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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Large Language Models Must Be Taught to Know What They Don’t Know | Accept (poster) | Summary: This work investigates the calibration of LLMs, i.e. how to estimate reliable confidence in their responses that correlates well with the actual likelihood of being correct. Through experiments, the authors reveal that existing methods fall short of achieving accurate confidence estimates, and their calibratio... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and supportive review!
Here is our response to the weaknesses you listed pointwise:
### Response 1
Yes, our method requires access to the model parameters. Luckily for our method, there are incredibly strong open-source LLMs available. The most recent a... | Summary: The paper investigates uncertainty calibration via fine-tuning models with correct and incorrect answers. The paper concludes that training through the features of a model on a thousand graded examples using LoRA outperforms prompting-based method and linear probes. The paper also finds that models can be used... | Rebuttal 1:
Rebuttal: We appreciate your feedback. We respond to your comments below, providing numerous clarifications and new results inspired by your comments.
## Technical Novelty
We would like to clarify that our goal in the paper was to show that more complex methods, including those with technical complexity... | Summary: This paper studies calibration of large language models. Firstly, the paper points out the zero-shot outputs of LLMs are poorly calibrated, especially in an open-ended generation setting. Secondly, the paper explores fine-tuning a separate LLM for calibration, and demonstrates its good properties such as OOD g... | Rebuttal 1:
Rebuttal: We’re glad that you find the paper informative and appreciate your feedback. Thank you for sharing the additional reference, which we will incorporate into the related work section. We respond to your questions below, and make several clarifications. We also provide additional results in the gener... | Summary: ### Summary
This paper presents a method to quantify the confidence of predictions from an LLM. Unlike some previous work, the authors propose a training-based approach to teach a model to generate reliable confidence scores. The experiments report Expected Calibration Error (ECE) and AUROC, commonly used in ... | Rebuttal 1:
Rebuttal: Thank you for your review! We respond to your comments below.
## Connection between Base Model and Uncertainty Estimate
As you note, the model that generates uncertainties is not exactly identical to the model that generates answers. However, the assertion that these models are entirely independ... | Rebuttal 1:
Rebuttal: Thank you to all the reviewers for your feedback. We wish to emphasize that our paper is closely engaging with an extraordinarily significant and timely topic: how do we calibrate foundation models to select for factual accuracy? We consider many facets of this question, including even a human+LLM... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper investigates fine-tuning on a small dataset of correct and incorrect answers for an uncertainty estimator. Through various experiments, the authors show the trained estimator not only surpasses the previous prompting approach, also has generalizability to other models and subjects. Lastly, the autho... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful and supportive feedback!
## Additional Benchmarks
Inspired by your comments, we provide results on the MMLU-Pro benchmark, which is a more challenging multi-task language understanding benchmark. This dataset is much newer than any of the models we test and bears no... | null | null | null | null | null | null |
The motion planning neural circuit in goal-directed navigation as Lie group operator search | Accept (poster) | Summary: This paper describes how goal-directed navigation in a group-structured space can be framed as group operator search, describes some neural network implementations of this search, and relates one of these networks to the central complex of the famous fruit fly.
Strengths: The exposition is clear and, for the ... | Rebuttal 1:
Rebuttal: Thank you for your positive review on our work! Following are detailed replies to your comments.
>”First, it is very puzzling ... it cannot be fit into the framework.”
Thank you for mentioning PFL2 neurons, and we are happy to include more discussions in the revised manuscript. Let’s discuss som... | Summary: The paper presents a formulation of the problem of motor planning as a Lie group operator search. The authors define a 2-layer neural network implementing the transformation from sensory input to rotation direction, which presents similarities to the neural circuits of the Drosophila responsible for goal-direc... | Rebuttal 1:
Rebuttal: Thank you for your review!
We want to clarify about the writings first:
> ‘Suppose the motor system ...through translation and rotation of the sensory input?’
In the heading direction example, the fly’s brain could use its wings to rotate its heading (flying) directions. Here, the direction $s$ ... | Summary: Intelligence systems, both biological and artificial, need to leverage sensory information and sensory-motor feedback in order to plan their (motor) actions in order to interact with their environment. A specific behavioral scenario is goal-directed navigation, wherein an agent needs to plan its actions based ... | Rebuttal 1:
Rebuttal: Thank you for your positive comments on the strengths of our work, especially on how it links the theoretical framework to concrete neural circuits. Please check our Global Rebuttal for our replies to your concerns about the generalization of the proposed framework, and the equivariant assumption.... | null | null | Rebuttal 1:
Rebuttal: ## Global Rebuttal
### Generalizability to complex scenarios
All three reviewers asked how the proposed framework can generalize to complex scenarios. Yes, the proposed framework can generalize to complex scenarios in theory, but the challenge comes from the limited amount of experimental evidence... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Faster Differentially Private Top-$k$ Selection: A Joint Exponential Mechanism with Pruning | Accept (poster) | Summary: The paper presents a compute and memory efficient implementation of the differentially private top-$k$ algorithm proposed by Gillenwater et al. (2022) which is based on applying the exponential mechanism to all index sequences of length $k$. The implementation by Gillenwater et al. (2022) has the time complexi... | Rebuttal 1:
Rebuttal: Thank you for the suggestions. We will provide a clearer overview of the comparison between the approaches in the final version.
1. When there is a large gap between the $k^{\text{th}}$ and $(k + 1)^{\text{th}}$ largest counts, FastJOINT (our algorithm) and JOINT (by Gillenwater et al. 2022) can ... | Summary: This paper considers a core differentially private primitive of top-k selection. In this problem, there are $d$ items where each item gets a frequency $h[i]$ which is the sum of $n$ binary vectors $\{0,1\}^d$. The goal is top select (approximate) top-k most frequent items while preserving privacy with respect ... | Rebuttal 1:
Rebuttal: Thank you for the suggestion. We will order the reference list alphabetically in the final version, and include the $\varepsilon$ in the running time.
We will also clarify our contribution at the beginning of Section 4 in the final version:
1. Section 4.1 is novel. It includes
* a new "group... | Summary: This paper studies differentially private top-k selection i.e., selecting the top k sums from a set of d different ones. This problem has been well studied and mechanisms achieving asymptotically optimal error are known. Durfee and Rogers [2019] gives an optimal mechanism that takes O(d) time. However, in 2022... | Rebuttal 1:
Rebuttal: Thank you for being supportive of our manuscript.
1. On the movie dataset, the JOINT (orange color) has a larger variance than FastJOINT (green color).
In this dataset, there is a visually noticeable variation in the error plot around $k = 100$ (Figure 1).
However, this variation is actually ... | Summary: This paper gives a faster method of performing differentially private top k selection. Previous work by Gillenwater et al., introduced the joint exponential mechanism in the context of top k selection and gave a DP algorithm that used \tilde{O}(kd) time and space. The main contribution of this paper is a new s... | Rebuttal 1:
Rebuttal: Thank you for the suggestion.
We will add more background on the Joint Exponential Mechanism in the appendix.
Specifically, we will include brief explanations and proofs for the properties of the Joint Exponential Mechanism (Facts 3.5 and 3.6) to ensure completeness.
---
Rebuttal Comment 1.1:
... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their consideration of our manuscript and their constructive feedback. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Provable Tempered Overfitting of Minimal Nets and Typical Nets | Accept (poster) | Summary: This paper studies the tempered overfitting phenomenon in deep neural networks. The authors show the upper bound of test loss in both Min-size interpolators and random interpolators, which indicates the tempered overfitting.
Strengths: The authors considered the quantized networks, and gave theoretical result... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading and constructive feedback.
Below we address the reviewer’s concerns and explain how they’ll help us improve our paper.
> a) I am concerned about the claim made in your contribution (Line 40) ... Doesn't this only hold under specific data settings? I ... | Summary: This paper investigates the overfitting behavior of fully connected Deep Neural Networks (DNNs) with binary weights fitted to perfectly classify a noisy training set. The authors analyze the interpolation using both the smallest DNN (having the minimal number of weights) and a random interpolating DNN. They pr... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading and valuable feedback.
**Regarding Weakness 1:**
> This work focuses on the learning theory and presents the first theoretical results on benign or mitigated overfitting. It would be better if authors could provide necessary evaluation studies to supp... | Summary: The paper proves that both minimum size and random interpolators exhibit tempered overfitting in the case of binary classification using a threshold network with binary weights on a noisy training set.
Strengths: The paper proves tempered overfitting for minimum size and random interpolators in the presence o... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback.
> I wonder if there is any way one could empirically verify the tightness of the proven results, even for small networks.
Kindly see our comment on this in the general rebuttal response.
Briefly, our results in the independent setting agree with the lin... | Summary: The paper studies the generalization ability of the interpolated minimal quantized nets. It considers the task of classification with a binary sequence input using Quantized neural networks. It establishes the generalization error of the minimal-width neural network that interpolates the dataset and claims tha... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed constructive feedback.
Below we address the reviewer’s concerns and explain how they’ll help us improve our paper.
> (Weak.a) I'm not sure how the theoretical results … provide any insights into how people understand interpolated neural networks ... In othe... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their constructive and valuable comments which we will use to improve our paper in its revised version.
As the subject of **empirically validating** our results appeared in multiple reviews, we address it here in this general comment.
Finding min-size int... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Suppress Content Shift: Better Diffusion Features via Off-the-Shelf Generation Techniques | Accept (poster) | Summary: The paper describes a pipeline for improving feature extraction from pre-trained diffusion models for general-purpose task such as semantic segmentation. The focus is on "content shift", where extracted feature maps at a certain time step in the diffusion process show differences in the content composition com... | Rebuttal 1:
Rebuttal: Thanks for constructive comments. We would like to response as follows.
>**Q1:**
The paper is written on a quite high level with many details left out or in appendix.
**A1:**
We focused on content shift and how generation techniques affect it. Some details were thus omitted in the main body due ... | Summary: This paper explores how diffusion models, typically used for generative tasks, can also serve discriminative purposes by using inner activations as features. The authors point out that these features often suffer from a phenomenon named content shift, i.e., the features are semantically different from the inpu... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments, and we would like to make the following response.
> **Q1:**
Hence, it is recommended to provide more failure cases to validate the negative impact of the content shift.
**A1:**
We have included another failure case, IP-Adapter, in the rebuttal.
For deta... | Summary: The authors focus on the task of suppressing content shift in diffusion features using off-the-shelf generation techniques. The authors observe that diffusion features, which are extracted from pre-trained diffusion models, suffer from a phenomenon called content shift, where there are content differences betw... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments, and we would like to make the following response.
> **Q1:**
I wonder how to design these prompts. Does it require us to design prompts for each input?
**A1:**
We observe the common characteristics of the training images and design a general prompt for a... | Summary: This paper addresses the issue of content shift in diffusion features, which are inner activations extracted from pre-trained diffusion models used for discriminative tasks. The content shift refers to the phenomenon where there are content differences between the features and the input images. In the manuscri... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments, and we would like to make the following response.
> **Q1:**
Please provide a more detailed explanation and additional diagrams to clarify how attention features are extracted from the diffusion model in Fig.1 and Fig.2.
**A1:**
Modern diffusion models u... | Rebuttal 1:
Rebuttal: Dear SAC, AC, and reviewers,
Thanks for your valuable feedback. Based on your comments, we first offer a global response to some common questions.
> **GQ1:**
More details about the overall pipeline and how attention features are extracted.
**(Q1, Q2 of z9si, Q1 of RvKH)**
**GA1:**
Thanks fo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Cascade of phase transitions in the training of energy-based models | Accept (poster) | Summary: This paper analytically demonstrates a cascade of second-order phase transitions in the training of a simple RBM model and numerically validates this theory in the training on real datasets.
Strengths: - This paper theoretically analyzes the learning dynamics of weight parameters in the Binary (or Bernoulli)-... | Rebuttal 1:
Title: Detailed answers to the questions (part 1)
Comment: **Detailed anwers to the questions:**
1. The referee is right, we should have been more careful when defining the quantity. The distribution of the hidden nodes is indeed of zero mean and variance of order $1/N_v$. The value is indeed $N=N_v$. This ... | Summary: This paper presents an analysis of the phase transitions of RBMs through an analysis of the weight matrix. They find the dynamics tend toward the center, then diversity into modes. The theoretical results are supported by empirics on 3 ML datasets.
Strengths: - This expands the important field of exploring th... | Rebuttal 1:
Rebuttal: We thank the reviewer for reading our paper and for their comments. We do not entirely agree with the reviewer regarding the target audience of this article.
First, the RBM itself is a model introduced by Hinton and Sejnowski for the purposes of machine learning. This model has been used in ML fo... | Summary: This paper analyses the phenomenon of phase transitions in the training dynamics of restricted Boltzmann machines (RBM) in theory and practice. In the theory part, the phase transition is characterised for a toy model: The RBM with a hidden node per feature is fitted to data characterised by one feature vector... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and for their careful reading of our paper. First of all, we will of course revise the notation to keep it consistent between the two parts and also make the transition and connections between the two parts clearer. We thank the reviewer for their advice.
... | Summary: This work examines the relation between the internal representations of an RBM and the data that it is trained on as the training procedure goes forward. The work begins with a theoretical exposition for Binary-Gaussian RBMs trained CW-like models with 1 or 2 preferred encoding patterns. The theoretical analys... | Rebuttal 1:
Rebuttal: We thank the reviewer for carefully reading the manuscript and for their interest in it.
We agree with the reviewer that the next natural update of this project will be to investigate these features in more complex generative models. Furthermore, we still believe that it is important to establish... | Rebuttal 1:
Rebuttal: Dear reviewer,
we join the pdf bringing more details on the learning of MNIST when using Contrastive Divergenge with one Monte Carlo step (CD-1).
Pdf: /pdf/55858b10ae6a4249d5da0b41942edae63b870602.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts | Accept (poster) | Summary: The manuscript presents a model for handling complex distribution shifts in graph data. The proposed GraphMETRO model employs a mixture-of-experts (MoE) architecture, with a gating model to identify distributional shifts and multiple expert models to generate shift-invariant representations. This method aims t... | Rebuttal 1:
Rebuttal: We are grateful for your detailed suggestions! We provide responses below:
## **Comment 1,3: A thorough review on related works and highlights on the novelty**
Thanks for the comment! We added more related works, including those in [2]:
- **Added Related work**: Recent methods for improving OOD... | Summary: This paper introduces a novel Graph Neural Network (GNN) architecture designed to handle complex distribution shifts in graph data. GraphMETRO uses a Mixture-of-Experts (MoE) model, with multiple expert models each targeting specific distribution shifts. Additionally, this paper uses a gating model to identify... | Rebuttal 1:
Rebuttal: We appreciate your efforts and insightful comments! To address your concerns, we provide detailed responses below.
---
## **Comment 1: Does each expert encode undesired information from the reference model?**
Great catch! Previously we conducted experiments to explore the impact of aligning eac... | Summary: This paper proposed GraphMETRO, a novel Graph Neural Network (GNN) architecture that try to models complex and natural distribution shifts in real-world graph data. The key innovation is a mixture-of-experts (MoE) approach, where the model decomposes the distribution shift into multiple shift components, each ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments. Please find our responses to each point below.
---
## **Comment 1: Real-world graph shifts may be more varied than defined shifts**
Great question! We discussed this in `Appendix G: Open Discussion and Future Works`, focusing on `The applicabil... | Summary: This paper proposes a novel model, GraphMETRO, that applies mixture of experts (MoE) to facilitate generalizable graph neural network (GNN). It uses different expert networks to mitigate different types of distribution shifts, such as adding edges, dropping nodes, noisy features, etc. When facing a distributio... | Rebuttal 1:
Rebuttal: We are grateful for your positive feedback and detailed suggestions! We provide responses below to address your remaining concerns.
## **Comment 1: The considered distribution shifts may not fully represent real-world shifts.**
Thanks for the comment! We discussed this in `Appendix G: Open Disc... | Rebuttal 1:
Rebuttal: # **General response**
We truly appreciate the reviewers' efforts and valuable suggestions in reviewing our paper. We are glad that most reviewers reached a positive consensus on our work's motivation, presentation, novelty, and experimental effectiveness. Since we received a decent number of rev... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The study investigates how to use mixture-of-experts (MoE) to model complex distribution shifts. It explores leveraging various expert modules, each tailored to different distribution shifts, to generate embeddings that enhance OOD generalization on graphs.
Strengths: 1. This work proposes an novel idea to ma... | Rebuttal 1:
Rebuttal: We appreciate your comments! We believe the major concern might be due to a bit of misunderstanding on the referential invariant representations and our objective. Please feel free to correct us if otherwise! Here we provide justification from a theoretical perspective for simplicity.
---
## **C... | null | null | null | null | null | null |
Generative Modeling of Individual Behavior at Scale | Reject | Summary: This paper adapts Maia, a group-level model and variant of the AlphaZero model proposed by McIlroy-Young et al. (2020), to function at an individual level. The authors focused on scaling the fit to individual behavior using techniques like LoRA for fine-tuning LLMs. Moreover, the learned embeddings were shown ... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and questions! We respond to each one below, and in some cases refer to the general responses above.
**Benchmarking and comparing accuracy gains**
Fig. 2 compares MHR-Maia (our approach) to individually fine-tuning a model for each player. We believe the ... | Summary: The paper explores modelling user behaviour for chess and rocket league using a PEFT-based method, wherein users are modelled using a composition of MHR adapters. The authors evaluate their approach both for predicting which player played a particular game and for predicting the next move of a given player, an... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and questions! We respond to each one below, and in some cases refer to the general responses above.
**Comparison to individual model fine-tuning**
We agree that for larger numbers of games, MHR-Maia is expected to have lower accuracy than individual model... | Summary: Authors found a way to obtain player stylometry using BC on a massive amounts of data. Their idea uses multiple LoRAs and routing matrix to obtain a style vector for each player.
Strengths: - Very interesting technical solution and innovative way to use LoRA and routing matrix to obtain player style vectrors ... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and questions! We respond to each one below, and in some cases refer to the general responses above.
**Applications of our work**
Please see our general response on the applications of behavioral stylometry; this includes two concrete use cases based on th... | Summary: In this paper, the authors propose to solve the problem of behavior stylometry, which is to identify the style of a player’s policy in the game, by regarding it as a multi-task learning problem. Each player’s style is a distinct task. Previous methods are either not scalable or not generative, in that they can... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and questions! We respond to each one below, and in some cases refer to the general responses above.
**Novelty and new capabilities**
Please see our general response above.
**Significance of behavioral stylometry and human-like agents**
We will add more... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful comments, suggestions, and ideas. We provide responses to some common questions below as well as individual responses after each review.
**Novelty and new capabilities [Ff76, ALah]**
We do not claim novelty over the base models and PEFT techniques use... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
PhyRecon: Physically Plausible Neural Scene Reconstruction | Accept (poster) | Summary: This paper addresses scene surface reconstruction in an object-compositional format. Unlike previous work that only considered rendering constraints, the proposed PhyRecon framework incorporates physical constraints into the model training. To identify interaction points, the paper introduces a differentiable ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive comments on our work. We will thoroughly address each of the concerns raised. Notably, we have added visualizations of intermediate results during training in the global rebuttal PDF. We hope these visualizations will help address your questions... | Summary: The paper introduces a novel method for physically plausible 3D scene reconstruction. It enforces the reconstructions to be in a static equilibrium state, which is achieved by minimizing the displacement of 3D points over time in a differentiable rigid body simulator. The experiments are conducted on scannet/r... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive comments on our work. We will thoroughly address each of the concerns raised. Notably, we have added visualizations of intermediate results during training in the global rebuttal PDF, along with sensitivity experiments regarding the max simulati... | Summary: This paper proposes to improve implicit surface representations by considering object dynamics (physics) in their resting state. It made a clever observation that the objects should be stable with respect to their supporting planes if the surface representation is correct. This observation leads to utilizing o... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive comments on our work. We will thoroughly address each of the concerns raised.
**1. Our method requires object masks, whereas MonoSDF does not.**
From neural scene reconstruction which treats the entire scene as a whole (e.g., MonoSDF[1]), addi... | Summary: This paper proposes a framework for scene reconstruction using neural implicit function with an emphasis on physical plausibility. The key idea is to integrate differentiable physical simulation into the neural scene reconstruction such that the reconstructed shape is stable under gravity. A surface points mar... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive comments on our work. However, we believe the reviewer may have some misunderstandings regarding our method. We will clarify the novelty of our approach as the first effort to incorporate physical constraints into neural scene reconstruction, an... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their efforts in reviewing this manuscript and for their constructive comments. We particularly appreciate their recognition of our proposed framework as "mind-blowing, insightful" (qV3n) and "novel" (fqR5, mFxQ, qV3N), with "extensive experiments and ablat... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Rethinking Exploration in Reinforcement Learning with Effective Metric-Based Exploration Bonus | Accept (spotlight) | Summary: The authors propose a robust metric for state discrepancy evaluation backed by comprehensive theoretical analysis. Previous exploration algorithms measure state discrepancy strictly on $L_p$ norms which is limiting since for environments where state differences are minimal in the observations the intrinsic rew... | Rebuttal 1:
Rebuttal: We gratefully thank the reviewer for recognizing our contributions, writing and novelty. The additional ablation experiments have been included in the PDF file and all the references mentioned in the response can be found in the reference list in the global comment. We provide our response below:
... | Summary: This paper proposes a new exploration algorithm in reinforcement learning (RL), particularly in environments with sparse rewards. The authors critique existing exploration bonus methods using state discrepancy, highlighting their limitations in scalability and theoretical guarantees. They propose a novel metho... | Rebuttal 1:
Rebuttal: We would like to thanks to the reviewer for their insightful feedback. Additional exps have been included in the uploaded pdf, for the references mentioned in the response, please find the reference list in the global comment and we provide our response below:
*W1:(1)evaluating on more Atari game... | Summary: This paper introduces the Effective Metric-based Exploration-bonus (EME), a novel approach for enhancing exploration in reinforcement learning (RL) tasks. The paper identifies key limitations in existing metric-based exploration methods, such as their reliance on count-based scaling factors and approximation g... | Rebuttal 1:
Rebuttal: We gratefully thank the reviewer for recognizing our contributions! The additional ablation experiments have been included in the uploaded pdf, we provide our response below:
*W1:The performance of EME with feature encoder is mixed; this may hamper the scalability of EME to realistic scenarios,... | Summary: The authors introduce the Effective Metric-based Exploration-bonus (EME) to address the limitations of existing state discrepancy methods by proposing a robust metric for evaluating state differences and a diversity-enhanced scaling factor for exploration bonuses. Extensive experiments demonstrate that EME out... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for valuable suggestions, the requested exp have been included in the uploaded pdf, for all the references mentioned in the response, please find the reference list in the global comment, and we provide our responseas follows.
*W1:The ultimate method seems incredibly co... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful and valuable feedback. Overall, reviwers appreciate the novelty, clarity and contribution of our work, we summarize the major concerns and provide new experimental results with discussions below:
- ### Additional ablation studies and other experiments
... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards a Theoretical Understanding of the 'Reversal Curse' via Training Dynamics | Accept (poster) | Summary: The paper provides a theoretical analysis of the recently discovered reversal curse phenomenon of LLMs [1], whereby a model trained or fine-tuned on the sentence "A is B" often fails to generalize to the reverse direction "B is A". The authors study the gradient dynamics of two auto-regressive models, a biline... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments. Below are our responses.
**Meta-learning:**
First, we emphasize that the near-orthonormality of the embeddings does not require the dimensionality to be much higher than the number of relevant tokens. In fact, it only requires the dimension $d$... | Summary: This paper explores the difficulty auto-regressive LLMs face with simple logical reasoning tasks, such as reversal curse. Through theoretical analysis and experiments, the study examines the training dynamics of gradient descent in bilinear models and one-layer transformers, suggesting that the asymmetry in mo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments. Below are our responses.
**Answer to Question 1**:
Thank the reviewer for pointing out this interesting question. Note that no matter which rule we are choosing, the model is not able to infer B<-A trained on A->B. Although sometimes this is a c... | Summary: This work analyzes the optimization trajectory of (a) a bilinear model trained on sequences of random Gaussian vectors, and (b) a simplified one-layer transformer on sequences of three tokens. It studies how well the trained model generalizes to sequences in reverse order. This study is motivated by an empiric... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments. Below are our responses.
