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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Towards Calibrated Robust Fine-Tuning of Vision-Language Models | Accept (poster) | Summary: The paper proposes a novel framework for robust fine-tuning for CLIP. To enhance out-of-distribution accuracy and calibration, the author incorporates a singular-based constraint term, self-distillation, and EMA. Extensive experiments on synthesized data and ImageNet demonstrate that CaRot can achieve better O... | Rebuttal 1:
Rebuttal: **R4-1**
> The overall framework is not well-motivated. Why do we need to incorporate self-distillation and exponential moving average (EMA)? These two techniques are not the main contributions of this paper and are not directly relevant to the primary theoretical analysis of singular value constr... | Summary: his paper aims to improve the accuracy and reduce the calibration error on OOD data for fine-tuning VLM models. The authors first demonstrate that the OOD calibration error and the OOD classification error can be bounded by the ID calibration error and the smallest singular value of the ID input covariance mat... | Rebuttal 1:
Rebuttal: **R3-1**
Thank you for pointing out the misleading notations. We will revise the notation in the theorem (line 113) as $x[i], \; i=1,...,d$.
**R3-2**
We appreciate reviewer's interest and details look on our theorem! As we noted at line 112 of our manuscript, we set the input $x$ as a represen... | Summary: VLMs have shown to be effective in a wide-area of applications, though they can fail under certain domain shifts. In this work, the authors first observe that the the upper bound of generalization and calibration under domain shifts is bounded by the ID calibration error and the smallest singular value of the ... | Rebuttal 1:
Rebuttal: **R2-1**
> (a) In certain cases, such as Imagenet-R on Table 2 and 3, it appears that the zero-shot CLIP is better than any of the fine-tuning methods including the proposed approach in terms of both the accuracy and calibration. Furthermore, the calibration after fine-tuning with CaRoT is worse t... | Summary: This research paper presents a novel fine-tuning approach for improving out-of-distribution (OOD) generalization and calibration in Vision Language Models (VLMs). By identifying a shared upper bound for OOD accuracy and calibration errors, the authors develop a constrained multimodal contrastive loss framework... | Rebuttal 1:
Rebuttal: ## R1 (g5ML)
**R1-1**
> (a) The theory part is a bit obscure. Is there an intuitive interpretation of the smallest singular value of ID input covariance matrix in the studied context?
We derive the smallest singular value into the bounds for OOD errors through two steps: 1) We frist derive OOD c... | Rebuttal 1:
Rebuttal: ## Summary of Rebuttal
*We sincerely thank all four reviewers for their constructive feedback and valuable comments.*
**The strengths of our work, as highlighted by reviews:**
* The motivation behind this work is clear, addressing an important but under-explored problem.
* There is a strong conn... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
What Is Missing For Graph Homophily? Disentangling Graph Homophily For Graph Neural Networks | Accept (poster) | Summary: This paper analyzes graph homophily and distinguishes three types of homophily: label, structural, and feature homophily. Theoretical analysis is based on CSBM-3H (contextual stochastic block model with three types of homophily). Based on this analysis, a new combined measure Tri-Hom is proposed. The relation... | Rebuttal 1:
Rebuttal: Thanks for your careful reviews. We have fixed the typos and revised our manuscripts accordingly. Here are our responses to your concerns.
## Part 1/4
## W1: Terminological inconsistency of graph homophily and structural homophily.
## RW1:
Graph homophily is a general concept that describes ... | Summary: The paper combines three different metrics (label, structural, and feature) for homophily proposing a Contextual Stochastic Block Model (CSBM-3H) describing these three types of homophily. By doing so it can control the topology and feature generation based on these three homophily metrics. Furthermore, there ... | Rebuttal 1:
Rebuttal: ## Part 1/2
## W1: The paper validates performance only under the node classification task. It would be very interesting to also validate Tri-Hom for the task of link prediction.
## RW1
Thanks for your valuable suggestions. It is a good topic to evaluate Tri-Hom and other homophily metrics for l... | Summary: The paper proposes a novel approach to understanding graph homophily by disentangling it into label, structural, and feature homophily. The introduction of the Tri-Hom metric combines these aspects to provide a more comprehensive measure of GNN performance. CSBM-3H is used to study the impact of these types of... | Rebuttal 1:
Rebuttal: ## Q1 The definitions of structural and feature homophily are somewhat abstract. Could you provide more concrete examples or case studies to illustrate these concepts?
## RQ1
Thanks for your valuable suggestions. In line 116 and 146, we show the differences of structural and feature homophily re... | Summary: This paper evaluates graph homophily from the perspectives of label, structure, and feature, which disentangle the dependencies of these three aspects. The theoretical analysis and experimental evaluations demonstrate the effectiveness of Tri-Hom.
Strengths: 1. This paper is innovative and significant in eval... | Rebuttal 1:
Rebuttal: ### Part 1/2
## Q1: Suggestions for designing models.
## RQ1
Thanks for your positive rating and constructive suggestions. It is interesting to see how our conclusions can guide the model design. Due to the page limitation, we did not share suggestions in model design. Here we show some guidelin... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their valuable feedback. In this author rebuttal PDF, we provide diagrams and visualizations to help better understand our proposed definitions.
Figure 1 shows the definition of three types of homophily. Label homophily $h_L$ measures the label consist... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Omnigrasp: Grasping Diverse Objects with Simulated Humanoids | Accept (poster) | Summary: The paper introduces Omnigrasp, a method for controlling a simulated humanoid with dexterous hands to grasp and manipulate a wide variety of objects along complex trajectories. The authors leverage a universal humanoid motion representation to improve training efficiency and scalability. This method achieves s... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments! We have revised the paper to provide more discussion for sim-to-real transfer and downstream tasks. To address your concerns and questions:
---
> **Transfer to Real Humanoid**
We acknowledge that Omnigrasp in its current form could not be applied to the rea... | Summary: The paper introduces a method for controlling a simulated humanoid robot to grasp and move objects along a trajectory with the use of a dex hand, This approach enables the robot to handle diverse objects with diverse trajectories. The key contribution is a humanoid motion representation that enhances training ... | Rebuttal 1:
Rebuttal: Thank you for your insightful suggestions and feedback. We have revised the paper to provide additional baselines (AMP and PHC), add details about dexterous hands, and discuss decoupled body and hand prior. To address your questions:
---
> **Additional Baselines**
In Table 2 of the global PDF,... | Summary: This paper proposes an approach to learning a humanoid controller that can manipulate objects to follow trajectories. It first assembles a dataset of human bodies and hands motions, and learns a control policy from the state transitions in the dataset. Then, they distill this policy using a VAE to obtain an ac... | Rebuttal 1:
Rebuttal: Thank you for the helpful feedback and comments. We have revised the paper to include robustness tests, a discussion about kinematic latent space, and failure case analysis. To address your concerns:
---
> **Robustness**
In Table 1 of the global PDF, we add uniform random noise [-0.01, 0.01] to... | null | null | Rebuttal 1:
Rebuttal: # General Response
The authors would like to thank the reviewers for their time and constructive feedback. We hope that our responses clarify and address their concerns. We are glad that the reviewers find our work a "significant advance" (z5bR), our results "achieve a high success rate" (gXSs, z... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Abductive Reasoning in Logical Credal Networks | Accept (poster) | Summary: This paper addresses abductive reasoning tasks such as generating MAP and Marginal MAP (MMAP) explanations in Logical Credal Networks (LCNs). Given that LCNs encode sets of distributions over their interpretations, a complete or partial explanation of the evidence may correspond to multiple distributions. Thus... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. We provide responses to your questions and concerns below.
We would like to emphasize that our paper provides the first study dedicated to MAP and MMAP inference in LCNs and to the best of our knowledge there are no other baseline algorithms for ... | Summary: This paper proposes how to solve MAP and Marginal MAP queries for Logical Credal Networks (LCNs). LCNs are a class of graphical probabilistic logic models with the expressiveness to represent cycles as well as marginal and conditional probability bounds on logical formulae. The authors first present three sear... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. We provide responses to your questions and concerns below.
We will expand the background section to include more examples of LCNs. For now, we refer the reader to the previous work on LCNs which we cite in the paper. We agree that Section 2.2 is ... | Summary: This is a paper about inference on logical credal networks, a class of graphical models that cope with interval-valued probabilistic statements on propositional logic formulae. The novelty of the paper is that it focuses on marginal MAP inference (and hence also MAP as a special case). Exact and approximate al... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. We provide responses to your questions below.
Q1: The hardness of MAP/MMAP inference in LCN isn’t actually known yet. We suspect it is an NP^NP^PP-hard task but proving this result formally is an open problem. We will include a discussion of this... | Summary: The paper presents an approach for MAP and marginal MAP in logical credal networks using search based algorithms. Compared to PGMs, MAP and marginal MAP is harder in LCNs since a MAP assignment could correspond to one of several distributions and therefore, to even evaluate the MAP, we need to perform marginal... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. We provide responses to your questions below.
To the best of our knowledge, this paper provides the first study dedicated to MAP and MMAP inference in LCNs and therefore, there are no other baseline algorithms for solving MAP/MMAP queries in LCNs... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their valuable feedback and thoughtful suggestions.
Since several reviewers have asked for more justification for the formalism, we would like to emphasize that LCNs offer a language to deal with many AI settings where probabilities and constraints interac... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: Logical Credal Networks (LCNs) are a probabilistic logic framework designed for representing and reasoning with imprecise knowledge. While previous research on LCNs has focused on marginal inference, there has been a lack of exploration in abductive reasoning within this context. This paper addresses this gap ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. We provide responses to your questions and concerns below.
The MAP and MMAP inference tasks have been extensively studied over the past decades in the context of classical graphical models such as Bayesian networks or Markov networks. However, al... | null | null | null | null | null | null |
SongCreator: Lyrics-based Universal Song Generation | Accept (poster) | Summary: This paper introduces SongCreator, a novel song-generation system designed to create complete songs with both vocals and accompaniment from given lyrics, addressing a significant gap in music generation. The system incorporates a dual-sequence language model (DSLM) and an innovative attention mask strategy, en... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's careful reading of our paper. We hope the following addresses all concerns mentioned.
**Regarding the differences from Suno and Udio**
We appreciate the reviewer’s constructive comments and will revise the introduction to better highlight our unique contributions. Si... | Summary: The paper presents a novel approach for lyrics-based song generation. The method leverages language models for semantic tokens modeling and then applies latent diffusion model to generate final music. A dual-sequence language model (DSLM) is introduced to not only handle vocals and accompaniment but also integ... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our work. We appreciate the constructive comments the reviewer provided, which will help us further improve our paper. We are delighted to have the following discussion with the reviewer.
**Regarding the latency discussion**
The reviewer is correct that Son... | Summary: The authors present SongCreator, a music generation system capable of simultaneous generation of vocals and accompaniment tracks. SongCreator consists of a language model generating two streams of BEST-RQ ([57] in the paper) semantic tokens, one for the vocals and the other for the musical accompaniment, a non... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's overall positive response and the consstructive comments provided. We address the concerns and questions below.
**Regarding the audio quality, semantic tokenizer and using the None strategy 20% of the time**
We take these concerns seriously and have provided a ... | Summary: The authors introduce a novel system for lyrics-based song generation. It can handle various inputs (lyrics, vocal prompts, accompaniment prompts) and generate different outputs (full songs, vocals only, etc.). The paper proposes a dual-sequence language model (DSLM) that separately models vocals and accompani... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's overall positive response. We will address the specific suggestions regarding Figure 2 and the information mentioned in the rebuttal in the final version of the paper. The other concerns and questions raised are addressed below.
**Regarding the training dataset*... | Rebuttal 1:
Rebuttal: We sincerely appreciate the detailed feedback and constructive comments from all reviewers, which are extremely helpful to us in revising this paper. We are grateful for your recognition of the **comprehensiveness of our experiments**, and we are also glad that our approach is recognized for **its... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper presents SongCreator, a system for full song generation including the vocals and accompaniments. The system comprises several steps:
- First a quantizer is trained to tokenize audio, which is used to tokenize the song, vocals, and accompaniments.
- Next, a language model (DLSM) conditioned on various... | Rebuttal 1:
Rebuttal: We appreciate the thorough review regarding our study. We provide detailed responses to your concerns, as summarized below.
**Regarding the clarity of paper**
We are thankful for the reviewer's constructive comment, which we take seriously to revise this paper for a clearer presentation. Some of... | null | null | null | null | null | null |
Learning Low-Rank Feature for Thorax Disease Classification | Accept (poster) | Summary: The authors proposed to use Low-rank Feature Learning (LRFL) to improve model performance specifically for thorax diseases classification. Ideas come from the assumption that the low-rank features capture the majority of the information. They implemented the LRFL by adding a self-modified regularization term, ... | Rebuttal 1:
Rebuttal: We appreciate the review and the suggestions in this review. The raised issues are addressed below.
**1. ...how did the truncated nuclear norm work? May need references.**
The truncated nuclear norm is defined in line 171 of our paper. Existing works [1,3, 4] perform low-rank learning by minimiz... | Summary: The paper is concerned with the problem of thorax disease classification in radiographic images. The authors propose a novel low-rank feature learning (LRFL) method which is applied on pre-trained masked autoencoders (MAE) and evaluated on two datasets (CheXpert and COVIDx). The authors also provide theoretica... | Rebuttal 1:
Rebuttal: We appreciate the review and the suggestions in this review. The raised issues are addressed below.
**1. ...the authors do not provide any evidence that ...more robust to noise or background than the baselines...**
Please refer to the robust GradCAM results in our rebuttal PDF file using the sug... | Summary: The paper introduces LRFL, a method for reducing the effect of noise and background or non-disease areas in radiograph images for Thorax Disease Classification. LRFL utilizes low-rank regularization to leverage low-rank features during network training.
Strengths: 1-The motivation for reducing the adverse of... | Rebuttal 1:
Rebuttal: We appreciate the review and the suggestions in this review. The raised issues are addressed below.
**1. Why did the author not use another radiograph to prove his/her claim that their approach can be used broader with any classification problems in radiographic images?**
Thank you for your sugg... | Summary: This paper introduces a novel Low-Rank Feature Learning (LRFL) method to effectively reduce noise and non-disease areas in radiographic images, enhancing disease classification. The LRFL method, which is theoretically and empirically motivated, demonstrates superior classification performance compared to state... | Rebuttal 1:
Rebuttal: **1. LRFL is based on LFP, and truncated nuclear norm is added as a regularization term. Nothing more. In this sense, there are so many similar methods with different regularizations.**
We respectfully but strongly disagree with this claim since the significant contributions of this paper are mis... | Rebuttal 1:
Rebuttal: We appreciate the review and the suggestions in this review. We have posted our response to individual reviews addressing all the raised concerns. Here we provide global responses itemized below.
**1. Significance and novelty of this paper**
While this paper uses the truncated nuclear norm (TNNR... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Active Set Ordering | Accept (poster) | Summary: This submission proposes a novel Mean Prediction (MP) method for active set ordering problem. MP selects pre-defined k inputs with highest Gaussian Process posterior mean values through a novel sampling strategy. Theoretical analysis on the regret, the prediction and the sampling strategy of proposed method is... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for taking the time to review our paper and for acknowledging the theoretical justification, motivation, and sampling strategy. We will now address the remaining concerns as follows.
> 1. My first concern regards the dimensionality of the problem setting.
We ... | Summary: The paper poses a novel active learning problem formulation of active set ordering, in which we aim to identify the data points that yield the top- and bottom-$k$ values via a given objective function.
This active learning goal serves as a compromise between Bayesian experimental design which focuses solely on... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for dedicating their time to review our paper and for acknowledging the interesting research problem situated at the intersection of experimental design, level set estimation, and Bayesian optimization. We are delighted to learn that the reviewer finds our paper... | Summary: This paper generalizes the best $k$-arm identification problem to Gaussian processes with the goal to estimate the set of the best $k$ function evaluations $f(x)$ on a finite domain $X$, where $k=1$ corresponds to standard Bayesian optimization. The proposed regret notion is a natural adaptation of the common ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for taking the time to review our paper and for appreciating our interesting research problem. We will address the questions and concerns raised by the reviewer as follows.
> the significance of the results is not fully clear to me, as the paper seems to be mos... | Summary: This paper introduces the "active set ordering" problem, which aims at recovering the top-k actions in a set by strategically sampling actions. The authors formally define the problem in the regret minimization setting and propose an algorithm for that. They authors upper bound the regret and run experiments t... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for dedicating their time and effort to reviewing our paper and for recognizing the regret analysis and the experimental results. Additionally, we would like to draw your attention to two other contributions: multiple top-$k$ sets, and the new perspective of ord... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their time and effort in reading and evaluating our paper. We are encouraged by the positive feedback and appreciation of our work regarding its novelty (reviewers Qpjm and i8Rj), theoretical soundness (reviewers Qpjm and i8Rj), and experimental results (re... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
HOI-Swap: Swapping Objects in Videos with Hand-Object Interaction Awareness | Accept (poster) | Summary: This paper presents HOI-Swap, a diffusion-based video editing framework for object swap editing. HOI-Swap involves two stages. In the first stage, the authors train an image-editing model to swap object in one frame. In stage II, the authors first warp a video from the edited frame using the optical flow sampl... | Rebuttal 1:
Rebuttal: We thank reviewer rdgi for the helpful comments and for providing thoughtful feedback on our work.
---
**1. HOI awareness in stage I**
> How can the image-editing model in stage I perceive HOI?...maybe some more ablations
The model’s ability to perceive HOI fundamentally relies on the data it ... | Summary: This work presents a novel approach for object insertion in hand-object interaction scenarios. The proposed approach consists of two stages: image-based editing to for precise alignment of hand with the inserted object, and video-based editing for motion alignment with the original video. The model is trained ... | Rebuttal 1:
Rebuttal: We thank reviewer gbKB for the helpful comments and for providing thoughtful feedback on our work.
---
**1. Clarification on experimental setup**
***(i) Data split***
As we focus on the problem of “object” editing, videos are split based on object instances (Ln 567-568). The held-out videos fe... | Summary: This article focuses on proposing a two-stage network HOI-Swap, a video editing framework designed for precise object edits with HOI awareness.To address the problem of real perception of HOIs as well as spatial and temporal alignment with the original video, HOI- Swap first stage focuses on solving HOI awaren... | Rebuttal 1:
Rebuttal: We thank reviewer NtRs for the helpful comments and for providing thoughtful feedback on our work.
---
**1. Limited innovation**
> The methodology of this paper is a combination of existing work
We respectfully disagree. As acknowledged by three other reviewers, our paper presents both a novel... | Summary: The work considers the task of swapping the objects in ego-centric short clips with hand-object interactions. The manuscript claims to introduce this sub-task within the field of generative video editing. Concretely, HOI-Swap starts from RGB video, object area bounding box, and an image of the target object, a... | Rebuttal 1:
Rebuttal: We thank reviewer ih2o for the helpful comments and for providing thoughtful feedback on our work.
---
**1. Downstream applications**
***(i) Entertainment.*** As showcased in Figure 1 of the main paper and Supp. video (pages 2-3), HOI-Swap can be applied in scenarios where object modification i... | Rebuttal 1:
Rebuttal: We thank all reviewers for their thoughtful and constructive review of our manuscript.
Three of the four reviewers recommend accepting. We were encouraged that the reviewers found our problem and approach novel (ih2o, gbKB, rdgi), technically sound (ih2o), and that our paper pinpoints and addres... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CorDA: Context-Oriented Decomposition Adaptation of Large Language Models for Task-Aware Parameter-Efficient Fine-tuning | Accept (poster) | Summary: This paper proposes CorDA, a context-oriented decomposition adaptation method that initializes LoRA adapter with different components from the weight decomposition to support two different options: knowledge-preserved adaptation and instruction-previewed adaptions. Through extensive experiments on LLaMA-2-7b a... | Rebuttal 1:
Rebuttal: Thank you for recognizing the contributions of our work and your valuable comments.
> (1) One major concern is the lack of theoretical support, and some arguments are not well justified. Is there any support for Line 45-47 or is this a pure intuition? Why SVD on $WC$? What does $WC$ represent?
W... | Summary: The paper proposes a context-oriented decomposition adaptation method for large language models (LLMs) called CorDA. This method constructs learnable adapters by considering the context of downstream tasks or world knowledge. It can bridge the performance gap between parameter-efficient fine-tuning (PEFT) and ... | Rebuttal 1:
Rebuttal: Thank you for recognizing the contributions of our work and your valuable comments.
> (1) It is necessary to add a description of the experimental environment and provide parameters to offer readers more possibilities for replication and ensure authenticity. Lacks specific quantification of cata... | Summary: This paper introduces a new parameter-efficient fine-tuning (peft) method called Context-oriented Decomposition Adaptation (CorDA). Although fundamentally similar to LoRA, it differs in that it initializes two low-rank matrices using the SVD results of pre-trained weights reflecting the data context. To incorp... | Rebuttal 1:
Rebuttal: Thank you for recognizing the contributions of our work and your valuable comments.
> (1) Originality: methodological similarities with PiSSA, and comparisons with existing works like AdaLoRA
_Similarity with PiSSA:_
Both our method and PiSSA adopt SVD for the pre-trained weights, however, our... | Summary: The paper proposes an initializes algorithm for LoRA based fine-tuning, with the aim to maintain world knowledge and also improve training performance on the fine-tuning task at hand. The authors carefully explain the reasoning behind their initialization scheme, and conduct extensive studies to showcase the e... | Rebuttal 1:
Rebuttal: Thank you for recognizing the contributions of our work and your valuable comments.
> (1) How did the authors decide to maintain the last $r$ eigenvectors for world-knowledge? Cite relevant works or make proper justification on this design choice.
As shown in Figure 1 and Eq. (4), in the knowle... | Rebuttal 1:
Rebuttal: We thank AC and all reviewers for reviewing our submission and recognizing the contributions of our work. We are grateful for the valuable comments and suggestions.
### 1. Response to each review
For each review, we address the major question/concern in the rebuttal. We leave discussions and an... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improving Decision Sparsity | Accept (poster) | Summary: The paper extends the notion of decision sparsity called the sparse explanation value (SEV). Cluster-based and tree-based SEV are introduced, as well as some algorithms to optimise the decision sparsity are considered. The core of the paper -- SEV -- is defined as the number of factors that need to be changed ... | Rebuttal 1:
Rebuttal: Thank you so much for your review! We really appreciate it! See below for our response to your questions and concerns.
> Line 85: "humans have no intuition for why a point belongs to one class or the other". I cannot completely agree with the statement, and it would be helpful to provide some exa... | Summary: The authors build on top of the Sparse Explanation Value approach by
Sun et al and provide improvements in terms of closeness and
credibility.
Strengths: Sensible problem, well presented solution.
Weaknesses: The main limitation I can see in the work is its very incremental nature with respect to the approac... | Rebuttal 1:
Rebuttal: Thank you so much for your review! We really appreciate it! See below for our response to your questions and concerns.
> The work is its very incremental nature with respect to the approach by Sun et al. cluster-based SEV is a trivial extension, while tree-based SEV only works if the underlying m... | Summary: This paper proposes several ways to create closer, sparser, and more credible explanations for the SEV, along with two optimizing models. The results of the experiments on various datasets support the paper's claims.
Strengths: 1. Before reading this paper, I was unfamiliar with the sparse decision field. How... | Rebuttal 1:
Rebuttal: Thank you so much for your review! We really appreciate it! See below for our response to your questions.
> In Table 1, what do the gray numbers represent?
Thank you for pointing out the missing explanation for the gray numbers. The gray numbers in Table 1 represent the query feature values that... | null | null | Rebuttal 1:
Rebuttal: Thank you to all reviewers. The following pdf goes with the response for Reviewer xH4u.
Pdf: /pdf/5285496e6b221504bb4ad4cd8b6d9db68f5983bc.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A distributional simplicity bias in the learning dynamics of transformers | Accept (poster) | Summary: The paper investigates whether transformers trained on natural language data using masked language modelling (MLM) exhibit a "simplicity bias" - learning increasingly complex interactions between input tokens over the course of training. The work provides both theoretical and empirical evidence for a simplicit... | Rebuttal 1:
Rebuttal: We want to thank you for your positive comments and your enthusiam for our work. We really appreciated the
series of language evolution studies on simplicity biases that you suggested,
and we decided to include them in the revised version of our manuscript.
Below we reply to your questions and c... | Summary: This paper demonstrates that masked language models like BERT approximate distributions with increasing complexity in terms of the number of interactions, as tracked over the course of training. They do this by approximating lower order interactions with provably limited models.
Strengths: This paper has one ... | Rebuttal 1:
Rebuttal: We thank you for your careful reading of the manuscript, your
detailed review, and the many references. Below we reply point-by-point; we hope that our replies alleviate your concerns. If so, we would appreciate it if you could revisit your rating; if not, we look forward to discussing further dur... | Summary: The paper investigates the simplicity bias in BERT-style Transformers trained with MLM. The study reveals that these models initially learn simpler interactions between tokens and gradually learn higher-order interactions. This finding aligns with simplicity biases observed in many neural network architectures... | Rebuttal 1:
Rebuttal: We thank you for your detailed questions and your feedback, which led us to conduct additional experiments. Below we reply point-by-point; we hope that our replies alleviate your concerns. If so, we would appreciate if you revisit your rating; if not, we look forward to discussing further during t... | Summary: This paper shows that transformers sequentially learn high-order interactions between input tokens during the training process, which echoes the simplicity bias prevalent in neural networks. Specifically, the paper proposes a method to extract certain orders of interactions from the original training set, and ... | Rebuttal 1:
Rebuttal: We thank the referee for their detailed questions and their feedback. Below we
reply point-by-point; we hope that our replies alleviate your concerns. If so, we would appreciate if you revisit your rating; if not, we look forward to discussing further during the discussion period.
> Support for ... | Rebuttal 1:
Rebuttal: We thank the reviewers for taking the time to review of our manuscript. In our paper, we show that BERT-style transformers trained using masked language modeling learn increasingly complex interactions among input tokens. To conduct this analysis, we develop a procedure to generate clones of a giv... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Continuous Heatmap Regression for Pose Estimation via Implicit Neural Representation | Accept (poster) | Summary: This paper introduces a new heatmap representation for 2D human pose estimation. Prior approaches use a quantized representation of heatmaps, where a confidence score is assigned to each pixel. In addition to being dependent on the image resolution, this representation does not match the usual ground truth coo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review. Below we address all the concerns.
***W1: The Related Work section lacks discussions and comparisons with other approaches attempting to address the quantization error ...***
The quantization error problem caused by heatmap discretization is a wel... | Summary: This paper proposes a new approach to predict continuous heatmap for keypoint localization. The proposed method adopts a MLP that receives position coordinates and corresponding feature and output the confidence score of this position. Then this method can query candidate points and select the point with maxim... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful review. Below we address all the concerns.
***Response to weaknesses:***
***Efficiency:*** We compare the efficiency and performance of NerPE and existing methods as shown in **Tab. A3 of the attached PDF**. Although implicit neural representations help us r... | Summary: The authors tackle 2D human pose estimation through a continuous heatmap representation. Specifically, instead of representing the heatmap as a grid of values, they use an implicit neural representation, where the coarse feature vector along with queried coordinates are fed to an MLP, which predicts the heatma... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback. Below we address all the concerns.
***W1: Some important alternative techniques from the literature are not discussed, and this impacts the conclusions. First, ...***
Here we analyze and compare our proposed method HerPE with the alternative techn... | Summary: This paper mainly studies the quantization problem of discrete representation of heatmap, especially in the case of low resolution. It proposes to use a continuous Implicit Neural Representation (coordinate-input MLP) to coarse-to-fine query to generate heatmap at arbitrary resolution, which achieves better pe... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments. Below we address all the concerns.
***W1: For training (Fig. 2), why not consider adding additional supervision ...***
Theoretically, additional supervision in the area near the ground truth can improve the performance of NerPE, but it also appl... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their time and their constructive feedback. We appreciate their assessment of our work NerPE as a "well-motivated" approach for "an important research topic" (xx7e), “a more principled solution to producing arbitrary-resolution heatmaps” (k3b9), a "new idea... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Neuronal Competition Groups with Supervised STDP for Spike-Based Classification | Accept (poster) | Summary: This paper aims to enhance the classification capabilities of SNNs using STDP learning rule. The authors introduce the Neuronal Competition Group architecture to address the limitations of existing WTA mechanisms in supervised STDP classification. This architecture promotes balanced intra-class competition and... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We appreciate their encouragement regarding local learning and time-to-first-spike coding. We respond to their comments below.
