1. *...How feasible is it to implement these theoretical constructs in practical neural network architectures? Have the authors considered the computational complexity and resource requirements for applying these methods...	Summary: This work generalizes the ridgelet transform to equivariant neural networks, providing constructive proofs of universality in the general case as integrations over parameter distributions. Although such a direction had been taken up in prior work [33], they generalize it from scalar activations to vector activ...	Rebuttal 1: Rebuttal: We appreciate your taking the time and detailed comments and suggestions. We are grateful for crediting the strict improvement from the previous study. Let us point out and correct some major misunderstandings. - *Summary: This work generalizes the ridgelet transform to equivariant neural ne...	Summary: The authors present a generalization of the work by Sonoda et al. by extending their formulation of universal approximation theorems applicable to a specific class of neural networks namely scalar-valued joint-group-invariant feature maps for "formal deep network" to a much larger class of learning machines. T...	Rebuttal 1: Rebuttal: We appreciate your taking the time and detailed comments and suggestions. - Q1. The authors allude to cc-universality in the Limitations section, can you briefly explain the term and is it the same as defined in Micchelli et al., 2006 etc Yes, it is the same. We supplement with a brief explan...	null	null	Rebuttal 1: Rebuttal: We thank the reviewers for their valuable comments and detailed questions. In response to several questions, we have supplemented two additional examples. - In A.1, we present a new network for which the universality was not known. - In A.2, we present a clarification of depth-separation. Pdf: /p...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
$\text{Di}^2\text{Pose}$: Discrete Diffusion Model for Occluded 3D Human Pose Estimation	Accept (poster)	Summary: This paper introduces a discrete diffusion method for 3D human pose estimation (HPE). Recent works have successfully applied diffusion models for HPE. However, they need a lot of training data and sometimes output non-anthropomorphic poses. In this work, the authors propose to use discrete diffusions, which le...	Rebuttal 1: Rebuttal: ## Q1: Clarifications for Method 1. The dimensions of F and T are provided from the beginning (L137). 2. We have re-examined our code and confirm that the L1 loss function ($L_{PQ}=\|\|P-{\hat{P}}\|\|_1$) was indeed used throughout our experiments. While this was a typographical error in ...	Summary: The work aims to solve the occlusion in 3D human pose estimation, which is an interesting and inherent topic in this field. The authors critiqued that the current continuous diffusion-based pose estimation method requires a large amount of data in the training, while 3D pose datasets are commonly insufficient ...	Rebuttal 1: Rebuttal: Thank you for the detailed comments. We are willing to address all your questions. ## Q1: Effectiveness of pose quantization for addressing data dependency The pose quantization step is designed to convert a 3D pose into multiple quantized tokens, which can be modeled in the latent space by the ...	Summary: This work claims that the 3D human pose of a single frame is discrete, and learns the local pairing relationship between joint points to generate the human pose under occlusion. At the method level, VQ-VAE is used for human skeleton quantization, and then combined with the diffusion model to solve this discret...	Rebuttal 1: Rebuttal: Thank you for the detailed comments. We are willing to address all your questions. ## Q1: Extended experiments on more complex datasets Clarification. In 3D skeleton-based HPE task, the 17-joint annotation of the Human3.6M is a widely-used and standard benchmark. Most mainstream meth...	Summary: The paper presents novel diffusion-based framework for occluded 3D Human Pose Estimation (HPE) that operates in discrete space. Di2Pose leverages a two-stage process: a pose quantization step and a discrete diffusion process. The pose quantization step captures the local interactions between joints and repres...	Rebuttal 1: Rebuttal: Thank you for the detailed comments. We are willing to address all your questions. ## Q1: Detailed explanations and insights about the two main parts: pose quantization and discrete diffusion. The proposed Di²Pose is a two-stage framework designed to address the challenges of occluded 3D human p...	