> Unclear motivation of the theoretical framework: ...
First, we want to emphasize that according to our analysis in this work, the reversal curse is hard to mitigate even on a well-structured , synthetic dataset both th... | Summary: This paper works toward a theoretical explanation of why the "Reversal Curse" occurs in language models. This "curse" refers to the empirical phenomenon that even when trained on certain relationships between entities ("Sue is an aunt of Alice"), the models fail at reversing those relationships in a semantical... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments. Below are our responses.
>If I understand correctly, the ultimate conclusion is that there's no real hope beyond reversal training, i.e., having both A-->B and B-->A in the training data. It would be stronger if the analysis revealed some kind of... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their great effort in reviewing our paper and providing helpful and insightful feedback. Below we first address some common questions.
**Small embedding dimension and number of layers:** We first acknowledge that the assumption $d \sim poly(m)$ is a bit loose in The... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Multi-Agent Imitation Learning: Value is Easy, Regret is Hard | Accept (poster) | Summary: The paper studies the problem of an mediator coordinating the actions of a collection of agents by learning from the demonstrations of an expert. Unlike previous work the paper considers strategic deviations of the agents given the recommended actions instead of just matching the experts recommendations. It is... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our submission! Please find our replies to your questions/comments below.
Response to W1: We designed the [proof] link after each theory statement to add a hyperlink to the proof in the appendix. This allows readers to easily navigate from the theorem state... | Summary: In this work, the authors 1.initiate the study of an alternative objective for multi-agent imitation learning termed the regret gap; 2. investigate the relationship between regret gap and the value gap; 3. provided 2 provably efficient algorithms to minimize the regret gap under coverage assumption on the expe... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our submission! Please find our replies to your questions/comments below.
Regarding the assumptions:
1. The theorems in this paper do not require realizability to hold. However, if we want to further analyze the computational complexity of the algorithm (... | Summary: This paper addresses the challenge of a mediator coordinating a group of strategic agents by recommending actions. Unlike previous work that focuses on non-strategic users who follow recommendations blindly, this study explores strategic users who may deviate based on their personal utility functions. The auth... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our submission! Please find our replies to your questions/comments below.
Response to W1: Our proposed algorithms are indeed built on the previous approaches taken by algorithms like DAgger and ALICE (as the names suggest). However, we have to develop a non... | Summary: The paper studies the problem of multi-agent imitation learning (MAIL), with a focus on the differences and relationships between the value gap objective and the regret gap objective.
The set-up consists of a multi-agent Markov game, where a central mediator attempts to coordinate the behaviors of the agents.... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our submission! Please find our replies to your questions/comments below.
Regarding the significance of theoretical results: We thank the reviewer for bringing up paper [1]. [1] shows that in the repeated Stackelberg game, not being able to observe the foll... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Toward Dynamic Non-Line-of-Sight Imaging with Mamba Enforced Temporal Consistency | Accept (poster) | Summary: 1. This paper build a new dynamic NLOS dataset crafted for learning from synthetic data and evaluating models on real-world data for dynamic NLOS reconstruction.
2. This paper introduce a Mamba-based method tailed for dynamic NLOS imaging.
Strengths: 1. This paper build a new dynamic NLOS dataset crafted for ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the time with our paper. While we have provided detailed responses to address the main concerns below, one thing needs to be highlighted first: we respectfully cannot agree that our work "lacks innovation" due to inheriting certain “existing modules”. In contras... | Summary: The paper tackles the problem of non-line-of-sight imaging. As a spin on the commonly attempted task, they solve the problem in dynamic scenes and use transients captured at different time points to aid the recovery of the scene at a specific time point.
To solve the problem they introduce a Mamba inspired a... | Rebuttal 1:
Rebuttal: We are highly encouraged by the positive recommendations and comments from the reviewer. The
main concerns are addressed below.
__Q1:__ Suggestions for the title.\
__Reply:__ We appreciate your insight that renaming it to "NLOS Imaging by Using Dynamic Cues" would more accurately reflect our appr... | Summary: This paper introduces the first Mamba-based method for dynamic NLOS imaging, featuring three key modules: the spatial-temporal Mamba, the cross ST-Mamba, and the phasor field loss. The spatial-temporal Mamba extracts and integrates both causal and non-causal transient components, while the cross ST-Mamba combi... | Rebuttal 1:
Rebuttal: We are highly encouraged by the positive recommendations and comments from the reviewer. The main concerns are addressed below.
__Q1:__ The explanation for the causality in transients.\
__Reply:__ First, let us review the definition of causal signals: Causal signals refer to signals where the out... | Summary: The paper proposes a dynamic non-line-of-sight imaging approach that uses Mamba to enforce spatio-temporal consistency (called ST-Mamba). This is done using a mutlistage model that takes in a set of transient frames, performs temporal downsampling, followed by spatial and temporal SSM per frame and across fram... | Rebuttal 1:
Rebuttal: We are highly encouraged by the positive recommendations and comments from the reviewer. The main concerns are addressed below.
__Q1:__ Lack video results.\
__Reply:__ We sincerely appreciate your kind reminder. After preparing the video as requested, however, we find that external links are not ... | Rebuttal 1:
Rebuttal: # This is the global rebuttal for all the reviewers including a PDF containing only figures and tables. Please find individual responses to each reviewer below.
The content is:
__Fig1:__ The reconstruction results via the traditional method (RSD) for the high-resolution transient measurements, w... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Physics-Constrained Comprehensive Optical Neural Networks | Accept (poster) | Summary: The authors present a novel approach on using optical neural networks by integrating physical constrains into the training process. This work is motivated by the issues that ONN models present, the difference between the ideal, simulated system and the real, physical implementation — which significantly degrad... | Rebuttal 1:
Rebuttal: Thank you for your positive comments and valuable suggestions for improving the quality of our manuscript. As you rightly pointed out, our work is indeed motivated by the challenges presented by ONN models, specifically the differences between the ideal, simulated systems and their real, physical ... | Summary: In this paper, the author proposed the use of a 4f optical system to implement alternative neural networks due to the advantages of reduced latency and lower power consumption. The main challenges identified are optical misalignments caused by dispersion and imperfections in optical fiber fabrications.
Streng... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and for acknowledging the motivation and improvements in our study. Our work addresses errors in practical optical neural networks, enhances experimental performance, and aims to advance reliable optical computing. We will reply your questions and suggestions b... | Summary: This paper proposes to use two analytical error terms to model laser jitter noise and camera exposure time error, and use a DNN-based error compensation network to correct unknown noises for higher accuracy after deployment.
Strengths: It has thorough ablation study on the effectiveness of the error compensat... | Rebuttal 1:
Rebuttal: I am very grateful to your comments for the manuscript. Your questions were answered below.
1: Novelty is quite limited. (1) Using physical or analytical error modeling in training for error compensation is common practice. (2) Using a neural network to fit unknown errors to emulate the response ... | Summary: This work presents a physics-constrained framework to improve optical neural networks for image classification. The study identifies and reducing two key errors: light source instability and exposure time mismatches.The authors introduce a physics-constrained ONN learning framework that includes a specialized ... | Rebuttal 1:
Rebuttal: We greatly appreciate your thorough review and the positive feedback on our work. As you mentioned, the core aim of our study is to narrow the gap between simulated and physical ONNs and to provide a pathway for more accurate and reliable optical computing applications. Your description is a very ... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their thoughtful comments. We appreciate their findings that our work focuses on narrowing the gap between simulated and physical optical neural networks(8n42) by addressing the inevitable errors encountered during the physical realization of these networks(W9o... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LIVE: Learnable In-Context Vector for Visual Question Answering | Accept (poster) | Summary: This paper aims to improving the in-context learning performance of multimodal models on VQA tasks. The paper proposes a Learnable In-Context Vector (L-ICV) method to distill essential task information from demonstrations into a single vector, reducing computational costs and enhancing accuracy in VQA tasks. E... | Rebuttal 1:
Rebuttal: **W1: Generalizability of L-ICV**
We also follow your suggestion to test L-ICV on the VL-ICL Benchmark[A] and the results are given in Table C. Due to the rebuttal's time constraints, we only tested two subtask performances in the VL-ICL Benchmark. We also tested L-ICV on IDEFICS v2 and some new ... | Summary: This paper proposes a method by introducing the idea of In-Context Vector in the field of NLP, by introducing L-ICV with a smaller number of parameters in LMM to replace in-context demonstrations to introduce external knowledge, and obtain through training The appropriate shift direction is combined in the que... | Rebuttal 1:
Rebuttal: **W1: Inference Time Comparison of TV and FV**
TV, FV and L-ICV have similar inference time. Specifically, TV takes 57.14 ms, FV takes 57.08 ms, L-ICV takes 56.81 ms for one sample, and Zero-Shot takes 56.69 ms. This similarity in inference times is because TV and FV replace the ICDs with a singl... | Summary: The study introduces a Learnable In-Context Vector (L-ICV) for Visual Question Answering (VQA) tasks. This approach extracts task information from demonstrations to improve the performance of Large Multimodal Models (LMMs) while reducing computational costs.
Strengths: * The writing of this paper is excellent... | Rebuttal 1:
Rebuttal: **Q1: trade-off between reduced inference time and additional training time**
We believe that performing additional lightweight training to reduce inference time is worthwhile.
Firstly, the initial one-time training investment is minimal and yields substantial long-term benefits. For instance, i... | Summary: This paper introduces learnable in-context vector (L-ICV) that extends the standard ICV method by training it on task samples. The method achieves performance benefits similar to ICV and the training methodology enables the L-ICV to outperform while still preserving the benefits of few-shot in-context learning... | Rebuttal 1:
Rebuttal: **Q1 & W1: Task Recognition (TR) and Task Learning (TL)**
Thanks for your recommendations of these studies. In our final discussion, we are happy to include these recent works on the interpretation of ICL abilities of LMMs. After thoroughly reviewing [A,B], we think that theoretically, L-ICV is m... | Rebuttal 1:
Rebuttal: We gratefully thank all the reviewers for their valuable and constructive feedback. We are pleased to see that the reviewers recognize our motivation: to reduce inference time costs while maintaining the performance of in-context learning (ICL) for LMMs. We are encouraged to see that they find our... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Predicting Future Actions of Reinforcement Learning Agents | Accept (poster) | Summary: This paper investigates methods for predicting future actions and events of reinforcement learning agents during deployment. It compares the predictability of explicitly planning, implicitly planning, and non-planning agents using two approaches: an inner state approach that leverages the agent's internal comp... | Rebuttal 1:
Rebuttal: We are thankful for the reviewer’s comments and for recognizing the significance of the problem statement. The questions are addressed as follows:
> What's the rationality behind the choices in different methods?
The rationality is mainly based on our intuition of which information is useful. We... | Summary: This paper focuses on predicting future actions and events using policies learned by different types of planning agents. Namely, the authors distinguish between explicit and implicit planning agents, where explicit planning agents learn a model of the environment dynamics/transitions. Given this distinction be... | Rebuttal 1:
Rebuttal: We are thankful for the reviewer’s comments and for recognizing the significance of the problem statement. The questions are addressed as follows:
**W1**: As the goal of the figure is to compare the effectiveness of different approaches on different agents instead of the prediction variability, i... | Summary: The paper presents a comparative analysis of future action and event predictions in Reinforcement Learning settings by comparing inner-step approaches (that predict via inner computations like by generating a plan or via neuron activations) and simulation-based approaches (that simulate a rollout in a learned ... | Rebuttal 1:
Rebuttal: We are thankful for the reviewer’s detailed comments. The questions are addressed as follows:
**W1**: Please see our global response. Briefly, our results largely accord with our hypotheses, and we will add the hypotheses to the introduction following the list of research questions.
**W2**: We d... | Summary: The authors present two approaches for predicting future states and actions in an RL setting. This is done in a supervised setting, where the inputs are the state and action and the target is either the future action sequence or the time of occurrence of some event. The first approach leverages using the inner... | Rebuttal 1:
Rebuttal: We are thankful for the reviewer’s comments and for recognizing the significance of the problem statement. The questions are addressed as follows:
> If the action space is rolled out and appended to the input, would this not result in the potential future actions already being included with the i... | Rebuttal 1:
Rebuttal: We are thankful for the reviewer’s comments. Before addressing the questions, we would like to clarify the context of the paper. In this paper, we consider a problem statement, namely predicting future actions and events for agents trained with different RL algorithms. As far as we are aware, this... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Role of Information Structure in Reinforcement Learning for Partially-Observable Sequential Teams and Games | Accept (poster) | Summary: This paper proposes an abstract model of RL with information structures. Under this mode, the paper investigates the necessary conditions for sample-efficient learning.
Strengths: The results of this paper are very general, in the sense that they encompass the results of several previous works.
Weaknesses: (... | Rebuttal 1:
Rebuttal: **Summary of review.** While the reviewer acknowledges the generality of our framework and technical results, they are skeptical about what advantage or insight this analysis grants compared to existing work, and what concrete new tractable model classes it identifies.
Thank you for your review. ... | Summary: The paper presents a new representation of sequential decision making problems with an information structure. They show that the information structure can be used to estimate the complexity of the decision-making problems and can guide learning algorithm development.
Strengths: 1. The authors present a good s... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback. We hope to address of each of your concerns in turn.
---
**Finite vs Continuous Spaces**
> ... the authors are dealing with finite state and action spaces ... If this is so, I see it as a major weakness
Our results are stated and proved for finite spaces.... | Summary: This paper studies the tractability of modeling and learning general sequential decision-making problems with an explicit representation of information structure. This gives a unifying framework that captures a range of commonly studied RL models. Through a graph-theoretic analysis it characterizes the complex... | Rebuttal 1:
Rebuttal: Thank you for your engagement with our work and your valuable feedback. We are glad our work resonated with you.
We hope to address your concerns and answer your questions below, in turn.
---
**On the identifiability condition and hardness results**
> The m-step $\mathcal{I}^\dagger$-weakly re... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Conditional Lagrangian Wasserstein Flow for Time Series Imputation | Reject | Summary: The authors introduce the conditional Lagrangian Wasserstein flow method for time series imputation.
The time series imputation task is treated as a conditional data generation problem. The authors use flow matching to learn an ODE sampler for generating the missing time series data. They further propose to en... | Rebuttal 1:
Rebuttal: Many thanks for the insighful comments.
## Weakness 1:
Our proposed method can also be implemented via an SDE sampler by adding a time-dependent volatility term (please see Eq. (12), Sec 3.2, Page 4), but in order to achieve the optimal empirical performance in terms of RMSE and MAE on the time s... | Summary: In this paper, the authors introduce a new time series imputation model based on the conditional Lagrangian Wasserstein flow. Different from previous diffusion-based models, the proposed model leverages the optimal transport theory and Lagrangian dynamics to improve the data generation performance.
Strengths:... | Rebuttal 1:
Rebuttal: Many thanks for the valuable feedback .
## Weakness 1:
To project the intermediate samples from the Euclidean space to the Wasserstein space, We adopt the technique descried in [1] by drawing mini-batches of the initial noise and target samples to compute the corresponding optimal transport maps.... | Summary: Inspired by optimal transport and Lagrangian dynamics, this work proposes to use the conditional Lagrangian Wasserstein flow to impute time series data. The method requires less model evaluation steps to generate high quality samples compared to existing diffusion models. Moreover, a task-specific energy funct... | Rebuttal 1:
Rebuttal: Many thanks for the insightful comments.
## Weakness 1:
The value of the potential function's variance do effect the Rao-Blackwellized sampler.
If the variance is too small the effect of the Rao-Blackwellization is negligible, if the variance is too large the performance will decrease.
In futu... | Summary: This work trains a conditional flow model from noise to time series data. A VAE is used to estimate the data density then perform interleaved flow and density gradient ascent steps to generate new time series. This is referred to as a Rao-Blackwellization procedure. It is shown empirically that the model perfo... | Rebuttal 1:
Rebuttal: Thank you very much for the constructive feedback and insightful comments
## Weaknesses (Quality) 1:
We have conducted the ablation study on the impacts of sampling steps of CSDI and CLWF.
The results show that CLWF has better performance when the simulation steps are small.
Please also take a l... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their insightful comments and try our best to address all the questions regarding this work.
To this end, we have included additional experimental results from an ablation study.
Specifically, we conducted new experiments using synthetic data to valid... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper presents Conditional Lagrangian Wasserstein Flow (CLWF), a new method for time series imputation. Using (entropic) optimal transport theory and Lagrangian mechanics, CLWF generates high-quality samples. Enhanced with a Rao-Blackwellized sampler, CLWF incorporates prior information through a variation... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback.
## Weakness 1:
Indeed, CLWF is trained through the simulation-free training of the Schrödinger Bridge. However, in this work, based on the Lagrangian mechanics framework, we specifically proposed the Rao-Blackwellized sampler to further enhance the model's pe... | null | null | null | null | null | null |
Grasp as You Say: Language-guided Dexterous Grasp Generation | Accept (poster) | Summary: This paper proposes a novel robotic grasping task that utilizes natural language as guidance to generate grasping poses for dexterous hands that satisfy corresponding intentions. To accomplish this task, the paper first creates a large-scale language-guided dexterous grasping dataset. Moreover, the authors pro... | Rebuttal 1:
Rebuttal: Thanks for your reviews. Below are our responses to your questions.
**Q1**: Intention consistency metric.
A1: To further evaluate, we employ Fréchet Inception Distance (**FID**) [1], which is commonly used in generative task [3]. We use sampling point cloud features extracted from [2] to calcula... | Summary: This paper tackles language-guided dexterous grasping by creating a large-scale dataset pairing grasps and language guidance and presenting a grasp generation pipeline based on language instructions. The dataset, DexGYSNet, leverages HOIR and LLMs to scale up the size of grasps and annotations while keeping th... | Rebuttal 1:
Rebuttal: Thanks for your reviews. Below are our responses to your questions.
**Q1**: The full point cloud and end-to-end pipeline.
A1: Our work is focused on language-guided dexterous grasp generation task and is the **base** of future work of partial observation and end-to-end pipeline. What’s more, ou... | Summary: The paper introduces "Dexterous Grasp as You Say" (DexGYS), a novel framework that integrates natural language instructions with robotic dexterous grasping. To support this integration, the authors present DexGYSNet, a dataset featuring 50,000 pairs of annotated dexterous grasps with corresponding human langua... | Rebuttal 1:
Rebuttal: Thanks for your reviews. Below are our responses to your questions.
**Q1**: Evaluation Metrics.
A1: To further evaluate, we employ Fréchet Inception Distance (**FID**) [1], which is commonly used in generative task [6]. We use sampling point cloud features extracted from [2] to calculate P-FID ... | Summary: This work proposes a language-guided dexterous grasp dataset based on OakInk, named DexGYSNet utilizing LLM and a corresponding two-component framework DexGYSGrasp which generates dexterous grasps based on human language instructions. Real-world experiments are conducted to validate DexGYSGrasp's performances.... | Rebuttal 1:
Rebuttal: Thanks for your reviews. Below are our responses to your questions.
**Q1**: The main contribution.
A1: The main contribution of our paper is **NOT** just a dataset and a baseline. We believe our idea can contribute to robotics community. The main contribution can be summarized as:
1) This pa... | Rebuttal 1:
Rebuttal: Thanks for the constructive comments of all reviewers. We conduct additional experiments and analysis on the evaluation metrics and generalization. The figures and tables can be found in the **rebuttal PDF**.
(a) Evaluation metrics:
1. To further evaluate, we employ Fréchet Inception Distance (*... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Hybrid Mamba for Few-Shot Segmentation | Accept (poster) | Summary: This work introduces Mamba to FSS. Particularly, it indicates two issues suffered by the original Mamba when being applied to the cross attention case, namely, the support forgetting issue and the intra-class gap issue.
To address them, a hybrid Mamba block (HMB) is designed, which can effectively incorporate... | Rebuttal 1:
Rebuttal: > W1&L1: Efficiency comparison with cross attention-based methods.
The efficiency comparisons (**different testing episode number**) have been provided in Appendix C.3, where we compare our mamba-based method with a latest cross attention-based method HDMNet [30]. Note that (1) HDMNet employs 6 a... | Summary: This paper proposes a hybrid mamba network (HMNet) to capture inter-sequence dependencies for few-shot segmentation tasks (FSS). The authors identify two issues when applying the original Mamba network to cross-attention scenarios: the support forgetting issue and the intra-class gap issue. The proposed HMNet ... | Rebuttal 1:
Rebuttal: > W1&Q1.2: Comparative analysis for efficiency and effectiveness across different quantity setting?
Thanks for your precious comment. Currently, we compare the efficiency and effectiveness of cross attention-based HDMNet [30] and our method in Appendix C.3, yet only with one image size (COCO data... | Summary: This paper proposes HMNet for FSS, which addresses the issues of support forgetting and intra-class gap in few-shot segmentation by designing support recapped Mamba and query intercepted Mamba, thereby utilizing support information more effectively to enhance segmentation performance. The authors evaluate HMN... | Rebuttal 1:
Rebuttal: > W1: Core idea of the proposed methodology.
Our main contributions consist of (1) a support recapped mamba (SRM) to address the *support forgetting issue* during the interactions of query and support features, and (2) a query intercepted mamba (QIM) to mitigate the *intra-class gap* issue. Both ... | Summary: This work proposes a hybrid Mamba network for few-shot segmentation, including a support recapped Mamba to periodically recap the support features when scanning query and a query intercepted Mamba to forbid the mutual interactions among query pixels.
Strengths: The idea of adapting Mamba for few-shot segmenta... | Rebuttal 1:
Rebuttal: > W1: Motivation of using mamba for FSS.
Thanks for this comment, and we agree it's better to make the motivation stronger:
1. (Discussion) Kindly remind FSS is a task where the model is trained on some base classes, and directly applied to test novel classes. During training, the model can easil... | Rebuttal 1:
Rebuttal: Dear Reviewers, ACs, SACs, and PCs,
Thanks for your insightful feedback on our manuscript: Hybrid Mamba for Few-Shot Segmentation!
We are encouraged that you find our contribution is good (hBYs, noa5), the soundness is good (hBYs, iSLu, noa5), the motivation of adapting Mamba for FSS is reasonab... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Facilitating Multimodal Classification via Dynamically Learning Modality Gap | Accept (poster) | Summary: This paper proposes a novel multimodal learning method to address modality imbalance by dynamically integrating unsupervised contrastive learning and supervised multimodal learning. The authors design two dynamic integration strategies: heuristic and learning-based. Experimental results show that the proposed ... | Rebuttal 1:
Rebuttal: Thanks for your comments.\
**Overall Response:** Our motivation is to explore the relationship between label fitting (one-hot label) and modality imbalance, thus to find a way to mitigate the impact of label fitting. We first utilize a toy noise label experiment to demonstrate that label fitting ... | Summary: This paper proposes a novel multimodal learning approach by integrating supervised MML and unsupervised contrastive learning dynamically to address the multimodal imbalance problem. The authors begin by examining the differences in unimodal performance, analyze the reasons for these differences, and introduce ... | Rebuttal 1:
Rebuttal: Thank you for your comments.\
**1.Answer for Question "Closely With the Learning Strategy Curve"**: We use the polynomial function $f(t) = a t^3 + b t^2 + c t + d$, with coefficients $a = 1.5 \times 10^{-4}$, $b = -6.5 \times 10^{-3}$, $c = 3.2 \times 10^{-2}$, $d = 1$ to fit the current learning ... | Summary: This paper discusses a core reason for modality imbalance in multimodal learning, i.e., fitting category labels. It proposes a novel multimodal learning approach to overcome the modality imbalance problem by dynamically integrating unsupervised contrastive learning and supervised 319 multimodal learning.