> Global learning methods, such as surrogate gradient and ANN-to-SNN conversions, achieve significantly better performance. Cons... | Summary: This paper introduces Neuronal Competition Groups (NCGs) with a two-compartment threshold mechanism to optimize Winner-Takes-All competition in spiking classification layers using supervised STDP. NCG integration with supervised STDP rules significantly boosts image recognition accuracy on CIFAR-10 and CIFAR-1... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and their time reviewing our work. We respond to their comments below.
> stdp is an unsupervised algorithm, and it is hard to associate it with supervised methods. Although there are some supervised stdp methods before, it is hard to believe its rationalit... | Summary: The authors proposed the Neuronal Competition Group (NCG) and a novel competition regulation mechanism based on two-compartment thresholds, to effectively implement intra-class WTA competition in a spiking classification layer employing first-spike coding and supervised STDP training, which improves classifica... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and value their enthusiasm for our work.
**W1:**
We clarified this aspect in Section 4.1: "Different samples from a given class can contain distinct, mutually exclusive patterns or combinations of patterns. Learning all these patterns concurrently with on... | Summary: The paper proposes Neuronal Competition Group (NCG), an architecture that maps each class in the task to a group of neurons using intra-class Winner-Takes-All (WTA) and competition regulation. The authors aim to implement effective WTA competition mechanisms in spiking neural networks (SNNs) employing first-sp... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive remarks, which have helped us improve the value of our work.
> The main limitation of the proposed method and experiments is that essentially all the ideas used in the method have been previously explored, so the contributions might be limited to comb... | Rebuttal 1:
Rebuttal: We sincerely appreciate the constructive feedback from the reviewers on our submission. We have made our best efforts to address all questions and concerns, which we believe have improved the quality of our work. We have attached a PDF file with a figure and two tables to support some of our respo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MeshXL: Neural Coordinate Field for Generative 3D Foundation Models | Accept (poster) | Summary: The paper is about 3D mesh generative models. The generative method is done by generating triangles one-by-one (auto-regressively). Each face is represented by 3 vertices (9 coordinates).
Strengths: The idea to generate faces sounds interesting than previous methods. Unlike PolyGen which needs to generate con... | Rebuttal 1:
Rebuttal: 📝 **W1: The limitation of sequence length.**
💡**A:** The main focus of our paper is to establish end-to-end methods with less inductive bias and pave the way for scaling up learning from large-scale 3D data. We restrict the number of faces to 800 for better alignment with PolyGen and MeshGPT. H... | Summary: The paper proposes MeshXL, a mesh generation model based on the Neural Coordinate Fields(NeurCF), which encodes discretized coordinates of mesh vertices into a sequence of tokens.
Then a decoder only transformer is trained to generate meshes unconditionally/conditioned on modality.
The model is trained on mu... | Rebuttal 1:
Rebuttal: 📝 **W1: Reasons why single stage method works, and analysis on previous works.**
💡 **A:** We thank the reviewer for helping us improve our paper.
1. **The coordinate sequence representation and auto-regressive generation make our method come true**. With a well-defined ordering system, each 3D ... | Summary: This paper proposes a way to use LLM to generate polygon meshes. The key idea is to model mesh generation as the next coordinate prediction, using a strategy similar to that of prior work like PolyGen and MeshGPT. The paper has shown the capability to create a mesh with reasonable quality.
I think the key co... | Rebuttal 1:
Rebuttal: 📝 **W1: The novelty of MeshXL.**
💡 **A:** The motivation of our MeshXL is to extend the **mesh representations**, **architecture design**, and **training strategy** in existing auto-regressive methods, i.e. PolyGen and MeshGPT, to support **efficient large-scale training on extensive 3D mesh da... | Summary: This paper addresses the challenge of generating high-fidelity 3D meshes by introducing Neural Coordinate Field (NeurCF), an effective representation for large-scale sequential mesh modeling. The authors present MeshXL, a family of generative pre-trained auto-regressive models, which applies modern large langu... | Rebuttal 1:
Rebuttal: 📝 **W1: The absence of domain knowledge to prevent potential artifacts**.
💡 **A:** In our work, we put emphasis on exploring a sequential way to model 3D meshes that better suits large-scale generative training on extensive 3D data. Therefore, the potential generation of surface artifacts is cu... | Rebuttal 1:
Rebuttal: We thank all reviewers for approval: 1. a **novel and elegant 3D mesh representation** (R1, R4) that 2) **effectively leverages LLM approaches** (R1, R2) for **end-to-end large-scale** training (R1, R3), and 3) a **stable training and better mesh generation quality by scaling up** (R2, R3) support... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Instruction Tuning With Loss Over Instructions | Accept (poster) | Summary: This paper proposes a new method called Instruction Modelling (IM) for training language models, which applies a loss function to both the instruction and output parts of training data, rather than just the output. Through experiments on diverse benchmarks, the authors show that IM can improve model performanc... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and constructive feedback. We are grateful for the reviewer's positive feedback: the fresh perspective, the extensive experiments, and the valuable analysis of crucial factors influencing IM's effectiveness. We would like to address the reviewer's... | Summary: The propose that when updating models using instruction tuning, the models should also be updated based on loss on the instruction itself.
This is a simple change that is un-intuitive, so the successful results are impressive.
Strengths: They include experiments from many different datasets and look at multi... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and constructive feedback. We are pleased to receive positive feedback on the extensive experiments and the consistency of our results across diverse settings. We would like to address the reviewer's valuable feedback as follows:
> It is unclear ... | Summary: This paper proposes Instruction Modeling (IM), which trains LMs by applying a loss function to the instruction and prompt part rather than solely to the output part. The method is found to be effective on NLP tasks and open-ended generation benchmarks. This paper found two key factors that influence the effect... | Rebuttal 1:
Rebuttal: We appreciate the effort and time of the reviewer (b1op). We are grateful for the positive feedback on our paper's comprehensiveness, novelty, potential impact, extensive experiments, and detailed presentation. We are particularly pleased that our well-written reasoning and thorough appendix were ... | Summary: In this work, authors propose to use instruction modeling (using loss over the full instruction-output pair) instead of just instruction tuning (using loss over the output given the instruction) as a method for supervised finetuning on LLMs. The authors demonstrate consistent gains over multiple benchmarks usi... | Rebuttal 1:
Rebuttal: We appreciate the effort and time of the reviewer (z7Cy). We are thrilled to receive positive feedback on the simplicity and scalability of our method, and the recognition of our efforts to characterise the gains. The reviewer's acknowledgement of our intuitive explanations for the results and the... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their thoughtful feedback and valuable suggestions. We are pleased that our work has been recognised for its novelty (`b1op`,`vkoa`), simplicity and scalability (`z7Cy`), extensive experiments (`b1op`, `kinw`, `vkoa`), and potential impact (b1op). Reviewers als... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Latent Learning Progress Drives Autonomous Goal Selection in Human Reinforcement Learning | Accept (poster) | Summary: This paper develops a new analysis of autonomous goal selection based on "latent learning progress". The key idea is that agents seek to maximize progress on a latent variable rather than an observed variable like performance. The paper reports an interesting experiment with humans that seeks to test whether p... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive assessment of our submission, and address their comments individually below.
---
**Weaknesses**
*Latent learning progress wasn't formally defined until well into the middle of the paper, and there it was only operationalized in terms of the specific exp... | Summary: This paper examines how humans select between different possible goals. The authors designed a hierarchical reinforcement learning task where the main dependent variable was which goal participants chose to work on for each trial. Then, they built descriptive computational models to assess what parameters expl... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments and their extremely detailed, helpful feedback. We address their suggestions for improvement below.
---
**Weaknesses**
*First, the experimental paradigm is very specific. Users have to pick from a limited list of goals with the explicit over-ar... | Summary: This paper looks at human goal selection during learning. A known useful signal for goal selection is learning progress (LP). LP measures performance from past observations, and is therefore only sensitive to measurable change in performance. The paper hypothesizes that goal selection is additionally driven by... | Rebuttal 1:
Rebuttal: We thank the reviewer for their excellent summary of our article and their thorough feedback. We address their suggestions for improvement below.
---
**Weaknesses**
*The definition of LLP introduced here is quite specific to a small and discrete space of action sequences. A more general defini... | Summary: This work presents a hypothesis of a latent learning process that can guide autotelic agents in goal selection. Human experiments provide evidence supporting this hypothesis.
Strengths: Autotelic agents represent an important research direction. This work provides evidence from human experiments on latent lea... | Rebuttal 1:
Rebuttal: We thank reviewer kdQC for examining our submission and recognizing its importance and potential impact on machine learning. In the rebuttal period, we hope the reviewer could also help clarify what would improve soundness and presentation beyond the point raised so far. Regarding contribution, we... | Rebuttal 1:
Rebuttal: We would like to express our gratitude for the reviewers’ positive and constructive feedback.
The reviewers highlighted several strengths of the paper, including the important research direction it falls in line with and its tackling a fundamental question. They noted the potential impact of our ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MiSO: Optimizing brain stimulation to create neural activity states | Accept (poster) | Summary: The authors proposed a framework for closed-loop brain stimulation to optimize the search of parameters in order to guide the measured brain responses towards certain predefined states. The proposed work is built on two blocks with the first one in charge of conducting latent space alignment to combine data se... | Rebuttal 1:
Rebuttal: > **Weaknesses**
> - The stimulation parameters have a large search space that is not defined.
In this work, a large search space means a combination of multiple electrodes among 96 electrodes. We can use any combination of 96 electrodes in theory. The number of possible stimulation patterns (i... | Summary: In this work, the authors propose MiSO (Microstimulation Optimization), a closed-loop stimulation framework designed to optimally generate microstimulation patterns to drive neural activity towards target states. The use of many electrodes for stimulation presents a challenge due to the curse of dimensionality... | Rebuttal 1:
Rebuttal: > **Weaknesses**
> 1. It is unclear why the authors do not fully integrate the CNN into the closed-loop system…
Please see the General Response to Reviewers, “Framework design. 3."
> 2. As mentioned in the discussion, the system is only tested with up to two electrodes…
Please see the Genera... | Summary: The paper presents MiSO (MicroStimulation Optimization), a closed-loop framework designed to drive neural population activity towards specified states by optimizing stimulation parameters over a large parameter space. MiSO addresses the challenge of the large search space by: latent space alignment and a convo... | Rebuttal 1:
Rebuttal: > **Weaknesses**
> - The work is preliminary with limited subjects, baselines, …
> - The applicability is tested on very less data and …
The reviewer is correct that we report the results from 5 closed-loop sessions in Fig. 2B. However, this is only a small subset of the data collected for this... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their constructive comments, which helped strengthen our submission. Here, we respond to comments shared by multiple reviewers.
## Limitation of MiSO
All reviewers inquired about the limitations of MiSO. We explain two main limitations of MiSO and how we can potentiall... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
First-Order Minimax Bilevel Optimization | Accept (poster) | Summary: This paper proposes two novel algorithms, FOSL and MemCS, for multi-block minimax bilevel optimization problems, avoiding the high complexity of computing the second-order gradient. Their theoretical analysis is quite solid and their experiments show proposed algorithms have superior performance and robustness... | Rebuttal 1:
Rebuttal: We thank the reviewer mptp for the time and valuable feedback!
**W1: Since the paper primarily claims the efficiency of the proposed first-order algorithms, the authors should discuss the scale of problems these algorithms are suitable for.**
A: From the perspective of scale, compared to deep AU... | Summary: This work proposes two novel fully first-order algorithms, named FOSL and MemCS, for multi-block minimax bilevel optimization problems. Specifically, the authors reformulate the lower-level problem as a value-function-based constraint and transform the minimax bilevel optimization into a surrogate minimax prob... | Rebuttal 1:
Rebuttal: We thank the reviewer pAAu for the time and valuable feedback!
**W1: I have concerns about the soundness of reformulating the minimax bilevel optimization as a minimax problem in Eq.(2). I hope the authors can conduct more analysis or provide some references that utilize the same technique.**
A:... | Summary: This work studies the multi-block minimax bilevel optimization problem. To address the high computation costs and high memory consumption issues of existing algorithms, this work proposes two fully first-order algorithms, i.e., FOSL and MemCS. Specifically, the authors convert the original minimax bilevel prob... | Rebuttal 1:
Rebuttal: We thank the reviewer Mt97 for the time and valuable feedback!
**W1: AUC-CT avoids the calculation of second-order matrices and shows comparable efficiency to the proposed first-order algorithm, which reduces the contribution of this work to improving algorithm efficiency. It would be better to p... | Summary: The paper introduces FOSL and MemCS that are two new first-order algorithms for multi-block minimax bilevel optimization, demonstrating superior sample complexities and robust performance in empirical evaluations on deep AUC maximization and robust meta-learning applications.
Strengths: The paper introduces F... | Rebuttal 1:
Rebuttal: We thank the reviewer iRgq for the time and valuable feedback!
**W1: The assumptions look quite strong, e.g., Assumption 5.3 requires strong convexity, and Assumption 5.4 requires several boundnesses on the derivatives and high-order derivatives, which usually are not satisfied in practice.**
A:... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Navigating the Safety Landscape: Measuring Risks in Finetuning Large Language Models | Accept (poster) | Summary: The paper introduces a novel concept called the “safety landscape,” which assesses the safety of generative language models. Within this landscape, the “safety basin” is defined as a safe local neighborhood around a model’s parameters. The key contribution is the introduction of a new metric called “Visage” th... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for acknowledging the significance of our findings and contributions! We hope the following clarifications can address the reviewer's concerns.
1. **Different refusal evaluation methods other than keyword search.**
We agree with the reviewer that safety does not n... | Summary: Inspired by the work of visualizing loss landscapes, the authors of this paper ask if there is a similar geometric interpretation of the weight space of LLMs and their respective vulnerability to answering unsafe questions. They provide a novel set of tools for perturbing the weight space of models, either alo... | Rebuttal 1:
Rebuttal: We are glad the reviewer finds our paper novel and considers the safety basin a very important tool for evaluating LLM safety finetuning. We also thank the reviewer for their constructive suggestions. We hope the following clarifications address the reviewer’s concerns:
1. **Measure a few other ca... | Summary: This paper aims to measure the LLM’s robustness against fine-tuning attacks by introducing the concept of “safety basin”. A new metric, VISAGE score, is proposed to measure the risk in fine-tuning without the need to actually fine-tune the LLM using a harmful dataset. The experiments demonstrate the proposed V... | Rebuttal 1:
Rebuttal: We thank the reviewer for all the constructive suggestions, and we hope the following clarifications can address the reviewer's concerns:
1. **Does the model still generate fluent output when ASR is high?**
We have conducted additional quantitative and qualitative experiments, which show that LL... | Summary: This paper looks at how robust/sensitive LLMs are in terms of safety training and finetuning. The authors study how robust models are by studying a "safety landscape" through perturbing the model's parameters in a random direction and evaluating the safety of the new perturbed model. They find many models exhi... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for acknowledging the significance of our findings and contributions! We especially appreciate that the reviewers find our metric practically useful and can be applied to future LLM safety training analysis. We hope the following clarifications can address the revie... | Rebuttal 1:
Rebuttal: # General Response
We sincerely thank all reviewers for their thoughtful feedback. We are excited that they highlight the strengths of our paper:
- **Safety basin, a new phenomenon observed universally in popular open-source LLMs, contributes significantly to the AI safety community and beyond.** ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SpGesture: Source-Free Domain-adaptive sEMG-based Gesture Recognition with Jaccard Attentive Spiking Neural Network | Accept (poster) | Summary: This work introduces an innovative framework for sEMG-based gesture recognition. It leverages Spiking Neural Networks (SNNs) and introduces a Jaccard Attention mechanism and Source-Free Domain Adaptation (SSFDA) to enhance model robustness and accuracy in real-world applications.
The framework achieves high a... | Rebuttal 1:
Rebuttal: ***To reviewer*** Thank you for your thorough review and insightful feedback on our work. We are delighted that you recognize the strengths of our proposed Jaccard-based attention mechanism, our SNN-oriented SFDA algorithm, and the high accuracy (89.26%) achieved on our newly collected sEMG gestur... | Summary: This paper proposes SpGesture, a surface electromyography (sEMG) based gesture recognition framework using Spiking Neural Networks (SNNs). The main contributions include: 1) A novel Jaccard Attention SNN (JASNN) model that enhances sparse spike sequence representations by directly applying Jaccard similarity c... | Rebuttal 1:
Rebuttal: ***To reviewer:*** Thank you for your thorough and insightful review of our paper. We are delighted that you found our contributions noteworthy. We appreciate your recognition of the Jaccard Attention SNN model, which **enhances feature representation while maintaining computational efficiency and... | Summary: The paper proposes a novel attention mechanism that utilizes the Jaccard similarity to replace the traditional dot-product approach. This allows Spiking Neural Networks (SNNs) to maintain their binary characteristics (0 and 1) during the forward pass，which is very important for hardware computation. Additional... | Rebuttal 1:
Rebuttal: ***To reviewer:*** Thank you for your insightful review of our paper. We are thrilled that you found the introduction of Jaccard Attention to be an innovative and computationally friendly algorithm for SNNs. Your recognition of our pioneering work on source-free domain adaptation in SNNs and its e... | null | null | Rebuttal 1:
Rebuttal: Dear Area Chair,
We would like to express our gratitude to you for your dedicated efforts and contributions and to the reviewers for their constructive feedback on our submission. We are encouraged by the positive evaluation from all reviewers. All three reviewers acknowledged the innovative aspe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Sample Selection via Contrastive Fragmentation for Noisy Label Regression | Accept (poster) | Summary: This paper targets the noisy label regression problem. Inspired by the classification loss which helps get good representation, they first propose to discretise continuous label space into pieces which thus divides data into disjoint fragments. Then they look at the F/2 maximally contrasting fragment pairs on ... | Rebuttal 1:
Rebuttal: **[Limitations, Q2]. Pair construction strategy is interesting but not well motivated by the noisy label topic casting concerns regarding contribution. The maximally contrasting pairs of fragments ignores order relation.**
We would like to clarify that ConFrag is strongly motivated by the topic o... | Summary: This paper aims at addressing noisy labels in real-world regression problems and propose the ConFrag method. ConFrag transforms regression data into disjoint fragmentation pairs to enhance the distinction between clean and noisy samples. It leverages neighboring fragments and expert feature extractors to ident... | Rebuttal 1:
Rebuttal: **W1. The motivation behind the idea should be stated more clearly in the introduction section. Since there is a close connection between labels and features, it is necessary to clarify why contrastive fragment pairing is introduced and whether the design of the data selection metric considers the... | Summary: This paper addresses the issue of label noise in regression tasks. The proposed method partitions the data and trains several binary classifiers for the most distant partition pairs. Noisy data samples are detected using the voted probability of all classifiers. The method outperforms other baselines on severa... | Rebuttal 1:
Rebuttal: **W1. Focus on regression is limiting. ConFrag transforms regression tasks into classification tasks; it could potentially be extended to address noise in classification**
We believe that noisy regression is an important task on its own! However, most noisy label learning research focuses on clas... | Summary: The paper introduces an innovative approach for the collective modeling of regression data, grounded in the idea that similar labels often correspond to shared significant features. The authors convert the data into separate, yet juxtaposed fragment pairs, employing a combination of adjacent fragments to detec... | Rebuttal 1:
Rebuttal: **W. Paper is overwhelmed with its presentation of too many details, which can move to the appendix to let up the reader focus on the important parts.**
We sincerely thank the reviewer for the suggestion. In order to balance the clarity and the detail, we will move some overly detailed procedure ... | Rebuttal 1:
Rebuttal: # Global Response
We thank the reviewers for their insightful comments and acknowledgment. We appreciate the recognition that our approach is technically solid and correct (Reviewer T5Bx), our presentation is commendable (Reviewer T5Bx, 2i51), and our extensive comparison of regression works is t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views | Accept (poster) | Summary: A variant of horizontal FedMVC is proposed to address more realistic scenarios involving heterogeneous hybrid views. It develops specific strategies and conducts theoretical analyses from the perspective of bridging client and view gaps. The proposed method demonstrates promising experimental results on severa... | Rebuttal 1:
Rebuttal: Response to Reviewer uC6c:
Thank you for your valuable feedback.
**Q1: Contrastive learning strategies have been widely used in multi-view clustering methods; thus, the synergistic contrast strategy proposed in this paper may not offer significant novelty.**
We would like to emphasize that the... | Summary: The authors introduce a novel method called Federated Multi-view Clustering via Synergistic Contrast (FMCSC), to simultaneously leverage the single-view and multi-view data across heterogeneous clients to discover clustering structures from hybrid views. This method bridges client and view gaps through a combi... | Rebuttal 1:
Rebuttal: Response to Reviewer jVcj:
We thank the reviewer for valuable comments and suggestions that have greatly improved our paper.
**Q1: The paper does not mention the data partitioning strategy of the proposed method. The authors need to provide details on how data are distributed among different cli... | Summary: This paper proposes a novel method i.e. Federated Multi-View Clustering in Heterogeneous Hybrid Views (FedCSC), which introduces a locally collaborative contrastive learning algorithm to achieve consistency between single-view and multi-view clients, thereby mitigating heterogeneity among all clients. Furtherm... | Rebuttal 1:
Rebuttal: Response to Reviewer s4wh:
**Q1: The dataset size of 10,000 is insufficient; validating the proposed methods requires a significantly larger dataset to ensure robustness and broader applicability.**
Thanks for the suggestion. We conduct further experiments on the large-scale YoutubeVideo dataset... | Summary: This paper proposes a novel Federated Multi-View Clustering method capable of handling heterogeneous hybrid views. By designing local-synergistic contrastive learning and global-specific weighting aggregation, the proposed method explores clustering structures across different clients. The effectiveness of the... | Rebuttal 1:
Rebuttal: Response to Reviewer 14Lq:
We sincerely appreciate your constructive comments and suggestions.
**Q1: Several key observations are presented in Section 3.2 on cross-client consensus pre-training; however, the purpose of these observations is not very clear.**
Thank you for your feedback. In Sec... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Nonparametric Evaluation of Noisy ICA Solutions | Accept (poster) | Summary: The authors consider the noisy ICA problem. They first propose a numerical objective function that can be used as a guide to assess the quality of an existing ICA algorithm. This function is not suitable for optimization and therefore, the authors propose to use it in a meta-algorithm, where the purpose is to ... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the challenging nature and wider applicability of the noisy ICA problem and for your kind words regarding our experiments with the Meta algorithm. All typographical and grammatical errors will be corrected in the revised manuscript and are not addressed individually her... | Summary: This paper proposes a nonparametric score to adaptively pick the best noisy ICA algorithm from a set of candidates. This “independence score” is based on the characteristic function-based objective (CHF) introduced by Eriksson&Koivunen in 2003.
In practice, this independence score evaluates the inverse mixin... | Rebuttal 1:
Rebuttal: Thank you for your kind words regarding our theoretical approach, literature review, and experiments. We address your comments, suggestions, and questions below:
**[Re: Recovery of source signals]** We address this concern in lines 60-65 of our paper. Under the noisy ICA model (Eq 1), both $\math... | Summary: The presented paper focuses on the problem of noisy ICA which remains a significant challenge in classical machine learning.
The authors introduces a nonparametric independent score extending the work in [21] to evaluate the estimation of the demixing matrices without requiring any prior knowledge of underlyin... | Rebuttal 1:
Rebuttal: Thank you for your kind words regarding our independence score, contrast functions, and our theoretical analysis. We address your questions and suggestions below.
**[Re: Gaussian noise assumption]** The classical noisy ICA problem adds Gaussian noise to a mixture of non-gaussian independent compo... | Summary: This work proposes a modification of the original CHFICA characteristic function to the noisy ICA case without requiring knowledge of the noise distribution parameters.
The modified independence score is then used to select the best out of multiple ICA methods based on the score assigned to their solutions, in... | Rebuttal 1:
Rebuttal: Thank you for your kind words regarding our work's originality, quality, clarity, and significance. We appreciate your detailed comments and suggestions, which we address below. All typographical and grammatical errors will be corrected in the revised manuscript and are not addressed individually ... | Rebuttal 1:
Rebuttal: We want to first thank all the reviewers for their valuable suggestions and insightful feedback. We believe we have addressed nearly all of their main technical questions. In what follows, we will address some important points each reviewer has raised. We will correct all the typographical issues ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training | Accept (poster) | Summary: This work propose a novel data selection method, FRHOzen (Frozen Reducible Hold Out Loss), which leverages an empirical Bayes-inspired approach to derive a simple and computationally efficient selection criterion based on the relative loss values of two auxiliary models. They provide an empirical evaluation of... | Rebuttal 1:
Rebuttal: We first want to thank the reviewer for their positive comments about how our simple method outperforms alternatives, has evidence of favorable scaling, and computational efficiency. We think these are all important strengths of the paper, and the other reviewers largely agreed.
Now we will addr... | Summary: This work proposes a simple, intuitive approach for data selection based on empirical bayes formulation minimizing the difference in the likelihood assigned to a candidate training sample by a model trained on a base distribution and a model trained on the base distribution plus a smaller sample of high qualit... | Rebuttal 1:
Rebuttal: We first want to thank the reviewer for their thorough review and very positive assessment of our paper. In particular, they highlight the intuitive and principled algorithm, the connections to related work, the improvement over influence functions, and the impressive scaling results.
In the rest... | Summary: The paper presents FRHOzen, a new data selection method for targeted pre-training of language models, which uses an empirical Bayes-inspired approach to derive a simple, efficient selection criterion based on the relative loss values of two auxiliary models. Evaluated on tasks such as domain adaptation from C4... | Rebuttal 1:
Rebuttal: We first want to thank the reviewer for their positive assessment of our paper. In particular, they highlight the clarity, the discussion of computational costs, the effectiveness of the method, and the scalability of the approach.
In the rest of this response we will address the weaknesses and ... | Summary: This paper proposes a data selection method that can improve the performance of language models on downstream tasks. The method uses two auxiliary models: one pretrained on the pretraining dataset, and the other is finetuned on the downstream task using that pretrained model. Then the method selects the data t... | Rebuttal 1:
Rebuttal: We first want to thank the reviewer for their thorough review and largely positive comments. In particular, they highlight that the method is novel, intuitive, well-formulated, situated wrt related work, and has strong experimental results.
In the rest of this response we will address the weakne... | Rebuttal 1:
Rebuttal: Thanks to all the reviewers for their constructive comments. We hope we have resolved any misunderstandings.
One experiment suggested by the reviews (particularly reviewers yqth and ZLEF) was to test if the data selected by FRHOzen for downstream tasks generalizes to new downstream tasks that are... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Discrete Flow Matching | Accept (spotlight) | Summary: This paper presents Discrete Flow Matching, a new method for generating discrete data, such as language. The approach uses a general family of probability paths between source and target distributions. It offers a formula for sampling from these paths using learned posteriors. By focusing on specific probabili... | Rebuttal 1:
Rebuttal: **Question: In line 32, you mention one advantage of FM is its flexibility in handling non-Gaussian target distributions. Have you demonstrated this case?** Yes, in fact all the probability paths we use in this paper are non-Gaussian. We worked with mask source distribution which corresponds to a ... | Summary: The paper proposes an approach to generative modelling for discrete data, i.e. multidimensional distributions where the variable along every dimension can take value in a finite set. This is an alternative approach to autoregressive generative modelling for discrete data which is currently actively studied for... | Rebuttal 1:
Rebuttal: **Question: Why do posteriors in Equation 15 define a proper distribution?** Equation 15 is a proper distribution as follows:
$$\sum_{x^i} \hat{w}^j_t(x^i|X_t) = \sum_{x_0,x_1} \overbrace{\big( \sum_{x^i} w^j(x^i|x_0,x_1)\big )}^{=1} p_t(x_0,x_1|X_t) = \sum_{x_0,x_1} p_t(x_0,x_1|X_t) = 1,$$
where... | Summary: The paper introduces a novel approach called Discrete Flow Matching, which adapts continuous flow models to discrete sequential data. It extends prior work by integrating discrete state spaces and time-dependent schedulers into a unified framework for non-autoregressive generative modeling. Methodologically, D... | Rebuttal 1:
Rebuttal: **Comment: I am a bit confused with Eq 10 especially the scheduler terms.** Equation 10 proposes a second instantiation of a conditional probability path $p_t(\cdot|x_0,x_1)$ where given some pair $(x^i_0,x^i_1)$ of source and target tokens, the token at time $t$ is: $x^i_1$ with probability $\ka... | Summary: The paper presents a discrete flow matching method for modeling discrete data with discrete state space. The paper presents unified frameworks for training and sampling from the discrete probabilistic model. Importantly, the paper also studied scaling up the model to 1.7B parameters and tested the model on cod... | Rebuttal 1:
Rebuttal: **Comment: The paper lacks some experiments on common benchmarks used in existing discrete diffusion models, e.g., LM1B and OWT.** First, please note that our experimental setup already includes the OWT dataset (see Table 2 and lines 268-273). Second, per the reviewer’s suggestion, we trained and ... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' insightful feedback on our paper. We address each of the comments/questions raised by the reviewers in the specific threads below. We would be happy to address any remaining concerns during the discussion period.