Rebuttal 1: Rebuttal: We thank all reviewers for recognizing our paper well-written (Reviewers tz5G, s6UY, ACgE), easy to follow (Reviewers s6UY, ACgE), and with novel ideas/methods (all Reviewers). We appreciate their careful reviews and constructive comments. We have revised our paper according to all comments. The...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Linear Transformers are Versatile In-Context Learners	Accept (poster)	Summary: This paper proves that linear transformer layers maintain a weight vector for implicit linear regressions, including a more challenging scenario where data is corrupted with different levels of noise. In the theoretical analysis, this paper shows the intrinsic mechanism of the gradient scent in linear attenti...	Rebuttal 1: Rebuttal: We thank the reviewer for raising these insightful questions, which highlight the importance of generalization of our findings. While a complete exploration of these aspects is beyond the scope of our current work, we would like to provide some initial thoughts and discuss potential future directi...	Summary: In this paper, the authors study linear transformers trained on linear regression problems and prove that each layer of every linear transformer maintains a weight vector for an underlying linear regression problem. Furthermore, the authors consider the mixed linear regression problem with varying noise levels...	Rebuttal 1: Rebuttal: We thank the reviewer to take their time to evaluate the paper and for valuable suggestions on improving the presentation. Indeed, the linear transformer is trained on various generated sequences of noisy linear regression. We will make it more obvious in the introduction and preliminary section. ...	Summary: This paper demonstrates that linear transformers maintain a weight vector for an implicit linear regression problem. The authors provide theoretical analysis showing that (1) linear transformers maintain a linear regression model at every block and (2) a diagonal parameterization of attention heads does not co...	Rebuttal 1: Rebuttal: We thank the reviewer for their insightful comments and valuable feedback. We address their specific points below: > Empirical validation of Section 5.3 is lacking that the update of y occurs every two steps. Indeed, since $w_{xy}$ controls the how the current $y_t^l$ prediction affects the pred...	Summary: The paper tries to understand the reasons of the strong performance of Transformers. The authors study linear Transformers trained on linear regression problems. Moreover, the authors explores the problem of regression where the labels have variable noise levels. Strengths: - The problems studied are importan...	Rebuttal 1: Rebuttal: We thank the reviewer for taking time to look at our paper. We believe that the empirical evaluation provided in the paper is thorough and is appropriate to cover the claims and contributions presented in the paper. We would love to know what kind of empirical evaluation the reviewer has in mind t...	Rebuttal 1: Rebuttal: Here, we are attaching a PDF with example of learned weights for Full parametrization as requested by Reviewer KrhZ. The learned weights converge to near-diagonal matrix, which inspired us to try the diagonal parametrization. Pdf: /pdf/99e69dddd32c107135c70ae9fae615b2874cddcb.pdf	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
PromptFix: You Prompt and We Fix the Photo	Accept (poster)	Summary: The paper introduces PromptFix, a novel framework that significantly enhances the capability of diffusion models in following human instructions for a wide range of image-processing tasks. The authors propose a comprehensive multi-modal dataset and further design a frequency-based diffusion model trained on th...	Rebuttal 1: Rebuttal: We sincerely appreciate your invaluable feedback and the opportunity to address your queries regarding our approach. > Q1: While instructions are necessary for users, the types of degradation tasks (such as snow removal and low-light enhancement) are clearly defined. In other words, for image...	Summary: This paper introduces PromptFix, a unified model designed to intelligently interpret and execute customized human instructions across a variety of low-level image tasks. To address the issue of spatial information loss in stable diffusion, PromptFix introduces a high-frequency guidance sampling strategy. Addit...	Rebuttal 1: Rebuttal: Thank you for your time, thorough comments, and valuable suggestions. We are pleased that you acknowledged our clearly explained idea, the well-written paper, and our convincing experiments. > Q1: The authors claim that when user-input instructions are discarded, PromptFix occasionally perfor...	