Stre... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments.\
**1.Answer for Question "Performance on Larger Dataset"**: We compare our method with the state-of-the-art method MLA on the VGGSound dataset using the experimental settings described in the literature [1]. Experimental results in Table 1 demonstrate that our... | Summary: This paper focuses on the notorious multi-modal imbalance problem. The authors attribute this issue to different modality-specific convergence rates caused by the inconsistent difficulty of fitting class labels. Therefore, this paper adopts unsupervised contrastive learning to mitigate the modality gap. Moreov... | Rebuttal 1:
Rebuttal: Thank you for your comments. \
**1. Answer for Question "Contrastive Learning can Mitigate Modality Imbalance"**: Our motivation aims to find a way to mitigate the modality performance gap caused by label fitting. As shown in literature [1], different modalities will compete with each other during... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Selective Attention: Enhancing Transformer through Principled Context Control | Accept (poster) | Summary: The authors introduce a modification to the widely-used self-attention mechanism, which modulates the queries and values in the attention computation with a per-token temperature parameter. They demonstrate that this allows the attention layer to model certain observations better for toy problems, and they als... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time, effort, and positive assessment.
**W1: demonstrate the performance improvement on the LM task is due to "strategic integration of SSA" rather than an increase in parameter count. Q1: Could we bolster the claim that SSA packs more benefit than just extra param... | Summary: This paper presents Selective Self-Attention (SSA), which introduces trainable temperature functions to adapt the contextual sparsity of attention weights. SSA uses a query temperature to adjust the sparsity for each query and its context position, and a value temperature to suppress irrelevant or noisy tokens... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort. We hope that our response below addresses their concerns and we would be grateful to respond to further questions during the discussion period.
**W1,2: Conducting experiments on downstream tasks, The perplexity improvement over the original models ... | Summary: The paper proposes token-aware and position-aware gating for query, key and value vectors in self-attention layers.
Strengths: 1. The position-aware gating is interesting, and, in theory, can alleviate dispersed attention and thus may lead to better length extrapolation.
2. The inductive bias introduced in th... | Rebuttal 1:
Rebuttal: **W1: The terminology and the positioning of the paper is misleading.**
- **Sparsity ve Spikiness:** We appreciate the feedback. We will clarify that our method is not about sparse approximation of the attention map and instead aims to control the “spikiness of attention” as the reviewer mentions.... | Summary: This work introduces Selective Self-Attention (SSA), an additional trainable module for standard transformer self-attention that applies temperature scaling to its three components: queries, keys, and values. By performing both token-based and position-based scaling to modulate the influence of different token... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and helpful feedback.
**W1: Computational overhead**
In our Common Response, we describe two strategies that reduce the number of parameter overhead below 0.5% while maintaining the benefits of SSA. These approaches are based on weight-sharing (0.47% overhead... | Rebuttal 1:
Rebuttal: ## Common Response
We thank all reviewers for their detailed and constructive feedback. To recap, our method Selective Self-Attention (SSA) enhances the approximation capability of attention through a learnable temperature scaling (TS) parameter. We theoretically establish the benefit of choosing... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning Distributions on Manifolds with Free-Form Flows | Accept (poster) | Summary: In this paper the authors propose to adapt the formalism in Draxtler et al. (2024) and Sorrensen et al. (2024) to Riemannian geometry. The idea they build upon is called Free-form flows and consists of: (i) relaxing the bijectivity requirement of Normalizing Flows by approximating the inverse transformation wi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and helpful feedback! Below, we address the points mentioned:
### No thorough runtime comparison study is performed. [...] How does concurrent work compare [...] at fixed performance [...] in terms of runtime (both training and inference)?
In a nutshell, M-... | Summary: This paper introduces M-FFF, a single-step generative model that operates on Riemann manifolds. The authors extend the existing FFF, which functions in Euclidean space, establishing the theoretical foundation for learning distributions on various Riemann manifolds and enabling faster generation than existing m... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and helpful feedback! Below, we address the points mentioned:
### Is it realistic to generate data in high-dimensional spaces? [...] There is no mention of limitations related to dimensionality
Yes, it is realistic. From a theoretical point of view, the tra... | Summary: The extends a recent class of generative models, called free-form flows, to Riemannian manifolds. These models are trained like normalizing flows, but relax the bijective constraint and instead employ separate encoder and decoder networks that are trained to be inverses of each other. The proposed method dif... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and helpful feedback! Below, we address the points mentioned:
### I'm not fully convinced by the likelihood based evaluation. [...] Since free form flows are not invertible, it is possible that the flow does not map to all of $M$
Yes, this could happen in p... | Summary: This paper introduces Manifold Free-Form Flows (M-FFF), an extension of free-form flows to Riemannian manifolds. M-FFF is easy to adapt to an arbitrary Riemannian manifold, since the method requires only a projection function from an embedding space. Moreover, the method itself relies only on a single function... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and helpful feedback! Below, we address the points mentioned:
### The geometry may be respected very approximately
No, this is a misunderstanding. M-FFFs always respect the geometry: In Eq. (17), we exactly project the output of an unconstrained neural netw... | Rebuttal 1:
Rebuttal: We thank all reviewers for their helpful and constructive feedback. Below, we collect and address the main concerns by the reviewers.
### Performance comparison (reviewers m8ZT, Z5qx)
Among single-step methods, **M-FFF is state-of-the-art in 12 out of 12 experiments**. Notably, it works with ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Safe Exploitative Play with Untrusted Type Beliefs | Accept (poster) | Summary: The paper considers a setting of stochastic bayesian games where the beliefs of the opponents’ strategies may not be fully trusted. The authors first analyze the problem in the stationary setting and then move to an MDP setting. The main outcome is that there is a trade-off between risk and opportunity dependi... | Rebuttal 1:
Rebuttal: Thank you so much for your feedback. We appreciate your recognition of the strengths in our paper.
***Alternative Forms of Beliefs:***
Regarding the issues addressed, we understand the need to discuss alternative approaches to decision-making based on trust in other forms of beliefs. Specificall... | Summary: The paper studies a setting in which an agent is trying to exploit opponents in a game based on uncertain beliefs about the opponents' strategies. In this setting, exploiting opponents can be risky if the beliefs might be wrong. The paper studies the trade-off between exploiting as much as possible, while mini... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and detailed review. Your positive comments on the fundamental contribution of the paper are greatly appreciated.
***1. Abstract and Introduction Clarity:***
We acknowledge the need for clearer exposition in the abstract and introduction to better communicate the co... | Summary: The paper explores how players in Bayesian games can manage the inherent uncertainty about other players in a strategic way, aiming to maximize their outcomes (exploitative) while minimizing potential risks (safe).
The main theme of the paper is the tradeoff between the opportunity and risk of exploiting unre... | Rebuttal 1:
Rebuttal: Thank you for your comments. We are glad that you find the research direction of this paper interesting.
***1. Technical Novelty***
***1.a Performance Benchmark:*** First, we'd like to highlight that the performance benchmark considered in this work is new. In particular, we focus on a payoff g... | Summary: The paper explores the dynamics of Bayesian games where players form beliefs about other players' types and update these beliefs based on observed actions. It specifically examines the risks associated with relying on potentially inaccurate type beliefs and how these inaccuracies impact the payoffs of strategi... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We appreciate your recognition of the strengths in our paper, particularly the contribution of new theoretical insights into safe and exploitative strategies in Bayesian games with incorrect type beliefs, and the formal characterization of the trade-off between opportu... | Rebuttal 1:
Rebuttal: We greatly appreciate all the questions received from the reviewers.
In this global rebuttal, we provide detailed discussions below regarding the additional experimental results, which can be found in the attached **PDF**.
***Tightness of Bounds:***
Thanks to the suggestions by **Reviewer** **... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper characterizes the trade-off between risk and opportunity in the games where the player need to infer other participants types. Upper and lower bounds in normal-form and stochastic Bayesian games are proved.
Strengths: This paper provides a solid theoretical foundation for normal-form and stochastic... | Rebuttal 1:
Rebuttal: We greatly appreciate all the suggestions and questions received. Below, we provide 1-1 responses to the issues raised.
***1. Heavy Notation***
Thank you for your constructive feedback. We understand that the notation-heavy nature of the problem can make our paper challenging to follow. There ar... | null | null | null | null | null | null |
LLMDFA: Analyzing Dataflow in Code with Large Language Models | Accept (poster) | Summary: This paper proposes an LLM-based approach for detecting bugs with data flow analysis. The proposed approach LLMDFA consists of three parts: source/sinks extraction, dataflow summarization, and path feasibility validation. LLMDFA allows LLMs to interact with different program analysis tools. The authors conduct... | Rebuttal 1:
Rebuttal: ### **I. Answers to Questions**
**Q1. Comparison with ML-based approaches**
Targeting a compilation-free and customizable dataflow analysis, we have conducted a comprehensive survey of existing literature and have not encountered any ML-based approaches that specifically tackle the same problem a... | Summary: The paper proposes LLMDFA, a compilation-free and customizable dataflow analysis, which takes a program and its CFG as input.
The authors decompose the problem into several subtasks including Source/Sink Extraction, Dataflow Summarization, and Path feasibility Validation, and introduce some strategies. They ev... | Rebuttal 1:
Rebuttal: ### **Answers to Questions**
**Q1. The costs of the three stages**
Please refer to the common concern in the global response.
**Q2. The statistics and details of path feasibility validation**
In Appendix A.3.2, we demonstrate the detailed statistics of path feasibility validation. In our evalu... | Summary: This paper presents LLMDFA, an LLM-powered compilation-free and customizable dataflow analysis framework for bug detection. To mitigate LLM hallucination, LLMDFA breaks down the dataflow analysis (DFA) task into multiple subtasks and delegates complex reasoning to specialized external tools. In bug detection e... | Rebuttal 1:
Rebuttal: ### **I. Answers to Questions**
**Q1. Technical design of dataflow summarization**
Many existing techniques, such as Pinpoint [R10] and SVF [R11], can derive dataflow paths deterministically. However, they all require the intermediate representations generated by a successful program compilation.... | Summary: The author proposes a data flow analysis tool based on LLM, named LLMDFA. This tool addresses the LLM hallucination problem by decomposing tasks. The method is divided into three stages: the first stage involves calling and extracting sources and links through LLM-generated scripts; the second stage generates ... | Rebuttal 1:
Rebuttal: ### **I. Answers to Questions**
**Q1. Experiments upon Other Languages**
Although we did not claim any contribution of multi-lingual support, it is easy to extend LLMDFA to support other languages due to its compilation-free design. During the rebuttal, we extended it to support C/C++/JavaScript ... | Rebuttal 1:
Rebuttal: We express our sincere gratitude to all the reviewers for their invaluable feedback and insightful questions. Prior to addressing each individual question and concern raised in the reviews, we present more experimental data, list several additional references, and address the common concerns of re... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ReF-LDM: A Latent Diffusion Model for Reference-based Face Image Restoration | Accept (poster) | Summary: - This paper addresses face image restoration using multiple reference images, employing the CacheKV technique to utilize these images effectively. To overcome the issue of loss of appearance information in latent diffusion models, a timestep-scaled identity loss is proposed.
- The design choices for CacheKV a... | Rebuttal 1:
Rebuttal: > **Q4-1. The redundant use of the diffusion model will cause slower speed and more computation, limiting applications like video face restoration.**
During inference, the CacheKV mechanism pre-computes and saves the keys and values (KVs) for each reference image with only one pass of diffusion n... | Summary: To enhance the performance of blind face image restoration, this paper proposes a reference-based method named CacheKV, which adapts LDM to restore lowquality (LQ) conditioned on several high-quality (HQ) images. As there is no dataset available for reference-based image restoration, therefore, the authors con... | Rebuttal 1:
Rebuttal: > **Q3-1. Explain how CacheKV works.**
We include an illustration in the PDF figure 3 (which is a simplified version of the paper figure 2) and explain as follows.
During inference,
- We first extract the CacheKV from reference images. Specifically, for a reference image, we encode the image $x_... | Summary: 1. This paper proposed ReF-LDM for reference-based face image restoration with CacheKV to utilize the input reference images.
2. This paper introduced a timestep-scaled identity loss, which considers the characteristics of diffusion models and helps ReF-LDM better learn the discriminating features of human id... | Rebuttal 1:
Rebuttal: > **Q2-1. Different impacts of reference images in comparison to style transfer works.**
The reference images affect model outputs differently because face image restoration and style transfer have different objectives. In our task, the model output is an HQ image, and the model inputs are refere... | Summary: This paper investigated the reference-based face restoration model based on latent diffusion model. It proposed a ReF-LDM method involving a CacheKV module and a timestep-scaled identity loss. A FFHQ-Ref dataset consisting of 20,405 HQ face images with corresponding reference images. Experiments are conducted ... | Rebuttal 1:
Rebuttal: > **Q1-1. Limited improvement over no-reference methods in Table 6.**
ReF-LDM achieves a significant boost in face similarity metric (IDS↑ 0.675 vs. 0.323 for the best no-reference method CodeFormer). As suggested by the reviewer, we evaluated an additional metric FID (with target distribution as... | Rebuttal 1:
Rebuttal: ## Response to all reviewers and area chairs for a brief summary
We sincerely thank all reviewers for their careful reading and insightful comments. We are encouraged by the positive feedback and recognition of our work's key strengths noted by the reviewers:
1. A new reference-based face restor... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning | Accept (poster) | Summary: In this paper, the authors proposed a method to address the problem of transfer learning under FL. Specifically, after a thorough theoretical analysis, the authors observe that the cross-client averaged Jacobian norm and its variance control the bound on the target loss. Therefore, they propose FedGTST to prop... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. Our answers are as follows.
1. **We additionally conducted experiments on DomainNet, and the results are presented below.**
Following FedSR, we applied a leave-one-out strategy, where one domain is treated as the target domain and the other five a... | Summary: This work addresses transferability of models pre-trained using Federated Learning (FL). Through a thorough theoretical analysis, the authors identify cross-client averaged Jacobian norm and cross-client Jacobian variance as a key factor influencing transferability. To retain good transferability, FedGTST con... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. Our answers are below.
1. **Additional Baseline**. We discuss Scaffold, a baseline suggested by the reviewer (and will cite this and other suggested works). They are as follows:
* 10 clients:
| | MNIST → MNIST-M (LeNet)| MNIST → MNIST-M (ResNet) | CIF... | Summary: The paper presents FedGTST, a novel federated transfer learning approach that achieves transferability over the global learning domain and accurate assessment of the degree of transferability. The proposed method utilizes a new regularizer that encodes cross-client statistics and adjusts the local training pro... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. Our answers are as follows.
1. We agree with the reviewer that there are other non iid sampling methods that may be more frequently used than ours. We therefore implemented the Dirichlet sampling. Following the reviewer’s suggested references, we set th... | Summary: This work aims to improve the transferability of pre-trained models under federated learning. It established a cross-domain generalization error bound under federation. Motivated by this, the authors devise a regularization method leveraging cross-client statistics to minimize the generalization error upper bo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. Our answers are as below.
* **Experimental Setting:**
1. *Federated Pre-training* was explained in the original manuscript.
In Lines 148–149, we said: “...federated pretraining where the source domain is a union of the agents’ local domains.” Hence,... | Rebuttal 1:
Rebuttal: Dear Reviewers and AC,
We would like to thank all reviewers for their careful reading and insightful suggestions on our paper. We have prepared a detailed, point-by-point response to each concern raised by the reviewers. Below, we summarize the major questions posed by multiple reviewers:
- Theo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Data-Efficient Learning with Neural Programs | Accept (poster) | Summary: This paper looks at optimizing "neural programs", which are composites of a deep neural network (DNN) followed by a program written in a traditional programming language or an API call to a large language model (LLM). This paper proposes ISED (Infer-Sample-Estimate-Descend) for learning neural programs that on... | Rebuttal 1:
Rebuttal: > While Section 1 and 2 is nicely written and can be understood by readers new to this field, it is challenging to read Section 3 due to over-formalism and the lack of end-to-end running examples.
Thank you for this feedback. We agree that the lack of end-to-end running examples makes the algorit... | Summary: This paper proposes an algorithm ISED for learning a composition between a neural model (e.g., using DNN to classify objects in an image) and traditional programming (e.g., calling GPT-4 to identify room type). ISED is based on reinforcement learning, which summarizes the input-output samples of the traditiona... | Rebuttal 1:
Rebuttal: To address these concerns, we ran additional experiments (highlighted in bold) for tasks involving GPT-4 which we summarize here.
### Leaf Classification
| Architecture | Test Accuracy |
| ------------- | ------------- |
| Purely Neural | 78.50% |
| CLIP | 20.15% |
| **GPT-4o Vision... | Summary: The paper introduces a sample-efficient algorithm called ISED (Iterative Sampling for Efficient Differentiable Programming) for learning neural programs i.e. programs that have a neural network component (that needs to be learned) and another fixed program. The fixed program could be a python program, a call t... | Rebuttal 1:
Rebuttal: > The paper should present results for all benchmarks in Table 3. It is misleading to show mostly positive benchmarks in the main paper and relegate the negative ones to the appendix.
We did not intend to be misleading, but we understand how we gave off that impression. We note that this paper go... | Summary: This paper is about learning the parameters for a neural network (or other differentiable structure) where its output is then provided to a different black-box procedure, and we can compute a loss for the (second) output from that procedure.
The procedure consists of four parts: infer, sample (+ execute), and... | Rebuttal 1:
Rebuttal: > The method seems to lack support for the case where outputs of the program can be real-valued.
We support program outputs that are real-valued, and the handwritten formula (HWF) benchmark is an example of this. As we mention on L136 and L158, after the sampling step, floating-point output space... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful comments. Our work explores the idea of composing DNNs with any program that performs reasoning over the network’s predictions. We introduced a novel algorithm for learning such composites with a black-box program component. We introduced new benchmarks ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Grounded Answers for Multi-agent Decision-making Problem through Generative World Model | Accept (poster) | Summary: The paper proposes a new approach to multi-agent decision-making problems by integrating a language-guided simulator into the reinforcement learning pipeline.
Learning before Interaction (LBI) uses a world model to generate trial-and-error experiences and improve the answers to complex decision-making proble... | Rebuttal 1:
Rebuttal: **1. Motivation seems misleading, and results are limited to the SMAC benchmark.**
We aim to improve the output of large language models by constructing a differentiable interactive environment and then learning the policy on this interactive environment. The paper focuses on constructing such an... | Summary: This paper proposes a method of improving the sample efficiency and performance of multi-agent reinforcement learning (RL) on the StarCraft Multi-Agent Challenge (SMAC) benchmark by learning to simulate the environment. The simulator consists of an image tokenizer, a dynamics model, and a reward function, all ... | Rebuttal 1:
Rebuttal: **1. The exploration methods (EMC and IIE) for offline data collection**
EMC is curiosity-based to boost multi-agent exploration for novel states, i.e., reduce out-of-distribution regions in the offline datasets. IIE belongs to go-explore algorithms that perform imagination without exploration, i... | Summary: This paper introduces a pipeline called Learning before Interaction, a approach to improve generative models' answers for multi-agent decision-making problems. The key idea is to integrate a language-guided simulator into the multi-agent reinforcement learning pipeline.
The main contributions are
- Creating ... | Rebuttal 1:
Rebuttal: **1. It requires significant computational resources**
We listed the hyperparameters in the interactive simulator in Tables 8 and 9, which requires computational resources. However, we believe this framework is meaningful. We utilize the generative model as an interactive environment for RL to en... | Summary: The paper presents a novel approach called Learning before Interaction (LBI) that integrates a language-guided simulator into the multi-agent reinforcement learning process to enhance the quality of solutions for complex decision-making problems. The LBI paradigm uses a world model comprising a dynamics model ... | Rebuttal 1:
Rebuttal: **1&2. Trivial framework and lack of investigation of related multi-agent world model literature.**
If I understand correctly, the distinct characteristic of multi-agent scenarios you mentioned should be decentralized execution, which is a common way to simplify computational demands during the e... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Rule Based Rewards for Language Model Safety | Accept (poster) | Summary: This paper proposes an innovation to reward modeling, where rule-based rewards are added to a helpful-only reward model. These rule-based rewards have only a small number of parameters that can be fit with a small amount of data. This is meant to help with fast-changing behavior policies for LLMs, to update th... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for the constructive feedback and for pointing out areas where the clarity of our submission can be improved.
**Terminology and Conceptual Clarity:** Thank you for pointing out that the large amount of terms can lead to confusion. As mentioned in the general respon... | Summary: The paper presents a novel approach to enhancing the safety behavior of Large Language Models (LLMs) through Rule-Based Rewards (RBRs). The authors argue that fine-tuning LLMs with human preferences can improve their capabilities and safety but may lead to unintended behaviors due to underspecified instruction... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments and suggestions.
**Generality of the Approach:** Our integration of rule-based systems with reinforcement learning is designed to be model-agnostic, and we believe it should generalize to other LLMs. In a revised version, we will include an addi... | Summary: The paper proposes a method for quickly incorporating safety mechanisms (i.e., refusal to obey the query) in LLMs. Given that production-use cases of LLMs can result in rapidly evolving safety requirements, the paper proposes a mechanism to incorporate a safety-based reward function into the LLM's RLHF / RLAIF... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough evaluation and constructive feedback.
**Relation to Prior Work and Novel Contributions:** Our work builds upon and extends concepts from prior studies such as Sparrow, particularly in using rule-based approaches for enhancing safety in LLMs. However, our ap... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful and constructive feedback. In this global response, we aim to inform reviewers of some additional content we provide and respond to common themes. We include this additional information in the rebuttal PDF.
**(1) Code and Data release:** We release some... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Tetrahedron Splatting for 3D Generation | Accept (spotlight) | Summary: This paper proposes a method for 3D mesh generation from a text. The idea is to improve gradient propagation ability of DMTet framework by splatting multiple tetrahedra instead of rendering only one surface triangle per pixel. Qualitative results on 3D mesh generation show good quality results with competitive... | Rebuttal 1:
Rebuttal: ### **Response to Reviewer Tjej**
Thank you for your valuable feedback and constructive suggestions. We've addressed your concerns in the revised manuscript and our response below.
**Q1: Explanations of pipeline and experiment.**
We apologize for any confusion. Here, we further summarize our pi... | Summary: The paper presents a novel method for generating 3D models from the given inputs. The authors, for that purpose, propose the use of tetrahedra instead of 3D Gaussians in the recent proposed Gaussian Splatting framework.
Strengths: The paper proposes the use of TeT Splatting, that are well formulated and demon... | Rebuttal 1:
Rebuttal: ### **Response to Reviewer dtJc**
Thank you for your valuable feedback and constructive suggestions. We've addressed your concerns in the revised manuscript and our response below.
**Q1: Results from multiple viewpoints.**
We apologize for the lack of videos.
We are not allowed to provide link... | Summary: The paper presents a new differentiable 3D shape representation based on DMTet. It borrows the efficient rasterization-based rendering techniques from Gaussian Splatting to improve the global optimization of DMTet. The new TeT splatting is shown to perform well in the task of text-to-3D.
Strengths: - The pape... | Rebuttal 1:
Rebuttal: ### **Response to Reviewer 7DJZ**
Thank you for your valuable feedback and constructive suggestions. We've addressed your concerns in the revised manuscript and our response below.
**Q1: Sharpness of texture.**
As discussed in the limitation section of the Appendix, lines 521-523, TeT-Splatting... | Summary: This submission is proposing a new representation for 3D content generation combining the three following benefits:
- easy to optimize,
- real-time rendering,
- allowing the extraction of precise meshes.
Recent advances in novel view synthesis, i.e. NeRF and then 3D Gaussian Splatting (3DGS) are eventually bri... | Rebuttal 1:
Rebuttal: ### **Response to Reviewer 39Vr**
Thank you for your valuable feedback and constructive suggestions. We've addressed your concerns in the revised manuscript and our response below.