Here we summarize the new experiments we performed duri... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
BehaviorGPT: Smart Agent Simulation for Autonomous Driving with Next-Patch Prediction | Accept (poster) | Summary: BehaviorGPT: Smart Agent Simulation for Autonomous Driving with Next-Patch Prediction introduces a model architecture for multi-agent simulation of dynamic traffic actors. Improved simulation capabilities are essential to the safe and rapid development of autonomous vehicles. BehaviorGPT structures multi-age... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive suggestions, which greatly help improve the quality of our manuscript. In the following, we attempt to address some critical concerns you raised.
**1. Scope down claims slightly**
(1) **Sample efficiency**: We evaluate our models trained with different p... | Summary: This work focuses on multi-agent simulation for autonomous driving. Instead of commonly used encoder-decoder structure, the authors propose a decoder-only autoregressive architecture for better data utilization, and achieve SOTA on Waymo SimAgents Benchmark.
Strengths: * The low data utilization problem using... | Rebuttal 1:
Rebuttal: We sincerely thank your valuable feedback. To resolve your concerns, we try our best to conduct some scaling experiments under the constraints of limited time and computing budget.
**Q1: Can BehaviorGPT scale with data and computation?**
**A1:** We answer this question regarding the quantity of ... | Summary: This paper proposes a new decoder-only learning scheme for autonomous driving dynamics. Rather than using an encoder-decoder type architecture, the model uses spatial, temporal, and “social” attention between map-agent, time-agent, and agent-agent respectively in a time-autoregressive manner. Further, the work... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful comments. We will revise the statements, notations, and figures according to your suggestions. In the following, we attempt to address your critical concerns by giving more analyses and clarifying the implementation details.
**Q1: Comparisons with timeserie... | Summary: This work presents BehaviorGPT, a model for trajectory prediction which is decoder only and respects temporal causality by employing a autoregressive sequence model. They opt for a coarser time resolution of the sequence they call "patching" for reasons of efficiency and larger context, in analogy to word-lev... | Rebuttal 1:
Rebuttal: Thank you for the insightful comments! We fully understand your concerns and hope to explain our motivation as well as some subtle details regarding your doubt.
**Q1: Does decoder-only architecture really matter?**
**A1**: Indeed, it is possible to achieve very good results with an encoder-decod... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Please find the qualitative results and the loss curves in the attached PDF file.
Best,
Authors
Pdf: /pdf/12407c41ccd44466581dd1d00db0b608f1e6bdb0.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes a decoder only model for traffic simulation. The paper proposes to not predict the next state/token but to predict the next patch a small trajectory segment. The network can select several possible patch generators which are based on RNNs. The resulting architecture achieves strong results w... | Rebuttal 1:
Rebuttal: We sincerely thank you for the valuable feedback. In the following, we answer your questions to resolve some critical concerns.
**Q1: Details of final models and ablation studies.**
**A1**: We list the detailed configurations of each group of experiments as follows, which will be added to the re... | Summary: This paper presents BehaviorGPT, a model for multi-agent traffic simulation. BehaviorGPT's architecture is based on a decoder-only transformer that autoregressively predicts patches of trajectories. The key insight is that predicting patches forces the model to learn longer-horizon reasoning. Then, to predict ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful comments and constructive suggestions. In the following, we attempt to address your critical concerns by giving more analyses and clarifying the implementation details.
**Q1: What makes BehaviorGPT outperform SOTA with fewer parameters?**
**A1:** Indeed, ... | null | null | null | null |
Pre-training Differentially Private Models with Limited Public Data | Accept (poster) | Summary: This work theoretically justifies the loss of utility under DP pre-training under the lens of a Hessian. This theoretical result can then be leveraged to perform efficient pre-training of models on public data (small amount) to significantly improve DP-trained model's performance.
Strengths: The problem autho... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments. We will address them point-to-point and hope the reviewer can raise the score if satisfied.
> My fundamental issue with the positioning of this work is that we already know that public data improves DP - [1,2,3,4,6]. We also know that DP training... | Summary: This paper addresses the challenge of DP pretraining, which has been limited due to performance degradation. The authors first analyze per-iteration improvements of the optimization process and then propose a novel DP continual pre-training strategy that uses a small amount of public data to mitigate the negat... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and the comments. We will address them point-to-point and hope the reviewer can raise the score if satisfied.
> The authors claim that a certain amount of public data can mitigate deceleration. However, Section 4 lacks discussion about deceleration. The given cl... | Summary: This paper proposed a Hessian-based analysis of the per-iteration loss for DP-SGD and non-private training, and provide a theoretical explanation for slower convergence of differentially private training. Authors identified the *decelerator* compontent in per-iteraton loss improvement, associated with the grad... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and comments. We will address them point-to-point. The answers to some questions are merged in the response to the weaknesses.
> The major weakness of the paper (at least its theoretical part) is the assumption that per-sample gradient clipping does... | Summary: This paper provides a theoretical framework to analyze impact of various aspects (parameters) of DP training on the performance of resulting models. The framework uses hessian of per-sample gradient to compute per-iteration loss improvement. Using the framework, the paper shows how DP impacts performance of mo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and comments. Given that the weaknesses are mostly about clarification and explanation, we are happy to address them if the reviewer can be slightly more specific, besides the questions which we address below.
> It looks like the conclusions made i... | Rebuttal 1:
Rebuttal: We thank all the reviewers for the comments and put every effort to address them. Please let us know if there are further questions (though revision is not allowed this year). We provide the algorithm of DP continual pretraining in PDF here (Appendix D).
Pdf: /pdf/daf3ed256bb2eaf287e9201b745c7d6d7... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Differentially Private Set Representations | Accept (poster) | Summary: The paper considers private representations of sets (under the neighboring relation of adding/removing an element). The goal is to answer set membership queries correctly with some nontrivial 1-alpha probability of being correct. (For a universe of size 1 this can be addressed by randomized response, so this p... | Rebuttal 1:
Rebuttal: We thank the reviewer ys34 for their valuable and comprehensive feedback.
In response to the reviewer's suggestion, we will improve the clarity of the privacy proof and incorporate additional context within the main body of the paper. We acknowledge the importance of explicitly explaining why ret... | Summary: The paper studies the problem of optimal differentially private representing a set $S$ with size $k$ on universe $U$, under the setting where the set size $k$ is significantly smaller than the universe size $|U|$. In such settings, the authors propose algorithms to compute $(\varepsilon, \delta)$-DP set repres... | Rebuttal 1:
Rebuttal: We thank the reviewer VHxw for their valuable and constructive feedback.
We will first address the reviewer's question regarding the error probability. We think that the proof presented in our paper is accurate. We believe the misunderstanding may arise from the way the pseudocode in Algorithm 1 ... | Summary: The paper addresses the problem of releasing a set $S $ of $ k $ elements from a potentially very large universe $ U $ in a differentially private manner. Here, two input sets $ S $ and $ S' $ are considered neighboring if their symmetric set difference is at most one; that is, $ S $ and $ S' $ differ by addin... | Rebuttal 1:
Rebuttal: We thank reviewer 1hbz for their constructive feedback.
The reviewer raised a concern about the privacy analysis of our paper, suggesting that the size of the published vector could compromise privacy.
We want to clarify that the parameter $k$ is an upper bound on the size of the set $S$ to be e... | Summary: The paper presents new differentially private (DP) mechanisms for representing sets of size k from a large universe. It introduces two algorithms: one for epsilon, delta -DP representations and the other for pure epsilo-DP representations, with faster decoding. Both algorithms achieve optimal privacy-utility ... | Rebuttal 1:
Rebuttal: We thank the reviewer Gmoe for their valuable feedback.
To illustrate the usefulness of our schemes, we will revisit the installed apps use case briefly mentioned in the paper and provide a concrete example. Imagine an analyst at an app software company looking to gather statistics on the percen... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Retraction-free optimization over the Stiefel manifold with application to the LoRA fine-tuning | Reject | Summary: The authors propose a retraction-free Riemannian optimization scheme on Stiefel and oblique manifolds to perform parameter-efficient fine-tuning (PEFT) in LoRA style. The proposed approach exploits the theory of landing flows on Stiefel manifolds. Theoretical results demonstrating convergence of this iterative... | Rebuttal 1:
Rebuttal: Thank you for appreciating our work and providing detailed comments. The following are our responses.
### Weaknesses:
All the presented theory is developed on a function defined on $\text{St}(d, r)$, while LoRA fine-tuning gives rise to an objective function $f(B, A) = L(BA)$, where $B \in \text... | Summary: Retraction-free optimization algorithms on the Steifel manifold have been proposed in [1,18,19,41] etc. The motivation is that if the cost of the objective function/gradient evaluation is significantly larger than the evaluation of a retraction, then the retraction-free optimization algorithms show their advan... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper. The following are our responses.
### Weaknesses:
1. Though the value of mu and an upper bound of $|x_0 - \bar{x}_0|$ are given concretely, the choice of step size is unknown. Theoretically, the step size needs to be sufficiently small (See Theorem 1). Any theor... | Summary: This paper considers solving optimization problems with constraints that have orthonormal columns (i.e. the matrix belongs to the Stiefel manifold). The leading method for solving such problems is the Riemannian optimization. However, Riemannian optimization requires a costly retraction operation. The authors ... | Rebuttal 1:
Rebuttal: Thank you for carefully reading our manuscript and appreciating our work. The following are our responses.
#### Novelty:
1. Citation [1] considers optimization with constraints on the orthogonal group (i.e., St(n,n)). It seems that the core idea on how to implement retraction free optimization alr... | Summary: This paper proposes a new algorithm, Manifold-LoRA, which incorporates the Stiefel manifold constraint to accelerate low-rank adaptation (LoRA) in fine-tuning LLMs. It also provides theoretical and experimental validation for the retraction-free and penalty parameter-free optimization methods.
Strengths: This... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper. The following are our responses.
1. **W1: Some experimental results are unclear and not well defined.**
**Reply**: We have revised the descriptions of numerical experiments accordingly. Specifically, we report the overall (matched and mismatched) accuracy fo... | Rebuttal 1:
Rebuttal: **Continued rebuttal for Reviewer ohUo**
4. Without looking in detail in the proofs, my guess is that changing the penalty step size $\mu$ affects how close you need to be to the manifold (the value 1/8), which affects how small the step size need to be. In other words, the authors load all the c... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Simulation-Free Training of Neural ODEs on Paired Data | Accept (poster) | Summary: The paper revisits using neural ordinary differential equations (NODEs) for modeling deterministic maps on paired data, e.g., maps that solve regression and classification problems. The authors propose utilizing flow-matching (FM), a recent simulation-free training method for NODEs, to overcome the computation... | Rebuttal 1:
Rebuttal: >**Q1.** The paper lacks formal and rigorous explanations of the method and experimental settings. For instance, adding noise to labels (L164).
**A1.** We appreciate the comment and will revise the presentations in the method section to be more clear in our final version of the manuscript. For ad... | Summary: This paper presents an approach for instantiating a flow matching method for paired data $(x, y)$ without relying on iterative ODE solvers. The method uses an input encoder with a pair of target decoder and encoder to project the original data into a latent space. By imposing the form of the dynamics in latent... | Rebuttal 1:
Rebuttal: >**Q1.** Presentation issues in Table 1 and repeated definition of abbreviations in the main text.
**A1.** We thank the reviewer for highlighting these presentation issues. We acknowledge that some abbreviations (e.g., NFE or NODE) are defined repetitively. In the final version of our manuscript,... | Summary: The authors propose to use the flow matching loss, which directly matches the dynamics of a neural ODE (NODE) model to the pre-defined (simple) vector field, for supervision tasks. While the flow matching with simple linear vector fields is efficient, it cannot work well for supervision tasks because the paire... | Rebuttal 1:
Rebuttal: >**Q1.** While this paper provides useful insights into NODEs, such as the crossing trajectory problems and the use of latent dynamics, these are well-known topics within the community of NODEs. Therefore, I believe this paper should be evaluated based on its practical application rather than its ... | Summary: This paper develops the Flow Matching (FM) algorithm to connect paired data. Due to the issue of crossing trajectories, FM in the data space cannot perfectly match associated pairs. To address this, the authors perform FM in an embedded space. Ultimately, they encode source and target data through an encoder e... | Rebuttal 1:
Rebuttal: >**Q1.** Embedding data into a latent space does not guarantee the prevention of trajectory crossings. The improvement in accuracy may be due to the additional embedding networks rather than resolving the issue. Please show that the proposed method experimentally prevents the issue.
**A1.** We wo... | Rebuttal 1:
Rebuttal: Dear reviewers,
We appreciate the constructive feedback provided by all reviewers, which has significantly contributed to the improvement of our paper. We are encouraged by the positive recognition our paper has received, including:
- "begins with a reasonable motivation and is well-presented" (Y... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Trap-MID: Trapdoor-based Defense against Model Inversion Attacks | Accept (poster) | Summary: The paper proposes Trap-MID, a novel defense method against model inversion attacks, drawing inspiration from backdoor attacks and shortcut-based defenses against adversarial examples. The core idea involves adding poisoned data samples to the target model's training data. This data poisoning is based on the B... | Rebuttal 1:
Rebuttal: Thank you for your insightful review and valuable feedback. We address the specific weaknesses and questions raised in your comments below:
**W1: The defense should be tested against black-box/label-only attacks**
The experiments show that BREP-MI, a label-only attack, is ineffective on Trap-MID... | Summary: The paper introduces a backdoor-based MI attack called Trap-MID. In this method, a trapdoor is integrated into the model to predict a specific label when the input is injected with the corresponding trigger. Consequently, this trapdoor information acts as a "shortcut" for MI attacks, causing them to extract tr... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and helpful comments. We address the specific weaknesses and questions raised in your review below:
**W1: “MI attacks leverage a discriminator to approximate generic prior and ensure natural outcomes” This statement might not generalize to MI attacks that don'... | Summary: The paper proposes Trap-MID, a trapdoor-based defense mechanism to protect deep neural networks (DNNs) against Model Inversion (MI) attacks. The technique involves integrating trapdoors into the model to mislead MI attacks, causing them to extract trapdoor triggers rather than private data. The authors provide... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We address the specific weaknesses raised in your review below:
**W1: The proposed method involves multiple optimization processes, making it computationally expensive and potentially impractical for large-scale or resource-constrained applications.**
We app... | Summary: This paper presents Trap-MID, a novel defense mechanism against model inversion attacks that utilizes trapdoor injection techniques. By incorporating a trapdoor into the model, Trap-MID misleads MI attacks into extracting trapdoor information instead of private data, effectively preserving privacy. The paper c... | Rebuttal 1:
Rebuttal: Thank you for your detailed and insightful review. We address the specific weaknesses and questions raised in your comments below:
**W1: Trap-MID assumes trust between data providers and model owners, which isn't always feasible.**
This limitation is discussed in Appendix A.2. We follow a common... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback that helped us improve our paper. We are encouraged that they found Trap-MID to be a **novel** (MuQ4, bcsy), **clever, and conceptually straightforward** (bcsy) approach to defending against MI attacks. The feedback that our work **fills a gap in ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Visual Sketchpad: Sketching as a Visual Chain of Thought for Multimodal Language Models | Accept (poster) | Summary: This paper proposes visual sketchpad that aims to aid the existing multimodal language models (MLMs). Specifically, the visual sketchpad serves as a task-specific prompting technique that calls tools to draw sketches for the input problem as additional context to help solve the problem. This prompt technique i... | Rebuttal 1:
Rebuttal: Thank you for your insightful and constructive review! We are honored that you believe visual sketchpad is interesting and effective. We address each question as follows. Hope that our response clarified your concerns, and we would be grateful if you could consider improving the rating after seein... | Summary: This work proposes Visual SKETCHPAD, a framework designed to incorporate visual reasoning into chain-of-thought and tool-use paradigms. Specifically, a multimodal LLM (Large Language Model) addresses a query by (1) generating a plan, (2) executing an action, (3) updating the current context with the result of ... | Rebuttal 1:
Rebuttal: We appreciate your constructive feedback! We are encouraged that you acknowledge the originality, quality, clarity, and significance of our work. We address your concerns as follows, and hope that they can clarify your concerns, and hope that you can improve the rating after seeing the responses!
... | Summary: This paper studies the problem of using language models to generate code to draw for intermediate reasoning. Particularly, the idea of chain-of-thought is applied to facilitate the reasoning process, such that the auxiliary "drawings" enhance the LM's reasoning ability. The proposed method is tested both on ma... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback! We are honored that you believe the pipeline is reasonable, achieving good results, and the paper well-written. We address your questions below. Hope that we addressed your concerns, and we would be grateful if you could consider improving the rating after seeing... | Summary: The Visual SKETCHPAD framework integrates sketching capabilities into multimodal language models, enabling them to iteratively draw, plan, and reason using visual artifacts - similar to how humans leverage sketching to facilitate problem-solving. Unlike prior approaches that relied on text-to-image models, Vis... | Rebuttal 1:
Rebuttal: Thanks for your kind and insightful feedback! We are honored that you believe Sketchpad is a great idea. We address your questions as follows:
**1. More discussion about the robustness (repeatibility) is needed, since a common problem for these commercial LVLMs is the instability. Does the method... | Rebuttal 1:
Rebuttal: We appreciate all reviewers for their timely and positive feedback. We are encouraged that the reviewers believe visual sketchpad is “a good idea” (Reviewer BpTp), “interesting and effective” (Reviewer jSat), with “originality” and “significance” (Reviewer 2LyD). Also, all reviewers believe that t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs | Accept (poster) | Summary: The paper introduces MR.BEAN, a benchmark designed to evaluate the meta-reasoning capabilities of large language models (LLMs). This benchmark focuses on the models' ability to detect and correct errors in reasoning steps, addressing the limitations of existing outcome-based benchmarks.
Strengths: - The paper... | Rebuttal 1:
Rebuttal: We truly appreciate your kind review and insightful questions. We are more than happy to address them as follows:
> **W1: The ACC_reason metric’s dependency on the judgments of different LLMs or human evaluators could lead to variability in scoring**
We would like to argue that due to the caref... | Summary: This paper introduces a new benchmark for evaluating the reasoning capabilities of large language models (LLMs). Current methods primarily focus on final outcomes and not sufficiently capture the intricacies of the reasoning process. To address this issue, this paper propose MR.BEAN, a process-based benchmark ... | Rebuttal 1:
Rebuttal: Thank you for your kind review and insightful comments. We are committed to addressing your concerns and providing clarifications.
> **W1: Missing a thorough comparison between the model's automatic annotations and human annotations.**
(Since all the instances in MR. Bean are annotated manually,... | Summary: This paper introduces MR.BEAN, a comprehensive benchmark for evaluating meta-reasoning capabilities of large language models (LLMs). Comprising 6,006 questions across various subjects including physics, chemistry, logic, coding, and more, MR.BEAN requires LLMs to analyze and correct errors in automatically gen... | Rebuttal 1:
Rebuttal: Thank you for your kind review and insightful and to-the-point comments. We are committed to addressing your concerns and providing clarifications.
> **W1: How does our evaluation mechanism measure meta-reasoning abilities rather than general language understanding or domain knowledge**
We belie... | Summary: This paper proposes a meta-reasoning benchmark for evaluating the solutions generated by a large-language model (LLM) to shift the focus more to process-based evaluation of an LLM's reasoning abilities rather than outcome-based evaluation.
*Evaluation*:
On a variety of question-solution pairs, the model is as... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and insightful comments. We hereby address your concerns below:
> **W1: Mixing metrics into a singular score**
Given the **interdependent and progressive** nature of the three tasks (MCC - ACC_step - ACC_reason), we can either choose to combine them **organic... | Rebuttal 1:
Rebuttal: Dear PCs, SACs, ACs 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 corresponding rebuttal sections. If our answers satisfy your queries, we would be grateful if you could consider revising y... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Antigen-Specific Antibody Design via Direct Energy-based Preference Optimization | Accept (poster) | Summary: This paper uses a pre-trained conditional diffusion model for antibody design. This diffusion model is fine-tuned using a direct energy-based preference optimization method, focusing on optimizing residue-level energy preferences to enhance the generation of antibodies with desirable structures and high bindin... | Rebuttal 1:
Rebuttal: Thank you for your strong support! Please see below for our responses to the comments.
**Q1: Lack of explanation of SE(3)-equivariant neural network. The author uses the diffusion model with such an equivariant neural network from Luo et al. Lacking such an explanation may hurt the understanding ... | Summary: This paper proposes a new perspective for antibody design-- incorporating the energy factors aiming to minimize the overall energy of designed sequence and structure. It involves diffusion to maintain the sequence-structure co-design. Towards the variance of energy factors, the paper proposes the idea of gradi... | Rebuttal 1:
Rebuttal: Thank you for your helpful feedback! We address your questions as follows.
**Q1: AAR is about 10% lower than dyMEAN. The authors did not provide a solid reason for what caused the result.**
A1: As mentioned in Appendix A, AAR is easily hacked and conceals numerous issues. Certain intrinsic patte... | Summary: The paper proposes an approach for fine-tuning diffusion models for the design of antibodies. The core diffusion-based generative model comes from Luo et al. [36] and to my understanding there are no technical changes to it. The second component is direct preference based optimization, inspired by fine-tuning ... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback. Please see below for our responses to the comments.
**Q1: The approach can design better binders, but comes at the expense of increasing the number of hydrophobic residues which is typically associated with non-specific binding.**
A1: This is exactly why we ... | Summary: This paper applies direct preference optimization to antibody design. Specifically, it uses Rosetta binding energy to guide a pre-trained diffusion model to generate antibody CDR structures with low binding energy.
Strengths: * Optimizing antibody binding energy is an important problem.
* The proposed gradien... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Please see below for our responses to the comments.
**Q1: It is computationally expensive to guide diffusion models using Rosetta.**
A1: Yes, the computational expense is indeed a drawback of Rosetta. We are aware of Rosetta's limitations and have discussed them in t... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for your constructive feedback. We placed Figure 1 and Figure 2 in the PDF file.
Pdf: /pdf/1c5dfaf432d4c963cf05fb886437fcf7c3123c7d.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DeepITE: Designing Variational Graph Autoencoders for Intervention Target Estimation | Accept (poster) | Summary: The paper introduces a deep learning framework for identifying intervention targets in causal systems by amortizing the problem, although assuming that the causal graph is known. The framework, called DeepITE, employs a variational graph autoencoder (VGAE) that learns from both labeled and unlabeled data acros... | Rebuttal 1:
Rebuttal: _Q1 summarizing table_
We agree that LIT does not require a given graph and will mention DeepITE requires a given graph explicitly in the introduction. In response to your suggestion, **we have provided a comparative analysis of the assumptions inherent in various methods in Table R1 of the PDF a... | Summary: The paper presents DeepITE, a novel deep learning framework designed for Intervention Target Estimation (ITE) in complex systems. DeepITE addresses these issues by employing a variational graph autoencoder (VGAE) that can learn from both unlabeled and labeled data across various intervention targets and causal... | Rebuttal 1:
Rebuttal: _Q1 theory_
We acknowledge the importance of theoretical analysis. However, due to the time limit, **we instead provide a comparative analysis of the assumptions inherent in various methods in Table R1 of the PDF attached to our global response.** This table highlights that the primary advantages... | Summary: This paper proposes a deep learning approach for Intervention Target Estimation (ITE) which is an important problem in causal discovery and inference. The authors argue that traditional methods in this area can only independently process each instance and is computationally inefficient. To address these limita... | Rebuttal 1:
Rebuttal: Thank you very much for your positive evaluation and encouraging feedback on our paper. We deeply appreciate your constructive comments and valuable suggestions.
_Q1 direct use of DAG-GNN_
DeepITE does not directly use DAG-GNN. **We have explcitly discussed the relation between DeepITE and DAG-... | Summary: Given a causal graph, this paper describes an autoencoder-based approach specifically designed for the purpose of designing intervention targets. This algorithm aims to be data and computation-efficient by by-passing the task of having to recover the causal graph, and also by incorporating specific architectur... | Rebuttal 1:
Rebuttal: _Q1 code availability_
We are committed to open-sourcing the code upon acceptance of the paper.
_Q2 lack of clarity_
1. Datasets and Evaluations: We have provided descriptions of the datasets used, including their sources, preprocessing steps, and evaluation metrics in Appendix G (please refer... | Rebuttal 1:
Rebuttal: **Global response to all reviewers**:
We sincerely thank all the reviewers for their valuable suggestions. We are delighted by the unanimous recognition of our work and appreciate the reviewers' positive feedback on the carefully designed network architecture and extensive experiments in DeepITE.... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Convergence of Loss and Uncertainty-based Active Learning Algorithms | Accept (poster) | Summary: This is a technical paper, whose subject of interest is the convergence of stochastic gradient-based learning algorithms which include a stochastic step size mechanism, whose value is allowed to be influenced by losses or other "uncertainty" related quantities that are computed at training time.
Their main th... | Rebuttal 1:
Rebuttal: We thank the reviewer for a well-rounded summary of our results and for recognizing their potential interest to a NeurIPS-like community.
# Weaknesses
The review focuses on some presentation issues. First, it argues that focusing the paper's exposition around active learning may be confusing and ... | Summary: The paper considers the active learning algorithms based on uncertainty and loss functions. The learner queries the label of an unlabeled sample with probability proportional to (some function of) the uncertainty/loss and updates the parameter according to some step size scheme. The authors generalize previous... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing insightful and useful comments.
# Weakness 1
We would like to clarify the reviewer's comment that we generalize previous results under the strictly separable binary classification setting and the general classification setting with convex loss and smooth equiva... | Summary: This submission studies the convergence guarantees of and bounds on the expected number of samples used when using loss based active learning. They additionally propose a new sampling scheme that combines loss based sampling and a Polyak step size and provide convergence guarantees. Their analysis covers multi... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating the importance of our work in ensuring the effective deployment of loss-based sampling strategies in practice, and suggesting where we can improve the presentation quality.
# Question 1
In response to the first question raised by the reviewer: the reviewer's... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful comments. We appreciate the positive feedback recognizing the problem we study as interesting, novel, and important, as well as the positive assessment of our results, which include both theoretical and experimental analyses. We also value the technical ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Language Models as Hierarchy Encoders | Accept (poster) | Summary: This paper proposes a method that leverages a non-Euclidean representation space to better encode hierarchical relationships between entities. Specifically, a hyperbolic embedding space is defined, wherein items closer to the origin are higher-level concepts, and items further from the origin are lower-level c... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback. We address your comments and questions below:
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**Regarding Weakness 1**:
Our current focus in this paper is on enabling transformer encoder-based language models to explicitly encode hierarchies. We recognise the importance of evaluating the preser... | Summary: The paper introduces a method to re-train transformer-based language models as Hierarchy Transformer encoders (HITs), using the properties of hyperbolic space to enhance their ability to encode hierarchical structures in language.