Summary: This paper employs prompts to perform low-level image restoration tasks using pretrained diffusion models. To facilitate this, a substantial paired dataset with image restoration instructions was collected. The proposed method relies on latent diffusion models, incorporating the input low-quality image as an a...	Rebuttal 1: Rebuttal: Thank you for the time, thorough comments, and nice suggestions. We are pleased to clarify your questions step-by-step. > Q1: The dataset is collected by manually performing the degradation which has a distribution gap with the real-world low-quality images. How will the method perform for th...	Summary: This paper addresses low-level image processing tasks using a unified, Diffusion-based method. The key idea is to construct a dataset comprising pairs of editing instructions and targets for a variety of tasks and fine-tune a pre-trained text-to-image Diffusion Model on this dataset. Further innovations includ...	Rebuttal 1: Rebuttal: Thanks for your constructive suggestions. Your endorsement of our method and experiments gives us significant encouragement. > Q1.1: Lack of novelty. Instruction-based image editing using Diffusion models dates back to InstructPix2Pix, which similarly utilizes text conditioning to unify arbit...	Rebuttal 1: Rebuttal: # General Response to Reviewers and ACs We thank the reviewers for their detailed and valuable comments. To better support our response, we have uploaded a rebuttal PDF (need to download it) containing the supporting materials. The figures within this PDF are labeled using Roman numerals, such as...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Infinite Limits of Multi-head Transformer Dynamics	Accept (poster)	Summary: The paper analyzes scaling limits of transformer models w.r.t. key-query dimension $N$, head count $H$ and depth $L$ using dynamical mean-field theory. For the $N\to\infty$ limit it is shown that $1/N$ scaling for the pre-attention scores is required for stable learning and all heads become degenerate. Convers...	Rebuttal 1: Rebuttal: ### Strengths We thank the reviewer for their supportive comments and for their detailed reading of our work. Below we try addressing the questions and mentioning ways we aim to improve the paper. ### Questions *1. Besides specifying the scaling exponents which seem to be somewhat intuitive fr...	Summary: The authors investigate multi-head transformer dynamics by scaling to infinite limits in key/query dimension, heads, and depth respectively using dynamical mean field theory and discover different statistical behaviors. Strengths: - Give detailed analysis (and closed form) on dynamics of the updates - Conduct...	Rebuttal 1: Rebuttal: ### Strengths We thank the reviewer for appreciating these aspects of our paper and for their support. ### Weaknesses *The paper is hard to understand, the notations are convoluted and most of the community might not be familiar with dynamical mean field theory , the authors might want to offer ...	Summary: The authors identify parameterizations that lead to nontrivial feature learning as the limits of $N, H, L \to \infty$. Specifically, the study demonstrates the following: - Under the limit $N\to\infty$ the $\mu P$ rule is required, causing all heads to collapse into the same dynamics. - Under the limit $H\to\...	Rebuttal 1: Rebuttal: ### Strengths We thank the reviewer for appreciating these aspects of our contributions. ### Weaknesses Below we try to clarify our limits and also add a citation to the "Feature-Speed Formula" paper and discuss the similarities and differences of the conclusions we make in this paper. We hop...	Summary: The authors study transformer training dynamics under various limits (infinite embedding dimension, infinite number of heads, infinite depth). They point out interesting and subtle behaviours that can happen in these limits. For example: - taking the embedding dimension to infinity can make heads redundant wit...	Rebuttal 1: Rebuttal: We thank the reviewer for their detailed feedback and for allowing us the chance to clarify our theoretical methods. We hope that upon implementing these proposed changes the reviewer will consider improving their score. ### Strengths We appreciate these comments on these strengths of our contri...	Rebuttal 1: Rebuttal: ## Global Response We thank the reviewers for all of their detailed comments and advice on ways to improve the paper. Below we go through some of the concerns which arose in comments from many reviewers and outline how we plan to address them in the newer version of the draft. ### Repeated Conc...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null

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