**Q1: Measures of surface quality.**
We thank the reviewer for raising this question. The best results from our me... | Rebuttal 1:
Rebuttal: We provide two additional figures in the attached PDF:
1) **Figure 1: More viewpoints of the generated assets shown in the teaser.**
2) **Figure 2: Comparison of different encodings used for modeling signed distance field.**
Pdf: /pdf/419353ef0bcda12abb8e7cb666c6c8013372992d.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Accuracy is Not All You Need | Accept (poster) | Summary: This paper introduces "flips" to measure the difference between quantized models with the original ones. Flips can be simply calculated as the % changes in model predictions from correct to incorrect and vice versa. Experiments demonstrate that compressed models while maintaining similar accuracy to baseline m... | Rebuttal 1:
Rebuttal: ## 1. Deeper theoretical insights
We agree that a theoretical treatment of the effect that each compression scheme has on a model’s outputs would be useful, however, it requires more progress on AI theory research before a rigorous theoretical framework can be created. Hence we leave this to futur... | Summary: This paper examines the evaluation of quantized language models, arguing that accuracy alone is insufficient as a metric. The paper introduces the concepts of flips for MCQ based tasks like MMLU/Hellaswag/PIQA/ARC etc., where flips measure the correct $\rightarrow$ incorrect and incorrect $\rightarrow$ correct... | Rebuttal 1:
Rebuttal: ### 1. Evaluation of MCQ tasks using second setup
We re-evaluate the MMLU task using the second setup recommended by the reviewer (full answer text probability by summing log probabilities, as described in the HuggingFace blog, this was the EleutherAI Harness implementation as of January 2023). N... | Summary: The authors analyze changes in model predictions as an additional performance metric to evaluate quantization schemes applied to large language models (LLaMa2 and Yi). The authors show that during model quantization that the predictions for a substantial number of examples change, beyond the limited number of ... | Rebuttal 1:
Rebuttal: **1. Consistency of flips**
The following table shows the % of questions whose answers are changed by 0-6 quantization schemes (for Llama2-70b MMLU task ~15K questions):
| % of Questions | 84.0 | 7.7 | 4.2 | 2.5 | | 1.1 | 0.3 | ~0.0 | |
|-------------------------------|---... | Summary: * The paper investigates the fine-grained effects of compression beyond just evaluating and measuring accuracy on benchmarks such as MMLU/Hellaswag.
* The paper finds that while aggregate level metrics demonstrate a very slight drop in performance (~2%), examining the individual responses highlights that a sig... | Rebuttal 1:
Rebuttal: **Additional evaluation**
We perform additional evaluations as asked by the reviewer. In particular, we evaluate the following tasks:
- BFCL (Berkeley Function Calling Leaderboard) to test function calling
- SCROLLS (QuALITY split) to test long context understanding which would be useful in retri... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable suggestions. We address some common questions here.
### **New experiments bolsters our 'accuracy alone is misleading' thesis**
As suggested by the reviewers, we have performed additional evaluations based on several *new tasks* (Berkeley Function Callin... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Bias in Motion: Theoretical Insights into the Dynamics of Bias in SGD Training | Accept (poster) | Summary: This paper presents a theoretical analysis of bias dynamics in machine learning models during the training process.
1. The authors introduce a high-dimensional teacher-student framework called the Teacher-Mixture (TM) model to study how bias emerges and evolves during stochastic gradient descent (SGD) trainin... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their time reading and assessing our paper. We are happy that the reviewer found our paper well-written and, in the following, we would like to address their main comments touching an important point, i.e. the robustness of our result in more complex setting... | Summary: The manuscript tries to understand the training dynamics in SGD training, through the lens of synthetic data with sub-populations from the Gaussian-mixture model. The paper leverages high-dimensional analysis to prove that the solution converges to a set of ODEs and thus characterizes the evolution of bias thr... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their general appreciation of our work and for their comment.
**Impact of the value of $m$**
Thank you for your insightful question. We plan to incorporate the following points into the revised manuscript:
* A higher value of $m$ would introduce addit... | Summary: The paper analyzes how different subgroups of the data affect optimization dynamics. The analysis is done in a large-dimension limit by analyzing squared error of a linear student classifier modeling a piece-wise linear teacher one. This analysis provides insights into dynamic loss behavior of loss on differen... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation of our analysis and their comments. We feel that these are important comments to improve the clarity of our work and we thank the reviewer for pointing them out, we ran additional experiments (discussed below) to address them.
**Limitations of the mode... | Summary: The paper investigates the emergence of bias in the transient phase of learning by gradient descent (after initialization and before convergence). The authors theoretically study a mixture of Gaussians dataset in a teacher-student framework. The paper then analytically characterizes several properties of a lin... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments and careful consideration of our paper.
**On the practical significance of our work**
Thank you for highlighting this important point. We plan to address the practical implications of our findings by adding the following discussions to the r... | Rebuttal 1:
Rebuttal: We thank all reviewers for their insightful comments and careful consideration of our paper. Their feedback has helped us identify ways to improve our presentation and convey our message more clearly. It has also prompted us to conduct additional experiments, which we hope demonstrate the robust... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: In this paper, the authors explore how bias evolves while learning features across different sub-populations. They particularly emphasize the transient phase of learning, which is less understood compared to the early and late phases of the learning dynamics. To this end, data from various sub-populations are... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation of our characterisation of the non-asymptotics training dynamics and their constructive comments.
**Regarding the Use of Linear Classifiers:**
As the reviewer points out, our theoretical analysis is limited to linear classifiers to ensure analytica... | null | null | null | null | null | null |
DN-4DGS: Denoised Deformable Network with Temporal-Spatial Aggregation for Dynamic Scene Rendering | Accept (poster) | Summary: This paper proposes a novel framework for modeling dynamic scenes. To address the noisy 3D Gaussians in canonical space, the authors introduce a Noise Suppression Strategy (NSS) with a two-layer Deformation Field to resolve this issue. Additionally, they propose a Decoupled Temporal-Spatial Aggregation Module ... | Rebuttal 1:
Rebuttal: 1. Thank you for your suggestions. We will cite the relevant works as recommended.
2. We appreciate your understanding of HyperNeRF and the insights regarding both HyperNeRF and Deformable-GS. In response, we have conducted additional visual comparisons in **Figure 2** of the **attached PDF**, inc... | Summary: The author proposed a Denoised Deformable Network to enhance the rendering performance of 4D-GS. In this network, a two-stage deformation prediction method was introduced to suppress noise and achieve better performance.
Strengths: Based on the performance on three dynamic datasets, the proposed method outper... | Rebuttal 1:
Rebuttal: ### **1. Novelty** ###
The primary motivations of this paper are twofold:
1. **Noise in Canonical + Deformable Design**: In the canonical + deformable design, point-to-point relationships within the canonical Gaussians are chaotic and erroneous (noisy), which can be transferred into the deformat... | Summary: This paper presents a two-stage pipeline for reconstructing 4D scenes with 3D Gaussians. Similar to previous works, the authors defined a set of canonical 3D gaussians and use MLPs as the deformable fields to deform them into new frames. The key difference is that the authors notice that the deformable field c... | Rebuttal 1:
Rebuttal: ### **Weaknesses:** ###
We apologize for any confusion caused by our expression. Below, we will clarify your questions.
1. **"Noise"**: In the canonical + deformable design, we input the canonical Gaussian coordinates $x, y, z$ and time $t$ into the deformable network. The deformable network ess... | Summary: This paper proposes DN-4DGS, a deformation-based Gaussian splatting technique for dynamic scene rendering. DN-4DGS utilizes a grid-based representation to model the deformation field. It features a two-stage deformation inference pipeline that aggregates temporal and spatial information to reduce noise in the ... | Rebuttal 1:
Rebuttal: 1. Thank you for pointing that out. It was indeed a typo, and we will correct "$G_t(i)$" to "$G(i)$".
2. Thank you for your reminder. We will correct the above formulas and add these two parts.
Formula 3: $\Delta x, \Delta y, \Delta z = \Psi(x, y, z, T_n)$, where $T_n$ represents the set... | Rebuttal 1:
Rebuttal: Thank you to the reviewers for their valuable reviews, which will help us further improve the quality of our work. We have addressed each reviewer's questions individually within their respective sections, and we look forward to further discussions during the discussion phase. Below and in the att... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning Commonality, Divergence and Variety for Unsupervised Visible-Infrared Person Re-identification | Accept (poster) | Summary: This paper proposed a Progressive Contrastive Learning with Multi-Prototype method for USVI-ReID, which learns both shared and diverse information. The Hard Prototype Contrastive Learning method aims to mine distinctive yet significant information, while the Dynamic Prototype Contrastive Learning method is des... | Rebuttal 1:
Rebuttal: ### Weaknesses
**W1: From Table 2, it is evident that the method is based on a relatively strong baseline. Does the paper use different baselines, like ADCA, still achieve good results?**
A1: We acknowledge the importance of evaluating our method with different baselines. As shown in following T... | Summary: This paper proposes a method for unsupervised visible-infrared person re-identification called Progressive Contrastive Learning with Hard and Dynamic Prototype. This method focuses on capturing commonality, divergence, and variety. By introducing hard prototype contrastive learning to emphasize divergence and ... | Rebuttal 1:
Rebuttal: ### Weaknesses
**W1: While introducing hard prototype contrastive learning can capture divergences, selecting hard samples may lead to model instability during the early training stages. Although progressive contrastive learning strategy is introduced to mitigate the above problem, balancing the ... | Summary: The paper is presented in the field of unsupervised visible-infrared person re-identification (USVI-ReID) which aims to match individuals in infrared images to those in visible images without annotations. Existing methods often use cluster-based contrastive learning, which fails to account for divergencies by ... | Rebuttal 1:
Rebuttal: **Q1: The use of the farthest sample as a method for representing diversity in the HPCL (as described in Equations 6 and 7) relies on the assumption that distance is an adequate measure of sample spread within a cluster. Given that distance can be a weak indicator and variance might offer a better... | Summary: The paper proposes a method for unsupervised visible-infrared person ReID. Towards this goal, it extends the PGM method with a progressive contrastive learning with hard and dynamic prototypes. Initially a typical centroid prototype contrastive learning approach is used. A mixed batch of images is encoded thro... | Rebuttal 1:
Rebuttal: ### Weaknesses
**W1: L215 states that each batch contains 16 person IDs and 16 samples per person. Is this a confusion and it should be based on the assignments from the clustering? Or are these actually person IDs from the annotations?**
A1: We apologize for the confusion. The statement in L215 ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their careful reading of our paper and help with improving our manuscript. We sincerely appreciate that you find our work:
- sense idea (edeS)
- well-writting (mgXA, i5Cg, n3oM)
- coherent and engaging (mgXA, i5Cg)
- innovative idea and fresh perspective (mgXA)
... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SelectIT: Selective Instruction Tuning for LLMs via Uncertainty-Aware Self-Reflection | Accept (poster) | Summary: The paper introduces SelectIT, a novel instruction tuning (IT) method that leverages the intrinsic uncertainty in large language models (LLMs) to select high-quality IT data without requiring additional resources. The authors present a curated IT dataset, Selective Alpaca, derived from the Alpaca-GPT4 dataset ... | Rebuttal 1:
Rebuttal: Thank you for your insightful review.
>Q1: Figure 2 is not clear to me. I suggest giving a brief description of each selection stage in the caption for easy understanding.
Thank you for your advice. Following your suggestion, we will revise the caption of Figure 2 to: `Overall Framework of Our P... | Summary: In this paper, authors leverage LLMs to score the training data in terms of the token and sentence and model. They train various models on their select models to show the effectiveness on various benchmarks. Their main contribution is to apply foundations to judge the quality of training data from different gr... | Rebuttal 1:
Rebuttal: Thank you for your insightful review.
>Q1: From the table 3, it seems Sentece-R works the best so what if we trained model on the data just from the Sentece-R? BTW, what is the ID 6, 7, 8 means in that Table?
Thank you for your suggestion. Actually, in Table 1, we have provided the experiment th... | Summary: This paper presents SelectIT, a new way to improve how large language models (LLMs) follow instructions using their own uncertainty to pick better training data. This method is cost-effective as it doesn't require extra tools or data. The authors developed a new dataset called Selective Alpaca using SelectIT, ... | Rebuttal 1:
Rebuttal: Thank you for your insightful review.
>Q1: The paper states initially that it does not use extra resources for its new method. However, it mentions later that they used different sizes of LLaMA 2 models to help choose the best instruction data. This contradicts their first claim about not needin... | Summary: The paper introduces a data selection approach to instruction tuning (IT) for large language models (LLMs) by leveraging the intrinsic uncertainties within the models themselves. This method, termed SelectIT, aims to enhance IT data selection without the need for additional external resources, making it more c... | Rebuttal 1:
Rebuttal: Thank you for your insightful review.
>Q1: Reliance on Tuning K: The method depends on tuning the parameter ( K ), which can significantly affect model performance. Will optimal K be training dataset dependent?
The performance of SelectIT is not critically dependent on the parameter $K$, based o... | Rebuttal 1:
Rebuttal: We thank the reviewers for the insightful comments and constructive suggestions, which will serve to improve the paper considerably. We will attend to all comments to the best extent.
> Q1: Applying the SelectIT on other representative instruction tuning datasets.
To further demonstrate the robu... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Aligning Audio-Visual Joint Representations with an Agentic Workflow | Accept (poster) | Summary: This paper deals with the problem of audio-visual representation learning and proposes a method for reducing the misalignment between audio and video streams. More specifically, the proposed method feeds audio and video streams into multi-modal LLMs to obtain textual descriptions for both streams separately an... | Rebuttal 1:
Rebuttal: We thank for the valuable comments and answer the raised questions below.
> W1: Details in the Planning Section
The 'Planning' stage of our workflow utilizes a predefined module that automatically selects appropriate actions based on audio-visual misalignments identified by the LLM. Each action... | Summary: This paper proposes a data-centric agentic workflow (AVAgent) to improve the alignment of audio-visual joint representations. The workflow is controlled by LLMs and consists of cyclic tool use, planning, and reflection steps. This paper claims the proposed method can analyze and adaptively modify the audio str... | Rebuttal 1:
Rebuttal: We thank for the valuable comments and answer the raised questions below.
> Capability of the Proposed Workflow
We agree that there is much room to improve our actions. At present, we focus on the audio-visual downstream scenarios such as audio-visual classification, sound localization, segment... | Summary: The paper introduces a new method to improve audio-visual joint representations by strategically aligning audio with visual data. This method employs an LLM-based assistant, AVAgent, which uses multi-modal LLMs to analyze audio and visual content separately and then plans corrective actions to better align the... | Rebuttal 1:
Rebuttal: We thank for the valuable comments and answer the raised questions below.
> W1, Q1: Dataset Specification Clarity
For our evaluations, we specifically utilized the AVSBench-semantic subset, which focuses on semantic segmentation tasks. This choice is pivotal as it directly relates to our method... | Summary: The authors propose to leverage large language model with agentic workflow such as planning, reflection and tool usage for better audio-visual alignment in training data applied to various downstream tasks such as classification, localization and separation.
Strengths: - The experiments with various downstrea... | Rebuttal 1:
Rebuttal: We thank for the valuable comments and answer the raised questions below.
> W1: Clarification of Visual Alignment and Temporal Synchronization Scores.
In our framework, visual alignment scores are computed using ImageBind, which assesses the similarity between visual data and modified audio desc... | Rebuttal 1:
Rebuttal: Dear AC and all reviewers,
We sincerely appreciate your time and efforts in reviewing our paper. We are glad to find that reviewers recognized the following merits of our work.
- **Innovative Approach** [RJkZ, siDn, GoMh]: Our novel agentic workflow employing LLMs for audio-visual alignment intr... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SSDiff: Spatial-spectral Integrated Diffusion Model for Remote Sensing Pansharpening | Accept (poster) | Summary: This paper introduces SSDiff, a model that improves the resolution of satellite imagery by combining high-resolution panchromatic images and low-resolution multispectral images. SSDiff separates image details into spatial and spectral components, and processes them through different network branches, then merg... | Rebuttal 1:
Rebuttal: >**W1.** Efficiency of the method: Evaluating and discussing the efficiency of the DDPM-based method used in SSDiff would help provide a more comprehensive understanding of the practical advantages and limitations of the proposed approach. This information could be valuable for readers to assess t... | Summary: For the task of multispectral and panchromatic image fusion (Pansharpening), this paper proposes a spatial-spectral integrated diffusion model (SSDiff). The framework is novel, utilizing spatial and spectral branches to learn spatial details and spectral features separately. It introduces an alternating projec... | Rebuttal 1:
Rebuttal: >**W1.** Figure 4 is not clear.
Thank you for your valuable suggestion. We will fix this.
>**W2&Q1.** To ensure the fairness of the experiment ..? \& What does the first row of data in Table 5 represent? ..
Thank you for your detailed review. The first row's data is the result after training f... | Summary: This paper propose a novel spatial-spectral integrated diffusion model called SSDiff based on subspace decomposition, and its main contributions contain: 1) Considering existing DDPM-based methods have not yet designed models for the discriminative features required in the pansharpening task, SSDiff divide the... | Rebuttal 1:
Rebuttal: >**Q1.** Why introduce another two features from the features of spatial domain and spectral domain separately?
Thank you for your thorough review and feedback. Firstly, PAN images and LrMSI are obtained from different sensors and contain distinct feature information. PAN images exhibit richer sp... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Generate Universal Adversarial Perturbations for Few-Shot Learning | Accept (poster) | Summary: The authors proposed an attacking framework to generate transferable universal adversarial perturbations on the few-shot learning scenario. The authors align proxy tasks to the downstream tasks and leverage the generalizability of pre-trained encoders to tackle the task shift and the semantic shift. Experiment... | Rebuttal 1:
Rebuttal: We truly appreciate your valuable comments. In the following, we respond to the concerns.
> Q1: It is confusing between the meaning of task and class. How can the authors state that the downstream task becomes less similar to the pre-training task with higher ASR in Fig. 3 (a)?
Based on the intr... | Summary: This paper proposed a framework for generating Universal Adversarial Perturbations (UAPs) by considering the task and the semantic shift in the context of few shot learning. The authors introduces proxy tasks and proxy protos to enhance the transferability of UAPs in different FSL tasks. Results are validated ... | Rebuttal 1:
Rebuttal: We truly appreciate your valuable comments. In the following, we respond to the concerns.
> Q1: It’s not clear if the task and semantic shifts still hold on larger-scale datasets like meta-dataset.
Thank you for your valuable suggestion. Due to constraints of time and space, we select three disti... | Summary: This paper addresses the adversarial attack on image classification tasks, especially for the few-shot learning setup with universal adversarial perturbations. Authors claim that the traditional approach does not work well in the FSL setup, which has two properties including task shifts and semantic shifts. Th... | Rebuttal 1:
Rebuttal: We truly appreciate your valuable comments. In the following, we respond to the concerns.
> Q1: All datasets are natural images, and I wonder if the experiments can be improved by trying on out-of-domain datasets, e.g. Omniglot or CUB.
Thanks for your constructive suggestion. In addition to the o... | Summary: Authors argue that Univeral Adversarial Perturbation (UAPs) do not work well in few-shot learning (FSL) scenarios due to task and semantic shifts between the data. To this end, they proposed a method for crafting UAPs in FSL scenarios. The threat model assumes access to a pre-trained model with no access to pr... | Rebuttal 1:
Rebuttal: We truly appreciate your valuable comments. In the following, we respond to the concerns.
> Q1: Authors attributed the bad transferability of GAP in FSL to the task shift. However, one more relevant issue could be the small number of examples used for generating GAP-based UAP.
Thanks for the insi... | Rebuttal 1:
Rebuttal: We thank all reviewers for the detailed and constructive reviews. We have revised the paper based on your suggestion. Here are some highlights in the paper revision:
1. **Expanded Experimental Scope**: We have augmented our study with additional experiments conducted on the CUB, Omniglot, and GTS... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DeeR-VLA: Dynamic Inference of Multimodal Large Language Models for Efficient Robot Execution | Accept (poster) | Summary: This paper aims to reduce computation time and enhance inference efficiency when deploying Multimodal Large Language Models on real robots. It introduces a dynamic neural network that selectively activates only a small portion of the network while maintaining performance levels comparable to the original netwo... | Rebuttal 1:
Rebuttal: Thank you for your time and constructive feedback. Please see the response below.
**W1/L1**: `Experiments are limited in simulated environments `; `show more analysis which can show that it can be effective in the real world `
Thanks. We admit that the submitted version of manuscript limits in t... | Summary: The paper presents a framework called Dynamic Early-Exit for Robotic MLLM (DeeR) aimed at improving the computational efficiency of Multimodal Large Language Models (MLLMs) used in robotic applications. The DeeR framework tries to address the challenge of deploying MLLMs on robots with limited computational re... | Rebuttal 1:
Rebuttal: Thank you for your time and constructive feedback. Please see the response below.
**W1/Q1**: `How well the approach generalizes to other robotic tasks and environments? `; `Are there any limitations when applying DeeR to different tasks?`
In principle, DeeR is designed for general robotic tasks... | Summary: DeeR provides an effective solution to the computational inefficiencies of MLLMs in robotic applications, enabling their use in resource-constrained environments while maintaining high performance. The paper emphasizes the practical implications of this approach, making advanced robotic capabilities more acces... | Rebuttal 1:
Rebuttal: Thank you for your time and feedback. Please see the response below.
**W1**: `uniformly sampling is totally different from the way in inference`
We recognize that uniform sampling during training does not perfectly replicate dynamic inference conditions. However, we believe this approach is rel... | Summary: This article explores the application of large language models in robotic manipulation tasks from a very interesting perspective. The authors present a notable observation: the majority of procedures involved in robot control for diverse task execution comprise relatively simple scenarios, which can be effecti... | Rebuttal 1:
Rebuttal: Thank you for your time and thoughtful review. Please see the response below.
**W1/W2**: `the effectiveness of the proposed method is not verified in real-world robot configuration`; `Reporting the FPS of the model's operation`
Thanks. We admit that the submitted version of manuscript limits in... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Understanding the Expressivity and Trainability of Fourier Neural Operator: A Mean-Field Perspective | Accept (poster) | Summary: In this work, the authors investigate the expressive power and trainability of the Fourier Neural Operator (FNO). A mean-field theory is adapted for the FNO to examine the behavior of randomly initialized FNOs from the perspective of the 'edge of chaos'. The investigation focuses on understanding the expressiv... | Rebuttal 1:
Rebuttal: Due to the character limit, we have addressed some questions in the global rebuttal. Please refer to our global rebuttal. We appreciate your attention and review.
> Q1. The discovery in their work, ...
The following part of the code is different from our He initialization, as it only initializes... | Summary: This paper leverages mean-field theory, developed for neural networks, to study the expressivity and trainability of random Fourier neural operators (FNOs). It provides practical insights for the appropriate initialization of FNOs.
Strengths: The authors address a significant question in the neural operators ... | Rebuttal 1:
Rebuttal: Due to questions similar to those from other reviewers, we have addressed these in the global rebuttal. Please refer to our global rebuttal. We appreciate your attention and review.
> Q1. The covariance matrix should be introduced in section 3.2.
The covariance $\Sigma_{\alpha, \alpha’}^{(\ell)... | Summary: This paper presents a comprehensive theoretical analysis of the Fourier Neural Operator (FNO) using mean-field theory. The main contributions to me at least are:
1. Establishing a mean-field theory for FNO, analyzing its behavior from an edge of chaos perspective.
2. Examining the ordered-chaos phase transiti... | Rebuttal 1:
Rebuttal: > Q1. The authors don't fully explore very deep FNOs or a wide range of hyperparameters (in particular sweeping over the number of modes). Additional experiments on larger-scale problems or more complex PDEs could further strengthen the empirical validation.
Thank you for your valuable suggestion... | Summary: This work explores the expressivity and trainability of Fourier Neural Operators (FNOs) by establishing a mean-field theory and analyzing their behavior from an edge-of-chaos perspective.
Strengths: The study examines the ordered-chaos phase transition of the network based on weight distribution, highlighting... | Rebuttal 1:
Rebuttal: > Q1. The summary of related work can be improved, particularly by including recent neural operator architectures
We have revised Section 2.1 as follows. (Only newly added references are cited in numbered form.)
**line 48:** The FNO (Li et al., 2021) is one of the well-established methods for so... | Rebuttal 1:
Rebuttal: > Q1. **(reviewer ePA1)** I recommend providing further motivation for all concepts and referencing previous works where appropriate.
**(reviewer gSyo)** It would be beneficial to provide a clearer explanation and discussion of the theoretical results and their implications for readers without a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Upping the Game: How 2D U-Net Skip Connections Flip 3D Segmentation | Accept (poster) | Summary: This paper proposes a novel element for 3D medical image segmentation, called uC (U-shaped connection) that replaces standard skip connection with 2D convolutions.
Strengths: - uC 3DU-Net shows state-of-the-art results on 4 datasets.