Strengths: 1. The utilization of hyperbolic space to encode hierarchical struct... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback. We address your comments and questions below:
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**Regarding Weakness 1**:
In Introduction, we acknowledge that hierarchical information has been considered in existing language model studies. Our claim emphasises the lack of **explicit geometric int... | Summary: This paper proposes a new way to retrain encoder-based language models into hierarchy encoders. Specifically, they propose to recast the output embedding space onto a Poincaré ball and retraining with the designed loss functions for organizing entities into hierarchy. The experiments on real world datasets lik... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback. We address your comments and questions below:
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**Regarding Weakness 1**:
Our current method is specifically designed for encoder-based language models due to their ability to produce embeddings with **more straightforward semantic meanings**. Deco... | Summary: This paper presents a novel approach called Hierarchy Transformer encoders (HITs) to retrain transformer-based language models to better encode hierarchical structures in language. The method involves situating the output embedding space of pre-trained language models within a Poincaré ball and training on hyp... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback. We address your comments and questions below:
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**Regarding Weakness 1**:
Our HiT models **can deal with new entities** and the capability of predicting subsumptions between arbitrary entity pairs is one of the key highlights. Our Mixed-hop predi... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Beyond Efficiency: Molecular Data Pruning for Enhanced Generalization | Accept (poster) | Summary: The paper presents a new framework MolPeg aimed at improving the efficiency and generalization of training models in molecular tasks using pretrained models. MolPeg introduces a novel DP technique that maintains two models with different update paces during training, leveraging the loss discrepancy between the... | Rebuttal 1:
Rebuttal: >**1. The effectiveness of MolPeg is highly dependent on the quality and suitability of the pretrained models utilized.**
Thanks for your valuable comments. We agree with the reviewer’s point that the quality of pre-training can influence the effectiveness of our method. In source-free transfer l... | Summary: By utilizing the pre-trained models, this paper presents a plug-and-play framework (MolPeg) to prune target data without source dataset. By maintaining two models with different updating paces during training, this paper introduces a novel scoring function to measure the informativeness of samples based on the... | Rebuttal 1:
Rebuttal: > **(Minor) Missing recent works: 1) static data pruning [1,2], 2) dynamic data pruning [3]**
Thanks for your recognition of our work and constructive suggestions. We apologize for omitting any relevant works. For the related works provided by the reviewer, we have added experimental results on t... | Summary: The paper introduces MolPeg, a molecular data pruning framework designed to enhance generalization when applying data pruning to pretrained models for molecular tasks. MolPeg uses two models with different updating rates to develop a new scoring function that assesses the informativeness of data based on loss ... | Rebuttal 1:
Rebuttal: > **1. Although the paper claims to be pioneering in applying data pruning to pretrained models, the motivation may require further exploration. Specifically, how can it ensure that OOD samples crucial for each task are not pruned, which could potentially undermine the very purpose of the pretrain... | null | null | Rebuttal 1:
Rebuttal: ### **General Response**
We would like to thank all reviewers very much for their extensive reviews and constructive critiques. We are encouraged that reviewers find that our approach is efficient and lightweight (Reviewer zkte), that the experiments are comprehensive and verify the effectiveness... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Differentiable Quantum Computing for Large-scale Linear Control | Accept (poster) | Summary: This paper introduces an end-to-end quantum algorithm for linear quadratic control problem. The proposed quantum-assisted differentiable simulator is suitable for large-scale dynamical systems where the dimension of system state is huge. Sample complexity is also provided when apply quantum computation in such... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the novelty and computational advantage of the proposed quantum application.
Due to the page limit, we only consider the classic LQR problem in this paper. Nevertheless, our approach can be readily generalized to other optimal control problems, such as distri... | Summary: The paper "Differentiable Quantum Computing for Large-scale Linear Control" introduces a quantum algorithm for linear-quadratic control problems, offering provable speedups. It utilizes a policy gradient method enhanced with a novel quantum subroutine for solving the matrix Lyapunov equation, leading to more a... | Rebuttal 1:
Rebuttal: We appreciate the reviewer recognizing the super-quadratic speedup achieved in this paper as a significant advancement in quantum computing for control problems.
Regarding the reviewer’s comment on the insufficient experiments as a main weakness of this paper, while the key contribution of this ... | Summary: This proposes an end-to-end solution to the quantum-assisted LQR problem. Based on a policy gradient method, the proposed algorithm incorporates a quantum subroutine for solving the matrix Lyapunov equation, achieving a super-quadratic speedup.
Strengths: 1. To the best of my knowledge, this is the first end-... | Rebuttal 1:
Rebuttal: We appreciate the reviewer recognizing our work as the first end-to-end quantum application to linear control problems with a provable quantum advantage.
One main concern in the review is related to the performance and reliability of the proposed quantum algorithm on noisy quantum hardware. The p... | Summary: This paper studies the problem of applying quantum computing to linear quadratic regulator (LQR) control. The approach is based on an efficient quantum estimation of the policy gradient. When the dimension n of the state space is large, the proposed approach can achieve orders of magnitude improvement on the t... | Rebuttal 1:
Rebuttal: We thank the reviewer for confirming that our work is of interest to the learning theory community, given that the technical part is sound. In what follows, we address the concerns regarding the weaknesses and technical details mentioned in the review.
In Table 1, the “(Model-based) policy gradie... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their invaluable comments on our submission. We are particularly grateful for the reviewers' recognition of the novelty of our paper as the *first end-to-end quantum application to optimal control* (Reviewers v7Mi, 7tHw), the acknowledgment of the *super-qu... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Consistency of Neural Causal Partial Identification | Accept (poster) | Summary: This paper studies theoretically the partial identification capabilities of neural causal models (NCMs), that is, the extent to which this type of models can approximate the interval on which a certain causal query falls on, $\theta(\mathcal{M}) \in [\underline{F}, \overline{F}]$. To this end, the authors deve... | Rebuttal 1:
Rebuttal: Thank you for your feedback! Below is our response. We hope our clarification can solve your problem.
***1. The method only works under the assumption that we know the exogenous distribution of the ground-truth model.***
We thank the reviewer for their advice. However, we would like to kindly e... | Summary: This paper provides a novel perspective and solid contribution to the case where continuous and categorical variables both exist.
Strengths: The presentation and organization are concise and clear; the contribution is solid.
Weaknesses: The assumption is somehow strong (e.g., assumption 2).
Technical Qualit... | Rebuttal 1:
Rebuttal: Thank you for your comments and questions! Below is our response. We hope our clarification can solve your problem.
***1. The assumption is somehow strong (e.g., assumption 2).***
Thank you for your feedback. Assumption 2 is a relatively new assumption about the data generation process of catego... | Summary: The paper extends neural causal models (NCMs) to the continuous and mixed-type variables. NCMs is a neural-networks based tool to perform an automated point/partial identification and estimation of causal queries. The authors provided new theoretic results (1) on how to construct a canonical representation of ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the helpful suggestions, which make our paper clearer and more readable. Below are the changes we make.
***1. Provide more examples of how to construct canonical representations for some simple SCMs and then the corresponding neural architectures of the NCMs.**... | Summary: This paper develop consistency results for partial identification via neural causal model with both continuous and categorical variables. Their results shed light on the impact of the neural network architecture and Lipschitz regularization during training. The resulting method can be trained via gradient-base... | Rebuttal 1:
Rebuttal: Thank you for your advice to improve our paper! Below is our response. We hope our clarification can solve your problem.
***1. The empirical validation of the algorithm/result is not extensive.***
Thank you for your feedback. As we point out in the paper, the methodology that we analyze has been... | Rebuttal 1:
Rebuttal: Thanks for all the reviewers' helpful feedback. We include all the figures in our response in this PDF file.
Pdf: /pdf/b59bba06267ff362b8fbea6ee338f8dae54ab4aa.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Erasing Undesirable Concepts in Diffusion Models with Adversarial Preservation | Accept (poster) | Summary: This paper investigates erasing undesirable concepts from stable diffusion. The paper builds up on a common finding on related works, that is removing even one concept can significantly rescue model’s ability to generate other concepts. Existing methods typically select a neutral concept, such as "a photo" or ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and insightful suggestions. We would like to address the remaining concerns as follows.
Due to the space limitation, some responses are provided in the global rebuttal and the author comment section.
**Q: Why using a CLIP alignment score is a reli... | Summary: The present paper addresses the challenge of erasing content from text-to-image diffusion models with a focus on reducing the degenerative impact on other concepts. To this end, the authors propose a novel approach that focuses on identifying and preserving adversarial concepts—those which are most affected by... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and insightful suggestions. We would like to address the remaining concerns as follows.
**Q: Better discussion**
We thank the reviewer for pointing this out. We will revise the comparison with CA in the revised version, i.e., our method is slight... | Summary: This paper focuses on the problem that existing concept erasing methods struggle to address the trade-off between the generation capability of erased concepts and remaining concepts. To address this problem, this paper proposes a method that erases the target concept while minimizing the impact of other concep... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments and suggestions. We would like to address the remaining concerns as follows.
Due to the space limitation, some responses are provided in the global rebuttal.
**Q: Do all related concepts need to be preserved? Boundary between preserved and erasin... | Summary: This paper studies the memory-forgetting tradeoff for concept removal. The authors systematically summarize the tradeoff problem, and propose the idea of adversarial concepts to solve it. Specifically, this approach automatically detects the most sensitive concepts that will be affected by unlearning, and enfo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments and suggestions. We would like to address the remaining concerns as follows.
Due to the space limitation, some responses are provided in the author's comment section.
**Q: Additional experiments with SOTA methods such as ConAbl and SPM, and evalu... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful comments and suggestions. Below are our responses to some important questions raised by the reviewers. We kindly request the reviewers to consider raising the scores if our responses adequately address the remaining concerns.
**Q: Compare to MACE a ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
GL-NeRF: Gauss-Laguerre Quadrature Enables Training-Free NeRF Acceleration | Accept (poster) | Summary: Volume rendering requires numerical integration for estimating output colors. This work proposes using the Gauss-Laguerre quadrature to reduce the number of samples and improve integration accuracy. The paper demonstrates that this method can be a plug-and-play module for any NeRF model. Experimental results s... | Rebuttal 1:
Rebuttal: We’re thankful for your time and the valuable insights you’ve shared. Your input has significantly advanced our project. In response to your feedback, we proposed a brand new perspective for volume rendering with a strong math foundation and validated it with experiments. We’ll address your concer... | Summary: This paper proposes a computational method for volume rendering using Gauss-Laguerre quadrature. In the context of NeRF, volume rendering is performed by evaluating MLPs (or other data structures) at a sequence of query points on a ray and integrating the weighted results. The proposed method reduces the numbe... | Rebuttal 1:
Rebuttal: Thank you for dedicating your time and providing such perceptive feedback. Your recommendations have considerably enhanced our work. Based on your comments, we have provide a brand new general framework for computing volume rendering and validate its accessibility on 2 baselines. We will address y... | Summary: This paper focuses on accelerating novel view synthesis using neural radiance fields (NeRF). Unlike previous works that concentrate on designing lightweight networks, this study is motivated by the specific volume rendering formula, which includes a negative exponential term in the integration function. By emp... | Rebuttal 1:
Rebuttal: We appreciate the time and thoughtful feedback you've given. Your suggestions have greatly improved our work. Considering your insights, we have provided a highly theoretical framework for calculating volume rendering and validated it using 2 baselines. We will address your concerns below.
>In th... | Summary: This paper presents a method for reducing the number of color samples needed for volume rendering in Neural Radiance Fields. The method works by applying Gauss-Laguerre quadrature as a replacement for the importance sampling used by some NeRF methods to reduce the number of calls needed for their fine color ML... | Rebuttal 1:
Rebuttal: We are grateful for your time and insightful comments. Your valuable suggestions have significantly elevated our work. In light of your comments, we have proposed a brand new mathematical perspective for volume rendering and proved its effectiveness on speedup using two baselines. We will address ... | Rebuttal 1:
Rebuttal: # General Response
We’d like to thank all the reviewers for their valuable feedback, especially for acknowledging our contributions regarding the theoretical foundation and the effort we made to validate and show the plug-and-play attribute of GL-NeRF. Regarding common concerns among reviewers, w... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Lightweight Frequency Masker for Cross-Domain Few-Shot Semantic Segmentation | Accept (poster) | Summary: The paper presents a novel approach to cross-domain few-shot semantic segmentation (CD-FSS) by introducing a lightweight frequency masker that aims to improve performance by filtering different frequency components for target domains. The authors propose an amplitude-phase-masker (APM) module and an adaptive c... | Rebuttal 1:
Rebuttal: **1. Compare with other frequency-based methods**
We answered this question in the global response. We hope this could resolves your concerns.
**2. The complexity analysis**
We present the results of the complexity analysis, showing that our APM and ACPA are extremely lightweight, modules with... | Summary: This paper discover a phenomenon that simpy filtering different frequency components for target domains can lead to a significant performance improvements. Then the paper delve into this phenomenon for an interpretation, and propose an approach based on this phenomenon, which achieves futher performance improv... | Rebuttal 1:
Rebuttal: **1. Our method can be applied to other tasks**
Our method can also be applied to cross-domain few-shot learning (CDFSL). Following BSCD-FSL[1] we implemented our method under this task setting (5-way 1-shot), and experimental results show that our method is effective in CDFSL as well.
| ... | Summary: This paper presents a novel approach to cross-domain few-shot semantic segmentation (CD-FSS) by introducing a lightweight frequency masker. This masker aims to enhance the robustness of models against domain gaps by filtering different frequency components during the testing phase. The authors claim that their... | Rebuttal 1:
Rebuttal: **1. Compare with directly constraining the model**
We answered this question in the global response. We sincerely hope this could resolve your concerns.
**2. Comparing with [27]Channel Importance Matters in Few-Shot Image Classification**
We compared with [27] in the global response, and here ... | Summary: This paper makes several notable contributions to the field of cross-domain few-shot segmentation (CD-FSS). The authors discover that filtering different frequency components for target domains can lead to significant performance improvements, attributing this to reduced inter-channel correlation in feature ma... | Rebuttal 1:
Rebuttal: **1. Filtering on features damages the original feature structure?**
Filtering certain frequency components does not damage the original feature structure; instead, it is beneficial. Since not all frequencies are advantageous for the current domain, we dynamically adjust the mask and take its inve... | Rebuttal 1:
Rebuttal: **1. Compare with other frequency-based methods**
Here, we elaborate on the differences between our work and previous frequency-based methods.
DFF [1] explores and retains frequency information beneficial for generalization during training while filtering out frequencies that are not. GFNet [2] ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model | Accept (poster) | Summary: This paper introduces the Materials Knowledge Graph, a pioneering graph database designed for materials science. It leverages advanced NLP methods and LLMs to extract and organize a vast amount of high-quality research into structured triples. It streamlines the discovery process by organizing information into... | Rebuttal 1:
Rebuttal: We would like to express our gratitude for the thorough review and valuable feedback on our manuscript. Your insights are highly appreciated and have been carefully considered to enhance our work. In this text, we will address each of your comments in detail, clarifying how we have addressed or pl... | Summary: This paper presents an innovative way on leveraging the power of Large Language Models for the construction of a Material Knowledge Graph (MKG) and link prediction. The method includes annotating few scientific articles (abstracts) related to material science which are used for training and finetuning LLMs. Af... | Rebuttal 1:
Rebuttal: We would like to express our gratitude for the thorough review and valuable feedback on our manuscript. We will answer all your questions and concerns, clarifying how we have addressed the concerns raised.
**Question 1: What would you consider limitations of this work?**
Response: We would like ... | Summary: The study presents an innovative pipeline for Knowledge Graph (KG) construction, specifically designed for efficient extraction of triples from unstructured scientific texts. The methodology enables fine-tuning of Large Language Models (LLMs) with limited annotated datasets, which is then utilized to extract s... | Rebuttal 1:
Rebuttal: We would like to express our gratitude for the thorough review and valuable feedback on our manuscript. In this text, we will address each of your comments in detail, clarifying how we have addressed or plan to address the concerns raised.
**Question 1: Does the inclusion of DOIs affect the resou... | Summary: The paper on constructing and applying a materials knowledge graph (MKG) in multidisciplinary materials science via a large language model (LLM) is valuable and well-written. However, it primarily focuses on application rather than strong technical contributions, with issues in experimental design, lack of non... | Rebuttal 1:
Rebuttal: We would like to express our gratitude for the thorough review and valuable feedback on our manuscript. Your insights are highly appreciated and have been carefully considered to enhance our work. We also want to emphasise some points in the paper to answer your question.
**W1 Response:**
We wou... | Rebuttal 1:
Rebuttal: We would like to express our sincere appreciation to all the reviewers for your valuable feedback on our work, and we have responded to all your questions (in the corresponding rebuttal sections). We also add some supplementary experimental results in the *pdf* file, mainly to reproduce the KG con... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Metric from Human: Zero-shot Monocular Metric Depth Estimation via Test-time Adaptation | Accept (poster) | Summary: This work proposes a test time training technique to turn a monocular relative depth estimation model into a metric monocular one. The core insight of the work is to rely on the prior of a text-to-image model (Stable Diffusion v2) to generate humans in the scene for which we are interested in knowing metric de... | Rebuttal 1:
Rebuttal: Thank you for the time and effort to review our paper. Below please find our specific answers to the questions.
1. **Practicality.**
We acknowledge that MfH is not currently efficient. The runtime shown in Figure 5 is based on a sequential generate-and-estimate process, where painted ima... | Summary: This paper introduces Metric-from-Human (MfH), a method to infer metric depths from images without needing metric depth annotations. Using humans as landmarks, MfH extracts scene-independent metric scale priors from generative painting models, overcoming the challenge of scene dependency in Monocular Metric De... | Rebuttal 1:
Rebuttal: Thank you for the time and effort to review our paper. Below please find our specific answers to the questions.
1. **Experimental results.**
We acknowledge that currently our zero-shot MfH does not always outperform state-of-the-art many-shot methods. However, we would like to highlight ... | Summary: This paper enables monocular depth estimation to output metric-scale depth maps from only single images. To do this, the key idea of this paper is to leverage painting human 3D models into the input images in test-time adaptation, whose motivation is that human painter depicts subjects in consideration of scen... | Rebuttal 1:
Rebuttal: Thank you for the time and effort to review our paper. Below please find our specific answers to the questions.
1. **Results from DSLR and smartphone.**
We demonstrate qualitative results for DSLR and smartphone captured images in Figure R2 of the attached PDF. Depth predictions are trun... | Summary: This paper presents target zero-shot monocular metric depth estimation in the wild. They propose to use humans as landmarks to achieve metric scale and without any other information, such as focal length used in metric3D and zerodepth. The key ideas are creative and well-motivated, addressing an important cha... | Rebuttal 1:
Rebuttal: Thank you for the time and effort to review our paper. Below please find our specific answers to the questions.
1. **Comparisons with state-of-the-art methods.**
We will update Table 1 in our revised manuscript as Table R1 in the attached PDF. Our previous Table 1 aims to provide a fair ... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their insightful feedback. They acknowledge the results as showing clear improvements (4QJ3), impressive (bPK1), superior (2dzs), and decent (xSFg). Reviewer 4QJ3 further finds our key idea creative and well-motivated while reviewer bPK1 sees our method establi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Knowledge Composition using Task Vectors with Learned Anisotropic Scaling | Accept (poster) | Summary: This paper presents a method called aTLAS, which leverages task vectors to enhance transfer learning in neural networks. Task vectors represent the difference in weights between a pre-trained model and its fine-tuned variant. aTLAS introduces anisotropic scaling to these task vectors by learning different coef... | Rebuttal 1:
Rebuttal: We are encouraged that the reviewer find our method interesting and the experiments comprehensive and insightful. We are thankful for the feedbacks. Below, we address the questions and concerns.
**W1. Applicability across different model architectures**
For task arithmetic, we included results w... | Summary: The paper introduces a method named aTLAS, which leverages task vectors and anisotropic scaling to enhance knowledge composition and transfer in pre-trained models. The authors investigate whether components of task vectors, particularly parameter blocks, exhibit similar characteristics and how these can be us... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for recognizing the novelty, parameter efficiency and thorough experimentation in our work, and we are thankful for the feedbacks. We now address the questions and concerns as follows.
**W1. Knowledge composition and transfer are limited to specific pre-trained model ar... | Summary: The paper enhances the performance of task arithmetic, a recent model editing technique based on weight interpolation, in vision-language models. Instead of the original task- and parameter-independent scaling coefficients of the task vectors, it proposes to learn anisotropic scaling coefficients from validati... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's recognition of the novelty, quality and impact of our work, and we are thankful for the feedbacks. In what follows, we now address the questions and concerns.
**W1. Comparison to AdaMerging (Yang et al., 2024)**
We thank the reviewer for suggesting this comparison, a... | null | null | Rebuttal 1:
Rebuttal: We would like to thank each reviewer for dedicating their time to reviewing our paper. We are encouraged that the reviewers find our work novel/original (Reviewers [RiZ9](https://openreview.net/forum?id=G9OJUgKo4B¬eId=PnDeqZQ2DM), [GVMe](https://openreview.net/forum?id=G9OJUgKo4B¬eId=PnDeqZQ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Relational Concept Bottleneck Models | Accept (poster) | Summary: The authors propose Relational Concept Bottleneck Models, a family of relational deep learning methods that utilize concept bottleneck models to provide interpretable task predictions. R-CBMs are shown to predict well in various settings, matching the performanace of black-box models.
Strengths: The paper stu... | Rebuttal 1:
Rebuttal: *R-CBM vs CBM comparison on non-relational datasets:*
**Vanilla CBMs are special cases of R-CBMs in non-relational domains (as described in L158-167)**, hence R-CBMs’ results on non-relational datasets (where predicates are unary) would be identical to propositional CBMs’ results (architecture ... | Summary: This paper proposed a Relational Concept Bottleneck Models(R-CBM), which merge CBMs and GNNs together. To be more specially, it encode atom into concept like CBM and did message passing afterwards like GNN.
Strengths: 1. The idea of combining GNN and CBM is novel and it enable the CBM to learn relational data... | Rebuttal 1:
Rebuttal: *Table 4 lists easy relations that R-CBM learned. Could you show complex relations?*
**Please notice that Table 4 shows rules learnt by R-DCR, other R-CBMs do not learn rules, but rather enable concept interventions (which is the main purpose of concept-based models).** R-DCR is the relational ... | Summary: The paper introduces Relational Concept Bottleneck Models (R-CBMs), which address the challenge of designing interpretable deep learning models that operate in relational domains. Existing Concept Bottleneck Models (CBMs) are interpretable but lack the capability to manage relational data, while Graph Neural N... | Rebuttal 1:
Rebuttal: *Concepts’ evaluations, like e.g. concept completeness:*
**We report the completeness scores of each concept-based model wrt the relational baseline**, following Equation 1 in \[Yeh, et al.\]. The results are shown in Table D of the attached pdf (we added this result in Table 6, Section 5). An... | Summary: This paper introduces Relational Concept Bottleneck Models (R-CBMs), a family of relational deep learning models that can provide some degree of interpretability and explainability; R-CBMs generalise both CBMs and GNNs.
According to the authors, R-CBMs 1) match the generalisation performance of existing black... | Rebuttal 1:
Rebuttal: *Relations with Neural Theorem Provers:*
**More scalable variations of NTPs, such as CTP and Minerva, have been proved to be weaker baselines than other baselines (e.g., RNNLogic) that we considered in the experiments, e.g. the results in Table 3 from the RNNLogic paper \[24\]. This is why we d... | Rebuttal 1:
Rebuttal: **Answer to all reviewers and ACs**
--------------------------------------
We first thank the reviewers for their thoughtful and insightful feedback. We think that by working on their comments, the quality of our manuscript has certainly improved, and we hope to have addressed all the raised conc... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences | Accept (poster) | Summary: This paper presents a new distributed learning method called Byz-VR-MARINA-PP, which can achieve Byzantine robustness and partial participation at once. The authors theoretically analyze the convergence and Byzantine robustness of Byz-VR-MARINA-PP. Numerical results of Byz-VR-MARINA-PP are also provided in thi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and time. Below we address the concerns and comments raised by the reviewer.
>**The proposed method is a combination of Byz-VR-MARINA and clipping, and the convergence analysis is similar to that of Byz-VR-MARINA. In light of these, the novelty of the paper ... | Summary: This paper addresses an important problem: how to achieve Byzantine robustness when the clients partially participate in distributed learning and the Byzantine clients form a majority of sampled clients in some rounds. To solve this problem, the authors propose using the gradient clipping technique to control ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and time. Below, we address the concerns and comments raised by the reviewer.
>**The novelty is limited since the proposed method is a simple combination of the existing method Byz-VR-MARINA and gradient clipping. The idea of using gradient clipping to bound... | Summary: The paper studied the federated learning problem with Byzatine clients and partial participation. The paper proposed a new algorithm called Byzantine-tolerant Variance-Reduced MARINA with Partial Participation or Byz-VR-MARINA-PP and proved its convergence upper bound when the aggregator is a $(\delta, c)$-Rob... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and time.
>**The proposed algorithm and its analysis focused on the case when the number of local update is 1.**
>**Can the results be extended when the number of local update is larger than 1?**
Thank you for raising this point! We understand that in a Fede... | Summary: This paper proposes Byzantine robust approaches in the case of partial participation
Strengths: The paper is well written, but the claims of novelty are problematic, cf bellow.
Weaknesses: The paper starts with a bold claim, "literally, *all* existing methods with provable Byzantine robustness require the fu... | Rebuttal 1:
Rebuttal: >**The paper starts with a bold claim, "literally, all existing methods with provable Byzantine robustness require the full participation of clients." Such a claim overlooks tens, if not hundreds, of papers on Asynchronous Byzantine machine learning that have been published in the past decade. All... | Rebuttal 1:
Rebuttal: We thank the reviewers for their feedback and time. Since several reviewers had concerns about the novelty, which we kindly but firmly disagree with, we prepared a general message addressing this.
As we mention in the introduction (page 2, left column, lines 49-54) and explain in Section 3 (parag... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This work considers the problem of Byzantine robustness in the framework of federated learning. The main contribution of this work is proposing and analyzing a novel federated algorithm, Byz-VR-MARINA-PP that utilizes gradient clipping. This algorithm is an extension of prior work, Byz-VR-MARINA, but important... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback and time. We appreciate your positive evaluation of our work.
>**As I mentioned before most of the tools used in this work are not novel and as a result the novelty of this work is somewhat limited.**
We kindly ask the reviewer to check our general response. ... | null | null | null | null | null | null |
Confusion-Resistant Federated Learning via Diffusion-Based Data Harmonization on Non-IID Data | Accept (poster) | Summary: This paper proposes an importance sampling method with a diffusion model to achieve data harmonization in federated learning with non-i.i.d. data. The proposed method utilizes the indicator function from self-paced learning to measure the reliability of loss on each client and calculates the optimal data distr... | Rebuttal 1:
Rebuttal: # W1. Discussion on Indicator Function.
**Response:** We appreciate the reviewer's insightful comments. Below is a comprehensive explanation:
(1) **Motivation of the Indicator Function**. The Indicator Function $I_{\lambda}(l_i, \sigma_i)$ is designed to dynamically adjust sample weighting ba... | Summary: This paper presents a framework called CRFed to address the significant challenges posed by non-i.i.d. data in federated learning environments. This work introduces a diffusion-based data harmonization mechanism that effectively reduces disparities in data distributions across different nodes. Additionally, th... | Rebuttal 1:
Rebuttal: # W1. Some notations could be more clearly defined.
**Response:** Thanks for pointing this out. In our framework, $\tau$ represents a confidence threshold that determines the difficulty level of samples based on their loss values. The term $(l_i - \tau) \sigma_i$ in the Indicator Function adjust... | Summary: This paper introduces CRFed, a framework designed to handle the challenges of non-i.i.d. data in federated learning. By using a diffusion-based data harmonization mechanism and a confusion-resistant strategy, CRFed aims to reduce data distribution differences among participating nodes and improve model consist... | Rebuttal 1:
Rebuttal: # W1. Sensitivity analysis.
**Response:** We appreciate the reviewer's insightful comment regarding the sensitivity analysis of our hyperparameters. To address this, we conducted additional experiments to analyze the sensitivity of the key hyperparameters in our CRFed framework, namely the varian... | Summary: The work proposes a new FL approach, called CRFed, for addressing data heterogeneity in FL settings. CRFed relies on a diffusion based approach for harmonizing clients data heterogeneity by performing data noise injection and iterative denoising, followed by a curriculum learning approach, which employs an in... | Rebuttal 1:
Rebuttal: # W1: Further elaboration on the selection of the methods
**Response:** Thanks for pointing this out. Below, we offer a detailed explanation and theoretical basis for the key methods employed in CRFed framework.
1. The Indicator Function $I_{\lambda}(l_i, \sigma_i)$ is designed to dynamically ad... | Rebuttal 1:
Rebuttal: # Conclusion
We sincerely thank all the reviewers for their insightful and valuable comments! Overall, we are encouraged that they find the contributions of our work noteworthy and valuable. Here is a summary of the key points acknowledged by the reviewers:
- The proposed CRFed framework, includ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Nimbus: Secure and Efficient Two-Party Inference for Transformers | Accept (poster) | Summary: This work proposes a new secure two-party computation (2PC) protocol called Nimbus for Transformer models to improve the efficiency and effectiveness of large matrix multiplication and non-linear layer approximation in Transformer inference. First, this work exploits client-side outer product and output compac... | Rebuttal 1:
Rebuttal: We thank the reviewer for your helpful comments, and address your concerns as follows. We also appreciate the reviewer's attentive reading for pointing out the typo in the Appendix. We will rectify this in the revised version.