- It reduces computational complexity and number of parameters
- It is aime... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review of our work! Please allow us to address your concerns and answer the questions.
>W1: Paper is hard to read at times.
Your suggestions are pivotal, we will comprehensively optimize the readability of the manuscript.
>W2: Section 3.2 is essentially empty...
Se... | Summary: The manuscript proposes using 2D U-Net connections to enhance 3D U-Net segmentation. The innovative approach demonstrates excellent performance across different imaging modalities and achieves state-of-the-art (SOTA) results with fewer parameters and lower computational costs on four public datasets. The manus... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments to our work! We appreciate the opportunity to address your concerns and respond to your questions.
**W1 & Q1:**
The motivation for employing DFi and its underlying principles can be futher discribed: In conventional 3D U-Net architectures, skip connections i... | Summary: The study introduces uC 3DU-Net, a 3D medical image segmentation method combining 2D U-Net skip connections with 3D CNNs.
Strengths: - Experiments were conducted across multiple datasets with several relevant baselines.
- Conducted comprehensive ablation studies and model hyperparameters are reported.
Weakne... | Rebuttal 1:
Rebuttal: Thank you for your careful review of our work! We would like to take this chance to address your concerns and respond to your questions.
W-Motivation:
>W1: The justification provided for...
The utilization of 2D convolutions potentially undermines spatial relationships between slices in 3D imagin... | Summary: This paper proposes an approach to combine 3D and 2D features for medical image segmentation. The key observation is that medical images often have high in-plane resolution (2D) and lower through-plane resolution (across 2D slices). They introduce a U-shaped connection which uses 2D convolutions in place of st... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review of our work! Below are the responses to your concerns.
>W1: More experimental evaluation.
We conducted experiments involving two attention-based segmentation models, D-LKA-former[1] and UNETR++[2], across four datasets, with results presented in pdf.Tab.1. Upd... | Rebuttal 1:
Rebuttal: Dear Reviewers,
The authors extend profound gratitude for the meticulous time and expertise invested in reviewing our manuscript. The invaluable insights and critiques provided have been pivotal in guiding comprehensive revisions. In response, substantive modifications have been executed, meticul... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models | Accept (poster) | Summary: This paper proposes Reference Trustable Decoding (RTD), a new paradigm that allows models to adapt to new tasks without fine-tuning and has lower inference costs compared to in-context learning (ICL). RTD uses the last-hidden states of the input sample to retrieve similar samples in the training examples and o... | Rebuttal 1:
Rebuttal: We sincerely appreciate your willingness to thoroughly read our paper. The time you’ve invested in our work is truly invaluable to us. Your recognition of our method’s clear motivation, extensive experiments, and ease of optimization is an honor. Additionally, we thank you for pointing out the sho... | Summary: The paper introduces Reference Trustable Decoding (RTD), a novel framework that allows large language models to adapt to new tasks without the need for fine-tuning. RTD constructs a reference datastore from training examples and optimizes the LLM’s final vocabulary distribution by selecting suitable references... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time you’ve spent on our paper. Thank you for recognizing our approach, experiments, and writing. The following is our response regarding the weaknesses, questions, and limitations, you raised. We aim to clarify any potential misunderstandings and indicate the direction... | Summary: Adapting large language models (LLMs) to specific tasks remains costly and complex. In-context learning (ICL) and parameter-efficient fine-tuning (PEFT) still suffer from inference latency and training costs, respectively. To address these problems, this paper proposes Reference Trustable Decoding (RTD), a tra... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough reading of our paper. The time you’ve invested in our work is truly invaluable to us. Your endorsement of the efficiency, orthogonality, and practical significance of our proposed RTD method means a great deal, and we are genuinely grateful for your recognitio... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
WorldCoder, a Model-Based LLM Agent: Building World Models by Writing Code and Interacting with the Environment | Accept (poster) | Summary: The authors propose a model-based agent by building a Python program to represent the world model through interacting with the environment.
The authors define logical constraints to explain the interactions and to achieve optimism in the face of (world model) uncertainty.
The authors prove the polynomial ... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review. Please see below for our responses.
> What if integrating the proposed approach, in particular, \phi_1 & \phi_2 to deep RL, if feasible, maybe together with an LLM for language understanding?
Interesting idea! One way we've been thinking about this is to fram... | Summary: The paper presents WorldCoder, a system for learning world models through program synthesis with an LLM. The framing is model-based reinforcement learning, where the agent interacts with the environment and collects data that is used to learn the model. WorldCoder uses a general-purpose language to synthesize ... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review. We really think we can improve the paper to address your main points. Please see below.
> GPT-4, the model used in the experiments, certainly was trained on the code implementing the models of the domains used in the experiments. How much of what we see is fro... | Summary: - This work deals with the transfer of text-based LLMs to the domain of agents, enabling LLMs to build a (world) model of an environment.
- The paper introduces the algorithm 'WorldCoder', that creates and refines Python programs for approximation of state transition and reward function
based on sampled data... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review, and for your support. Please see below for our responses.
> Task generalization achieved by prior knowledge: Env generalization with uncertain/incomplete prior knowledge of LLMs is not properly addressed. E.g., the dynamics behavior of grid worlds is obviously... | Summary: This paper introduces a novel model-based agent for sequential decision-making. A structured world model and goal-conditioned reward function are created by an LLM and refined as new information or task changes come in. This is based on logical constraints. For selecting the right candidate world model (ie obj... | Rebuttal 1:
Rebuttal: Thank you for the comprehensive review and for being supportive of the paper. Please below for answers to your questions.
> The method lacks generality, needing a hand-defined curriculum in minigrid and a handcrafted language-based reward bonus on AlfWorld;
We think this might be a misunderstand... | Rebuttal 1:
Rebuttal: Thank you everyone for the helpful input! Although there is a separate response to each review, we wanted to also include a global response to highlight a new experimental result this week performed to help better understand how the system adapts to new environments that are different from what it... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning | Accept (spotlight) | Summary: This paper provides an analysis of existing Offline Meta-RL (OMRL) methods by considering which informational quantity is maximized by which approach, and how they are related.
The show, under a certain assumption on data generation process, that early approaches like FOCAL maximize an upper-bound of the quant... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive comments, which really help us make the paper stronger. Our response to your concerns/questions:
# Justification of Assumption 2.2
For clarification of $I(Z; M; X_b) \ge 0$ or equivalently $I(Z; M) \ge I(Z; M|X_b)$, please see our following re... | Summary: In this paper, the authors propose a unified mathematical framework that encapsulates a subset of the developments conducted in Context-Based Offline Meta-Reinforcement Learning. They achieve this by carefully describing each previous attempt to solve this problem, and how the incremental improvements have bee... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive comments and the appreciation of our work. Our response to your concerns/questions:
Q1. Eqn 7
Eqn 7 concludes that $I(Z; M)\equiv -H(M|Z)$, which by definition of equivalence $\equiv$ in Thm 2.3, suggests that the two terms are equal up to a c... | Summary: This work introduces two new algorithms for COMRL based on an information theoretic decomposition of behaviour and environment information. The primary insight is that when encoding the contextual task representation as much environment information should be maintained while as little behaviour information is ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive comments and the appreciation of our work. Our response to your concerns/questions:
# Clarity
Thank you for your advice. We will make sure to improve the presentation of figures and theorems in the final version.
Theorem 2.4 mainly bounds th... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers, ACs and PCs for ensuring high quality review of the paper. We find all reviews constructive and helpful for making our paper stronger. As requested, the attached PDF provides result of UNICORN vs. a new baseline, ContraBAR, as well as hyper-parameter optimization ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs | Accept (poster) | Summary: The paper proposes two novel classes of MDPs in the function approximation setting:
* Mildly Smooth MDPs: in which the bellman optimality operator outputs smooth functions of degree $\nu$.
* Locally Linearizable MDPs: in which there exist a state-action feature mapping into $\mathbb R^d$ and a finite partition... | Rebuttal 1:
Rebuttal: Q: _The regret bound has factors that are exponential in $d$._
Lower bounds show that exponential dependence is $d$ cannot be avoided. If we look at Gaussian bandits with the squared-exponential kernel, a problem family that is strictly contained also in the Strongly Smooth class, [1] ensures an ... | Summary: This paper introduces a notion of local linearization, which is then applied to smooth MDPs. It generalizes the "Eleanor" algorithm into "Cinderella," which, by avoiding a "cardinality of N" term on the suboptimality with respect to the inherent Bellman error, gets sublinear regret for all classes of smooth pr... | Rebuttal 1:
Rebuttal: Q: _The extent of the discussion seems limited to the sentence "Difference with ELEANOR stays in the fact that parameters relative to different regions are learned separately" ... What, if any, are the new analysis techniques developed for this setting?_
We thank the reviewer for giving us the op... | Summary: The paper introduces the concept of Locally Linearizable MDPs, a class of MDPs that generalizes existing ones like Linear MDPs and MDPs with low inherent Bellman error. In this model, the state-action space is partitioned into $N$ regions, where the Q-functions belong to a class that allows the result of the B... | Rebuttal 1:
Rebuttal: Q: _However the proposed approach results in a regret bound that scales exponentially with
, the dimension of the feature space. This raises similar concerns about the practical feasibility of the setting. (...) Does [24] also have similar dependency in_
$d_{\nu_*}$? _If not, I wouldn't consider... | Summary: The paper discusses the concept of local-linear MDPs which is a general representation class of MDPs that extends previous works on learnable (sublinear regret in $K$) and feasible (polynomial regret in $H$) episodic MDPs.
Strengths: The paper considers important complexity questions associated with the conti... | Rebuttal 1:
Rebuttal: We point out that the regret of our algorithm does _not_ depend exponentially in $H$. Exponential dependence cannot be avoided for the Lipschitz class, and this work is also born to find a class of MDPs that is able to elude this undesirable phenomenon.
Q: _Can you give some concrete examples (ca... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper studied a structural property called local linearity that makes continuous MDPs learnable. In particular, local linearity means that the continuous state-action space can be partitioned into multiple regions and in each region, the Q-function is linear w.r.t. a unique (different) parameter. The pape... | Rebuttal 1:
Rebuttal: Q: _The proposed algorithm is computationally inefficient. It needs to run over all regions while the number of regions might be exponentially large according to section 4._
It is true that the number of regions is exponential in $d$. This makes the computational complexity exponential in this pa... | null | null | null | null | null | null |
Fast TRAC: A Parameter-Free Optimizer for Lifelong Reinforcement Learning | Accept (poster) | Summary: This paper proposes a new method for avoiding plasticity loss in 'lifelong RL', a setting in which distribution shift occurs during RL training. Here, the setting is split into a number of discrete tasks which change at a fixed interval. The method involves including a penalty on updates, rooted in convex opti... | Rebuttal 1:
Rebuttal: We thank you so much for your insightful review, feedback, and opportunity to improve our work!
## PACE vs. Mechanic
Firstly, we address your feedback by conducting a more rigorous evaluation of Mechanic across our benchmarks. Implementing your suggestion has substantiated our claim that PACE pe... | Summary: Many methods have been proposed recently for continual learning that can maintain their plasticity (e.g., through resetting or regularization towards initialization). There is, however, one critical limitation of those methods, which is their hyper-parameter tuning. Although those methods are designed for life... | Rebuttal 1:
Rebuttal: Thank you for your very thorough review! Especially, we appreciate your valuable comments on the writing and organization of this paper. We'll incorporate those in the camera-ready revision.
Regarding your questions: we realized that we went a bit too fast when introducing the algorithm. So firs... | Summary: This work proposes a new method designed to mitigate the loss of plasticity when a model is continuously retrained on a set of tasks. The approach uses online convex optimization (OCO) via the minimisation of a regret function. The approach is compared against two baselines: PPO with ADAM (fixed learning rate)... | Rebuttal 1:
Rebuttal: Thank you for your insightful review and constructive feedback!
## Lifelong RL baselines
Firstly, we acknowledge your comment on the lack of baselines. We recognize the important contributions of Ben-Iwhiwhu et al., 2023, Rolnick et al., 2019, Schwarz et al., 2018, and others in the field of lif... | Summary: The paper tackles the problem of plasticity loss in RL, where a neural network may have reduced performance on learning new tasks after a task switch. The authors develop a hyperparameter-free algorithm, PACE, using ideas from online convex optimization. It can be interpreted as an automatically-tuned weight d... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and for highlighting the strengths of our work!
## Layer normalization helps:
Thank you for suggesting the use of layer normalization, as highlighted by Lyle et al. for addressing plasticity loss, in our experiments. We implement layer normalization in our con... | Rebuttal 1:
Rebuttal: We thank all reviewers for their detailed feedback. We appreciate their recognition of our parameter-free approach to mitigating plasticity loss in lifelong RL as novel (Reviewers Jqmp, Jcag), its evaluation across a broad set of lifelong RL benchmarks (Jcag, 346D, eCfR), the extra analysis in the... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The surprising efficiency of temporal difference learning for rare event prediction | Accept (poster) | Summary: The paper studies the estimation of rare event statistics in discrete-time, discrete-state Markov chains. It shows that when the transitions probabilities of the chain satisfy certain locality assumptions then temporal difference estimators are exponentially more efficient than Monte-Carlo estimators. Concrete... | Rebuttal 1:
Rebuttal: Thank you very much for your positive feedback and acknowledgement of our work’s contributions. While our specific contributions are statistical in nature, we feel they are very relevant to machine learning based prediction for rare events. For example, in the context of extreme event prediction f... | Summary: The authors consider the problem of efficiency comparison between temporal difference (TD) and Monte Carlo (MC) methods for learning value functions (policy evaluation) especially in conditions involving rare events. The authors identify two challenges in policy evaluation with respect to rare events - long ti... | Rebuttal 1:
Rebuttal: We thank the reviewer for the opportunity to respond to detailed questions. We respond to specific comments below. Space for our responses is limited, so please let us know if you have additional questions.
**Weaknesses**:
For a detailed response regarding our numerical experiments, please see ... | Summary: This paper provides a rigorous sample complexity comparison between MC sampling and LSTD method, and shows that for rate event, LSTD can provide relatively accurate estimation with a much smaller dataset compared to MC sampling. This contradicts to the intution from the classical worst perturbation bounds.
St... | Rebuttal 1:
Rebuttal: Thank you for your review of the paper and supportive comments.
For a detailed response regarding our numerical experiments, please see our General Response.
Given space constraints, our goal in Section 1.2 was to *very* briefly summarize basic matrix perturbation and quasi-stationary limit res... | null | null | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their time reviewing our work, and a number of thoughtful suggestions for how to improve the presentation of the paper. We have responded to all individual comments below, but please let us know if any other questions arise.
There is a shared point of disc... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning Optimal Tax Design in Nonatomic Congestion Games | Accept (poster) | Summary: This paper investigated how to design the optimal tax in congestion games to minimize the social cost, with partial information. Specifically, the tax designer cannot observe the cost function directly. Instead, whenever a tax assignment is announced, the designer will observe the Nash equilibrium and cost.
S... | Rebuttal 1:
Rebuttal: Thanks for your appreciation! We address your questions below.
- **Typos:** Thanks for pointing out the typos! We will correct them in the final version.
- **Definition of the marginal cost function $c'_f$:** Yes it is the derivative of the cost function.
- **Knowledge of $\beta$:** The algorithm... | Summary: This manuscript proposes an algorithm for obtaining optimal tax design on nonatomic congestion games that decreases the social welfare of equilibrium state. Although the optimal tax design for nonatomic congestion games has a closed form of cost function, the cost function is generally unknown for tax designer... | Rebuttal 1:
Rebuttal: Thanks for your appreciation! We address your questions below.
- **Equilibrium feedback:** Thanks for pointing out the usage of similar concepts as equilibrium feedback in prior works. We will comment on this and make clear that we are the first studying it in the tax design setting in the final ... | Summary: The paper studied the classic congestion pricing problem, which can be framed as a Stackelberg game in which a leader (a tax designer) imposes congestion tolls in a congested network used by many self-interested travelers (followers), with the goal of minimizing the total social cost. The proposed algorithm c... | Rebuttal 1:
Rebuttal: Thanks for your appreciation! We address your questions below.
- **Literature review:** Thanks for pointing out references! We will include MPEC hardness and the complexity of the pricing problems in the final version. We will also move the discussion on Stackelberg games in the Appendix to the m... | Summary: The paper considers the problem where a system designer endeavors to guide players to welfare-maximizing behavior via imposing a tax function. One key challenge is that the designer can only observe equilibrium feedback from a Nash equilibrium, a stable state of the system, after imposing the tax. The main con... | Rebuttal 1:
Rebuttal: Thanks for your appreciation! We address your questions below.
- **Nash equilibrium assumption:** Under Assumption 1 (standard assumption used in nonatomic congestion game [Nisan et al., 2007]), the potential function of the game is convex and computing the Nash equlibrium is equivalent to solvin... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper focuses on the challenge of designing tax mechanisms to maximize social welfare in congestion games, where players' self-interested behaviors can lead to suboptimal outcomes for the overall system. The study introduces an innovative algorithm to learn the optimal tax with limited feedback, termed "eq... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful review. We acknowledge the importance of empirical validation and **have provided it in Appendix F**. In the final version, we will ensure this is better highlighted.
- **Assumptions:** Monotonicity is a standard assumption in the nonatomic congestion game literature [N... | null | null | null | null | null | null |
Learning rigid-body simulators over implicit shapes for large-scale scenes and vision | Accept (oral) | Summary: This work presents a GNN-based rigid-body simulator augmented with learned signed distance fields (SDFs). The core idea is to connect surface nodes between objects by testing whether their signed distances are within a certain threshold. This way, the resultant graph networks have sparser edges than prior work... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful feedback and insightful comments.
**Representation of methods like MeshGraphNet and DPI is misleading. SDF-Sim cannot resolve deformable solids and fluids**
We fully agree that MeshGraphNet and DPI can handle broader types of physical systems. We are happy t... | Summary: In this paper, a learning-based simulator based on graph neural networks (GNNs) is proposed that leverages signed distance function (SDF) shape representations of objects for efficient parallel processing. The GNN operates on a set of nodes and edges where the nodes are defines by the object center of masses a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback. We will add the discussions below into the paper. We will also correct the reference to FIGNet* and DANO description in the related work.
**Are euler integration and shape matching differentiable?**
Both Euler integration and shape-matching (... | Summary: This work tackles the problem of learning how to simulate rigid-body objects, scaling up to very large scenes that may consist in hundreds of objects and around a million mesh vertices. Graph neural network (GNN) baselines were usually used but become intractable for large scene, with high memory and computati... | Rebuttal 1:
Rebuttal: We thank the reviewer for positive evaluation of our paper and for insightful comments.
**Learned SDFs …. accuracy is limited by the network's capacity.**
We agree that the accuracy of Learned SDFs depends on network capacity. However, we found that even a basic MLP with 8 layers and 32 units p... | Summary: This paper presents a neural network-based simulator of rigid body dynamics with contacts that is memory-efficient and thus scalable to scenes with hundreds of objects and up to 1.1 million nodes on a single GPU. The key idea is to use SDF as the geometry representation to simplify collision detection compared... | Rebuttal 1:
Rebuttal: We thank the reviewer for highly appreciating the value of the paper and for valuable comments.
**The wording "small-scale datasets"**
Indeed, here we mean the datasets have small-scale scenes with up to 10 objects, while later in the paper we scale the simulator to scenes with hundreds of objec... | Rebuttal 1:
Rebuttal: We thank the reviewers for the thoughtful and constructive comments.
We are happy that the reviewers appreciated the contributions of our paper, specifically that the paper *addressed an important and interesting problem … and has significant potential value in various fields, including robotics,... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Gradient Accumulation Method for Dense Retriever under Memory Constraint | Accept (poster) | Summary: This paper proposes a method for training with contrastive loss with more memory efficiency and stability. The paper focuses on setups with memory constraints. In such settings, the batch size cannot be too large which leads to limited negative samples for computing per-batch contrastive loss. Prior work, Grad... | Rebuttal 1:
Rebuttal: # Weakness
**Broader Applicability**
ContAccum has the potential to be applied to various tasks and modalities. As mentioned in the global rebuttal, while ContAccum was initially proposed for training dense retrievers, its application can be extended to other domains due to the similarities in ... | Summary: The paper begins by addressing the weaknesses of traditional methods, specifically pointing out that Gradient Accumulation utilizes fewer negative passages and Gradient Cache requires extensive training time. To overcome these issues, the authors propose a novel method called Contrastive Accumulation. This met... | Rebuttal 1:
Rebuttal: Weaknesses
===
**Detailed Analysis Compared to GradAccum and GradCache**
As explained in the global rebuttal, ContAccum achieves similar training speed to GradAccum and faster than GradCache while outperforming both in terms of performance. This is because ContAccum utilizes simple forward/back... | Summary: This work proposes ContAccum, a novel approach to address the challenges of training dense retrievers within limited memory resources. This method employs a dual memory bank structure to leverage previously generated query and passage representations. It allows for the reuse of past representations without the... | Rebuttal 1:
Rebuttal: # Weaknesses
**Novelty of ContAccum compared to pre-batch negatives**
ContAccum directly addresses the primary limitation of PreBatch: its inherent instability during the training process. PreBatch did not consistently enhance performance throughout the training epochs, a problem highlighted in bo... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank the three anonymous reviewers for their insightful feedback. We have made our best efforts to respond to all points raised and provide clarification where needed. Furthermore, These comments have provided us with an opportunity to strengthen our statement through additional expe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Cross-video Identity Correlating for Person Re-identification Pre-training | Accept (poster) | Summary: This work explores learning the ReID model from a large number of unlabeled videos. The identity associations within the video and across the videos are mined. Extensive experiments under different backbones and downstream ReID settings are conducted.
Strengths: 1. Learning from unlabeled videos makes sense f... | Rebuttal 1:
Rebuttal: Sincerely thanks for your constructive and valuable comments. The concerns are answered as follows.
**(i) Analyses of technical contribution.**
Thanks for your nice concern.
Person Re-ID, a fine-grained visual retrieval task, centers on learning a feature distribution that robustly captures the... | Summary: The paper introduces a Cross-video Identity-cOrrelating pre-traiNing (CION) framework for person re-identification. CION addresses the limitations of existing instance-level and single-video tracklet-level pre-training methods by leveraging identity-invariance across different videos. The framework uses a prog... | Rebuttal 1:
Rebuttal: Sincerely thanks for your constructive and valuable comments. The concerns are answered as follows.
**(i) Include results from other datasets.**
Thanks for your suggestion.
Our work focuses on proposing a superior pre-training framework to enable the model to achieve exceptional performance on... | Summary: The authors propose an unsupervised method for mining identity relations in large-scale video data, incorporating self-supervised learning to pre-train the general models (like CNN, transformer). This approach demonstrates superior performance in person ReID tasks compared to other pre-training models.
Streng... | Rebuttal 1:
Rebuttal: Sincerely thanks for your valuable comments. The concerns are answered as follows.
**(i) Repective contribution analyses.**
Thanks for your concern. In Sec. 4.4, the 2nd and 3rd experiments inherently quantify the respective contributions of the identity correlation seeking and the large scale o... | Summary: This paper present a novel framework CION, to learn identity-invariance from cross-video person images for person re-identification pre-training. In particular, they model the identity correlation seeking process as a progressive multi-level denoising problem, with a novel noise concept defined. Also, they pro... | Rebuttal 1:
Rebuttal: Sincerely thanks for your constructive comments. The concerns are answered as follows.
**(i) Comparison with other identity-ignored pre-training methods.**
Thanks for your nice concern. Firstly, the experiment in Tab. 4 has yielded the following results:
when pre-trained on exactly the same da... | Rebuttal 1:
Rebuttal: We sincerely appreciate all reviewers' time and efforts in reviewing our paper.
We are glad to find that reviewers generally recognized our contributions including the novelty and significance of our method (Reviewer hfuf, uzbJ, 3WDg, 5xRM), the comprehensive experiments (Reviewer uzbJ, 3WDg, 5x... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Leveraging partial stragglers within gradient coding | Accept (poster) | Summary: In distributed learning, workers in large scale clusters may be slower than expected promised or are prone to failure. Gradient coding tries to address this by introducing redundancy within the assignment of chunks (of data) to workers. There, ideas from coding theory are used to recover the full gradient.
E... | Rebuttal 1:
Rebuttal: _[Weakness-1]_ - We thank the reviewer for pointing this out. We are happy to act on suggestions from the reviewer about this issue.
_[Weakness-2]_ _and_ _[Questions 1 \& 2]_ - You are considering a situation where the PS dynamically adjusts the chunk assignment based on the status of the clust... | Summary: This paper provides a coded distributed computation solution when the parameter server needs to compute the gradient (exactly or approximately) over a very large dataset. The idea relies on distribution dataset into chunks and assigning chunks to different workers, such that each worker computes the gradient f... | Rebuttal 1:
Rebuttal: _[Weakness-1]_ - The reviewer is correct in stating that our algorithm needs more communication. However, we would like to clarify certain points here. Most work in the gradient coding including the original protocol and ours assumes a setting where communication is not noisy. This is easily justi... | Summary: This paper considers the gradient coding (GC) framework for distributed learning systems to address the issue of stragglers. Compared to the standard GC protocol, the authors propose new approaches that utilize the partial work done by partial stragglers. These protocols aim to enhance computation and communic... | Rebuttal 1:
Rebuttal: _[Question 1 and second part of Weakness 1]_ - The reviewer's proposed solution works only in the case where we consider $\ell=1$, i.e., in the case when we are not communication-efficient. In this situation, it is potentially possible to arrive at a protocol where each $g_{\mathcal{D}_i}, i = 1, ... | Summary: This paper addresses inefficiencies in gradient coding protocols for distributed learning systems, specifically targeting the issue of stragglers—workers that are slower than expected or fail to complete their tasks. The authors introduce novel gradient coding protocols that incorporate the partial work perfor... | Rebuttal 1:
Rebuttal: _[Weakness-1]_-We would like to respectfully disagree with the assertion about limited novelty. While the original gradient coding protocol and the follow-up works have made several contributions in our understanding of the area, they all suffer from the major limitation that the training takes un... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful comments. We have provided exhaustive bulleted responses to each question and weakness that has been pointed out by the reviewers. In some cases, essentially the same comment appears as a weakness and a question. In this case, we have provided a join... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Iteration Head: A Mechanistic Study of Chain-of-Thought | Accept (poster) | Summary: This paper describes an algorithm called the "iteration-head" by which autoregressive transformers can implement simple iterative functions, which may be related to the chain of thought (CoT) reasoning capabilities of language models. The paper shows empirically that simple two-layer transformers can learn to ... | Rebuttal 1:
Rebuttal: Thank you very much for your positive review.
We will integrate a longer discussion to compare our circuit with induction heads. We understand that induction heads were introduced as an example of how to move information across "registers" (the $(e_{t,l})$) with copying mechanisms. The induction ... | Summary: - The authors try to understand Chain of Thought in LLMs by studying tiny toy autoregressive transformers trained on algorithmic tasks (like parity, or polynomial iteration) that are much easier to solve iteratively, and which try to be a proxy for problems LLMs solve with CoT
- These toy tasks have the form "... | Rebuttal 1:
Rebuttal: Thank you for your detailed and thoughtful review of our paper. We appreciate your feedback and your many valuable suggestions. To answer your major concerns, we detail our perspective below.
Regarding the relevance of our toy model to real-world LLMs, we agree that our setup is simple and may no... | Summary: The authors propose a simplified formatting of the input, that represents the structure of chain-of-thought reasoning. They then propose a theoretical circuit that can efficiently copy information from different indices in the input to the appropriate position in the chain-of-thought to perform an additional ... | Rebuttal 1:
Rebuttal: Thank you for your positive review and valuable feedback on our paper. We appreciate your concerns and will address them below.
Regarding the relationship between iteration heads and other copying mechanisms in language models, such as induction heads, we understand that induction heads were intr... | Summary: This paper provides a mechanistic study of small transformers trained to perform simple algorithmic tasks (polynomial iteration, parity, binary copy) with scratchpads. Findings show that trained transformers exhibit attention patterns dubbed "iteration heads": heads that iterate through the input, performing i... | Rebuttal 1:
Rebuttal: Thank you for your positive review and valuable feedback on our paper. We appreciate your concerns and will address them below.
We acknowledge that there is some ambiguity between the concepts of "scratchpad" and "chain-of-thought" and will review our manuscript to make it clearer. In our view, s... | Rebuttal 1:
Rebuttal: We appreciate the time and effort the reviewers spent reviewing our paper. The overall reception has been positive. The reviewers appreciate the "iteration head" definition and experiments regarding its transferability across tasks.
Reviewers expressed concerns that will help us greatly improve o... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
An Offline Adaptation Framework for Constrained Multi-Objective Reinforcement Learning | Accept (poster) | Summary: This paper presents a novel framework for multi-objective RL that eliminates the need for handcrafted target preferences. Instead, it uses demonstrations to implicitly indicate the preferences of expected policies. The proposed offline adaptation framework can also handle safety-critical objectives by utilizi... | Rebuttal 1:
Rebuttal: We thank the reviewer for all the valuable comments. Please refer to the point-to-point responses below.
**W1 & Q1: Impact of reward correlation on preference match**
We might not fully understand the reviewer's concern about reward correlation, so we would appreciate further clarification if we... | Summary: The paper presents a new multi-objective RL setup to learn a policy offline that can adapt to unknown target preference online, utilizing posterior estimates of the true target preference.
Strengths: 1. The paper is very clearly written.
2. The base policy is reasonable (decision transformer and diffusion pol... | Rebuttal 1:
Rebuttal: We thank the reviewer for all the valuable comments. Please refer to the point-to-point responses below.
**W1: About contribution**
First, we would like to clarify that our primary contribution is not to develop a new MORL algorithm parallel to MODF or MORvS but to propose a flexible and minimal... | Summary: This paper addresses the issue of handcrafted target preferences in multi-objective RL (MORL) by proposing an offline adaptation framework called Preference Distribution Offline Adaptation (PDOA), which implicitly learns the preferences from a small number of demonstrations. Specifically, PDOA involves two ste... | Rebuttal 1:
Rebuttal: We thank the reviewer for all the valuable comments. Please refer to the point-to-point responses below.
**W1: Lack of offline IL baselines**
Applying offline IL to our problem faces several challenges: 1) Offline IL requires complete policy training for each adaptation, which is resource-intens... | Summary: The paper tackles the important problem of adapting multi-objective policies to diverse preferences and constraints at deployment. This work uses state-of-the-art multi-objective RL methods to learn a set of policies that can align with diverse preferences. At deployment time, it doesn't assume access to the e... | Rebuttal 1:
Rebuttal: We thank the reviewer for all the valuable comments. Please refer to the point-to-point responses below.
**W1: Unavailable access to the explicit preferences**
We agree that unavailable access to preference labels is indeed a problem in real-world MORL applications. Some unsupervised techniques,... | Rebuttal 1:
Rebuttal: We appreciate all the valuable comments from the reviewers. We have provided point-to-point responses to each reviewer's questions and will revise our manuscript according to their suggestions.
Pdf: /pdf/9da6b448a81ddde23a0802f4ec616870e8857680.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Cherry on Top: Parameter Heterogeneity and Quantization in Large Language Models | Accept (poster) | Summary: The paper introduces a quantization technique for LLMs, inspired by an empirical study on parameter importance. The authors identify a small subset of parameters, termed "Cherry parameters," which exhibit significantly higher importance than others. They propose an optimization strategy: retaining full precisi... | Rebuttal 1:
Rebuttal: **W1**: The method depends on back-propagation, which could lead to computational and memory overhead challenges in resource-limited scenarios.
**Response**: While the quantization indeed requires higher computational resources, this is a one-time operation. Once performed, the quantized model w... | Summary: This paper investigates the phenomenon of parameter heterogeneity in large language models (LLMs) and proposes a novel quantization method called CherryQ. The authors find that a small subset of parameters, referred to as "cherry" parameters, have a disproportionately large influence on model performance, whil... | Rebuttal 1:
Rebuttal: **W1**: What is real improvement in terms of efficiency?
**Response**: We have now conducted real-world tests on a single A100 GPU to measure the generation speed and memory consumption of CherryQ when generating 128 tokens. The results for LLaMA2-7b are summarized in the table below. Compared t... | Summary: This paper presents a new post-training quantization strategy called "CherryQ" that identifies "cherries" --- meaning parameters that disproportionately impact the model's loss --- stores those in fp16, and quantizes the rest of the model parameters using a standard symmetric min-max quantization (with groups)... | Rebuttal 1:
Rebuttal: **W1 and Q1**: It would have been useful to **directly** confirm that the proposed way of approximating impact on loss is actually a good approximation of the impact of a parameter on the loss.
**Response**: Following your suggestion, to directly validate the accuracy of our impact approximation,... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
What makes unlearning hard and what to do about it | Accept (poster) | Summary: This paper studies the difficulty of unlearning with respect to different unlearning sets. Specifically, the authors examine two factors, namely entanglement between forget and retain sets, and memorization. The authors further propose a framework called RUM which first divides the forget set into different su... | Rebuttal 1:
Rebuttal: **Re: “leave-one-out experiments” and “did you use any approximation?”**. Let us clarify the setup.
While the reviewer is correct that Feldman's definition of memorisation implies a 'leave-one-out' training, in practice this evaluation is prohibitively expensive, as pointed out. As a more efficie... | Summary: This paper looks at measurable qualities of forget/retain sets in machine unlearning contexts, which can make it difficult to 1) unlearn the forget set at all 2) unlearn the forget set without also unlearning the retain set. The paper puts forward well-reasoned metrics that get at entanglement/homogeneity in t... | Rebuttal 1:
Rebuttal: **Re: More discussion about efficiency / costs of RUM**. This is a great point! Many researchers in this area do not bother with addressing issues of efficiency. Due to space constraints, we may have also been guilty of not paying as much attention as needed on this topic. This is unfortunate as i... | Summary: This paper studies how two properties of “forget sets” make machine unlearning hard: similarity/entanglement between the forget set and an accompanying retain set, and a memorization score for the forget set. The authors hypothesize and experimentally confirm that (1) high entanglement makes unlearning harder,... | Rebuttal 1:
Rebuttal: **Re: Weaknesses of ToW**, we discern two interesting issues raised: 1) using average accuracies, 2) that it might be preferable to base ToW on another metric like MIA.
For 1), we computed for each example the output of the unlearned and retrained models, and we count the number of different pre... | Summary: This paper investigates the characteristics of forget data for machine unlearning algorithms and demonstrates their impact on the effectiveness of various unlearning methods. The paper further proposes refining the forget set into subsets based on data characteristics and selecting suitable unlearning algorith... | Rebuttal 1:
Rebuttal: **Re: limited to classification models.** We agree it is important to study different modalities, discriminative and generative models. However, these settings differ in three key ways.
1) Unlearning definitions: The problem we study is removing the influence of a particular subset of training da... | Rebuttal 1:
Rebuttal: We thank all reviewers for the valuable feedback. We are pleased to hear that the reviewers found our analysis “valuable”, that we study “very sensible questions”, “very comprehensively”, and our paper “makes excellent contributions to the unlearning science”; that our findings are good both “in t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FashionR2R: Texture-preserving Rendered-to-Real Image Translation with Diffusion Models | Accept (poster) | Summary: This paper proposes a diffusion-based method for enhancing the realism of fashion images generated through computer graphics pipelines. To this end, the paper introduces a Texture Preserving Attention Control for improving the appearance of the textures in garments in the generated images, and a domain-knowled... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for acknowledging the importance of the problem, the contribution of our dataset, the sound method and our presentation, the effective evaluation, and additional information in code and supplementary. The suggestions and questions also inspire us to improve our work. Bel... | Summary: This paper proposes a method to translate synthetic renderings of fashion shots into photorealistic images. It is based on Stable Diffusion; SD is first fine-tuned on the target domain; then a negative guidance token for the rendered domain is learnt; then the image is translated by DDIM-inversion in original ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the recognition and encouragement on our novel pipeline, significantly more real qualitative results, effective quantitative results, and generally well-written paper. We would also like to thank the reviewer for the rigorous analysis and suggestions to help us improve ou... | Summary: The paper presents an advancement in the field of rendered-to-real image translation, particularly for fashion images. Its novel approach, focus on texture preservation, and contribution of a new dataset make it a valuable addition to the literature.
Strengths: The Texture-preserving Attention Control (TAC) m... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's positive comment and acknowledgement on the TAC design of our method, the value of our SynFashion dataset, and the experimental results. The suggestions on user studies and further discussion are very helpful. Below we address them in detail:
> The paper acknowledges t... | Summary: This paper proposed a modified framework for generating realistic fashion photos. The framework enhances existing diffusion models through a knowledge injunction pre-training process and an attention control mechanism during the generation process. Additionally, the author introduced a new dataset including hi... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our contributions in the proposed dataset and "impressive" performance of our framework, as well as the valuable feedback and suggestions. Below we address the concerns and questions in detail:
> There is no updating on diffusion structure but relies on pre-t... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable time and insightful comments to help us improve our paper. In particular, we are encouraged by the positive feedbacks:
1. "The paper tackles an important problem". (Reviewer L3jv)
2. The proposed high-quality SynFashion dataset "fills a gap in relevant... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Bridge-IF: Learning Inverse Protein Folding with Markov Bridges | Accept (poster) | Summary: This work introduces the bridge process generative model for inverse protein folding using Markov bridges to learn the probabilistic dependencies between protein backbone structures and sequences. This model aims to address the limitations of existing methods, such as error accumulation and the one-to-many map... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer for the insightful and constructive comments!
> While the authors show the accuracy / PPL and TM-scores, I wonder whether it is possible to show that, experimentally, the proposed method can make discovery of novel protein sequences, or engineer existing proteins... | Summary: The authors propose a Markov Bridge-based model for the protein inverse folding problem, incorporating a language model. This model outperforms all baseline methods on the CATH dataset. However, achieving state-of-the-art results on accurate crystal test datasets alone is not sufficient in practice. In real-wo... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer for the insightful and constructive comments!
> Experiment on de novo proteins.
We conducted experiments based on your suggested setups. We sample 10 backbones at every length [100, 500] in intervals of 5 using Chroma. Inspired by the finding of ProteinMPNN that... | Summary: The paper proposes a new method called Bridge-IF for the problem of inverse protein folding. The main objective is to develop a diffusion bridge generative model called Bridge-IF that can generate high-quality protein sequences from a structure-aware prior. The authors claim that they outperform state-of-the-... | Rebuttal 1:
Rebuttal: We deeply appreciate your critical comments on our paper. However, we believe some of the concerns are caused by potential misunderstandings, and we hope that our response can address your concerns.
> **W1**: The authors have toned down the baseline results. For example, KWDesign reported recover... | Summary: In this paper the authors introduce Bridge-IF, an inverse folding approach based on a novel Markov bridge sampling formulation and both protein sequence and structure encoders. They show that leveraging both the information from pre-trained protein language models and a probabilistic, iterative sampling proced... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer for the insightful and constructive comments!
> **W1**. Because of the effectiveness of the pLM in approximating the Markov bridge process, it is not as clear how central the Markov bridge approach itself is to the results, versus augmenting an “autoregressive di... | Rebuttal 1:
Rebuttal: Dear Area Chairs and Reviewers,
In the author response period, we made diligent efforts to address reviewers' concerns and provided additional experimental results to further verify our contributions. The summary of our main efforts is presented as follows:
- We have provided a detailed explanat... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MoE Jetpack: From Dense Checkpoints to Adaptive Mixture of Experts for Vision Tasks | Accept (poster) | Summary: The authors propose MoE Jetpack, a method to transform dense checkpoints into a SoftMoE-like network. They introduce checkpoint recycling to utilize dense checkpoints as initial weights for MoE models. Additionally, the authors enhance the SoftMoE with the proposed Expert Regularization and adaptive Dual-path ... | Rebuttal 1:
Rebuttal: > **W1-3:** The comparison in the main results appears to be unfair... MoE Jetpack is initialized with a pre-trained model and then fine-tuned on the evaluation dataset. However, the baseline (SoftMoE) is trained from scratch solely... you should use a SoftMoE pre-trained on ImageNet-21k and fin... | Summary: This paper introduces MoE Jetpack, a novel method to efficiently fine-tune dense model checkpoints into sparsely activated mixture of experts (MoE) models, reducing the need for extensive data and computational resources. Key techniques include checkpoint recycling and the hyperspherical adaptive MoE (SpheroMo... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their valuable comments and suggestions. We have carefully considered each point and provided our responses below.
> **W1:** The authors need to carefully check the formatting details of the paper...
We acknowledge the formatting issues identified. We will co... | Summary: This paper introduces MoE Jetpack, a novel method for fine-tuning pre-trained dense model checkpoints into mixture of experts (MoE) models. It leverages checkpoint recycling to accelerate convergence and enhance accuracy, and incorporates a hyperspherical adaptive MoE (SpheroMoE) layer for optimized integratio... | Rebuttal 1:
Rebuttal: Thank you for your review and constructive comments on our paper. We appreciate the opportunity to address your concerns and clarify our work.
> **W1:** The work consumes extensive computational resources on small-scale datasets. It is recommended to conduct more extensive experiments on ImageNe... | Summary: The paper proposes several ideas to convert dense checkpoints into MoEs for vision tasks. In particular, four alternatives are presented to reuse checkpoints of dense models to be use as an initial checkpoint for an MoE model for a later fine-tuning phase. Of the four alternatives presented, Importance-Based W... | Rebuttal 1:
Rebuttal: Thank you for your detailed review of our paper and for highlighting the novelty and effectiveness of our method. We will address each of the concerns you raised in detail.
> **W1:** All experiments are done with quite modest backbones ...
We acknowledge the use of modest backbones due to limit... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We would like to express our gratitude for your insightful feedback and suggestions, which have been instrumental in updating and enhancing our submission. We are particularly grateful for your recognition of the novelty of our approach (R 7BZ9, ysa3), the thoroughness of our expe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning an Actionable Discrete Diffusion Policy via Large-Scale Actionless Video Pre-Training | Accept (poster) | Summary: The authors propose an approach that takes advantage of human-object interaction videos to improve policies. Their proposal involves three steps: 1) learn a video tokenizer common to both human and robot videos. 2) Learn a discrete-diffusion model that performs video token denoising on both human and robot vid... | Rebuttal 1:
Rebuttal: Thank you for your thorough comments and valuable suggestions! Below, we carefully address your concerns:
>1. ... how much the human videos are adding to the policy performance? ... How does the model without human data perform given some more robot trajectories?...
Thanks for your question.
(1... | Summary: In this paper, the authors focus on the problem of policy learning via leveraging large video data without action labels. To this end, they employ a discrete diffusion framework which is first employed to predict future quantized video frames. Afterwards, the model is fine-tuned on robot data to learn the fina... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and for a positive assessment of our work. Here, we carefully address your concerns as follows:
>1. Technical novelty is limited. The approach is similar to a lot of other approaches: quantize videos using VQVAE, predict future tokens using transformer, somehow ... | Summary: The authors propose utilizing large scale internet videos along with robot data to train a diffusion model which predicts video future frames conditioned on past video frames and language description of videos. This diffusion model operated on the vector quantized embeddings of the video frames. Subsequently, ... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback and for a positive assessment of our work! We carefully address your concerns as follows.
>1. How does the method compare against other generalist imitation learning methods like Octo, RT-1 etc?
Thanks for your suggestions. We would like to clarify that Octo a... | Summary: The paper targets at transferring the knowledge from human videos to robotic manipulation policy. The paper proposes VPDD, a method that first pre-trains on video generation on both human and robot videos and then fine-tunes on robotic manipulation data with action labels. The intuition is, by learning to gene... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review and a positive assessment of our work! We carefully address your concerns as follows.
>1. ... how many robot videos are we using here? If the robot videos are too few, will it affect the resulting pre-trained model from this stage?...
As stated in Se... | Rebuttal 1:
Rebuttal: ## General Response
We thank all of the reviewers for their time and insightful comments. Furthermore, we are very glad to find that reviewers generally recognized our interesting idea, the superior performance of our method, and the clear presentation of our paper:
### Contributions:
- **Method**... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Near-Optimality of Contrastive Divergence Algorithms | Accept (poster) | Summary: The paper provides a non-asymptotic analysis of the Contrastive Divergence (CD) algorithms, demonstrating their ability to achieve parametric convergence rates and near-optimal asymptotic variance in the online setting. It also shows near-parametric rates in the offline setting, extending previous results. The... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful comments. We address their concerns below.
**Regarding the techincality of the paper** We agree that Section 4 is technical, and that it is crucial to increase its accessbility to a broad readership. We will take multiple actions to improve the accessibili... | Summary: This paper analyzes the contrastive divergence algorithm for learning exponential family distributions.
Both online and offline versions are studied. The main contribution of this paper is to establish the parametric convergence rate $O(n^{-1/2})$ of the estimator, which improves the previously best result of ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and suggestions. We address their concerns below.
**On the technicality of the paper** We agree that the paper in its current form is technical, and agree that increasing the accessibility of our work to a broad readership is important. The main message is... | Summary: The paper studies parameter estimation problem in naturally-parametrized exponential families using the contrastive divergence methods, i.e., SGD where the stochastic gradients are estimated via MCMC. Both online and offline variants of the algorithms are considered. Building upon existing works on stochastic ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive review. We address the reviewer's concerns below.
**Regarding the benefits of offline CD** We agree that the analysis of offline CD is more delicate than the one of online CD. On the other hand, we stress that while online CD is asymptotically opti... | Summary: The paper delivers a detailed non-asymptotic analysis of the Contrastive Divergence (CD) algorithm applied to unnormalized exponential family distributions. It significantly advances the understanding of CD by showing that, under certain regularity conditions, the algorithm can achieve the parametric convergen... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful comments. We address their concerns below.
**Regarding the regularity conditions required to achieve the $O(n^{-1/2})$ rate** One of the assumptions made in our theorems regarding online CD is that the parameter space $\Psi$ is compact. Under this assu... | Rebuttal 1:
Rebuttal: We thank the reviewers for their comments, which were very helpful to improving the current manuscript. We have addressed the concerns of each reviewer in their respective threads. We provide below a summary of the most common reviewer's comments, and of our actions taken to address them.
**Rega... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper studies the problem of finding a maximum-likelihood estimator for an exponential family. The paper proves guarantees for the Contrastive Divergence (CD) algorithm, which iteratively performs stochastic gradient descent on the cross-entropy loss between the estimated model and empirical data distribu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and in particular for challenging us regarding the significance of our work and its relevance to the NeurIPS community, which will help better position our contributions. We explain further the relevance and significance of our results below. We will make t... | null | null | null | null | null | null |
Almost Minimax Optimal Best Arm Identification in Piecewise Stationary Linear Bandits | Accept (poster) | Summary: he paper introduces a novel algorithm, PSεBAI+, designed for ε-Best Arm Identification in Piecewise Stationary Linear Bandits (PSLB). The algorithm addresses the challenge of identifying an arm with an average return close to the optimal under varying contexts and unknown changepoints. It incorporates change d... | Rebuttal 1:
Rebuttal: >Comparison with the current state-of-the-art methods.
We thank the reviewer for the question. As we stated in Line 101 in the manuscript, to the best of our knowledge, there is limited literature investigating the BAI problem in the nonstationary bandit setup (there is comparatively a much riche... | Summary: The paper introduces a piecewise stationary linear bandit (PSLB) model and presents the PSεBAI+ algorithm for identifying the best arm with minimal sample complexity. The PSLB model accounts for environments where contexts change at unknown points and are drawn from an unknown distribution. The PSεBAI+ algorit... | Rebuttal 1:
Rebuttal: >The justification for using a piecewise stationary model could be more robust, as real-world scenarios may involve more complex conditions.
We agree that the real-world scenarios may involve more complex conditions. However, even under our relatively benign setup, the proposed solution is alread... | Summary: The paper proposes a new model called PSLB, where the underlying parameter $\theta$ in the common linear bandit model is sampled from an unknown distribution at fixed but unknown time steps. The authors propose a naive algorithm and a sophisticated algorithm for this problem, along with their sample complexity... | Rebuttal 1:
Rebuttal: >Typo: dynamics --> algorithm?