# Q1: Details of the batch size for summarizing the input distribution
... | Summary: This paper proposes Nimbus, a secure inference protocol for transformers in the 2pc setting. They propose distribution-aware nonlinear function approximation to use low-degree polynomials to compute GELU and softmax. They showed that their method can preserve accuracy and achieve efficient performance by compa... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback on our paper. We appreciate your insights and would like to provide more clarification.
# Q1, Q2: The leakage of the approximation polynomial
The secure polynomial evaluation does not allow the client to learn the polynomial coefficients and comparison thresh... | Summary: This paper provides a hybrid method that uses both HE and additive secret sharing (Add-SS) to perform 2PC privacy-preserving transformers. Two main contributions are discussed in this paper: (1) Client-side Outer Product Protocol and (2) Lower Degree Polynomial Approximation and Smaller Rings.
Strengths: The ... | Rebuttal 1:
Rebuttal: We thank the reviewer for your helpful comments, and would like to address the main concerns as follows.
# Q1: Why HE + Add-SS is used for secure two-party inference
HE combined with additive secret sharing (Add-SS) is one of the most promising techniques for secure two-party DNN inference. The te... | Summary: This submission proposed secure inference protocols for Transformer-based model, involving two
crucial components: HE-based linear operations and approximation-based no-linear operations.
Experiments were conducted to verify the feasibility of the proposed protocols and to compare the
performance with prior... | Rebuttal 1:
Rebuttal: We thank the reviewer for your insightful feedback and suggestion. We address the main concerns as follows.
# Q1: Generalization of insight on activation distribution to other datasets
We supplement our study with additional experiments on the activation distribution (Figure 1 in the **supplementa... | Rebuttal 1:
Rebuttal: # Global response
Thank you for taking the time to review our work. Besides the separate response, we also include a **PDF** file under the global response that contains figures and a table related to the reviewers' comments. If you have any further questions or need more details, please don't hes... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
L-TTA: Lightweight Test-Time Adaptation Using a Versatile Stem Layer | Accept (poster) | Summary: This paper introduces L-TTA, a novel lightweight approach to test-time adaptation (TTA) that focuses on the stem layer of deep neural networks. The method incorporates a Domain Embedding Layer (DEL) using discrete wavelet transforms and a Gaussian Channel Attention Layer (GCAL) to minimize stem-layer uncertain... | Rebuttal 1:
Rebuttal: We are truly grateful for the depth of insight you have provided. We have carefully considered your review and have provided our detailed responses to each of your comments below. :)
**Weakness 1.**
>- Our proposal aims to adapt to an extremely constrained environment, *i.e.,* by backward only in... | Summary: This paper proposes a novel test-time adaptation (TTA) method, L-TTA, that minimizes uncertainty instead of entropy. The method involves remodeling the stem layer of the network to minimize uncertainty, which significantly reduces memory overhead and enables rapid adaptation to the target domain. The stem laye... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough review on our manuscript. Please see our detailed responses to your comments outlined below. Please see our detailed responses to your comments outlined below.
**Weakness 1.**
>1. GCAL is designed based on the squeeze and excitation layer (SE Layer), where th... | Summary: In this paper, the authors enhance the efficiency of test-time adaptation (TTA) without necessitating forward/backward passes of the main model. To this end, they introduce a domain embedding layer consisting of a two-level discrete wavelet transformation, which extracts meaningful components in different freq... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and thoughtful comments on our work. We are grateful for your expertise and feedback with this much insight. :) We joyfully respond to each comment as shown below.
**Weakness-1.**
>- TENT also uses pre-trained weights; indeed, our methodology can be trained from... | Summary: The paper focuses on reducing the memory usage of test time adaptation (TTA) by remodelling the first layer. The authors apply discrete wavelet transform to input features and use squeeze and excitation blocks to get the per-channel uncertainty. The proposed loss function minimizes the -per-channel uncertainty... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough review on our manuscript. Please see our detailed responses to your comments outlined below.
**Weakness-1.**
>- Yes, please see lines 230-231 of the manuscript.
“*Eq. 4 means minimizing uncertainty for all channels, which applies equally to pretraining and T... | Rebuttal 1:
Rebuttal: We are thankful to the reviewers for their thoughtful reviews. Our efforts to respond to your questions and comments have led us to identify improvements to be made in the manuscript, which we have incorporated in the final manuscript.
We have endeavored to provide detailed comment-by-comment re... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
An effective framework for estimating individualized treatment rules | Accept (poster) | Summary: This paper proposes a framework for estimating individualized treatment rules (ITRs) in precision medicine applications. The traditional methods for ITR estimation rely on inverse probability weighting (IPW) and L1-penalization, but these methods may have limitations such as statistical bias and computationa... | Rebuttal 1:
Rebuttal: > We sincerely thank the reviewer for the extremely insightful comments. Due to space constraints, we had to cut half of our initial response. We would be happy to have a more in-depth discussion.
>**W1:** We see that both of these works take a similar approach to ours by using weighting schemes ... | Summary: This paper presents an approach for estimating individualized treatment rules (ITRs) for linear-in-feature decisions involving multi-category treatments. Unlike previous methods, the authors utilize distributional covariate balancing instead of inverse propensity weighting, and apply a combination of $L_1$ and... | Rebuttal 1:
Rebuttal: > **Response for Weakness 1:**
>
>Thank you for your valuable feedback. We agree that clarifying the scope of our work is essential.
In this paper, we focus on linear decision boundaries, which are a standard approach in statistical literature due to their interpretability. One significant advanta... | Summary: The paper presents a framework for estimating individualized treatment rules (ITRs) with multi-category treatments to address the problem of misspecified propensity score in inverse probability weighting (IPW) and the computation bias of L1 penalization. The authors propose using energy balancing weights (EBWs... | Rebuttal 1:
Rebuttal: > **Response for Weakness 1:**
Thank you for the suggestion. We updated Figure 1 with an error bar with the standard error of the mean in the one-page author response.
> **Response for Weakness 2:**
Thank you for the insightful comment. Instead of reporting the standard deviation, we will rep... | Summary: The paper proposes an algorithm for treatment rule estimation under standard no unmeansured confounding + SUTVA assumption. The algorithm builds on the AD-learning approach but changes to energy balancing weights and different regularizations. The paper then concludes with both simulations and real data exampl... | Rebuttal 1:
Rebuttal: >**Weakness 1,2:** Thank you for the feedback. Our work views the ITR framework as a weighted convex optimization problem, focusing on using robust weights and the PGD algorithm to find sparse solutions effectively, a novel approach in this context. We believe that these contributions are signific... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thank you for reviewing our work and providing valuable comments. We appreciate the time and effort you have taken to provide feedback on our paper.
In response to your suggestions, we have made the following updates to Figure 1 in the one-page author's response:
- **Error bar**... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Where Do Large Learning Rates Lead Us? | Accept (poster) | Summary: This paper is an empirical study focusing on the effect of large learning rates (LRs) in neural network training. The authors aim to answer two main questions:
1 How large an initial LR is required for optimal quality?
2 What are the key differences between models trained with different LRs?
The study reve... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their thoughtful review of our work! We now address the raised concerns and questions.
> The experiments in the study are limited to specific datasets (CIFARor synthetic) and neural network architectures (Resnet).
We have conducted additional experiments showing that o... | Summary: This paper investigates the benefits of an initial large learning rate (LR) in training neural networks. In particular, the paper identifies three training regimes in a pretraining-finetuning/model averaging context, where different regimes depend on different pre-training LRs and have distinct impacts on the ... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their valuable feedback and overall positive assessment of our work! We respond to the comments and questions as follows.
> Some of the paper's results have already been shown by prior work, e.g., large LRs can lead to sparse features [1].
The results of [1] are closely... | Summary: This paper studies the effects of using initial (large) learning rates on the performance of the trained neural networks. Two key questions explored are:
1. how large are the optimal initial learning rates?
2. what's the difference between the model trained by different initial learning rates?
The paper id... | Rebuttal 1:
Rebuttal: We are grateful to the Reviewer for their positive and constructive feedback on our work! We address the concerns and questions below one by one.
**Scale-invariant setting**
As the Reviewer rightly noted, our main experiments are conducted in a specific scale-invariant setting to provide effect... | null | null | Rebuttal 1:
Rebuttal: We kindly thank all the reviewers for their constructive and valuable feedback that will help us further improve our paper!
We are very pleased that the reviewers assessed our findings as novel, practically important, and providing additional insights into the loss landscape geometry and feature l... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Transferability Bound Theory: Exploring Relationship between Adversarial Transferability and Flatness | Accept (poster) | Summary: The paper investigates the relationship between the transferability of adversarial examples and the flatness of adversarial examples. The paper shows that flatness alone is not sufficient to guarantee transferability. Based on this theoretical result, it derives an optimization method for adversarial examples ... | Rebuttal 1:
Rebuttal: **Q1:** The presentation of the the proof of Thm. 3.1 could be improved & The usage of D in the statement of Thm. 3.1 makes a mapping of terms in the proof to the result unnecessarily cumbersome.
**Response:**
In response to your suggestion, we have revised the proof and replaced $D$ to enhance c... | Summary: This paper focuses on the transferability of adversarial examples. The authors first derive an upper bound for the transferability loss used in the paper. Then, they propose a new loss function based on the derived bound to increase the adversarial transferability. The proposed TPA method is tested in both cla... | Rebuttal 1:
Rebuttal: **Question 1\&2:** The theoretical claims lack a strong and important assumption. \& Theorem 3.1 cannot well reflect the bound of the adversarial transferability of inputs.
**Response:**
For first issue, we do not assume $L(F'(x),y)-L(F(x),y) \approx 0$ (See the derivation below).
For second iss... | Summary: The paper proposes a theoretical investigation into the relationship between the flatness of adversarial examples and their transferability. The authors challenge the prevailing belief that flatter adversarial examples necessarily have better transferability. They introduce a new method called Theoretically Pr... | Rebuttal 1:
Rebuttal: **Question 1:** The tightness of our bound.
**Response:**
We would like to provide some clarifications regarding our bound.
First, as pointed out by Reviewer QVii, the first and second terms in Eq.3 should be squared.
The revised bound in Theorem 1 is: $$\mathbb{E} \{ ||D(x+\delta,y)||_2^2 \leq \... | null | null | Rebuttal 1:
Rebuttal: We would like to express our sincere appreciation for the efforts and feedback from all reviewers. We have taken into account reviewers' comments and suggestions, which have greatly enriched the quality of this manuscript.
As noted by some reviewers, there are minor errors and ambiguities in our ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Hybrid Generative AI for De Novo Design of Co-Crystals with Enhanced Tabletability | Accept (poster) | Summary: This paper presents GEMCODE, the first co-crystal design AI pipeline, which consists of four components:
- SMILES-based models for coformer generation.
- Classification models for co-crystal property prediction.
- An evolutionary algorithm for coformer optimization.
- A GNN for prediction of the probability of... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's useful comments and suggestions!
Below, we would like to provide our __answers to the questions__:
1. We are happy to provide a clarification for the high values of "Diversity of target" in Table 1. The diversity values were calculated for the molecules that have be... | Summary: This works presents a generative framework for co-crystal design which uses deep learning and evolutionary algorithms for optimization. The GEMCODE pipeline can be used to select optimal molecular pair combinations: an active pharmaceutical, and a coformer to control for the desired co-crystal properties. The ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the high rating and very valuable comments! We appreciate the detailed feedback on where we can improve the clarity of the manuscript. We will do so in the camera-ready submission.
Below, we provide our __answers to the questions__:
1. For the purpose of ranking molecul... | Summary: This paper presents GEMCODE, a novel pipeline for generating co-crystal designs with enhanced tabletability properties for pharmaceutical applications. The authors combine deep generative models, evolutionary optimization, and machine learning to create and evaluate potential co-former molecules. They train mo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful evaluation of our work and for the valuable feedback! We will certainly revise and address all the comments and suggestions in the camera-ready version of the paper and our future work.
Below, we provide __answers to the key questions and comments__:
1. W... | Summary: The authors investigate an interesting chemical problem of generating coformers given an organic molecule such that they would form co-crystals with desirable chemical properties. The authors use GAN/VAE-based methods to generate SMILES of potential coformers, which are then improved by evolutionary optimizati... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing a very valuable feedback!
We believe that some of the criticism was caused by a misunderstanding.
We would like to first __comment on the weaknesses__ outlined by the reviewer:
1. Polymorphism is certainly an important factor in the design of co-crystals as ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Achievable distributional robustness when the robust risk is only partially identified | Accept (poster) | Summary: This paper proposed a general framework of partially identifiable robustness to evaluate robustess in scenarios where the training distributions are not heterogeneous enough to identify the robust risk. They define 'the identifiable robust risk' and its correspondig minimax quantity. They show previous approac... | Rebuttal 1:
Rebuttal: We are glad that the reviewer appreciated the main idea of our paper. In the following, we hope to clarify the real-world motivation for our study and some theoretical assumptions.
**On real-world applications** The main motivation for our paper was expanding the existing assumptions in distribu... | Summary: This paper investigates the optimal minimax risk of a robust predictor when the robustness set is partially observable, under a structural causal model with hidden confounders. By decomposing the test covariance matrix of latent parameters into a component spanned by the training distributions and the orthogon... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating the contribution of the paper and precisely summarizing its main idea.
**Bias of the structural causal model** The reviewer is correct that the causal effect estimate would be biased when the cross-correlation between the noises $\xi$ and $\eta$ is not 0. W... | Summary: This paper proposes a new framework for distributionally robustness under the linear causal setting. Specifically, the authors minimize the so-called identifiable robust risk, which corresponds to the maximum of the robust risk for parameters in the observationally equivalent set. Under such partially identifi... | Rebuttal 1:
Rebuttal: We thank the reviewer for pointing out the novelty of the direction of our study. We are happy to fill in on the missing details below:
**Q1 (real-world example of partial identifiability)** Indeed we can give a toy example that illustrates our abstract setting in Section 3.1: suppose that we ar... | Summary: The paper studies a linear Structural Causal Model (SCM) for prediction under distribution shifts due to an an additive term to covariates that changes at test time, and an unobserved confounder between the covariates and the label. The key difference between the proposed analysis and those presented in other ... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating the overall direction and clarity of our paper, as well as originality of our work. We are grateful for the constructive comments and will incorporate the minor points in the revised version of the paper. We now respond to some of the major points and questio... | Rebuttal 1:
Rebuttal: We express sincere gratitude to all reviewers for their detailed reviews. It is encouraging to hear that the reviewers appreciate the novelty and overall direction of our work, especially its attempt to formalize partially identifiable robustness.
We also appreciate constructive feedback and we... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Continuous Temporal Domain Generalization | Accept (poster) | Summary: The paper introduces the problem of Continuous Temporal Domain Generalization. It extends the Temporal Domain Generalization problem, that aims at developing models under temporally varying data, to handle data collected at arbitrary and continuous time points. The paper proposes a framework based on Koopman t... | Rebuttal 1:
Rebuttal: Thanks for reviewing and acknowledging our work! We see your main concerns are about assumption and evaluations. Please read our answers along with the Rebuttal PDF.
> W1. discuss more the limitations; Assumption 1 that states the conditional probability distributions follow an ODE, need to discu... | Summary: The article introduces Continuous Temporal Domain Generalization (CTDG), which extends traditional Temporal Domain Generalization (TDG) methods by addressing the challenges posed by continuous and irregularly spaced temporal domains. The authors propose a Koopman operator-driven framework (Koodos) to handle da... | Rebuttal 1:
Rebuttal: Thanks for reviewing.
> W1. needs more empirical studies.
>
1. The Koopman operator is proven theoretically that can linearize any nonlinear dynamical system in [4].
2. Our empirical evidence supports its applicability across various domains. Datasets are summarized in Tab. 5
- Experiments ... | Summary: The paper presents a novel approach called Continuous Temporal Domain Generalization (CTDG), addressing the challenge of training predictive models under continuously evolving and irregularly observed temporal domains. Unlike traditional TDG methods that rely on discrete time intervals, CTDG captures continuou... | Rebuttal 1:
Rebuttal: Thanks for reviewing and acknowledging our work! Please read along with the Rebuttal PDF.
>W1. The explanation of how the article addresses domain changes with arbitrary temporal sampling is not very clear.
1. TDG assumes there is widespread smooth, predictable distribution changes over time, so ... | Summary: This paper introduces a new task: Continuous Temporal Domain Generalization (CTDG) to address the limitations of traditional TDG in handling continuously evolving and irregularly observed temporal data. By proposing the Koopman operator-driven framework (Koodos), this work leverages Koopman theory and optimiza... | Rebuttal 1:
Rebuttal: Thanks for reviewing and acknowledging our work! We see your main concerns are about related works and evaluations. Please read our answers along with the Rebuttal PDF.
> W1. Limited evaluation on small datasets/models; lacks high-dimensional datasets.
1. We provide a summary of the datasets and ... | Rebuttal 1:
Rebuttal: Thank you to each reviewer for their valuable time.
Please download the pdf file before reviewing the responses.
Best,
Authors
Pdf: /pdf/f7a1110a43d2cc9c7f0c49aad29c34dcf34b12fe.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment | Accept (poster) | Summary: The authors proposed a novel pipeline for once-for-all training multiple subnets in a supernet LLM under different resource constraints. The entire pipeline consists of a knowledge preserving subnet selection utilizing DP to sample depth and width and a new LoRA to resolve the gradient conflicts during traini... | Rebuttal 1:
Rebuttal: Thank you for recognizing the value of our work and for your constructive comments! We have addressed all your concerns below.
**1. The definition of depth and width in decoder models**
Thank you for pointing this out! In this work, depth is defined as a whole self-attention block, including bot... | Summary: This paper proposes AmoebaLLM: a one-for-all fine-tuning and compression framework for delivering pruned and accurate subnets from a pre-trained LLM at various pruning (both depth and width) ratios without the need to fine-tune individual subnets. AmoebaLLM consists of two components: 1) a dynamic programming-... | Rebuttal 1:
Rebuttal: Thank you for recognizing the novelty and interesting insights of our work, as well as for your constructive comments! We have addressed all your concerns below.
**1. Cite the results from Section 5.3 to motivate the issue of full model fine-tuning**
Thank you for the suggestion! We believe what... | Summary: The paper proposed a new framework, named AmoebaLLM, that adapts any LLMs to achieve optimal efficiency across different platforms and applications. In specific, the framework contains two stages. The first stage denoted as a knowledge-preserving stage, creates a subnet of the LLM by dynamic programming given ... | Rebuttal 1:
Rebuttal: Thank you for recognizing our work as practical and interesting, as well as for your constructive comments! We have addressed all your concerns below.
**1. The definition of latency and more real-device efficiency measurement**
We clarify that the latency used in Fig. 3 of our manuscript is star... | Summary: To address the problems of diverse resource constraints and deployment flows while using LLM for multiple real-world applications, this paper proposes an AmoebaLLM, featuring a knowledge-preserving subnet selection strategy, a shape-aware mixture of LoRAs and a distillation scheme with loss-magnitude balancing... | Rebuttal 1:
Rebuttal: Thank you for recognizing the idea and performance of our work, as well as for your constructive comments! We have addressed all your concerns below.
**1. Whether our DP-based depth shrinking strategy needs to evaluate the performance for each selection strategy and its overhead**
Yes, you are... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Diffusion of Thought: Chain-of-Thought Reasoning in Diffusion Language Models | Accept (poster) | Summary: The paper introduces Diffusion of Thought （DoT）, integrating diffusion models with Chain-of-Thought. The paper proposes two training-time sampling strategies to enhance self-correction during inference. Experimental results demonstrate the effectiveness of DoT in simple and complex reasoning tasks.
Strengths:... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer q6Ny for the review and are grateful for the time you spent with our submission. We wish to address your confusion and concerns by providing detailed responses to each of your comments.
**Weakness 1: Confusion about the coupled sampling strategy**
Thanks for pointing ... | Summary: This paper introduces "Diffusion of Thought" (DoT) to diffusion language models to improve upon their reasoning capabilities.
The method adapts the implicit chain of thought methodology (iCoT) for autoregressive models, which relies on per-task fine-tuning to distill reasoning into transformer layers, while ... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer Y1KX for your review and are grateful for the time you spent on our submission. Below we would like to give detailed responses to each of your comments.
**Weakness 1: Direct Comparison Baseline**
Thank you for your suggestion. We conduct the answer-only setting to fur... | Summary: The authors propose a chain-of-thought technique for diffusion language models. They achieve this by diffusing a set of hidden representations (thoughts) through time. Different sampling techniques are introduced to enhance error recovery including looking forward and conditioning on multiple previous thought ... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer 5GKr for your review and are grateful for the time you spent on our submission. We are also glad you think our paper is novel and significant. Below we would like to give detailed responses to each of your comments.
**Weakness 1: The presentation regarding color and f... | Summary: The work introduces Diffusion-of-Thought (DoT), a method that combines diffusion language models with the Chain-of-Thought technique to enhance their reasoning ability. DoT uses the flexibility of diffusion processes to allow reasoning steps to diffuse over time, improving performance in several mathematical t... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer E7FJ for your review and are grateful for the time you spent on our submission. We're pleased you find our method effective. Below, we provide a point-by-point rebuttal to clarify your concerns.
**Weakness 1: Discussion of recent work**
Thank you for sharing this pape... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: * The authors propose DoT, a chain of thought method for diffusion language models.
* DoT is applicable to both continuous embedding-based diffusion models and continuous-time Markov chain discrete diffusion models.
* DoT shows performance increase on digit multiplication, boolean logic, and GSM8K tasks, as we... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer abfr for the review and are grateful for the time you spent with our submission. We wish to address your confusion and concerns by providing detailed responses to each of your comments.
**Weakness 1: Simple datasets**
The reasoning ability contains arithmetic reasonin... | null | null | null | null | null | null |
Linguistic Collapse: Neural Collapse in (Large) Language Models | Accept (poster) | Summary: This paper empirically investigates the emergence of Neural Collapse (NC) properties during the training of causal language models. NC is a phenomenon observed in the top layer of deep-nets trained on one-hot classification problems, where the last-layer class-mean embeddings become equinorm, have maximal angu... | Rebuttal 1:
Rebuttal: > I don’t understand what you mean by *ambiguous* samples, ``which are not soft- or multi-label’’ (line 128). When a context appears several times in the training set, each time followed by a different next token, this training sample has a soft label, where the label (next token) can take on diff... | Summary: This work focuses on studying the Neural Collapse phenomenon in the context of language model training. The author first introduces the original NC properties, explaining how such metrics may not apply to the case of LLM training given 1) the ambiguity of language next token prediction, 2) large number of poss... | Rebuttal 1:
Rebuttal: > **(Major)** I appreciate the extensive experimental work on establishing a connection between the proposed NC metrics and validation accuracy; however, I believe the coefficient of determinations provided in Table 1 suggests a low correlation between NC properties and validation loss. While I se... | Summary: The final layers of classifiers show a property called neural collapse (NC), which are seen as beneficial to model performance. The authors study it’s appearance in causal language models (CLMs), and point out that CLMs do not respect those conditions (CLM are trained on noisy, unbalanced data, made on more to... | Rebuttal 1:
Rebuttal: > Apart from evaluation loss, model performance metrics from commonly used LLM benchmarks are not provided, making high level comprehension of the technical observations more complicated.
LLM researchers are indeed interested in performance metrics beyond the cross-entropy (CE) evaluation loss th... | Summary: This paper investigates neural collapse (NC) -- properties of the penultimate feature representation of DNN -- in causal language models (CLM). Previous NC studies have primarily focused on classification problems with balanced classes with few labels compared to the feature dimensionality. This paper finds th... | Rebuttal 1:
Rebuttal: > I’m somewhat unclear on the motivation for using TinyStories, a purely synthetic dataset. While I understand the benefit of using a small dataset to ease the computational burden of the experiments, I would have liked to see some further experiments on real data confirming the observed trends. F... | Rebuttal 1:
Rebuttal: ## The Choice of TinyStories
The study of NC in causal language modeling at the token level would be very expensive, so the motivation to use a small dataset is clear. However, most commonly used text datasets such as WikiText, BookCorpus, CommonCrawl, or most subsets from the Pile are much too c... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning Plaintext-Ciphertext Cryptographic Problems via ANF-based SAT Instance Representation | Accept (poster) | Summary: The paper explores an approach for predicting the satisfiability of Algebraic Normal Form (ANF) Boolean SAT instances by graph neural networks (GNN). The approach is similar to the framework of NeuroSAT but a new graph structure is proposed for handling the quadratic terms in ANN. Experiments indicate that the... | Rebuttal 1:
Rebuttal: ## Response to Reviewer ZcPU (Rating: 4 / Confidence: 4)
Thank you for your thoughtful response and for highlighting the key contributions of our paper. We are pleased that you recognized the appropriate technical depth of our work. Your elaboration on the significance of non-CNF constraints, par... | Summary: This paper introduces an approach to handling cryptographic problems by transforming them into Boolean Satisfiability (SAT) problems using a graph structure based on Arithmetic Normal Form (ANF) to efficiently manage XOR operations, which are prevalent in cryptography. It proposes CryptoANFNet, a graph learnin... | Rebuttal 1:
Rebuttal: Thanks for your valuable comment and for recognizing the significance of our work in formalizing the ANF formula as a Multivariate Quadratic (MQ) problem. We appreciate your acknowledgment of how this approach reduces the complexity of the problem. We are pleased to address our contribution in de... | Summary: The paper strikes an interesting endeavor for introducing machine learning to address the Plaintext-Ciphertext Cryptographic Problems, which is to my best knowledge, new in literature. It lies in the between of AI and security and specifically crypto which goes beyond the line of research either in learning fo... | Rebuttal 1:
Rebuttal: Thanks for your valuable comment and for recognizing its innovative aspects and experimental design. Your questions about the conclusions and insights offered by this paper are important and meaningful. We greatly appreciate the opportunity to elaborate on our contributions and address the points ... | Summary: In this manuscript, the authors propose an ANF-based graph structure that efficiently handles XOR and AND operations in cryptographic problems. Additionally, they introduce CryptoANFNet, a message-passing neural network model designed to predict the satisfiability of cryptographic problems. The authors also pr... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and for recognizing the innovative aspects of our work. We appreciate your positive assessment of our paper's contribution.Your feedback highlights the key strengths of our research, particularly the introduction of efficient representations and learning strate... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers’ time, valuable feedback, and constructive suggestions. Overall, the reviewers have deemed our work as "highly innovative" (imRj), "well-written" (uZGu, ZcPU), and having the appropriate level of technical detail (ZcPU). They have acknowledged our methodology... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Neural Network Reparametrization for Accelerated Optimization in Molecular Simulations | Accept (poster) | Summary: The paper proposes a novel neural network reparameterization approach which provides a flexible alternative to traditional coarse-graining methods for molecular simulations. Unlike CG methods that strictly reduce degrees of freedom, the proposed model can dynamically adjust system complexity, potentially incre... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We agree that the manuscript requires substantial revisions for clarity and flow. Below, we aim to address each of your concerns, and to resolve the criticisms thoroughly.
## Weaknesses
__1.__ We agree that intro and background will benefit from significant ... | Summary: This paper proposes an efficient approach for finding the optimal conformation in terms of the energy function. By identifying the slow modes, the proposed method can reduce the computational complexity while extracting the core movement for simulating the dynamics. Specifically, the authors observe a connecti... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time taken to review our work. Please check our shared rebuttal above for discussion on improving presentation and new experimental results.
## Weaknesses:
1. __Efficiency:__ We agree that models like AlphaFold take a different approach. However, we think our appro... | Summary: This paper presents a novel approach to molecular simulations using neural network reparametrization as an alternative to traditional coarse-graining methods. The key idea is to reparametrize fine-grained modes as functions of coarse-grained modes through a neural network, maintaining continuous access to fine... | Rebuttal 1:
Rebuttal: Thank you for your comments. We try to address them below. Please also read our general rebuttal above, which details our plan for improving presentation and more.
## Weaknesses:
1. __Unclear presentation:__ We agree. Please see above and the attached pdf for flow-chart and algorithm.