We are sorry for the confusion. There might be a cross citation problem in the manuscript which will be fixed in the revised version.
The "Dynamics" refers to the dynamics of the problem. Specifically, Dynamics $1$ on page 43 describes the dynamics of the proposed... | null | null | Rebuttal 1:
Rebuttal: **We would like to thank all the reviewers for their valuable suggestions and we sincerely hope their concerns have been properly resolved by the detailed response provided below.**
>In most of the linear bandit best arm identification literature, for example, references 1-5, the sample complexit... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Understanding the Limits of Vision Language Models Through the Lens of the Binding Problem | Accept (poster) | Summary: Vision Language Models (VLMs), including models that are able to describe images, such as GPT-4v, and models that generate images given a text description, such as DALL-E, display several failures in tasks that humans are able to perform effortlessly. Such tasks include counting, localizing a given object in a... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and valuable feedback. We appreciate the opportunity to address your comments and clarify our work. Below is our point-by-point response:
**Concerns about response parsing**
We thank the reviewer for pointing out this important concern. We have now eliminated t... | Summary: This work investigates the performance of the vision language model (VLM) GPT-4v and text-to-image model DALL-E-3 on multi-object reasoning tasks. According to the literature, humans perform well on multi-object tasks because they learn a compositional representation of the world and use serial processing to d... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and valuable feedback. We appreciate the opportunity to address your comments and clarify our work. Below is our point-by-point response:
**Concerns about generalizability and scope of our results**
We thank the reviewer for suggesting to run additional control... | Summary: **Summary:**
The paper investigates the counting abilities of VLMs. It puts forward a cognitive science inspired perspective, where VLMs are fundamentally constrained in their processing of multi-object scenes due to a lack of serial processing mechanisms.
**Decision:**
The paper is well structured and inve... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and valuable feedback. We appreciate the opportunity to address your comments and clarify our work. Below is our point-by-point response:
**Clarification on human-like capacity limits and the logic of our experiments**
The reviewer has asked for clarification r... | Summary: The paper puts forth the hypothesis that VLMs suffer from similar constraints as humans in multi-object visual processing, especially
with respect to "binding". Results from cognitive science show human constraints in numerical estimation, visual search, and relation
identification when not able to do serial ... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and valuable feedback. We appreciate the opportunity to address your comments and clarify our work. Below is our point-by-point response:
**GPT-4v’s ability to describe synthetic images, and relationship to concurrent work (‘Vision language models are blind’)**... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We sincerely thank you for your thoughtful and constructive feedback. In response to your comments, we have conducted several new analyses that we believe have significantly strengthened our work:
1. **Extended Model Analysis**: We expanded our study to include five VLMs (GPT-4v,... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
An End-To-End Graph Attention Network Hashing for Cross-Modal Retrieval | Accept (poster) | Summary: In this paper, a cross-modal hash retrieval method based on graph attention networks is proposed. The main framework of the authors' research contains three key components: feature extraction, graph attention classifier and hash code module. An end-to-end architecture is realized by combining CLIP and Transfor... | Rebuttal 1:
Rebuttal: Weaknesses:
Q1.The paper could delve deeper into the details of how the CLIP and Transformer models are integrated.
R1: Thank you for your comments. We would added more enough detail on the process of how the CLIP and Transformer models are integrated as follows. In the feature extraction module... | Summary: The paper mainly tries to address the issues about uncomprehensive feature representation and semantic associations of the existing cross modal retrieval works. In the paper, EGATH, an end-to-end graph attention network hash is proposed. EGATH adopts CLIP to improve the generalization ability in semantic consi... | Rebuttal 1:
Rebuttal: Weaknesses & Questions:
Q1.Some grammar, spelling, and symbol errors need carefully check.
R1: Sorry for this, we will carefully check to ensure that there are no grammatical, spelling, and symbol errors in the final version.
Q2. For the title, ‘Network Hash’ seems uncommon. Please check it
R2... | Summary: In this paper, the authors present an E2E graph attention network based hashing method for cross-modal retrieval task. The proposed method adopts CLIP and Transformer to extract the features of images or texts. In addition, it also uses a classifier based on graph attention network to predict labels to enhance... | Rebuttal 1:
Rebuttal: Weaknesses:
Q1. The contribution is not significant. Actually, some hashing methods have explored the use of CLIP, Transformer or graph attention network.
R1: Thank you for your comments. The main contributions of our work are as follows.
By combining CLIP and Transformer technologies, an end-to... | Summary: This paper proposes a method for cross-modal hash retrieval, unlike previous methods, the EGATH proposed in this paper combines multiple techniques with each other, which can effectively improve the performance of retrieval. At the same time, graph attention network is introduced to further process the informa... | Rebuttal 1:
Rebuttal: Weaknesses:
Q1. the paper does not provide enough detail on some specific components of the framework
R1:We would add more enough detail on the framework. we propose a new end-to-end cross-modal hash retrieval based on graph attention network, which contains three main components: feature extrac... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We appreciate the time and effort you have put into reviewing our paper titled “[An End-To-End Graph Attention Network Hash for Cross-modal Retrieval].” We value your feedback and have addressed each of the comments raised. Below, we provide detailed responses to the reviewers' co... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Nearly Minimax Optimal Regret for Multinomial Logistic Bandit | Accept (poster) | Summary: This paper studied the minimax optimal regret for a series of contextual MNL bandit problems, covering rewards—uniform and non-uniform cases and varying the value of the outside option $v_0$. The authors first derive the regret lower bound that provides explicit dependence on the key parameters. Then they prop... | Rebuttal 1:
Rebuttal: We are delighted that you enjoyed reading our paper and appreciated the value of our contributions. Thank you very much for your valuable feedback. Based on your detailed and insightful suggestions, we will make further improvements to our paper.
---
### **Regarding Assumption 2**
We would like ... | Summary: This paper studies MNL bandit problem and proposes computationally efficient algorithms in both uniform and non-uniform settings. The authors present lower bounds for these two settings and show their results are minimax optimal up to logarithmic factors.
Strengths: 1. The paper achieves the minimax optimal r... | Rebuttal 1:
Rebuttal: We appreciate your time to review our paper and your feedback.
We would like to clarify some of the possible misunderstandings regarding our main contributions.
Our main contributions are to establish **the first minimax regret for MNL bandits**, along with providing several **novel insights relat... | Summary: The paper closes the gap between upper and lower bounds for contextual MNL bandits over various scenarios, including for varying $v_0$ and uniform/non-uniform rewards. Furthermore, the paper proposes a computationally efficient algorithm that achieves such nearly optimal upper bound, which is validated numeric... | Rebuttal 1:
Rebuttal: Thank you very much for your positive evaluation of our paper and for your insightful and valuable feedback. Each question you raised is an intriguing research topic, shedding light on new areas for future study.
---
### **Advantage of optimistic bonus**
First, we want to clarify that the simple... | Summary: The paper studies the contextual multinomial logit bandit problem. In this problem a learner interacts with the environment over a sequence of rounds. At each round, the learner receives features for $N$ arms and decides to select up-to $K$ of them (called an assortment) and play them. The environment might se... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the value of our work and providing a positive evaluation. We will address your questions below and make improvements to our paper based on your suggestions.
---
### **Anything new to the logistic bandit**
There are interesting points that can be said about the logi... | Rebuttal 1:
Rebuttal: We would like to express our heartfelt thanks to all the reviewers for their constructive feedback. Your insights and suggestions are greatly appreciated.
In response to Reviewer 8Ks7's request, we conducted additional experiments for the logistic case ($K=1$) and for larger values of $K$ ($K=20... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors consider the problem of sequentially picking assortments of items in an online manner over a horizon of $T$ rounds, where a user then selects from the items or not according to a multinomial logit model with a fixed unknown preference vector. The items are represented as (known) feature vectors wh... | Rebuttal 1:
Rebuttal: Thank you very much for your insightful and valuable feedback. As we start our discussition, we first would like to ask for your patience for our lengthy responses. We have much to share as we have genuinely appreciated your feedback and look forward to our discussion with you! We will ensure that... | null | null | null | null | null | null |
On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries | Accept (poster) | Summary: This paper studies the problem of learning juntas from the perspective of restricted statistical query algorithms. Concretely, for some d-dimensional product distribution \mu_x^d and some conditional distribution \mu_{y|z}, the learner is given access (via samples or, in this paper, certain queries) to the dis... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation and their valuable feedback! Below we address the weaknesses and questions.
**Addressing the weakness:** Thank you for pointing that out. We will add a more intuitive summary of the proof, especially how Equality $(25)$ is derived and how the Leap expo... | Summary: This submission studies the computational complexity of learning low-dimensional functions (juntas) and examines a new type of statistical query termed DLQ, which captures the difficulty of learning using gradient-based queries. The authors identify quantities that dictate the computational lower- and upper-bo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation and their valuable feedback! Below we address the weaknesses/questions in order:
---
**Intuition behind easily learnable functions:** The simple characterization for the generative exponent in [Damian et al. 2024] is due to the setting they consider: 1... | Summary: This paper studies the complexity of learning sparse functions (i.e., juntas) using statistical and gradient queries. For that, it introduces Differentiable Learning Queries (DLQ) that can be seen as a generalization of correlation statistical queries beyond square loss.
With the notion, the paper gives a ch... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation and their valuable feedback! Below we address the weaknesses and questions.
**Simulations:**
Following the reviewer’s suggestion, we will include additional figures (beyond just the function $y_2$) in a separate section, as well as details on the arc... | null | null | Rebuttal 1:
Rebuttal: This is a continuation of the response to the Reviewer atP9; other reviewers are not subjected to read.
**Relation between $\mathsf{DLQ}_\ell$ complexity and the online SGD samples:**
In Section 7, we show that $\mathsf{DLQ}_\ell$ leap 1 functions correspond exactly to the class of functions th... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Computational Complexity of Private High-dimensional Model Selection | Accept (poster) | Summary: This paper addresses the challenge of model selection in high-dimensional sparse linear regression models under privacy constraints. It proposes a differentially private algorithm for best subset selection using the exponential mechanism, providing strong statistical utility guarantees under high-privacy regim... | Rebuttal 1:
Rebuttal: Thank you very much for recognizing the novelty and significance of the work. Below, we address all your concerns:
**(P1)** To address Question 1, we first point out that both $\pi(\cdot)$ and $\pi_t(\cdot)$ are data-dependent distributions, i.e., these distributions depend on the data, and hence... | Summary: The paper addresses the model selection problem in sparse linear regression, proposing a differentially private version of the best subset selection algorithm using the exponential mechanism. The authors prove that the proposed private algorithm requires $O(\sigma^2 s\log p/n\epsilon)$ samples to identify the ... | Rebuttal 1:
Rebuttal: We greatly appreciate your encouraging words about our paper. Below we address your concerns point by point:
**(P1)** We want to just briefly touch upon your comment regarding the weakness of the paper. You are absolutely right about the limitations of the correlation assumption (Assumption 4.2).... | Summary: This papers study the differentially private best subset selection (BSS) of sparse linear model selection.
Strengths: + It presents an O(s), where s is the sparsity parameter, improvement in the utility-privacy tradeoff.
+ The MCMC implementation of exponential sampling seems a promising direction which may... | Rebuttal 1:
Rebuttal: We thank you for your constructive comments. We will address all your concerns in the following points:
**(P1)** ``***The assumptions on both the sensitivity and margin need a better justification***'' -- We did not make any assumption on the sensitivity. It is a consequence of Assumption 3.2. Pl... | Summary: This paper studies the best subset selection (BSS) problem in high-dimensional sparse linear regression. The results of this paper are roughly as follows:
- First, adopt the exponential mechanism to design a DP BSS algorithm. The statistical/privacy guarantee of this approach can be derived based on standard t... | Rebuttal 1:
Rebuttal: We thank you for handling our paper and providing constructive comments. Below we provide point-by-point responses to your concerns:
**(P1)** We will add more plots for the auto-regressive design to improve the experiment section. Current experiments show similar results as presented in the curre... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Controlling Counterfactual Harm in Decision Support Systems Based on Prediction Sets | Accept (poster) | Summary: The paper introduces a notion of counterfactual harm for a classification setting in which a human is choosing among a subset of possible labels determined by an AI system, i.e., the automated system is used to "narrow down" the set of most likely labels for a sample. Under assumptions of counterfactual and in... | Rebuttal 1:
Rebuttal: **[W1]** In our paper, we follow the same notation used in Elements of Causal Inference by Peters, Janzing and Schölkopf, a standard book in causality research with over 2,000 citations in Google Scholar, also used in [18] (ICML 24). In particular, under the notation used by Peters et al. to denot... | Summary: This paper addresses the issue with prediction methods that generate a set of potential labels, from which a human makes the final decision. The authors identify a harmful scenario where the model's intervention leads to incorrect predictions that a human would have otherwise made correctly. To model this, the... | Rebuttal 1:
Rebuttal: **[Eq. 4]** In Eq. 4, the true label $Y$ is not a function of the predicted label $\hat{Y}$. The expectation is over the predicted label $\hat{Y}$ and, following standard notation used elsewhere, the distribution $P^{\mathcal{M} ; do(\Lambda = \lambda) | \hat{Y} = \hat{y}, X = x, Y=y}(\hat{Y})$ de... | Summary: This paper focuses on decision support designed to help humans in multi-class classification tasks via prediction sets. The paper defines the notion of counterfactual harm, namely when usage the system would lead the agent to a wrong prediction which would have been correct without the system’s ‘help’. The aut... | Rebuttal 1:
Rebuttal: **[y-hat & Y-hat]** The random variable $\hat{Y}$ always denotes the expert prediction from a prediction set $\mathcal{C}\_{\Lambda}(X)$ following the SCM specified in Eq.3. Under no intervention, $\Lambda=1$ and thus $\mathcal{C}_{\Lambda}(X) = \mathcal{Y}$, as noted in Line 131-132, i.e., the hu... | Summary: This paper relates to decision support systems based on prediction sets. It introduces a framework using conformal risk control to design prediction sets that balance accuracy with minimizing potential harm (understood as a human who has succeeded at predicting the ground-truth label of an instance on their ow... | Rebuttal 1:
Rebuttal: **[Practical examples]** We would like to first clarify that, in the medical domain, prediction sets are often referred to as differential diagnoses. A prominent example of a decision support system that uses patient history and skin condition images to provide differential diagnoses (prediction s... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their careful and insightful comments, which will help improve our paper. Please, find a point-by-point response below and a one page pdf with additional results attached. In the revised version of the paper, we will fix all typos pointed out by the reviewe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval | Accept (poster) | Summary: This work presents a novel method for user representation in retrieval and recommendation systems. The authors propose a density-based approach that leverages the density of user interactions to enhance the representation of users. This method is aimed at improving the performance of retrieval and recommendati... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and appreciation of our method's novelty, soundness, and presentation. We address your main concerns below.
**W1 Clarification on using the item categories**
We would like to clarify that many strong baselines we are comparing use the category information, su... | Summary: The paper introduces density-based user representations (DURs) using Gaussian process regression (GPR) to address limitations in existing user modeling methods for personalized recommender systems. The proposed GPR4DUR approach effectively captures user interest variability, incorporates uncertainty-awareness,... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We address your main concerns below.
**W1+Q2+Q3 Clarification on the difference of SUR, MUR, DUR**
SUR uses a single point (K=1) in the embedding space to represent a user, while MUR uses multiple points (K>1, e.g., K=4 in MaxMF) to represent a ... | Summary: The research problem addressed in this study revolves around the challenge of effectively capturing user interests to enhance the quality of personalized recommendations. Traditional methods often struggle to model users with diverse interests accurately, leading to suboptimal recommendations.
The authors emp... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and appreciation of our method's novelty, soundness, and presentation. We address your main concerns below and will add details and clarifications in the paper as needed.
**W1 The advancements of GPR4DUR over prior baselines**
Our proposed method, GPR4DUR, ad... | Summary: The authors propose GPR4DUR, which incorporates uncertainty-awareness and scales well to large numbers of users. This method leverages Gaussian process regression (GPR) to effectively capture the diverse and dynamic interests of users without the need for manual tuning. Through experiments with real-world off... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and appreciation of our method's novelty and in-depth analysis. We address your main concerns below.
**W1 Clarification on the focus of our work**
As mentioned in our paper (lines 119, 139, 212-213), our approach focuses on the retrieval phase and is best sui... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
AutoManual: Constructing Instruction Manuals by LLM Agents via Interactive Environmental Learning | Accept (poster) | Summary: This paper introduces AutoManual, a framework enabling LLM agents to autonomously build understanding of new environments and generate instruction manuals through interactive learning. The system comprises three main components: a Planner agent generating code-based plans for environment interaction, a Builder... | Rebuttal 1:
Rebuttal: 1- **Fine-tune Smaller Models:** We are unsure about the "generated data" referred to by the reviewer. Is this data generated through interactions between GPT-4 and the environment to create successful trajectories? This also seems to require the use of very capable LLMs. It is important to note t... | Summary: LLM agents show promise in many environments but require expert prompts in specific environments to perform well. This paper introduces the AutoManual framework which autonomously builds a 'manual' of rules through interactions to help adapt to new environments. The framework consists of 3 agents: the Planner ... | Rebuttal 1:
Rebuttal: 1- **Significance of Selected Ablations**:
Each row in the ablation study table in Section 4.2 was carefully chosen to demonstrate the contribution of different components of AutoManual to the overall results. Specifically:
- The first row, "Planner only," serves as our baseline, testing only... | Summary: The authors propose a new approach to interactive environment interactions by LLMs which they dub AutoManual. The approach takes an LLM and uses it in several different roles, differentiated by what context is included in their prompts, and some number of saved rules. The fundamental idea of the approach is to... | Rebuttal 1:
Rebuttal: ## Regarding the Exclusion of AdaPlanner's GPT-3 Results:
1- **Reproducibility Issues with AdaPlanner's GPT-3**: The high success rate claimed by AdaPlanner's GPT-3 is nearly impossible to replicate. We utilized the publicly available code from AdaPlanner and tested it multiple times using OpenAI... | Summary: The authors propose AutoManual, a method for improving LLM agents by collecting and updating rules online via rule-assisted interactions in the environment, where code form planning is used for the interactions. In addition to the management of rules, AutoManual also maintains skill and reflection libraries pe... | Rebuttal 1:
Rebuttal: 1- **Clarification on Environment-Specific Information:**
First, the environment-specific information provided by humans can be categorized into four types:
- (i) Brief description of the environment and roles (about 25 words)
- (ii) Actions that the agent can perform
- (iii) Example... | Rebuttal 1:
Rebuttal: We have provided detailed rebuttals for each reviewer's Weaknesses and Questions. We kindly ask reviewers to review the respective rebuttals, and if you find that our rebuttals have addressed your concerns, please consider raising your scores. Thank you very much for your patience. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MADiff: Offline Multi-agent Learning with Diffusion Models | Accept (poster) | Summary: The paper introduces MADiff, a generative multi-agent learning framework designed to tackle the challenges of coordinating multiple agents in offline MARL. Leveraging the attention-based diffusion model, MADiff effectively captures complex agent interactions, enabling effective teammate modeling and trajectory... | Rebuttal 1:
Rebuttal: **Q1: "The novelty is limited ... which is not a novel concept in the multi-agent domain."**
**A1:** Many successful design choices of the decision diffuser (DD) inspired and guided our method. DD also referred to the design of the diffuser [1], but it remains excellent work.
Compared with DM, w... | Summary: This paper presents a novel generative multi-agent learning framework named MADIFF. The framework employs an attention-based diffusion model (DM) to address the complex coordination problems in multi-agent settings. MADIFF combines centralized training with decentralized execution, enabling effective teammate ... | Rebuttal 1:
Rebuttal: **Q1: "The paper lacks a detailed discussion on the computational complexity and scalability of the proposed approach, which is crucial for practical applications."**
**A1:** The **computational complexity** of MADiff during sampling is $O(KN^3)$. The following three points warrant clarification:... | Summary: This paper proposes a novel diffusion-based offline multi-agent learning framework called MADiff. It extends previous diffusion-based offline RL work to offline cooperative MARL, particularly in CTDE and centralized control settings. The main contribution is the inclusion of an attention module in the diffusio... | Rebuttal 1:
Rebuttal: **Q1: "The novelty of the work is somewhat limited, being seen as a simple extension of Decision Diffuser to the MARL setting with an additional attention layer."**
**A1:** It is indeed that many successful design choices in single-agent diffusion learning have inspired us and guided us to derive... | Summary: The paper introduces MADiff, a generative multi-agent learning framework that leverages diffusion models (DMs) to address coordination challenges in multi-agent scenarios. It extends the previous work of using DMs for single-agent decision-making tasks, where the direct application to multi-agent problems is l... | Rebuttal 1:
Rebuttal: **Q1: "The proposed method is mostly evaluated across domains with a small number of agents (up to 8) and MADiff with decentralized execution requires each agent to predict all agents' trajectories, which is difficult to be applied to larger scales."**
**A1:** Thanks for your comment!
- Most ex... | Rebuttal 1:
Rebuttal: We want to express our sincere gratitude to all the reviewers for their valuable feedback and insightful comments. We appreciate the recognition of our paper's strengths, highlighted by the reviewers: **novelty** (nRbJ, hh3p), **superior / effective performance** (all reviewers), and **extensive ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning and Transferring Sparse Contextual Bigrams with Linear Transformers | Accept (poster) | Summary: The paper studies the training dynamics of a one-layer linear transformer on the sparse contextual bigram task. Using $\ell_1-$regularization and proximal gradient descent, they show that the training process goes through a sample-intensive phase and then a sample-efficient phase. They also extend the results ... | Rebuttal 1:
Rebuttal: Thank you for your reviews. We are glad to answer your questions.
### Initialization
We initialize the first layer weights with $a^{(k)}_t = 1/T$, which corresponds to the uniform attention. In practice,
weights are initialized to be small and softmax is used. As a result, the attention pattern ... | Summary: This paper proposes a new data model, Sparse Contextual Bigram (SCB), to study the training dynamics and sample complexity of transformers. The paper analyzes a one-layer linear transformer trained on data generated by the SCB model using a gradient-based algorithm. They prove convergence guarantees and provid... | Rebuttal 1:
Rebuttal: Thank you for your reviews! We'd like to answer your questions as follows.
## More complex architectures
We discuss in the global response on the relationship between our linear model and softmax transformers. At a heuristic
level, they are approximately equivalent, up to a change of learning ra... | Summary: The topic of this paper is understanding transformers. Since this is an ambitious goal, the authors make some reasonable simplications. Specifically, they study the training of a **one-layer linear** transformer on synthetic datasets generated by a novel data-generating model, called **Sparse Contextual Bigram... | Rebuttal 1:
Rebuttal: Thank you for your feedbacks. We'd like to address your concerns as follows.
## Meaning of $I_T$.
$I_T$ stands for the $T$-by-$T$ identity matrix. We are using one-hot positional embeddings
and attending only to positions here. Hence, the second $E$ is replaced by the identity matrix.
## Arch... | Summary: The paper studies a natural problem that generalizes the bigram model for language generation. In the bigram model, each token $x_t$ is only dependent on the previous token $x_{t-1}$. Therefore, to learn to predict the next token, one only needs to look at the bigram frequencies $P[i,j] = \Pr[x_t = i \mid x_{... | Rebuttal 1:
Rebuttal: Thank you for your reviews! We'd like to answer your questions as follows.
## Impact of non-linearity on the sample complexity
We discuss the relationship between softmax transformers and our linear model in the global response. Toward the
beginning of training, where the sample complexity is po... | Rebuttal 1:
Rebuttal: ## Relationship between our model and softmax transformers
As several reviewers
asked about the difference between the softmax transformers and our linear model, we'd like to further discuss their relationship. In short, they have qualitatively similar behaviors: there will be sample-intensive
i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fundamental Convergence Analysis of Sharpness-Aware Minimization | Accept (poster) | Summary: The authors provide a fundamental convergence analysis of sharpness-aware minimization algorithms,
including the normalized and unnormalized variants.