2. __Eff... | Summary: This paper proposes a novel approach for molecular simulations using neural network reparametrization. The authors first motivate the need for this work, specifically the traditional coarse-graining (CG) methods reduce the number of degrees of freedom (DOF) to improve computational efficiency. However, they re... | Rebuttal 1:
Rebuttal: Thank you, we appreciate your pertinent comments. Please also check our general response above about improvements to the presentation.
## Weaknesses:
1. The plot was mistakenly omitted. We are adding it to the appendix.
2. Thanks, adding the citations.
3. n is the number of particles, d is the... | Rebuttal 1:
Rebuttal: Thank you all for very constructive comments. Some issues were raised by multiple reviewer. Here we will address the shared concerns. Below, we respond point-by-point to each review.
## Presentation
We agree that the presentation of the paper needs significant improvement and reorganization, esp... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Scalable Neural Network Verification with Branch-and-bound Inferred Cutting Planes | Accept (poster) | Summary: Following the discussion with the authors, I am increasing my score to weak accept.
----
This paper presents a new branch-and-bound-based verification algorithm for neural networks with ReLU activation. The main idea is to produce additional constraints to the verification problem by identifying small combina... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments and valuable questions. We hope the reviewer can reevaluate our paper based on our response below:
- Q1: Reframe the contribution in the paper in terms of the existing related work about nogood learning and restarts.
Thank you for your valua... | Summary: The paper presents BICCOS: a method to derive cutting planes for use within a state-of-the-art neural network verification framework based on branch and bound (BaB). Given verified (UNSAT) subproblems, BICCOS tries to find a subset of the employed branching choices that led to the verification result, and appl... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments and valuable questions. We want to clarify a few key misunderstandings about feasibility and results. We hope the reviewer can reevaluate our paper based on our response below:
- For Weakness
* Presentation.
For presentation, we wi... | Summary: The paper extends GCP-CROWN, an existing toolkit for the verification of neural networks which is based on GPU-accelerated bound propagation combined with a branch-and-bound (BaB) approach. The strength of the existing algorithm is its ability to incorporate cutting planes into the bound propagation process. G... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments and valuable questions. We hope the reviewer can reevaluate our paper based on our response below:
* For Weakness.
- W1. The work is somewhat incremental compared to GCP-CROWN
GCP-CROWN and our work make orthogonal contributions. G... | Summary: This work proposes a new approach to produce cutting planes in the context of branch-and-bound-based solvers for neural network verification. Whenever an infeasible subproblem is encountered in branch-and-bound, this method generates a cut from the conflicting assignment that led to the infeasible subproblem (... | Rebuttal 1:
Rebuttal: We thank you very much for your constructive feedback and for correctly recognizing our key contributions. We appreciate your support and very helpful feedback. We provided additional experiments as requested and clarified the key questions below:
* Q1: Do You Consider Other Drop Heuristics?
... | Rebuttal 1:
Rebuttal: Submission of figures of added experimental results
Pdf: /pdf/1b7b808d5f9be625d16be50ca93707eaa7e402a2.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Power of Extrapolation in Federated Learning | Accept (poster) | Summary: The paper presents a new method FedExProx, a federated learning method based on proximal splitting using extrapolation. The method combines the proximal splitting approach from FedProx with the extrapolation from FedExp. FedExProx is shown to improve upon both previous methods in multiple ways, among others fa... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedbacks on our paper. Here is a detailed response to the weaknesses and questions the reviewer mentioned.
- Weakness 1: `FedExProx assumes that the prox problems can be solved exactly but the analysis of FedExp takes into account the number of local iterati... | Summary: This paper proposes and analyzes several server-side extrapolation strategies to enhance the theoretical and empirical convergence properties of FedProx.
The authors present the convergence properties of the proposed methods for smooth convex and strongly convex problems in the interpolation regime.
Theoretic... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedbacks on our paper. Here is a detailed response to the weaknesses and questions the reviewer mentioned.
- Weakness 1 & Question 1: `Assumption 2, the interpolation regime, seems too strong. Are there any other published papers that use this assumption? If... | Summary: In this paper, the authors present an enhanced version of the FedProx algorithm for federated learning, named FedExProx. Unlike the FedProx, this algorithm incorporates an extrapolation step on the server following the computation of the proximal operator on each client. The authors investigate both constant a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedbacks on our paper. Here is a detailed response to the weaknesses and questions the reviewer mentioned.
- Weakness 1: `First, the benefits gained from the extrapolation step are not evident. As shown in Table 2 of this manuscript, the only noticeable diff... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and effort.
The reviewers highlighted several key strengths of our paper. Notably, we introduced an extrapolation parameter to the FedProx algorithm for the first time, developed adaptive versions that eliminate dependence on the unknown smoothness parame... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improving Gloss-free Sign Language Translation by Reducing Representation Density | Accept (poster) | Summary: This work uses contrastive learning of sign language gestures to improve the discrimination power of learned representations of gestures. This is motivated by showing, via TSNE projections, that gloss-free representations (that do not benefit from an intermediate representation), are less well represented th... | Rebuttal 1:
Rebuttal: We sincerely appreciate your detailed comments and insightful suggestions. We provide point-wise responses to your concerns below.
**W1: Lack of proper statistical validation**
Thank you for your suggestion. We reran the representative experiments of Table 2 five times, using different seeds (23... | Summary: This paper identifies a critical issue of representation density in gloss-free sign language translation. A series of models were employed to verify the existence of this problem. To address this, the author proposed a straightforward and effective solution, SignCL loss. This objective improves the discriminat... | Rebuttal 1:
Rebuttal: We sincerely appreciate your detailed comments and suggestions. We provide point-wise responses to your concerns below.
**W1: The technical novelty and contribution are somewhat limited:**
We respectfully disagree with the statement that "The entire paper mainly introduces a contrastive loss". We... | Summary: This paper addresses the challenge of gloss-free sign language translation (SLT) by identifying and tackling the "representation density problem". The authors observe that visual representations of semantically distinct sign gestures tend to be closely packed in feature space, making it difficult for gloss-fre... | Rebuttal 1:
Rebuttal: We sincerely appreciate your detailed comments and insightful suggestions. We provide point-wise responses to your concerns below.
**W1: Lack of comprehensive sensitivity analysis on sampling strategy**
Thank you for your insightful suggestions. We conducted an additional comprehensive sensitivi... | Summary: This paper focuses on gloss-free sign language translation (SLT) and is largely motivated by the large cost to annotating glosses. The authors discussed a so-called "representation density problem" in gloss-free SLT where semantically dissimilar signs appear in a similar part of the latent space.
The first t... | Rebuttal 1:
Rebuttal: We sincerely appreciate your detailed comments and insightful suggestions. We provide point-wise responses to your concerns below.
**Q1: How do the proposed approaches help with face and motion errors?**
* Thank you for your question. Our SignCL approach samples positive and negative examples fro... | Rebuttal 1:
Rebuttal: We sincerely appreciate all reviewers for their detailed comments and insightful suggestions. We are encouraged that they find our paper identifies a **novel problem** (representation density) in gloss-free SLT [Reviewer rTJg and UuUf], and introduces a relatively straightforward SignCL to address... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The main content of this paper is about improving the performance of Gloss-free sign language translation. The author discovered the representation density problem in sign language translation, that is, in the feature space, the semantic visual representations of different sign language gestures tend to be clo... | Rebuttal 1:
Rebuttal: We sincerely appreciate your detailed comments and insightful suggestions. We provide point-wise responses to your concerns below.
**W1: The analysis and supplement of strategy selection can make this work more perfect**
Thank you for your suggestions. We have added a systematic and sensitivity ... | null | null | null | null | null | null |
Constrained Synthesis with Projected Diffusion Models | Accept (poster) | Summary: This paper proposes an approach to sample generation using diffusion models which adheres to a set of constraints. The approach is based on the score matching formulation of diffusion models, and applies a projection step which finds the nearest feasible sample to each iteration of SGLD. A theoretical justific... | Rebuttal 1:
Rebuttal: Thank you for their time reviewing our work and their praise of our submission. We appreciate your consideration of our work and would like to address your outstanding questions and concerns.
>**Weakness 1: I think a mathematical description of the projection operator and constraints for the expe... | Summary: This paper proposes Projected Diffusion Models (PDM) for constrained generative modeling. The key idea is to reframe the denoising process of diffusion models as a constrained optimization problem, iteratively projecting the generated samples onto a constraint set at every denoising step. The method is validat... | Rebuttal 1:
Rebuttal: *We will include abridged versions of our responses in this rebuttal window, but we ask that the reviewer refers to our complete answers in the comments.*
Thank you for your time and efforts in providing feedback on our paper. We appreciate your acknowledgment of the diversity of our applications... | Summary: This paper proposed Projected Diffusion Models (PDM) inspired by stochastic gradient Langevin dynamics, to generate samples that satisfy given arbitrary constraints and remain within the specified regions. The authors claimed that the proposed algorithm is compatible across various applications, including sati... | Rebuttal 1:
Rebuttal: *We will include abridged versions of our responses in this rebuttal window, but we ask that the reviewer refers to our complete answers in the comments.*
Thank you for your valuable feedback. Before addressing your specific questions, let us emphasize the significant contribution provided by our... | Summary: This paper proposes a diffusion model that imposes constraints on the generated output. However, the constraints here are not abstract verbal instructions but rather formalizable constraints. The authors propose projected diffusion model sampling to perform constraint conditional log-likelihood maximization at... | Rebuttal 1:
Rebuttal: Thank you for your time and efforts in providing feedback on our paper. First, let us emphasize the significant contribution provided by our work: Our proposed method provides constraint imposition with _formal guarantees_ for several important classes and for the first time in the general and com... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Structured Learning of Compositional Sequential Interventions | Accept (poster) | Summary: Estimating the causal effect of a sequence of interventions on another sequence is a central task in causal inference / treatment effect estimation. However, for large discrete spaces, canonical assumptions such as Markovian assumptions, short sequences, and so forth will not apply, while very general black-bo... | Rebuttal 1:
Rebuttal: Thank you for your review and your remarks that **“The work is original…”** and that is its **“generally very principled and should be very applicable in the field”**! The many comments about presentation are very useful.
**IMO the main drawback of the work is in clarity. 1a.: …time to parse…1b. ... | Summary: This paper considers a special case of a series of interventions where the interventions are categorical and sparse. And interventions can effect later timestamp. This is a form of causal extrapolation. Authors propose to study this using a conditional mean model that utilizes basis functions and subsequently ... | Rebuttal 1:
Rebuttal: Thanks for your review, and for agreeing that **“the problem is interesting and well-motivated”**! We would like to see more of that in the community.
**Section 2.1 is really hard to parse**
We hope the following helps. Eq. 1 takes a standard tensor decomposition format. It can be motivated by a... | Summary: This paper studies treatment effect estimation under sequential, discrete interventions.
It is assumed that all treatments are fully independent and impact all future outcomes.
It studies the problem of "causal extrapolation" where some combinations of sequential interventions may not have been observed during... | Rebuttal 1:
Rebuttal: Thanks for your review, and for the much appreciated point that the problem is **“important and, … an understudied problem in causality”**, which is one of the main messages we wanted to convey! We address all clarification questions below.
**Eq. 1: Where does this come from?**
This is an excel... | Summary: The authors propose CSI-VAE, a method that solves the problem of forecasting potential outcomes in multiple interventions. In particular, the authors seem to propose a solution to the problem of a large (combinatorial) space of future treatment plans using the controlled past treatment sequences for inference.... | Rebuttal 1:
Rebuttal: Thank you for your review, and for finding that our paper is **“quite well done”** and **“generally easy to follow”**.
The question about the benchmark is a good opportunity for further clarifications. In order to keep the paper focused, we stripped away complementary discussions about dealing wi... | Rebuttal 1:
Rebuttal: We take this opportunity to once again thank all reviewers for their time and suggestions! Individualized answers have been provided to each of you.
We use this space to report an update on experiments. To summarize the context, we chose the GRU family merely as an illustration of a modern black-... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LLM Dataset Inference: Did you train on my dataset? | Accept (poster) | Summary: The paper addresses the limitations of traditional membership inference attacks in identifying if specific text sequences belong to the training data of LLMs. The authors highlight the inadequacies of MIAs and propose a novel dataset inference method. This method focuses on detecting entire datasets used in mo... | Rebuttal 1:
Rebuttal: We appreciate your review and are happy to see that you found our work to pose a compelling argument for transitioning toward Dataset Inference, and found the paper well-written. We acknowledge your concerns and attempt to respond to them line by line below:
### Re: Decontamination of Validation ... | Summary: Large language models are trained on a vast amount of online available data, which has lead to copyright and privacy issues (e.g. New York Times vs. OpenAI, as pointed out by the authors). There are various methods that try to identify if a given data point x was used to train a large language model.
This pap... | Rebuttal 1:
Rebuttal: We appreciate your review and are happy to see that you found our analysis systematic and our paper technically solid with a high impact, along with an overall positive assessment of the work. We acknowledge your concerns and attempt to respond to them line by line below:
### Re: Generalization t... | Summary: In this paper, the authors investigate the commonly used membership inference evaluation for LLMs and find that previous attacks primarily detect features related to temporal changes, performing poorly under real IID scenarios. To address the challenge of individual sample membership inference attacks, the aut... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's valuable feedback and are happy to hear that they enjoyed reading our paper.
>**The experiments are conducted on a single series of models, rather than on various models trained with different datasets, algorithms, or even seeds. I think this is a little picky since i... | Summary: The paper addresses the challenge of identifying training data in large language models (LLMs) with rising concerns over privacy and copyright violations. As it has previously been studied, traditional membership inference attacks (MIAs), which determine if individual text sequences were used in training, are ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and feedback. We are happy to see that you appreciated the method’s robustness and overall quality of the draft. We address the individual concerns below one by one:
### **Re: Other work studying the failure of MIAs (W1)**
We would like to preface this answ... | Rebuttal 1:
Rebuttal: We appreciate the positive, encouraging, and constructive feedback. We are pleased that the reviewers recognize the significance of the problem (Reviewer VFio), consider the paper well-written (Reviewers VFio, pZrT, G1MR), and found it enjoyable to read (Reviewer Nmtj). Our work motivated by showi... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper addresses the problem of Dataset Inference in Large Language Models - namely, the ability to detect whether the given dataset was used by LLM developers in pre-training.
First, authors demonstrate the importance and feasibility of Dataset Inference task in comparison with Memebership Inference Attac... | Rebuttal 1:
Rebuttal: Thank you for your feedback and comments!
### **W1, Q2: Existence of IID validation dataset**
We acknowledge that obtaining labeled validation data sampled from the same distribution as the test data can be challenging. Indeed, this is the number one open problem that our work creates for future... | Summary: This paper tackles the dataset inference problem to detect a specifically trained dataset such as a licensed dataset. Firstly, the authors claim that previous membership inference attacks (MIAs) are not successful in discriminating between members and non-members from the same distribution (iid), which is a le... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review and positive feedback.
### **W1: Absence of hyperparameter gammas.**
We measure the success rate of MIAs using the Area Under the Curve (AUC) metric. The AUC metric is advantageous because it provides a comprehensive measure of the model's performance that is ... | null | null | null | null |
Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data | Accept (poster) | Summary: The paper introduces the Federated Transformer (FeT), a novel framework for Vertical Federated Learning (VFL) that addresses the challenges of fuzzy identifiers in multi-party scenarios. It incorporates three key innovations to enhance performance, privacy, and reduce communication overhead: positional encodin... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments; we have addressed all concerns below.
W1, L6. **How FeT Relies on Linkage Quality**: FeT is significantly **less reliant** on initial linkage quality than traditional VFL models like Top1Sim [4,5]. While Top1Sim trains only on records linked by privacy-preser... | Summary: This paper investigates vertical federated learning (VFL) linked with fuzzy identifiers and develops a new framework called Federated Transformer (FeT). The proposed framework leverages the transformer architecture to encode the identification information and distributes the subnets of the transformer across p... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments; we have addressed all concerns as follows.
W1, L1. **Real Application of Multi-party Fuzzy VFL**: In Figure 15 of "rebuttal.pdf," we present a real-world application involving travel cost prediction in a city through collaboration among taxi, car, bike, and b... | Summary: This paper introduces the Federated Transformer (FeT) framework, which is designed to support multi-party VFL.
It enhances the efficiency of model training among multiple parties using fuzzy identifiers while ensuring data privacy.
Experiment results show that FeT performed well when it scaled to 50 parties an... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments; we have addressed all concerns as follows.
W1.1 **Design of Dynamic Masking**: Dynamic masking is a simple two-layer MLP that can be easily optimized and performs well across all five datasets. Our ablation study in Table 3 of Appendix C.1 demonstrates that d... | Summary: This paper proposes the Federated Transformer (FeT) framework, which aims to address performance and privacy challenges in multi-party fuzzy vertical federated learning (VFL). FeT leverages the Transformer architecture to encode fuzzy identifiers and distribute training across different parties. The authors in... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments; we have addressed all concerns as follows.
W1. We will include a formal definition in Section 4 and the introduction. Our scenario extends the two-party fuzzy VFL model defined in FedSim [1] to a multi-party setting. Below, we clarify the key terms:
- **Mult... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their thoughtful efforts in reviewing our manuscript and providing valuable feedback. In response, we have made substantial revisions and added new experiments, visualizations, and real-world applications to the "rebuttal.pdf," including Figures 11-15 and T... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Federated Black-Box Adaptation for Semantic Segmentation | Accept (poster) | Summary: The authors proposed a black-box tuning method to address the problem of Semantic Segmentation in FL. Specifically, they propose to split the network into two parts and store them in server and clients separately. The server modules are optimized via first-order optimization while the client modules are optimi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments.
1) Reproduction of training data:
a) The clients can arbitrarily choose their networks. In the current setting, there is no dependence between the clients or the client and the server other than the output size of the client's last layer. The c... | Summary: This manuscript introduces a federated learning framework for semantic segmentation that neither requires knowledge of the model architecture nor involves transferring gradients, thereby preventing privacy leakage. BlackFed incorporates split neural networks and first/zero order optimization for training the s... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments.
1) Reproduction of Training data: As the reviewer suggested, the intermediate representations and segmentation masks can be used along with diffusion models. However, to train these models, one would still require the raw data to act as ground trut... | Summary: In this work, the authors introduce BlackFed, an FL algorithm that enables distributed learning without transfer of gradients or model weights. This characteristic distinguishes the approach as a black-box model compared to existing FL methods, which can be considered as white-box approaches. Recent research o... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments.
1) Comparison with the baseline from papers with code: The dataset of Cityscapes has data from 18 different centers. In the original splits of Cityscapes, the training data contains images from some of these centers, while the data from rest of the ... | Summary: A common issue now with federated learning systems is that they are not completely private owing to gradients transferred between the clients and the global server during training or by knowing the model architecture at the client end.
The paper proposed a workaround to this by removing the need for the pass... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments.
1) Low Performance on Polypgen: This case indeed serves as an important application area. Here, for 4 out of 6 centers (C1, C2, C4 and C5), it is beneficial to use the FL method. But for C3 and C6, the OOD performance decreases. This could be due to... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable comments. We tried to address all concerns in the respective rebuttal sections. Here, we would like to write the global rebuttal for two common concerns raised by the reviewers:
1) More comparison experiments: We added two new results in Table 1 of th... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
How Far Can Transformers Reason? The Globality Barrier and Inductive Scratchpad | Accept (poster) | Summary: The paper contributes a new metric for measuring the difficulty of a task for a transformer model to learn, called distribution locality. This refers to the amount of an input x that needs to be seen by a model for it to be able to return the corresponding output y. A high locality means that a large amount of... | Rebuttal 1:
Rebuttal: We thank the reviewer’s feedback on the writing of the paper, we will revise the paper accordingly to enhance its readability. In particular, we will use different colors alongside annotations for the inductive scratchpad examples to make reading and parsing them easier.
Further, note that we in... | Summary: The paper investigates the conditions for Transformers to learn algorithms with length-generalization. The paper proposes a formal definition of “distribution locality” and conjectures that the measure is highly correlated with the capability of Transformers to weakly learn. The (inverse of) conjecture is theo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback. We address the rest of the remarks and questions below.
> Q. Is the graph classification task possible to be solved by a Transformer regardless of training? This is quite important since the work is focused on the learnability of Transformers i... | Summary: In the context of Chains-of-Thoughts prompting, Transformer models can solve more reasoning problem when recording their intermediate reasoning steps on a 'scratchpad'. This paper attempts to formulate the hardness of reasoning tasks (i.e., locality barrier), and explore the limitation of 'scratchpad' in seq2s... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback. We address the rest of the remarks and questions below.
> Q. What is the connection to the “Towards Revealing the Mystery behind Chain of Thought: A Theoretical Perspective“ paper which considers dynamic programming tasks solved by scratchpad?
... | Summary: This paper proposes the concept of a "locality barrier" and conjectures that transformers can (weakly) learn to solve a problem only if it doesn't have a locality barrier, i.e., doesn't require global reasoning (as specifically defined by the authors). They prove, in particular, that for the cycle task that th... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback. We address the rest of the remarks and questions below.
> Q. Intuitively, why is global reasoning hard for transformers?
Please see first the global response. For more intuition: we train transformers using a gradient descent algorithm, which ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive comments. We address some of the remarks and questions in the list below.
**I. On the “locality/globality” message:** this paper puts forward the claim \-with both theoretical and experimental results supporting it- that Transformers can efficiently l... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
TabEBM: A Tabular Data Augmentation Method with Distinct Class-Specific Energy-Based Models | Accept (poster) | Summary: The authors address the problem of data augmentation for classification tasks by proposing a novel approach that utilizes separate energy-based models (EBMs) to generate synthetic data for each class. These EBMs are derived directly from the logits of a binary classifier whose goal is to distinguish real data ... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and constructive feedback! We address **all** your questions and comments below, and provide a **rebuttal PDF** in the general rebuttal. Due to space limits, we summarise the new results. We will update our manuscript with additional clarifications and results.
#... | Summary: The paper proposes TabEBM, a novel data augmentation method designed for low-sample-size tabular classification tasks. TabEBM generates synthetic tabular data using class-specific Energy-Based Models (EBMs) to learn the marginal distribution for each class. Experimental results on various real-world datasets d... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review! We address **all** your questions and comments below. Due to limited space, we summarised the new results. We will update the manuscript to include the complete new experiments and clarifications.
## >Q3: How does TabEBM perform on highly imbalanced datasets?... | Summary: The authors present a new method of tabular data augmentation, called TabEBM. The unique feature of TabEBM is that it creates distinct generative models for each class in a classification problem setting. With extensive and thorough evaluations, the authors prove that TabEBM sets the new state of the art.
Str... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review! We address **all** of your questions and comments below. Due to space constraints, we summarised the new results. We will update the manuscript to include the complete new experiments and clarifications.
## >Q2: Provide details on training models for the prop... | Summary: The paper introduces TabEBM, a class-conditional generative method for tabular data augmentation using distinct EBMs for each class. By modeling each class's data distribution individually, TabEBM generates high-quality synthetic data. Experiments demonstrate that using TabEBM for data augmentation improves cl... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review! We address **all** of your questions and comments below. Due to space constraints, we summarised the new results. We will update the manuscript to include the complete new experiments and clarifications.
## >Q1/W2: Do the datasets contain categorical features... | Rebuttal 1:
Rebuttal: We thank the reviewers for the feedback!
## **(1) Summary of positive things**
- **Novel method**
- `Cc3y`: *“a new method of tabular data augmentation”; “the unique feature is that it creates distinct generative models for each class”*
- `iojh`: *“Novel approach using class-specific EBM... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
gRNAde: Geometric Deep Learning for 3D RNA inverse design | Reject | Summary: gRNAde is a graph neural network designed to address the RNA reverse folding problem, a significant challenge due to the potential of RNA as therapeutic modalities and their unique data properties. RNA molecules have lower thermodynamic stability compared to proteins, resulting in fewer training samples, and t... | Rebuttal 1:
Rebuttal: Thank you for your encouraging and actionable review! We believe our revised paper will be strengthened by incorporating your suggestions. We hope our rebuttal further addresses your questions and concerns.
> Question 1
- We have ablated the inclusion of long (primarily ribosomal) RNAs in gRNAde’... | Summary: This paper proposes a geometric RNA design model. Specifically, it introduces multi-stage GNN to encode multiple conformations and aggregate these candidates, and further feed decoder to predict probabilities of a set of candidate sequences.
Strengths: 1. This work creates a new dataset for RNA inverse design... | Rebuttal 1:
Rebuttal: Thank you for your actionable review. We think details in our appendix and rebuttal responses address several of your questions and concerns – please do consider revising your score if you find the responses satisfactory and let us know if there is something we can further clarify.
> Weakness 1 ... | Summary: This paper introduce gRNAde, a geometric deep learning pipeline for RNA sequence design conditioned on one or more 3D backbone structures. gRNAde is superior to the physically based Rosetta for 3D 320 RNA inverse folding in terms of performance, inference speed, and ease of use. The method demonstrates signifi... | Rebuttal 1:
Rebuttal: Thank you for your review – please see our detailed responses below – we believe we have addressed several of your concerns and questions. Please let us know what further information we can provide to make you reconsider your vote to reject the paper.
> Question 1
- See global response ‘On margin... | Summary: This work designed gRNAde, a geometric deep learning pipeline for RNA sequence design conditioned on one or more 3D backbone structures. To achieve this, the authors created single-state and multi-state 3D RNA structure datasets, built a geometric graph representation, and proposed an architecture consisting o... | Rebuttal 1:
Rebuttal: Thank you for your actionable review. We hope that our rebuttal answers your questions sufficiently and makes you reconsider some points that you noted as weaknesses. Please do consider revising your score if you find the responses satisfactory and let us know if there is something we can further ... | Rebuttal 1:
Rebuttal: Thank you to the reviewers for their feedback and actionable suggestions! Everyone highlighted the following positives:
- Careful data preparation and experimental evaluation (vioU, h3v6, E2ka)
- Introduction of multi-state design and representation learning (E2ka, h3v6, Hi6M)
- New design capabil... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper introduced a multi-state geometric graph neural network for the RNA inverse folding problems. Experiments are conducted on carefully splited structural datasets that avoid data leakage. The results have shown convincing performance improvement over the physics-based methods such has FARFAR and Roset... | Rebuttal 1:
Rebuttal: Thank you for your encouraging review! We think that incorporating your comments will strengthen our revised paper and we hope our responses address your questions in the best way possible.
> Question 1:
- To clarify, ‘perfect’ in this sentence is from a machine learning context, not from an appl... | null | null | null | null | null | null |
Approaching Human-Level Forecasting with Language Models | Accept (poster) | Summary: This paper introduces a forecasting system based on Language Models that aims to achieve human-level forecasting capabilities. It presents a system that autonomously searches for relevant information, generates forecasts, and aggregates predictions. Through collecting a large dataset of questions from competit... | Rebuttal 1:
Rebuttal: Thank you for evaluating our work.
> Compare to baselines, the system requires significant computational resources due to its summary and multi-sampling operations. Although the authors use some methods to save the cost, report token statistics and cost used by the system and baseline may be nece... | Summary: The authors contribute a novel system that approaches human-level forecasting performance. The authors also contribute a dataset of forecasting questions submitted to various human forecasting websites. The authors show that their system generally approaches human crowds. In some settings, where the LLM can se... | Rebuttal 1:
Rebuttal: Thank you for evaluating our work.
> I would cite some crowdworking papers from other fields (e.g. HCI) just to highlight the effectiveness of crowd work in the related work section.
Thanks for pointing this out! We plan to cite the following review paper “Ghezzi, Antonio, Donata Gabelloni, Anto... | Summary: The authors benchmark LLMs ability to perform on the task of forecasting, or predicting the outcome of future events. They test several methods and find that ensembling pretrained and fine-tuned LLMs which have access to news sources produces predictions similar to the accuracy of humans.
Strengths: Authors c... | Rebuttal 1:
Rebuttal: Thank you for evaluating our work.
> Dataset is relatively small, as there is limited data in existence for which is there a human baseline.
We note that our dataset is the largest and most up-to-date available for automated forecasting. Compared to the latest work, which includes 3,833 binary q... | Summary: The authors develop a forecasting system that uses news article retrieval and reasoning to predict future events. The system performs with near-human capability and is also complementary to humans. Thorough ablations and evaluations are done to identify that each component of the paper's method provides meanin... | Rebuttal 1:
Rebuttal: Thank you for evaluating our work.
> More motivation in the introduction/general paper on why prediction is important: Other reviewers/readers may not understand the degree of importance prediction tasks have in fields such as social science.
We discuss the broader impact in more detail in Appen... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
UniAR: A Unified model for predicting human Attention and Responses on visual content | Accept (poster) | Summary: In this paper, the authors introduce a novel text-image framework designed to integrate various human-response tasks and multiple image domains. These tasks include attention map generation, scanpath prediction, and subjective preference evaluation, applied to images such as webpages, natural scenes, graphic d... | Rebuttal 1:
Rebuttal: Thank you for the comments and we address each point below.
---
**Metrics in Table 3 and Table 6**
A: We include the important metrics and baseline methods in Table 3 so that it is more readable with larger font size. We have included reference to Table 6 in Table 3 caption for completeness. So... | Summary: Noticing the existing issues in human behavior modeling, such as isolating the study of implicit, early-stage perceptual behavior (like human attention) from explicit, later-stage behavior (like subjective preferences), specified visual content type; in this manuscript, the author(s) aimed to build an integrat... | Rebuttal 1:
Rebuttal: Thank you for the comments and we address each point below.
---
**Model's limitations in real-world situations, such as how its performance is in dynamic environments or its adaptability to changes in user behavior over time.**
A: Current model does not consider dynamic environments or changes ... | Summary: This paper introduces ALOHA, a multimodal model that predicts human saliency, scanpath, and subjective rating of natural images, webpages, and graphic designs. ALOHA outperforms or performs similarly to baseline models across each of its prediction tasks while improving generalizability over task-specific mode... | Rebuttal 1:
Rebuttal: We appreciate you bringing the ethical concerns to our attention. We recognize that such concerns are common with machine learning models, particularly those involving user preference and behavior modeling. We are committed to addressing these issues to the best of our ability by expanding the eth... | Summary: In their paper "ALOHA: from Attention to Likes – a unified mOdel for understanding HumAn responses to diverse visual content" the authors describe a new unified model to predict human saliency(attention/importance), even more fine grained than that, scanpath, and ratings.
After nicely introduced the motivatio... | Rebuttal 1:
Rebuttal: Thank you for the comments and we address each point below.
---
**The writing can be improved in several places**
A: We will edit the paper according to the suggestions.
---
**How hyperparameters are tuned**
A: We did a training-validation split on our larger datasets and then tuned the hype... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thorough and constructive feedback. We have addressed each point in the individual responses. We have included some of the common points of discussion as below. Moreover, in the rebuttal pdf, we also included some figures to answer the questions of “Error Analysis”... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MoGenTS: Motion Generation based on Spatial-Temporal Joint Modeling | Accept (poster) | Summary: This paper present a new method for text to motion generation. In this method the human motion is represented as 2D tokens in a codebook. This allow the authors to apply 2D operation on 3D motions and use a 2D masking strategy. The architecture is composed of a VAE to learn the codebook and of a Transformer to... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and constructive suggestions! We hope our responses adequately address the following questions raised about our work. Please let us know if there is anything we can clarify further.
**1. Clarification of positional encoding.**
Sorry for the confusi... | Summary: This paper proposes an approach for text-conditioned motion generation. A common practice in this area is to use a quantized representation of human motion obtained with a VQ-VAE. However, most prior works represent the full body by a single token, which makes accurate reconstruction complicated.
In this work... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and constructive suggestions! We hope our responses adequately address the following questions raised about our work. Please let us know if there is anything we can clarify further.
**1. Clarification of the quantization.**
Sorry for the confusion ... | Summary: This paper proposes a novel approach to human motion generation by quantizing each joint into individual vectors, rather than encoding the entire body pose into one code. The key contributions are: (1) It quantizes each joint separately to preserve spatial relationships and simplify the encoding process. Then ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and constructive suggestions! We hope our responses adequately address the following questions raised about our work. Please let us know if there is anything we can clarify further.
**1. Computational overhead of the proposed method.**
Thanks for t... | null | null | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude to all the reviewers for their time and their valuable feedback. We deeply appreciate their recognition of our work, such as
**Reviewer g7ot:**
"a novel approach",
"a robust framework that effectively captures spatial-temporal dynamics",
"Extensive ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Is One GPU Enough? Pushing Image Generation at Higher-Resolutions with Foundation Models. | Accept (poster) | Summary: In this paper the authors propose a number of engineering tricks which enable generating at higher resolutions from a pre-trained txt2img diffusion model.
Notably, the requirements for the proposed method are relatively low.
In the proposed approach, an image of a standard resolution is generated first.
After... | Rebuttal 1:
Rebuttal: Thank you for the constructive criticism. We welcome any comments that can help improve the quality of our work.
>W1. While the authors several times mention that "the imaginary part in the frequency space contains most of the low-frequency information of the image" (line 187) and "imaginary part... | Summary: This paper proposes a training-free method for diffusion models to sample images at higher resolution with limited GPU memory, which introduces several tricks, such as fourier merging, chess-mask deduplication, and slider control. Experiments show the effectiveness of the proposed method.
Strengths: 1. The fo... | Rebuttal 1:
Rebuttal: Thank you for the constructive criticism. We welcome any comments that can help improve the quality of our work.
>W1. Is there any literatures to support that the imaginary part of the fourier transform corresponding to the low-frequency of the signal, or is there any deeper analysis beyond the a... | Summary: The paper introduces Pixelsmith, a framework designed to utilize pre-trained diffusion models to enable high-resolution image generation using only a single GPU.
Patch-based denoising ensures that the entire generation process can be accommodated on a single GPU.
The Slider mechanism balances the trade-off bet... | Rebuttal 1:
Rebuttal: Thank you for the constructive criticism. We welcome any comments that can help improve the quality of our work.
>W1. (full question)
We have restructured the content based on the suggestions. The Related Work and Foundations sections have been shortened. The Method section has been revised, as ... | Summary: This paper introduces a framework for generating high-resolution images from text prompts using pre-trained diffusion models. The key innovations are: A cascading approach that uses lower-resolution generated images as guidance for higher resolutions. A "Slider" mechanism to control the balance between followi... | Rebuttal 1:
Rebuttal: Thank you for the constructive criticism. We welcome any comments that can help improve the quality of our work.
>W1. Poor presentation. The writing of this paper needs substantial improvement to be published in a venue like NeurIPS. First, the language of the paper needs to be improved. Second, ... | Rebuttal 1:
Rebuttal: We thank the reviewers (Rvs) for the valuable feedback and the opportunity to improve our work.
## Acknowledged Strengths
**Results:** All 4 Rvs agree that the paper achieves good results compared to current works. It's worth emphasizing that these works are published at the most prestigious con... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
When LLM Meets DRL: Advancing Jailbreaking Efficiency via DRL-guided Search | Accept (poster) | Summary: This paper proposed a black-box jailbreaking framework, RLbreaker, it use Reinforcement learning to help the optimization of jailbreaking prompt.
At training each step, the designed agent of RLbreaker will select a mutator from a small set, and then the helper LLM will use the selected mutator to enhance the ... | Rebuttal 1:
Rebuttal: **The reviewer points out that all experiments are conducted on a 300-sample test set, which has the same distribution as the training set, making it difficult to prove the method's robustness and effectiveness.**
We thank the reviewer for the question. When splitting the training and testing set... | Summary: This work proposes a new method to jailbreak LLM to elicit harmful respones. It adapts deep reinforcement learning to learn a policy of sampling jailbreaking operations (modifying prompt) from a predefined pool. A LLM is then used to rewrite the query prompt complying with the sampled operation. To guide the p... | Rebuttal 1:
Rebuttal: **The reviewer suggests that GPT-3.5 Turbo may be outdated for black-box evaluation and recommends using GPT-4 instead.**
We thank the reviewer for this suggestion. We evaluate our attack on GPT-4, following the same experiment setup in Section 4. We select the latest GPT-4o-mini (07/18/2024) as ... | Summary: This paper proposes a new jailbreaking attack on LLMs with deep-reinforcement learning (DRL) techniques, which takes jailbreak prompts as states and mutations as actions.
Strengths: 1. This paper is the first to leverage DRL techniques to jailbreaking LLMs, bringing new insights to this community.
2. The expe... | Rebuttal 1:
Rebuttal: **The reviewer suggests improving the paper's organization to enhance readability. For example, Section 2 lacks discussion on the background of DRL and related work on jailbreaking.**
We thank the reviewer for the suggestions. Due to the space limit, we did not add a background section for DRL. ... | Summary: This paper introduces RLbreaker, a novel deep reinforcement learning (DRL) approach for generating jailbreaking prompts to attack large language models (LLMs). The authors frame jailbreaking as a search problem and design a DRL agent to guide the search process more efficiently than existing stochastic methods... | Rebuttal 1:
Rebuttal: **The reviewer raised concerns regarding the N/A values from GCG for GPT-3.5 Turbo in Tab.1. It could be run on other LLM and obtain the adversarial prompts to do a transfer attack.**
We thank the reviewer for the suggestion. Following the suggestions, we added an experiment to test GCG's perform... | Rebuttal 1:
Rebuttal: We thank the reviewers for the constructive feedback. Below, we summarize our responses:
**New experiments:**
We added all experiments suggested by reviewers (All the results are in the submitted PDF). Below, we give a brief summary.
1. **GCG transfer attack on black-box model.** We demonstrat... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Hierarchical Programmatic Option Framework | Accept (poster) | Summary: This work builds off of deep reinforcement learning generating programmatic policies, and adapts it to solve long-horizon, repetitive tasks. Concretely, this work proposes HIPO, short for hierarchical programmatic option framework, which retrieves history programs by its neural embeddings, and uses these progr... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough and constructive comments. Please find the response to your questions below.
>Is there any empirical evidence to show that the designed modules do improve the effectiveness, diversity, and compatibility of programs?
We thank the reviewer for this... | Summary: Utilizing human-readable programs as policies has been recently proposed to enhance interpretability in reinforcement learning. This work introduces the Hierarchical Programmatic Option Framework (HiPO) that first embeds the programs into a smooth and continuously parametrized space, obtains a diverse and comp... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough and constructive comments. Please find the response to your questions below.
>The execution time will differ from program to program, but HiPO does not consider this factor while incorporating a discount factor 𝛾 of 0.99.
During the option retrie... | Summary: The authors present HIPO, a method that use a Program embedding space to create options. Some are retrieve if diverse and efficient to create a set of option, later used by a learned high level policy. To evaluate their framework, as the KAREL benchmark does not include long and repetitive tasks, they introduc... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough and constructive comments. Please find the response to your questions below.
>A dedicated Limitation section could clearly help situate the advantages and drawback of HIPO, and compare it to other methods. Authors could step a little bit outside pr... | Summary: The paper presents HIPO, a method for learning programmatic options for solving problems with long-horizon and repetitive tasks. First, HIPO searches in the space defined by a domain-specific language for a set of diverse programs. This set of programs is generated while accounting for the diversity and compos... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough and constructive comments. Please find the response to your questions below.
> Option-learning baselines
As suggested by the reviewer, we additionally experimented with the option-critic architecture [1] and reported the comparison to our method b... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Target-Guided Adversarial Point Cloud Transformer Towards Recognition Against Real-world Corruptions | Accept (poster) | Summary: This paper introduces a framework consisting of two modules: the Adversarial Significance Identifier, which selects tokens with high importance, and the Target Guided Prompter, which selectively drops important tokens to achieve more generalized performance. This approach aims to mitigate the overfitting on sp... | Rebuttal 1:
Rebuttal: We sincerely appreciate your detailed and insightful reviews. We hope our response can address your concerns.
>**Q1.1: The term "local pattern" is ambiguous and lacks a formal definition. Does it refer to a graph constructed from the Point Cloud set tokens or the importance ranking of the tokens?... | Summary: The paper proposes a novel architecture called Target-Guided Adversarial Point Cloud Transformer (APCT) for robust 3D perception in the presence of corrupted data. The APCT integrates an Adversarial Significance Identifier and a Target-guided Promptor to augment global structure capture and enhance the model's... | Rebuttal 1:
Rebuttal: We sincerely appreciate your detailed and insightful reviews. We hope our response can address your concerns.
>**Q1: Augmentation methods like PointMixUp and PointCutMix can improve the robustness, the experiments should be performed.**
Thanks for your advice! As you suggested, we further evalua... | Summary: The paper introduces a novel architecture called the Adversarial Point Cloud Transformer (APCT). This model aims to enhance the robustness of 3D perception models against real-world corruptions. The APCT integrates two core components: the Adversarial Significance Identifier and the Target-guided Promptor. The... | Rebuttal 1:
Rebuttal: We sincerely appreciate your detailed and insightful reviews. We hope our response can address your concerns.
>**Q1: Disscussion about the impact of the proposed method on computational overhead could be important for practical implementations.**
Thanks for your advice! It is very necessary to... | null | null | Rebuttal 1:
Rebuttal: We sincerely appreciate all reviewers and community members for their efforts in evaluating the paper and writing suggestions that greatly help us improve the work！ Please find our responses to your individual questions below. We look forward to discussing any issues further should you have any fo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Can Language Models Perform Robust Reasoning in Chain-of-thought Prompting with Noisy Rationales? | Accept (poster) | Summary: This manuscript explores the challenge of noisy rationales in LLMs. The authors introduce the NoRa dataset, specifically designed to evaluate LLMs' robustness to noisy rationales. They reveal a widespread vulnerability among LLMs to such noise, despite advancements in in-context learning. To address this chall... | Rebuttal 1:
Rebuttal: Thanks for the valuable feedback. Please find the point-to-point responses below. Any further comments and discussions are welcome!
> Q1. About the baseline methods.
**Reply**: Thanks for this question. **We would like to kindly point out that we have included extensive baseline methods.**
We ... | Summary: While previous work focuses on LLMs' stability over noisy questions, this paper investigates the robustness of LLMs to noisy rationales in CoT prompting. The authors introduce the NoRa dataset for this task, which inserts irrelevant or inaccurate sentences into the reasoning steps. They show that LLMs are sign... | Rebuttal 1:
Rebuttal: Thanks for the valuable feedback. Please find the point-to-point responses below. Any further comments and discussions are welcome!
> W1. About the noisy rationales in in-context learning.
**Reply**: **The rationales in the demonstrations (T_1 to T_n) can be noisy in practice, which is the main ... | Summary: The paper proposes a new noisy rationales dataset, to evaluate LLMs' robustness of reasoning across various reasoning domains, covering math, symbolic, and common sense. The datasets is formed by adding irrelevant or inaccurate thoughts into rationales. Existing LLM like GPT 3.5 would struggle on this newly pr... | Rebuttal 1:
Rebuttal: Thanks for the valuable feedback. Please find the point-to-point responses below. Any further comments and discussions are welcome!
> W1. The generalization ability of the proposed CD-CoT method.
**Reply**: Thanks for this valuable comment.
**Please refer to the general response**, where we fu... | Summary: This paper introduces the NORA dataset and a new technique called Contrastive Denoising (CD) that paired with LLMs improves Chain-of-Thought (CoT) reasoning. The paper presents an extensive experimental evaluation over four different LLMs under all tasks in the NORA dataset and a lengthy comparison with CD.
S... | Rebuttal 1:
Rebuttal: Thanks for the valuable feedback. Please find the point-to-point responses below. Any further comments and discussions are welcome!
> W1. The presentation of the submission.
**Reply**: Thanks for this constructive comment!
We would kindly note that in the official guideline of NeurIPS 2024, “th... | Rebuttal 1:
Rebuttal: ### A General Response by Authors:
**We sincerely thank all four reviewers for their thoughtful suggestions on our submission.**
**We have received four reviews with positive ratings 6,6,5,5. We are glad that all the reviewers have good impressions of our work**, including
- an under-explored an... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Probing the Decision Boundaries of In-context Learning in Large Language Models | Accept (poster) | Summary: This study investigate in-context learning in LLMs by examining their decision boundaries in binary classification tasks. The authors investigate the performance of several mainstream models on these tasks. Despite achieving high test accuracy, the decision boundaries of these LLMs are often irregularly non-sm... | Rebuttal 1:
Comment: Dear Reviewer AvnH,
Thank you for your feedback. We hope to address your questions and comments below.
> Q1:"The tests are mainly conducted on binary classification tasks, making it unclear if the findings can be generalized to other tasks."
1. Our primary motivation for using synthetic 2D classi... | Summary: This paper investigates the decision boundary of in-context learning of Transformers. The paper shows that for three toy tasks, the decision boundaries of in-context learning of various pretrained models are not smooth. The paper then explores the method for improving the smoothness of decision boundaries and ... | Rebuttal 1:
Rebuttal: Dear Reviewer PktE,
Thank you for your feedback. We hope to address your questions and comments below.
> "The empirical results are limited to toy datasets, which may reduce the impact of the proposed smoothness-improving method."
1. Our primary motivation for using synthetic 2D classification ... | Summary: The authors study the decision boundaries of LLMs in binary in-context learning tasks, finding that decision boundaries can be non-smooth despite high test accuracy and linear separability of the task itself. They examine numerous methods for smoothing the decision boundaries, including SFT and training a tran... | Rebuttal 1:
Rebuttal: Dear reviewer WmaA,
Thank you for the positive and encouraging review! We are glad to find that you found our work to be insightful, well-written and robustly evaluated :) | Summary: This paper conducts a wide range of experiments exploring the smoothness of decision boundary generated by LLMs. Synthetic datasets from scikit-learn are used for the experiments, and experimental results are showing that various factors affect decision boundaries of LLMs.
Strengths: - There are analyses from... | Rebuttal 1:
Rebuttal: Dear Reviewer PtXB,
Thank you for your feedback. We hope to address your questions and comments below.
> Q1: There have been works trying to understand the mechanism of in-context learning by conducting experiments on NLP classification tasks. However, it seems unclear why the authors choose sci... | Rebuttal 1:
Rebuttal: To address the reviewer's concerns, we conducted additional experiments. Here is a summary:
- **NLP Multi-class Classification Tasks (Figure 1):**
We included additional experiments on six widely-used NLP classification datasets, addressing concerns about our use of toy datasets. Note, to handle... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Efficient Availability Attacks against Supervised and Contrastive Learning Simultaneously | Accept (poster) | Summary: This paper studies the transferability of unlearnable examples (UEs) across different learning paradigms, i.e., supervised learning and self-supervised contrastive learning. Different from existing works including both supervised UE generation methods and hybrid methods, this paper argues that strong data augm... | Rebuttal 1:
Rebuttal: We are grateful to the reviewers for their detailed and thoughtful reviews!
We aim to resolve the issues highlighted and believe our answers will do so.
**[Weakness1] Alignment and uniformity gaps**
- Alignment and uniformity are widely adopted illustrations for the mechanism of contrastive lea... | Summary: This paper explores efficient availability attacks that target both supervised and contrastive learning algorithms simultaneously. The authors propose two attack methods, named AUE (Augmented Unlearnable Examples) and AAP (Augmented Adversarial Poisoning), which utilize enhanced data augmentations to craft eff... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their valuable comments and suggestions! We hope our responses sufficiently address the concerns raised.
**[Weakness1] Theoretical guarantees and why the enhancing data augmentation works.**
- In fact, we conduct theoretical analysis for a toy model in th... | Summary: This paper claims to introduce a threat scenario where the adversary may use contrastive learning to bypass the unlearnable examples that are crafted only for supervised learning. In this threat model, the authors showcase that previous works on unlearnable examples crafted only for supervised learning may fal... | Rebuttal 1:
Rebuttal: We thank the reviewers for their constructive feedback and valuable suggestions! We hope that our clarifications address your queries and concerns effectively.
**[Weakness1] Topic novelty and previous works**
- As the reviewer mentioned, the CP and TUE papers were the first to point out the vu... | Summary: This paper aims to address the issue of effectively conducting availability attacks on both supervised learning (SL) and contrastive learning (CL) algorithms. Specifically, the paper highlights that existing methods fail to simultaneously achieve "unlearnability" for both SL and CL, posing risks to data protec... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their thorough and constructive feedback! We aim to clarify and address your concerns through our detailed replies.
**[Weakness1] Comparison with “One for All (14A)”** We believe that "14A" and our work are more orthogonal in nature and will explain it in deta... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to the reviewers for their careful consideration and valuable feedback!
In our responses to each reviewer, we have clarified and addressed the weaknesses and issues raised, including many additional supplementary experiments.
Due to the word limit of the re... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Euclidean distance compression via deep random features | Accept (poster) | Summary: The paper focuses on constructing sketches of point sets via (compositions of) random maps $\varphi_l$ into the discrete cube $N^{-\frac{1}{2}}\{-1,1\}^N$, and describes how to get an estimate of the squared Euclidean distance. The paper explains how the maps $\varphi_l$ are constructed and motivate the choice... | Rebuttal 1:
Rebuttal: Weakness 1: We agree and we only claim novelty in the case $l \geq 2$; versions of the 1-layer map have been discussed in several papers. We will clarify the relationship with [6] (Charikar) and [Li et al, COLT 2006] when the maps are introduced. See overall author rebuttal for a discussion of the... | Summary: This paper studies the bit complexity of storing the Euclidean distance between n points X up to (1 +- eps) error. The simplest setting assumes all points have ||x||_2 = 1, and all results depend on the "spread" of the point set m = min_{x_1,x_2 in X} ||x_1 - x_2||.
A good comparison for their results is v... | Rebuttal 1:
Rebuttal: Weaknesses: We believe our theoretical contributions are substantial because of the new algorithmic ideas and because our upper bound nearly matches an existing lower bound. See the overall author rebuttal for more details.
---
Rebuttal Comment 1.1:
Comment: Thanks for the rebuttal. I agree the... | Summary: The authors investigate the bit complexity of distance preserving embeddings of points on the unit sphere and in the unit ball.
Their main finding is that iterative application of Charikar's hyperplane SimHash could use slightly fewer bits of storage than snapping a random projection to an epsilon net for cert... | Rebuttal 1:
Rebuttal: Weakness 2: Of the quantization methods discussed on line 173, the most relevant one is known as "sign random projections" [6] and some of those other papers generalize sign random projections in various ways. We will add a remark saying the our 1-layer map $\varphi_1$ is the same as the original ... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for your constructive and helpful feedback. We have the following comments about our contributions and the experiments for all the reviewers:
The main contribution of the paper is theoretical, including algorithmic ideas and their analysis. We believe we have strong contrib... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Artemis: Towards Referential Understanding in Complex Videos | Accept (poster) | Summary: This paper introduces Artemis, a video-language model for video-based referential understanding. It can describe a target referred by a bounding box in a video. A referential video dataset named VideoRef45K is collected to train the model. Artemis is evaluated on HC-STVG benchmark and outperforms baselines ada... | Rebuttal 1:
Rebuttal: Thank you for recognizing the contributions of our work. We deeply appreciate your constructive comments and have provided point-to-point responses below. We hope our responses address all your concerns, and further comments are welcomed.
**Q1:** *The RoI selection step of Artemis clusters th... | Summary: This paper introduces Artemis as a robust solution for the video-based referential understanding task. This task involves analyzing complex videos, each spanning 20–30 seconds, where the target performs multiple actions. Given a video, the Multimodal Large Language Model (MLLM) attempts to answer questions suc... | Rebuttal 1:
Rebuttal: Thank you for recognizing the contributions of our work. We deeply appreciate your constructive comments and have provided point-to-point responses below. We hope our responses address all your concerns, and further comments are welcomed.
**Q1:** *As far as I know, Multi-Object Tracking (MOT) ... | Summary: The paper proposes to bring fine-grained understanding to multimodal LLMs (MLLMs) by introducing video-based referential understanding task. The paper motivates with the drawbacks of current image- and video-based MLLMs, and the need for region-specific features to answer region-specific questions . The propos... | Rebuttal 1:
Rebuttal: Thank you for recognizing the contributions of our work. We deeply appreciate your constructive comments and have provided point-to-point responses below. Further comments are welcomed.
**Q1:** *A fair baseline.*
**A1**: During the rebuttal, we fine-tuned Merlin on the same data (*i.e.* VideoRef... | null | null | Rebuttal 1:
Rebuttal: We thank reviewers for their meticulous work and the insightful comments provided to us.
All reviewers acknowledged the novelty and contributions of the proposed approach (Artemis).
**Reviewer U8HM&CDQu:** The paper is **well-written and clear** and the **motivation is well presented**.
**Rev... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Enhancing Large Vision Language Models with Self-Training on Image Comprehension | Accept (poster) | Summary: This paper addresses the problem of acquiring high-quality fine-tuning data for large vision language models (LVLMs) with minimum human effort. The paper presents STIC (Self-Training on Image Comprehension). STIC contains two stages: image comprehension self-training and description-infused fine-tuning. LVLM f... | Rebuttal 1:
Rebuttal: Thank you for the detailed feedback. Please find our responses below. We hope that our clarifications and additional experiments resolve the misunderstanding.
### **W1/Q3: In Table 2, much of the performance gain comes from DaR.**
We respectfully point out that there is a misunderstanding of th... | Summary: Summary:
The paper presents STIC, a method to enhance LVLM by reducing the need for labeled data. STIC generates image descriptions using unlabeled images and improves reasoning by reusing existing instruction-tuning data. It demonstrates performance gain across seven benchmarks, showing potential to effective... | Rebuttal 1:
Rebuttal: We appreciate your feedback and make clarifications as below.
### **W1. If there is any method needed to ensure the correctness of the well-crafted prompt?**
Thank you for raising this important question. The concern regarding the complexity of prompts is indeed crucial. To address this, we impl... | Summary: This paper proposes a two-stage method to enhance Large Vision Language Models (LVLMs) using unlabeled images. In the first stage, well-designed good and bad prompts are used to make the LVLM generate preferred and dis-preferred completions, respectively, conditioned on the unlabeled images (from COCO). Then, ... | Rebuttal 1:
Rebuttal: Thank you very much for your support and the constructive feedback that helped us improve our work. Please see our detailed response with additional experiments below.
### **W1a: Principles behind the prompt design.**
In short, we use GPT-4 to generate and sample multiple initial prompts, which... | Summary: This paper introduces Self-Training on Image Comprehension (STIC), which emphasizes a self-training approach specifically for image comprehension. First, the model self-constructs a preference dataset for image descriptions using unlabeled images. Preferred responses are generated through a step-by-step prompt... | Rebuttal 1:
Rebuttal: Thank you for your strong support and valuable feedback! We address your major comment as follows.
### **W1: The scalability of STIC**
To explore STIC's applicability to models with higher representation capacity, we conducted supplementary experiments using LLaVA-v1.6 (Vicuna-13B).
| **Model... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their insightful and encouraging feedback on our manuscript. We are grateful for the recognition of the significant performance gains achieved by STIC (B7oX, s8oQ), the novelty and efficiency of STIC (qFQt, s8oQ), the effective use of unlabeled images (qFQt... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Critical Evaluation of AI Feedback for Aligning Large Language Models | Accept (poster) | Summary: The paper explores the current experimental set up for learning from AI feedback (LAIF), specifically using demonstrations generated by a LLM that is weaker than the LLM used to assign preference labels both for training and evaluation. By comparing training several different base LLMs on demonstrations genera... | Rebuttal 1:
Rebuttal: Thanks for finding our paper well written, and our contributions for experimental design vital. We address your concerns and questions below:
> However, it seems some of the take aways discussed in "Current base LLMs are insufficiently responsive to AI feedback" (pg. 7) could apply to LHF. Usin... | Summary: The paper evaluates the extent to which AI feedback is helpful in aligning large language models (LLMs) within the commonly used two-step method of improving pre-trained LLMs. This method involves first performing supervised fine-tuning (SFT) and then fine-tuning with reinforcement learning (RL) or direct pref... | Rebuttal 1:
Rebuttal: Thanks for finding our experiments comprehensive and appreciating our bandit experiments! We answer your questions and concerns below:
> I am not entirely certain about the claim that “SFT on strong distribution minimizes any improvements from LAIF.” While this was the case for 3 out of the 4 res... | Summary: Learning from AI Feedback (LAIF) has become a popular alternative for improving the instruction-following abilities of large language models (LLMs). Despite its popularity, many unresolved questions remain regarding the actual improvements gained through LAIF. The authors address some of these questions, with ... | Rebuttal 1:
Rebuttal: > 10% rule doesn’t always hold
> Figures 4 and 7 show that the 10% threshold is ideal for certain models and settings. Have you experimented with a different percentage threshold?
Thanks for noting this. Using an increasingly larger split for SFT would weaken our claims compare LAIF with SFT (fo... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Edit Distance Robust Watermarks via Indexing Pseudorandom Codes | Accept (poster) | Summary: This paper constructs an LLM watermarking scheme which is robust to edit distance perturbations. The construction is carried out carefully in stages. The authors first construct pseudorandom codes (PRC) that are robust to constant fraction substitutions assuming the existence of local weak pseudorandom functio... | Rebuttal 1:
Rebuttal: Thank you for your positive comments. Regarding your comments on presentation: we will adjust our exposition accordingly. Regarding local weak PRFs, we do not know of a formal reduction showing that public-key cryptography implies local weak PRFs. We will adjust the discussion to add some addition... | Summary: This paper addresses the challenge of watermarking the outputs of language models with provable guarantees of undetectability and robustness to adversarial edits. The authors propose a novel watermarking scheme that maintains these properties even when subjected to a constant fraction of insertions, deletions,... | Rebuttal 1:
Rebuttal: Thank you for your review.
Many of the weaknesses and limitations you mention relate to the lack of implementation and experiments. We want to emphasize that developing a fully practical watermarking scheme for immediate use in LLMs is not the point of this paper. Rather, the purpose is to lay th... | Summary: This paper proposes a new pseudorandom code (PRC) called an _indexing PRC_ over a polynomially-sized alphabet that is robust to a constant number of adversarial edit corruptions (insertions, deletions or substitutions). It is constructed as a wrapper around a substitution-robust PRC, which has been studied in ... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments.
Minor questions:
- Eq. (2): Yes, dimension of $s$ should be $\ell(\lambda)$. And yes, we forgot an "=1" in the second term.
- Line 217: $\widetilde{Adv}^G$ refers to the output of the algorithm Adv (which is a 0 or 1) when, each time Adv decides to query G, it... | Summary: The paper presents an innovative approach to embed an undetectable and robust watermark into AI-generated texts. The authors takes three main steps to reach the goal. They first design a robust pseudorandom code (PRC) over a binary alphabet, and then turn it into a polynomial-sized alphabet PRC robust to a fra... | Rebuttal 1:
Rebuttal: Thank you for your review.
Question 1: Using techniques from our theoretical watermarking scheme to develop a practical implementation for use with LLMs is an important direction for future work. The main limitation of our present theoretical results which may complicate a practical implementati... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CLIP in Mirror: Disentangling text from visual images through reflection | Accept (poster) | Summary: This paper attempts to address typographic attacks by disentangling visual and language representations. The proposed framework, MirrorCLIP, leverages the observation that visual models struggle to recognize text semantics in mirrored images. By using both original and flipped images, MirrorCLIP compares their... | Rebuttal 1:
Rebuttal: **W1&Q1:** Does … still work … text and its mirrored version? The proposed … might be easily circumvented.
**A1:** Yes, the disentangling mask still works. Although there is a 10.22 drop (59.71 to 49.49) in performance compared to the accuracy with ordinary typography (See Table Ⅱ in attach... | Summary: This paper proposed a simple yet effective disentanglement framework for CLIP, leveraging the different characteristics of visual and textual semantics when facing mirror reflection and reveals that the CLIP model does not exhibit horizontal flip invariance for text, demonstrating a certain degree of innovatio... | Rebuttal 1:
Rebuttal: **W1:** Detailed experimental results of the disentangling framework when dealing with images containing flipped text were not provided in Ablation Experiment.
**A1:** Thanks for your thorough review of the paper. We show the detailed experimental results with images containing flipped text in Ta... | Summary: The paper highlights that CLIP may erroneously identify visual objects due to the influence of textual information, thereby reducing the accuracy of visual object recognition. The objective is to extract more precise visual and textual features from the image. The paper proposes that mirroring the image can pr... | Rebuttal 1:
Rebuttal: **W1&W2&Q1:** It would be better to have a discussion on whether MirrorCLIP can be explored for other downstream tasks or applications.
**A1:** To explore MirrorCLIP's applications and downstream tasks, we combined it with RegionCLIP and SAM, for detection and text region segmentation. Specific e... | Summary: This paper introduces a zero-shot framework, MirrorCLIP, to solve the confusing issues of CLIP facing text-visual images. Unlike existing methods, this method exploits CLIP’s invariance for visual factors and variance for textual factors of images when horizontally flipped. In particular, this paper reveals th... | Rebuttal 1:
Rebuttal: **W1:** It would be beneficial to delve deeper into the differences by comparing this approach to the existing CLIP-based method for textual and visual disentanglement in related works.
**A1:** Thanks for your constructive advice. Compared to other CLIP-based works, our MirrorCLIP is the only tra... | Rebuttal 1:
Rebuttal: Dear Reviewers,
**Please see the attached one-page PDF with added experimental results.**
We sincerely thank all the reviewers for their positive and constructive comments:
* All reviewers appreciate that our paper introduces a simple yet effective training-free approach to disentangle textua... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
StreamFlow: Streamlined Multi-Frame Optical Flow Estimation for Video Sequences | Accept (poster) | Summary: The paper proposes a multi-frame optical flow estimation model with a novel flow decoder that estimates a flow output for all frames simultaneously. The paper describes this process as a Stream-lined In-batch Multiframe (SIM) pipeline and argues that this leads to efficiency gains when processing video as eac... | Rebuttal 1:
Rebuttal: **Q1: The relationship between image size and memory requirements**
Thank you for your question. During inference, the memory is modest, as shown in Line 305. Moreover, the memory usage could be further reduced via packages like ``flash-attention``. We have compared StreamFlow with the recent met... | Summary: This work focuses on the task of multi-frame optical flow estimation. It challenges the traditional pair-wise flow estimation approach in multi-frame scenarios, which involves redundant calculations. To address this issue, a new framework is proposed that takes multiple frames as input and predicts successive ... | Rebuttal 1:
Rebuttal: **Q1: Eq.2, Eq. 5~9 could be presented with more clarity and Fig. 3 could be given more captions.**
Thank you for your suggestions. We will revise the formulas for clarity and provide additional captions for Fig. 3 as recommended. We provided a rather general explanation of Integration at Line 16... | Summary: The paper presents StreamFlow, a new optical flow estimation method tailored for video inputs. StreamFlow differentiates itself from earlier methods by incorporating a streamlined in-batch multi-frame pipeline that reduces duplicate computations across consecutive frames, thereby enhancing efficiency. Addition... | Rebuttal 1:
Rebuttal: **Q1: More discussions of related works such as SKFlow and VideoFlow could be included in Section 2.**
A: Thank you for your advice. We will add a more detailed discussion to the literature review in Sec 2. For instance, "To address the issue of occlusion，SKFlow begins by expanding the spatial r... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers,
Please refer to the attached one-page PDF that summarizes the added experimental results, which include:
**1. Results on the Spring dataset (CVPR' 23), and the comparison with the recent method MemFlow (CVPR' 24, on arxiv 2404)**
StreamFlow has achieved superior performance on t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Challenges of the Nonlinear Regime for Physics-Informed Neural Networks | Accept (poster) | Summary: The authors analyze the NTK perspective for PINNs for non-linear PDEs. Previous considerations derived for linear PDEs fall short for non-linear ones and the authors attribute this difference to the non-vanishing Hessian term. Therefore, they suggest to use second order methods and show to be able to converge ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the kind words regarding the structure and analysis in our paper. While we agree that some of the results might be expected, most of the existing literature focuses on the linear regime and only conjectures what might happen in the nonlinear case. To our knowled... | Summary: This paper studies the training dynamics of PINNs, especially for the nonlinear PDEs. The authors find that the previous recognized NTK viewpoint is not applicable to nonlinear PDEs, although it holds for linear PDEs. Therefore, the global convergence of gradient descent on nonlinear PDEs may not be guaranteed... | Rebuttal 1:
Rebuttal: We would like to extend our gratitude to the reviewer for their thoughtful feedback and for bringing this reference to our attention. Now, we will proceed to address the specific questions and concerns raised by the reviewer.
- **Q1** In the paper referenced by the reviewer, the convergence of th... | Summary: The paper studies the NTK of NNs trained on non-linear PDEs, showing that they exhibit different behaviours compared to standard analysis of NTKs. The paper then discusses the issue of spectral bias that arises from first-order methods, showing that they can be alleviated by the use of second-order methods.
S... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the positive feedback and the constructive comments on our paper. In particular, we acknowledge the second weakness highlighted by the reviewer. We plan to include this information in an improved version of our paper. At present, the reviewer can get a quali... | Summary: In this paper, the theory of the Neural Tangent Kernel(NTK) in the case of solving nonlinear partial differential equations using PINNs is investigated in detail. In particular, it is shown that typical results of the NTK framework do not hold when the simple gradient descent method is employed due to the wors... | Rebuttal 1:
Rebuttal: First and foremost, we would like to express our sincere gratitude to the reviewer for the encouraging feedback and thoughtful comments on our paper.
Regarding the reviewer's question, you are correct in noting that part of our theoretical framework is developed at the NTK level, i.e., for infini... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Replay-and-Forget-Free Graph Class-Incremental Learning: A Task Profiling and Prompting Approach | Accept (poster) | Summary: - The paper tackles the problem of graph class-incremental learning. The proposed TPP consists of two modules, that is, task profiling and graph prompting.
- The task prediction is accomplished by learning task prototypes based on graph Laplacian smoothing. Specifically, task profiling aims to accurately p... | Rebuttal 1:
Rebuttal: Thank you very much for the constructive comments and questions. We are grateful for the positive comments on our design and empirical justification. Please see our detailed one-by-one responses below. We will include the new results and discussions below into the paper.
>**Questions #1:** Point ... | Summary: This paper addresses the challenge of class-incremental learning (CIL) in graph data (GCIL) by proposing a novel Task Profiling and Prompting (TPP) approach. It leverages Laplacian smoothing-based task profiling to achieve accurate task ID prediction, thereby mitigating inter-task class separation issues, and ... | Rebuttal 1:
Rebuttal: Thank you very much for the constructive comments and questions. We are grateful for the positive comments on our design and empirical justification. Please see our detailed one-by-one responses below.
>**Weaknesses #1:** TPP is similar to L2P [Ref2].
Although our TPP and L2P both adopt promptin... | Summary: This paper studies the graph class-incremental learning problem with unknown task identities. Specifically, the unknown task identity is the key challenge, and this work proposes a Laplacian smoothing-based graph task profiling approach that is theoretically justified capable of predicting the task identities.... | Rebuttal 1:
Rebuttal: Thank you very much for the constructive comments and questions. We are grateful for the positive comments on our design and empirical justification. Please see our detailed one-by-one responses below.
>**Weaknesses #1/Questions #1:** Not clear experimental setup and the performance of TPP with d... | Summary: The paper proposes a Replay-and-Forget-Free Graph Class-Incremental Learning (GCIL) approach called Task Profiling and Prompting (TPP). This method addresses the challenges of class-incremental learning in graph tasks without relying on task identifiers during inference. By using Laplacian smoothing-based task... | Rebuttal 1:
Rebuttal: Thank you very much for the constructive comments and questions. We are grateful for the positive comments on our design and empirical justification. Please see our detailed one-by-one responses below.
>**Weaknesses #1:** The problem definition requires some clarification.
In the **Global Respon... | Rebuttal 1:
Rebuttal: Dear all reviewers,
Thank you very much for the time and effort in reviewing our paper, and for the constructive and positive comments. Our rebuttal consists of two parts: **Global Response** where we address the shared concerns from two or more reviewers and **Individual Response** where we prov... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Stacking Your Transformers: A Closer Look at Model Growth for Efficient LLM Pre-Training | Accept (spotlight) | Summary: The paper tackles the problem of model growth during training. The authors focus on the problem of efficient LLM pretraining and analyze a plethora of proposed growing techniques. The paper focuses on and established from the very beginning three clear and important objectives: 1. Comprehensive evaluation for ... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and valuable comments! Due to the word limit, we have omitted some of your partial questions and here are our pointwise responses:
1. _O1. Reported results in ..._
Thank you for your comments regarding the connections to existing work. The aim of this study i... | Summary: The paper introduces a novel method for pre-training large language models (LLMs) efficiently using model growth techniques. The authors tackle three main obstacles: lack of comprehensive evaluation, untested scalability, and absence of empirical guidelines. They propose a depth-wise stacking operator, "Gstack... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our work! Here are our pointwise responses:
1. _Although extensive, the experiments mainly focus on model performance from a computational and speed perspective. The impact on final model accuracy or downstream task performance is less explored, which could be c... | Summary: This paper studies the model growth technique for large language models, which expand a smaller pretrained large language model into a bigger one. The authors consider four natural way of expanding the parameters of the smaller pretrained large language models, and find that duplicating layers is the most effe... | Rebuttal 1:
Rebuttal: Thnak you for your appreciation and positive feedback on our work! Here are our point-by-point responses:
1. _[Minor] The experiments are conducted on Transformer architecture. Trying different model architecture such as SSM can be more interesting._
Yes, we incorporated an SSM-based LLM experim... | Summary: The presented work systematically investigated the major obstacles of applying model growth methods to large language models and the corresponding solution. The empirical results reveals that depthwise stacking methods works the best to LLMs. The paper then studied how to practically use the depthwise stacking... | Rebuttal 1:
Rebuttal: We appreciate your overall positive feedback and recommendation on our work!
Regarding the two weaknesses you mentioned, we acknowledge that the current draft lacks a thorough theoretical analysis of the depthwise stacking and a comparison to more sophisticated growth methods. This is because our... | Rebuttal 1:
Rebuttal: We sincerely appreciate all the reviewers for their time and effort in reviewing our paper. We are thrilled to receive positive feedback from all four reviewers and are honored that the reviewers generally acknowledge our strengths, including: 1. the paper is well-motivated and easy to follow [FXh... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Policy Improvement using Language Feedback Models | Accept (poster) | Summary: The authors introduce Language Feedback Models (LFM), a method filtering out desirable transitions (i.e. transitions considered as helping to solve a task) collected by an agent in an environment to then improve this agent by doing Imitation Learning on these transitions. The LFM method has three phases: 1) Fi... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our paper and provide us with valuable insights. We appreciate the acknowledgement of the generalizability and efficiency of our method, as well as improvements to model explainability.
## W1: Rejection sampling
We thank the reviewer for drawing... | Summary: This paper provides a policy improvement method with a Language Feedback Model (LFM) for decision making tasks.
The proposed method mainly consists of two stages: (1) training a Language Feedback Model (LFM) and (2) improving a policy model with the trained LFM. In the first stage, to train a LFM, the initia... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our paper and provide us with valuable insights. We appreciate the acknowledgement of the novelty of our method, as well as clear demonstrations of improvements using our method across three representative benchmarks.
## W1: sample-efficiency an... | Summary: The papers present a method to essentially filter which actions should be used to learn a policy via imitation learning. The method follows the online imitation learning setting and replaces the expert policy with a language feedback model (LFM) distilled from a LLM. The LFM evaluates which transitions from th... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our paper and provide us with valuable insights. We appreciate the acknowledgement of the novelty of our method, as well as clear demonstrations of improvements using our method.
## W1: interpretable feedback
To clarify, we have 2 claims. First,... | Summary: The paper proposes to train a language feedback model and leverage the language feedback model to conduct policy improvement in language-based task. The authors also propose a pipeline that can apply CLIP to convert images into textual description. Experiments over ALFWorld, ScienceWorld and Touchdown validate... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our paper and provide us with valuable insights. We appreciate the acknowledgement of the strengths of our paper, including its clarity, novelty, and demonstration of generality across a range of tasks.
## W1: improvement from distilling languag... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Conditioning non-linear and infinite-dimensional diffusion processes | Accept (spotlight) | Summary: The paper attempts to derive a means of conditioning a nonlinear diffusion process upon function-valued observations, via the $h$-transforms, adapting the method of _Jeremy Heng, Valentin De Bortoli, Arnaud Doucet, and James Thornton. Simulating diffusion bridges with score matching._ to a function-valued sett... | Rebuttal 1:
Rebuttal: Thanks for your positive review and the concrete suggestions. We're glad you appreciate the importance of our result!
It seems to us that the weaknesses you listed were almost entirely presentational. We have fixed the issues you point out in your review as follows.
**Clarity of introduction an... | Summary: This paper explores the conditioning of non-linear processes in infinite dimensions. To achieve this, the authors introduce an **infinite version of Doob’s $h$-transform** (contribution 1) that relies on the infinite-dimensional counterparts of Itô’s lemma and Girsanov’s theorem. They then discretize the condi... | Rebuttal 1:
Rebuttal: Thank you for the review and the insightful questions. We're glad you liked the paper and find the theoretical contribution noteworthy!
In the following, we address your questions one by one:
1. Assumption 3.3: Yes, you're right and we agree this would be nice to include!
We are aware of a r... | Summary: This paper addresses the challenge of conditioning infinite-dimensional stochastic processes, particularly non-linear ones, without prior discretisation. Traditional methods condition finite-dimensional data but struggle with infinite-dimensional, function-valued data. The authors employ an infinite-dimensiona... | Rebuttal 1:
Rebuttal: Thanks for the positive review!
In the following, we would like to briefly clarify the lack of related approaches (this work is, to the best of our knowledge, the first to operate in an infinite-dimensional, nonlinear setting)
and describe the computational complexity:
1. Comparison with related... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for their reviews and positive evaluations of our work. We are glad you all liked our contribution!
Many weaknesses seem to relate to presentational concerns, which we believe are easy to correct.
Below, we reply to all reviews in separate threads.
Attached is a PDF that co... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On Affine Homotopy between Language Encoders | Accept (poster) | Summary: The paper introduces formal mathematical concepts of intrinsic and extrinsic homotopy between language encoders. The first concept compares the behavior of two encoders on a concrete dataset, while the second concept compares them independently from the concrete dataset. The paper also demonstrates how to appl... | Rebuttal 1:
Rebuttal: Thank you for taking the time to read and review our work – we are grateful for all the suggestions. Further, we are happy to hear that our usage of novel non-trivial mathematical tools for the analysis of the space of encoders is appreciated. Below, we address the concerns raised in the review:
... | Summary: In this paper, the authors study a nature question "What does it mean for two encoders to be similar" and proposed an intrinsic measure of similarity that aligns with extrinsic performance on downstream tasks. It introduces the concept of affine alignment and explores its properties and implications for unders... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review and helpful feedback. We are pleased to read that you appreciate the novelty of the approach and the breadth of the ideas introduced in the paper. Below, we address the specific concerns raised in the review:
1. **“Elaborate your Motivation of usin... | Summary: The paper aims to formally define, derive and then analyse similarity between pretrained language encoders, focusing on aspects of intrinsic (task-independent) similarity and extrinsic (task performance-oriented) similarity. The paper is mostly of theoretical nature, aiming to properly and formally define the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read our work, as well as for their thorough review and helpful feedback. We are very happy to hear the positive comments about our formalization of the problem and our writing. We address the open points and concerns raised in the review below:
- **"T... | Summary: This paper presents theoretical analysis on "intrinsic alignment" of two or more pretrained language encoders (e.g. BERT trained with various seeds). The work proposes computing the intrinsic alignment between two encoders by first defining a algebraic metric space in which these two encoders exist, and then l... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review and the helpful feedback and ideas they provided! We are happy to hear that our theoretical results as well as practical implications are appreciated. In the following, we address the open points and concerns:
- **“It is difficult to see what the ri... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Vision Foundation Model Enables Generalizable Object Pose Estimation | Accept (poster) | Summary: This paper introduces VFM-6D, a two-stage RGBD-based method for generalizable object pose estimation. Given a set of reference images depicting objects of the arbitrary category, the proposed method first estimates a viewpoint using image matching, then based on the NOCS map of this matched reference image, it... | Rebuttal 1:
Rebuttal: >**Q1: Can the proposed method work well on unseen categories when the geometry/texture of reference and query objects are different?**
**A1:** Thanks for the comment. We suppose that our presented experiments on Wild6D and CO3D datasets could address this concern. For Wild6D evaluation, we have ... | Summary: The paper presents VFM-6D, a new framework for generalizable object pose estimation. VFM-6D integrates a 2D-to-3D feature lifting module and a shape-matching module, both of which utilize pre-trained vision foundation models to enhance object representation and matching accuracy. In open-set robotic manipulati... | Rebuttal 1:
Rebuttal: >**Q1: The method relies on depth maps and NOCS maps, which could limit its applicability in real-world scenarios.**
**A1:** Thanks for your comment. To address your concern, we explore the possibility of RGB-only pose prediction for our method. Due to the character limit, please refer to **Q1** ... | Summary: Authors introduce method for generalizable object pose estimation given RGB-D image. The approach builds upon DINOv2 model and adds two adapter blocks on top to accomplish better viewpoint estimation as well as object coordinate estimation. The resulting model is trained on synthetic data with contrastive loss... | Rebuttal 1:
Rebuttal: >**Q1: Clarify Fig.1. Why is DINOv1 used in Fig.1 and not much more performant DINOv2?**
**A1:** Thanks for pointing it out. In Fig.1, taking DINOv1 as an example, we aim to show that the pre-trained vision foundation model is not reliably used for category-level object pose estimation. This obse... | Summary: This paper addresses the task of category-level object pose estimation for unseen object categories from paired RGB-D imagery. To deal with unseen object categories, the authors leverage a vision-foundation model (Dino-v2 in this case).
The entire pipeline works in two stages. Given an RGB-D input, the first s... | Rebuttal 1:
Rebuttal: >**Q1: Ablation study on RGB-only object pose estimation.**
**A1:** Thanks for your suggestion. Due to the character limit, please refer to **Q1** in the global response for detailed evaluation results. As can be observed, the RGB-only variant achieves comparable pose accuracy on average when com... | Rebuttal 1:
Rebuttal: We are grateful to all reviewers for taking the time to review and provide constructive feedback. We are glad to see that most reviewers recognized the novelty of our method, the strength of our experimental evaluations, and the good presentation of our paper:
- “Presents a technically sound metho... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Generalized Linear Bandits with Limited Adaptivity | Accept (spotlight) | Summary: This paper addresses the generalized linear contextual bandit problem under limited adaptivity constraints. In a setting (M1) where the times for updating the agent's policy are predetermined, the first proposed algorithm B-GLinCB divides the entire timeline into batches, updating the policy at the end of each... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. We address the comments and questions below:
**Regarding Weakness 1**: *Prior knowledge of $\kappa$*.
We note that [7], in fact, assumes the knowledge of an upper bound on $\kappa$ in Procedure 1 for the non-contextual problem and while calculating $\tex... | Summary: The authors consider the problem of regret minimization in bounded generalized linear contextual bandits with limited adaptivity. Specifically, they consider two models of limited adaptivity: **M1** in which the update rounds must be chosen before the algorithm is run, and **M2** in which the algorithm can be ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and insightful review.
**Regarding Weakness 1**: *Numerical comparisons with DDRTS-GLB and EVILL*.
This is a useful suggestion. We will implement the additional empirical comparisons mentioned in the review.
On the theoretical front, it is, however, re... | Summary: This paper considered regret minimization for a generalized linear reward model with limited adaptivity, in which the set of arms $\mathcal{X}t$ is stochastically generated by unknown distribution $\mathcal{D}$, and after pulling $x_t \in \mathcal{X}_t$ the learner receives a reward $r_t$ sampled from the GLM ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. We address the comments and questions below:
**Regarding Weakness 1**.
*(1a) Unknown $\kappa$*.
It is relevant to note that an upper bound on $\kappa$ suffices for the mentioned use cases. $\kappa$ is an instance-dependent parameter that captures the no... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Reasoning Multi-Agent Behavioral Topology for Interactive Autonomous Driving | Accept (poster) | Summary: The paper presents a approach to address the challenges of autonomous driving in multi-agent scenarios and heterogeneous interaction. It introduces the concept of Behavioral Topology (BeTop) and its corresponding network BeTopNet. BeTop is based on braid theory and aims to provide a topological representation ... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. We address your questions below.
>**W1/Q1:** The proposed method infer one type of agent behavior topology from one mode of future trajectories (e.g., 8s), while the topology of multi-agent interaction for real-world autonomous driving is usually multi-modal and... | Summary: The paper introduces a novel approach to enhance the safety and social consistency of autonomous driving systems through improved multi-agent behavioral integration. To address inefficiencies and inconsistencies in current behavioral representations, the authors propose Behavioral Topology (BeTop), a topologic... | Rebuttal 1:
Rebuttal: Thank you for appreciating our work. We address your questions below.
> **W1:** While the paper demonstrates effectiveness on specific datasets, it remains uncertain how well the method generalizes to diverse driving environments and conditions not covered in the training data.
Thank you for yo... | Summary: The paper addresses the challenges of autonomous driving by integrating behavior among interactive agents, specifically focusing on issues caused by multi-agent scene uncertainty and heterogeneous interactions. To tackle this, the paper introduces a topological formation called Behavioral Topology (BeTop), de... | Rebuttal 1:
Rebuttal: Thanks for your valuable suggestions and we really appreciate your comments. We have carefully revised the draft for better readibility. We address each of the questions and confusion on the weaknesses as follows.
>**W1:** As far as I understand, the paper uses braid topology for just one step. C... | Summary: This paper introduces a new approach, called Behavioral Topology (BeTop), to address the challenges in modeling multi-agent behaviors in autonomous driving. By utilizing braid theory, BeTop explicitly represents the consensual behavioral patterns among multiple agents, facilitating better prediction and planni... | Rebuttal 1:
Rebuttal: Thanks for your appreciation and the helpful review of our work. We address your concerns below.
>**W1/Q1:** Lack of Discussion on Multi-Agent Settings: While the paper introduces a topological approach for multi-agent behavior modeling, it lacks an in-depth discussion on how this method scales a... | Rebuttal 1:
Rebuttal: *Dear Area Chairs and Reviewers,*
*We thank all the Reviewers for their careful reviews and valuable comments on our work. We have taken each comment into consideration, added more ablative experiments in the rebuttal, and clarified some technical details. Please see each response below. We are g... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
AutoTimes: Autoregressive Time Series Forecasters via Large Language Models | Accept (poster) | Summary: This paper argues that existing LLM based time series models have not fully exploited the inherent autoregressive property and the decoder-only architecture of LLMs. To address this problem, this paper introduces a novel AutoTimes model, which exploits the autoregressive property of LLMs. Experimental results ... | Rebuttal 1:
Rebuttal: ## Response to Reviewer k2uw
Many thanks to Reviewer k2uw for providing a valuable review.
**Q1**: Reclarify the contributions of the proposed method.
The reviewer mentioned that "the paper exploits the autoregressive property of the LLMs". We agree with this argument but also would like to hig... | Summary: The authors present AutoTimes, a method that utilizes large language models (LLMs) for time series forecasting. One of the key underexplored research topics addressed by the authors is the lack of models and pre-training mechanisms that result in foundation models capable of handling lookback and forecasting h... | Rebuttal 1:
Rebuttal: ## Response to Reviewer EAH7
Many thanks to Reviewer EAH7 for providing a thorough insightful review and recognizing our contributions.
**Q1**: Suggestion to improve the presentation of the paper.
Thanks for your valuable feedback regarding the color scheme and the mentioned typo. We will use a... | Summary: This paper proposes a model named AutoTimes to repurpose LLMs for time series forecasting. Different from previous methods that use flattening and linear projection to get a prediction, this model repurposes LLMs in an autoregressive way, which is closer to the pre-training process of LLMs. Specifically, the m... | Rebuttal 1:
Rebuttal: ## Response to Reviewer 1PWH
Many thanks to Reviewer 1PWH for providing a detailed and insightful review.
**Q1**: Whether AutoTimes truly leverages the capabilities of the pre-trained LLMs.
We noticed that the recent work[1] has raised questions about non-autoregressive LLM4TS methods. It is al... | Summary: The authors present in this paper an interesting approach where LLMs are leveraged to be fledged as time series forecasters. This proposed approach is based on freezing the LLM backbone to update a small amount of parameters to generate suitable time series embeddings which, together with time stamps as positi... | Rebuttal 1:
Rebuttal: ## Response to Reviewer acJd
Many thanks to Reviewer acJd for providing a detailed review and recognizing our contributions.
**Q1**: More baseline models for evaluation.
We acknowledge the importance of the recent works and will certainly incorporate the suggested references in our revision. As... | Rebuttal 1:
Rebuttal: ## Summary of Rebuttal
We sincerely thank all the reviewers for their insightful reviews and valuable comments, which are instructive for us to improve our paper further.
In this work, we proposed an effective approach (AutoTimes) to repurpose LLMs as **autoregressive forecasters**. Unlike previ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
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