The analysis of normalized variants is based on the theoretical results of inexact gradient descent methods,
while the analysis of unnormalized variants is base... | Rebuttal 1:
Rebuttal: Dear Reviewer cPcD,
Thank you for your precious time reviewing our paper. We answer your question as follows
*1. I think the analysis of the convex case (Sec 3.1) is meaningless. As for the convex function, usually, we can use some convex programming methods to obtain the global optimal solution... | Summary: This paper studies the convergence properties of Sharpness-Aware Minimization (SAM) and some of its variants. The authors use the inexact gradient descent to present convergence guarantees for convex and non-convex cases.
Strengths: I think it is interesting to unify different variants of SAM under the inexac... | Rebuttal 1:
Rebuttal: Dear Reviewer ZtLt,
We really appreciate your precious time reviewing our paper. However, we hardly agree with most of your opinions on the weakness of the paper.
*1. The results seem rather a straightforward application of prior work. I think the contribution of the work is limited.*
In this p... | Summary: In this paper, the authors provide a comprehensive understanding of the SAM and its variants. They establish the convergence for SAM under different settings. Unlike the convergence results in the literature, they establish the last-iterate convergence rather than the best-iterate convergence or function value... | Rebuttal 1:
Rebuttal: Dear Reviewer Yksm,
Thank you for your precious time reviewing our paper. We answer your question as follows.
*1. In line 480, you should refer to Proposition 3.9 in Khanh et al.*
It is actually Proposition 2.4 in the published version of the paper, as we cited.
*2. Without the KL assumption... | Summary: ### Summary:
This paper studies various notions of convergence for SAM. The results are summarized in Table 3. Indeed, in Theorem 3.2, they prove the convergence of SAM for smooth convex function for several definitions under diminishing stepsize. Moreover, in Figure 1, they argue that SAM cannot converge wi... | Rebuttal 1:
Rebuttal: Dear Reviewer wdGg,
We really appreciate your precious time reviewing our paper. However, we hardly agree with your opinions on the paper.
*1. The paper is not well-written.*
This comment is going against with opinions of other reviewers, as all of them they view the presentation of our paper i... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thank you all for the valuable feedback. We discuss questions and concerns raised in the answers below. If you have any further questions, please do not hesitate to contact us. We are more than happy to answer them!
Best regards,
Authors | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Statistical-Computational Trade-offs for Density Estimation | Accept (poster) | Summary: The paper provides a lower bound, and an upper bound w.r.t the hard instances, for the problem of Density Estimation. Their lower bound is based on the ‘list-of-points’ model of computation, which captures all the upper bounds from existing results. Their bounds quantify the necessary tradeoff between sampling... | Rebuttal 1:
Rebuttal: Thank you for the review! We address your question below.
>A quick question about the model of computation: do you suspect this to be the only and the main model of computation that is relevant for this problem? Are there any known algorithms (even inefficient) that do not fit this model of compu... | Summary: This paper considers the discrete density estimation problem supported on n points. Specifically, given a set of k discrete distributions and samples from one distribution, we would like to recover the underlying distribution. The main focus of this paper is on the interplay between sample complexity and query... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We address your comments below.
>This paper only considers discrete density estimation, while I think it might be more interesting to learn density in the continuous setting, such as in mixtures of Gaussians.
The main contribution of this paper is a strong lower boun... | Summary: This paper considers the problem of constructing a data structure for density estimation: given a set of k distributions over [n], construct a data structure. Given samples from one of the distributions, use the data structure to identify a nearby distribution quickly. There is a three-way tradeoff between the... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We address your comments below.
> I'm not sure that NeurIPS is the best venue, this seems much more theoretical.
We believe that NeurIPS is an appropriate venue, given that several related works (e.g., [4,7,18]) appeared in past NeurIPS conferences.
>The lower bou... | Summary: The authors study the density estimation problem, i.e., given k distributions p1,...,pk over a domain [n] and a query based on samples from a query distribution q, the goal is to output a pi close to q in the 1-norm, i.e., |q-p_i|\le eps (realizable case). Note that usually k>>n.
This leads to a natural trade-... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Please see our general response discussing the novelty of our techniques above. | Rebuttal 1:
Rebuttal: **General response: The novelty of our techniques**
Thank you for the reviews! We provide a general comment here on the novelty of our techniques and have individual rebuttals for each reviewer.
We believe that the main conceptual contribution of this paper (specifically, the lower bound part) i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Generative Hierarchical Materials Search | Accept (poster) | Summary: This paper proposes a hierarchical generative system for material search, which consists of a language model to translate user input into intermediate textual representation, a diffusion model which then generates crystal structure, and finally a property predictor used for sample selection.
The works leverag... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback of our work! Please find our response to your questions below.
> LLM finetunine baseline
The reason that we only included few-shot prompting baselines in the end-to-end evaluation of Table 1 is because, to our knowledge, there is no natural-language-to-crystal... | Summary: This work leverages the powerful capabilities of large language models to propose a generative framework that transitions from natural language to material structures. The framework divides material generation into high-level semantic information, which can be expressed as chemical formulas in text, and low-le... | Rebuttal 1:
Rebuttal: Thank you for the detailed review. Please see our response below.
> The "end-to-end" terminology.
We agree that our usage of "end-to-end" is not conventional and can be misleading. "End-to-end" in this paper’s context refers to end-to-end automation from formulae proposal to structure generation... | Summary: The paper presents a language-to-structure generation model for crystal structures. They propose a hierarchical approach that uses a language model to generate intermediate textual crystal information and then generate low-level crystal structures using diffusion models. They demonstrate their ability to gener... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful feedback. Please see our response below.
> More comprehensive baselines
The reason that we only included few-shot prompting baselines in the end-to-end evaluation of Table 1 is because, to our knowledge, there is no natural-language-to-crystal-structure dataset avail... | Summary: This paper presents Generative Hierarchical Materials Search (GenMS), a novel framework for material search using LLMs. GenMS consists of a language model that takes high-level natural language as input and generates intermediate textual information about crystals (e.g., chemical formulae). Specifically, it us... | Rebuttal 1:
Rebuttal: Thank you for recognizing the significance of this work! Please see our response to your questions below.
> Ad-hoc decisions in the framework.
We would like to clarify that the choice of $R_\text{hi}$ and $R_\text{lo}$ are not ad-hoc decisions made by the framework, but highlighting what practit... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Sharpness-Aware Minimization Activates the Interactive Teaching's Understanding and Optimization | Accept (poster) | Summary: The authors investigate the parameter update mechanism of interactive teaching, represented by co-teaching, in their article. They provide an algorithmic understanding from the perspective of Expectation-Maximization (EM) and utilize SAM (Sharpness-Aware Minimization) for interactive teaching optimization from... | Rebuttal 1:
Rebuttal: Thank you for your comments and for your interest in the experimental section. We will explain the rationale behind the experiments and provide details on updating certain formulas.
**Q1**: The description of the experimental section in the article is somewhat weak, and the authors need to presen... | Summary: This paper delves into the understanding and optimization of teaching strategies between artificial intelligence agents. Taking co-teaching as an example, the authors propose an understanding of co-teaching based on the EM algorithm within a probabilistic framework. Building on this, they point out that co-tea... | Rebuttal 1:
Rebuttal: Thank you for your thorough comments on this paper. Based on the comments provided, we will offer specific explanations.
**Q1**: The paper explores the understanding of interactive teaching from a probabilistic perspective, using co-teaching as an example. To introduce the EM framework, the autho... | Summary: In this paper, the author focus on the research of teaching method, choosing the well-known Co-teaching as a prototype to make deeper analysis. Co-teaching simultaneously optimizes dual networks and both networks select small-loss samples for the other larger loss landscape reduction. On the other hand, Sharpn... | Rebuttal 1:
Rebuttal: Thank you for your constructive question. In our response below, we further explain the motivation behind the article and provide additional experiments regarding the time complexity.
**Q1**: From my perspective, Co-teaching and SAM can be well combined from loss landscape aspect, which has been... | Summary: This work mainly proposes an understanding of the interactive teaching algorithm update mechanism led by co-teaching, as well as further optimization using the sharpness technique. The article proceeds from two aspects: one is to summarize that the co-teaching update process can be explained by the EM method, ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful question. We provide the following explanation in response to your question.
**Q1**: The authors' arguments and experiments are only on the interactive teaching of two neural networks. If conducted on three or even more networks, it is unclear whether the insights pr... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their detailed questions regarding this paper. We acknowledge that this work still requires additional clarifications and explanations, broadly in three areas: **core logic**, **experiments and complexity**, and **explanations of certain principles**. We provide the ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Extending Multi-modal Contrastive Representations | Accept (poster) | Summary: This paper introduces the Extending Multi-modal Contrastive Representation (Ex-MCR), an efficient method for learning multi-modal contrastive representations without relying on paired modality data. Unlike the previous C-MCR scheme, which connects two pre-trained modalities to a new embedding space, Ex-MCR ext... | Rebuttal 1:
Rebuttal: ## W1: Statements and Figure in the “Various Modality-centric Data” part
Thanks for pointing out the typos and providing suggestions about figures, we have fixed them in the new version.
**1.1 Unclear Figure 1(b) and its caption.**
We have uploaded the detailed captioned Figure 1(b) to the new... | Summary: This paper focus on the multi-modal contrastive representation of more than 3 modalities in multi-modal learning. To address the flaws of existing works, such as the dependency on large-scale, high-quality paired data and the expensive training cost, this paper introduces Ex-MCR, a training-efficient and paire... | Rebuttal 1:
Rebuttal: ## W1 & Q1 & Q2: Evidence for Information Loss & Support for Modality Alignment Forgetting
Taking the reprojecting CLIP representations to C-MCR space as an example, the following table(as a sub-table of Table 1) shows the performance differences before and after the projection:
| ... | Summary: The paper introduces Extending Multi-modal Contrastive Representations (Ex-MCR), a novel method for learning unified contrastive representations across multiple modalities without the need for paired data. Ex-MCR extends one modality's representation space into another, preserving semantic alignment and reduci... | Rebuttal 1:
Rebuttal: ## W1: the analysis of hyperparameter choosing
Please refer to the response to all reviewers(**part 3** The analysis of hyperparameter choosing).
## W2: 3D-Image-Text performance improvements are relatively limited.
For 3D-Image-Text experiments, we think there the main reason for the performan... | Summary: This paper proposes Extending Multimodal Contrastive Representation, a training-efficient and paired-data-free method to build unified contrastive representation for many modalities. Without using paired data, Ex-MCR achieves comparable performance to advanced methods on a series of audio-image-text and 3D-ima... | Rebuttal 1:
Rebuttal: ## W1 & W2: Lack of novelty in the article & the method is too simple
The main contribution of Ex-MCR is not to "apply contrast learning in multimodal retrieval tasks", but to efficiently integrate multiple pre-trained contrastive multimodal representation spaces into a new and more comprehensive... | Rebuttal 1:
Rebuttal: # Response to all reviewers:
## 1 Further clarification of the experimental setup
The final unified Ex-MCR representations are composed of CLIP's image, text representations, projected CLAP's audio representations, and projected ULIP's 3D representations (i.e. the $\bf{t_{i}^{I}}$, $\bf{v_{i}^{I... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LoD-Loc: Aerial Visual Localization using LoD 3D Map with Neural Wireframe Alignment | Accept (poster) | Summary: This paper proposes a hierarchical scheme for pose selection to progressively compute high-quality pose in a coarse-to-fine fashion.
Strengths: The scheme of the proposed method is easily understood and the paper shows good experimental results.
Weaknesses: The innovation of the paper is not clearly describe... | Rebuttal 1:
Rebuttal: **Q1: “As far as I am concerned, these steps are general steps for localization, what's your innovation?”**
**A1:**
We believe our innovation could be summarized in the following parts:
**Task:** We establish a novel task that utilizes Level-of-Detail (LoD) 3D maps for 6-DoF aerial visual local... | Summary: The paper presents a localization algorithm using LoD maps. Compared to conventional localization pipelines using SfM / SLAM maps, LoD maps are memory efficient and can offer privacy preservation. Nevertheless, due to the lack of texture in LoD maps, it is not straightforward to use pre-existing localization p... | Rebuttal 1:
Rebuttal: **Q1: “Several related works seem to be missing, including some recent papers on descriptor-free visual localization. […]”**
**A1:** Thank you for your suggestion. We will incorporate the related works in the revised paper.
**Q2: ”I wonder how the proposed method could be extended for larger-sca... | Summary: This paper introduces a method for visual localization of Unmanned Aerial Vehicles (UAVs) using a Level-of-Detail (LoD) 3D map and neural wireframe alignment. The UAV sensor provides coarse pose estimation. Then, LoD-Loc hierarchically builds a cost volume for uniformly-sampled pose hypotheses to describe pos... | Rebuttal 1:
Rebuttal: **Q1: “The importance of the major contribution of "Level-of-Detail" is not clearly described. Actually, Level-of-Detail has been widely investigated.”**
**A1:** The LoD model is a well-established concept used in building reconstruction and design to manage the complexity of 3D models. Compared ... | Summary: The paper presents an approach to provide an accurate camera pose of an aerial image according to a LoD 3D map, which is a lightweight representation to store the key information of a known map. For each query image, multi-scale wireframe probability maps are extracted by a trained U-net. These maps are then u... | Rebuttal 1:
Rebuttal: **Q1: “The definition of "in-Traj" and "out-of-Traj" is not clearly provided.”**
**A1:** Sorry for the confusion. The 'in-Traj' and 'out-of-Traj' scenarios refer to two distinct query image collections as introduced in **A.2 Query Image Collection** in the **Appendix** and depicted in "Figure 6:... | Rebuttal 1:
Rebuttal: We extend our sincere thanks to all reviewers for their insightful feedback. We are honored that our work has been recognized as "**practical**" (RdXx, UAbv, nkE1, H1Dr), our dataset as "**useful**" (RdXx, UAbv, nkE1), our method as "**novel**" (UAbv, H1Dr), "**interesting**" (RdXx, H1Dr), and "**... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes a method named LoD-Loc for visual localization of unmanned aerial vehicles (UAVs) using Level-of-Detail (LoD) 3D maps. Unlike existing algorithms that rely on complex 3D representations, LoD-Loc aligns wireframes derived from LoD model projections with the wireframes predicted by a CNN Unet ... | Rebuttal 1:
Rebuttal: **Q1.1: "What I observed from Table 2 even Table 3 is the result for CadLoc are all zeros. Is that a mistake?"**
**A1.1:** Upon thorough verification, we confirm that the results for CadLoc in both Table 2 and Table 3 are indeed zeros. The primary reason may be that advanced RGB-based retrieval a... | null | null | null | null | null | null |
Second-order forward-mode optimization of recurrent neural networks for neuroscience | Accept (spotlight) | Summary: The present work addresses the difficulty to optimize RNNs that are used in the realm of neurosciences to model biological data, which by design may not benefit from modern RNN inductive biases (e.g. gates), nor be trainable by the Adam optimizer and may inherently hit memory limits due to handling long tempor... | Rebuttal 1:
Rebuttal: We thank you for your thoughtful review (and for the extensive summary of our paper!). In the main rebuttal above, we have addressed the concerns common to all reviews, including your comments on small network sizes. Here, we wish to address your more specific concerns.
Thank you for bringing up ... | Summary: The manuscript introduces a second order optimization algorithm (called SOFO) for training recurrent neural networks on neuroscience tasks. SOFO does not do backpropagation, instead uses a batched forward-mode differentiation that is memory efficient for large (in time) computation graphs. Authors show that RN... | Rebuttal 1:
Rebuttal: Many thanks for your thoughtful review; we have been greatly encouraged this last week by your expressed willingness to potentially increase your score. In the main rebuttal above, we have addressed the concerns common to all reviews, including the effect of network size. Here, we address your oth... | Summary: In this work, the authors applied a second-order optimization with an approximated Hessian to train vanilla recurrent neural networks (RNNs). The authors have shown that the weight update using a random subspace approximation of Hessian can be implemented onto a GPU in a memory-efficient manner by using the Ja... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review; we are glad you found the paper clearly written and technically sound and that you found our experiments convincing. In the main rebuttal above, we have addressed the concerns common to all reviews, including your comment on the effect of network size. Here we... | Summary: In this work, the authors develop a method to train recurrent neural networks with biological plausibility constraints, which are required for neuroscience applications, and where standard BPTT doesn't tend to work well. They use forward-mode gradient calculation (RTRL) that makes the memory requirement indepe... | Rebuttal 1:
Rebuttal: We thank you for your thoughtful review; we are glad you found the paper well written and structured. In the main rebuttal above, we have addressed the concerns common to all reviews, including a more systematic study of the effect of $K$ (number of random parameter tangents used to sketch the GGN... | Rebuttal 1:
Rebuttal: We thank all reviewers for the time and effort they put into providing thoughtful reviews. We have addressed most of their concerns in reviewer-specific rebuttals, and here is an executive summary:
- We have added a comparison to FORCE learning, on a new task (3-bit flip flop) -- see Figure 1 of ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
From Transparent to Opaque: Rethinking Neural Implicit Surfaces with $\alpha$-NeuS | Accept (poster) | Summary: The paper proposes an extension of NeuS to also support transparent surfaces. It states a proposition that claims that NeuS is also unbiased for transparent surfaces, proposes an approach for simultaneous extraction of opaque and transparent surfaces, and presents a benchmark dataset.
Strengths: (1) The resul... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments.
**Q: Overview of NeuS**
A: We appreciate your feedback and will add the overview of NeuS in our revision. We prepare an initial overview as follows:
NeuS is a surface reconstruction method based on Neural Radiance Field (NeRF) that uses volume rendering t... | Summary: This paper proposes an adaptation to NeuS to extract semi-transparent surfaces. The method relies on training the original NeuS but with a modification to the mesh extraction. Instead of using marching cube; this paper proposes to extract the 0 level of the SDF field and also its local extrema that represent s... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments.
**Q: Results and discussion on opaque models (DTU).**
A: We have added additional results on the DTU dataset in Figure 4 of the supplementary PDF. The results of our $\alpha$-Neus and the original NeuS are similar.
We show the absolute values of the SDF l... | Summary: The paper proposed a extended version of NeuS that can reconstruct both transparent and opaque surfaces at the same time with unbiasedness. The unbiasedness is guaranteed by theoretical proof and a companying surface extraction method is also proposed. The authors evaluated their method on a small benchmark an... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments.
**Q: The evaluation set is relatively small, adding more synthetic cases would be fine.**
A: Thank you for your suggestion. We added a synthetic example in Figure 3 of the supplementary one-page PDF file as a test of an empty transparent object without ref... | Summary: The paper presents a theoretically justified method for surface reconstruction of transparent and opaque objects. The authors prove the theorem that the NeuS density function is unbiased even for transparent surfaces. They propose a method based on DCUDF for extracting unbiased surfaces. The method is validate... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments.
**Q: Related work on transparent objects.**
A: Thank you for highlighting several pertinent studies that we had initially overlooked. We will incorporate a discussion of these works in our revised manuscript. The works [1,2] address the reconstruction of f... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their insightful and constructive comments.
**Q: The quantitative evaluation with NeUDF.**
A: We have included further experimental results related to NeUDF in Figure 1 and 2 of the supplementary one-page PDF file. Generally, the NeuS backbone outperforms... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Inference via Interpolation: Contrastive Representations Provably Enable Planning and Inference | Accept (poster) | Summary: This paper presents a variant of contrastive learning to time series data. Contrastive learning has been used in many areas but the insight from this paper is joint distribution of representations is also Gaussian. In addition, the experimental results show that their theory can be applied to tasks up to 46 di... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for the detailed review and for the suggestions for improving the work. We have run an additional experiment on a Stock price datasets, and revised the paper to include the running time for each experiment. **Together with the responses below, does this fully address the ... | Summary: This paper shows that learning contrastive representations with the infoNCE objective combined with an L2 constraint on these representations permit Gaussian distributions on the representations. The authors then show for timeseries data, that, when we make a specific Markov chain assumption (a kind of linear ... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thanks for the detailed comments and suggestions for improving the paper. It's clear that a large amount of time was spent on this review – we appreciate it, and it will help improve the paper. We have added a new autoencoder baseline. To address the reviewer's concern about the no... | Summary: This paper proposes to use contrastive learning technique in time series feature learning, which are helpful for improving inference performance later on.
Strengths: The method proposed here is simple but effective as shown in the numerical experiments. Employment of contrastive learning in time series contro... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thanks for the detailed review, and for the insightful comment about the connection with Kalman filters. As noted by the reviewer, both Kalman filters and the representations that we analyze correspond to a probabilistic model of time series data. The key difference (again, noted b... | Summary: This paper addresses the challenges of probabilistic inference with high-dimensional time series data. It provides compact, closed form solutions in terms of learned representations by extending a prior work to show that the learned representations can be modeled as a Gauss-Markov chain. It demonstrates the ef... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thanks for the detailed comments and suggestions for improving the paper. It is clear that a lot of time went into carefully reading the paper! It seems like the main suggestion was to clarify a few sections of the text, which we have done. Per the reviewer's suggestion, we have al... | Rebuttal 1:
Rebuttal: Dear reviewers,
The attached rebuttal PDF contains the results of new experiments, which demonstrate stock prediction as a downstream application (Fig. R1) and which compare to a new autoencoder baseline (Fig. R2). Responses to each reviewer individually can be found in separate comments below.
... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Object segmentation from common fate: Motion energy processing enables human-like zero-shot generalization to random dot stimuli | Accept (poster) | Summary: Authors propose a study on the generalization of motion segmentation models to random dot kinematograms, where they study various learning based (optical flow) motion estimation models vs. a classical motion energy model. Their work is the first to explore such a motion energy model for the motion segmentation... | Rebuttal 1:
Rebuttal: Thank you very much for your review. We’re happy you find our paper to have “good contribution and novelty” and to provide a “good experimental analysis”. In the following we would like to address your concerns.
**Comparison to SOE-Net.** Thank you for making us aware of the work by Hadji and Wil... | Summary: The manuscript investigates whether artificial neural networks perceive moving random dots similar to humans. To this end, they propose a foreground-background segmentation framework to objectively measure several models. Two groups of models are explored: (1) driven by optical flow that finds matching points... | Rebuttal 1:
Rebuttal: Thank you very much for your positive review. We are happy that you think that our work “can open a new line of investigation to model human motion perception”.
**Human-machine comparison.** We see our study as an ideal observer analysis and argue that humans are not expected to reach the model p... | Summary: The authors attempt to use a classical evaluation of human gestalt perception, namely shape identification in moving random dot stimuli, to evaluate both a traditional model of motion perception (motion-energy model from Simoncelli and Heeger) against state-of-the-art deep learning-based optical flow methods. ... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We are happy that you see “many strengths” in our work.
**Comparison to Yang et al. (2023).** Indeed, this work is very related since it also compares various motion estimation models to human perception. Thank you for making us aware of this paper. The focus o... | Summary: The paper measures the zero-shot generalisation of motion energy (as described and estimated by Simoncelli & Heeger (1998)) when applied to the moving object segmentation task on the random-dot moving patterns. The comparison is performed with optical flow representation. Given a synthetic dataset of single-mo... | Rebuttal 1:
Rebuttal: We thank you for your thoughtful review. As argued in our general response, we see the main contribution of our work not to computer vision but to cognitive neuroscience; by establishing a compelling link between a biologically motivated model and high-level inductive biases of human perception.
... | Rebuttal 1:
Rebuttal: We thank all reviewers for their thoughtful reviews and are happy that the reviewers see “many strengths” (8R4V) and that our work “can open a new line of investigation to model human motion perception” (DKrM). The reviews also contain concerns and valuable suggestions that we are happy to address... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LoQT: Low-Rank Adapters for Quantized Pretraining | Accept (poster) | Summary: This paper proposes LoQT, a new method for efficiently training quantized models. LoQT is based on the low-rank decomposition of gradient matrices, and is inspired from GaLore [1] and LoRA [2] methods for transformers architectures. The work’s fundamental contributions are: 1) Propose a low-rank factors initia... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough feedback. We appreciate the recognition of LoQT’s efficient handling of optimizer state memory, its effective quantization error management and its periodic update strategy.
## 1. Additional Finetuning Experiments
Excellent suggestion; we agree that more ... | Summary: This paper introduces a novel metho, LoQT, to address the computational and memory challenges in training large neural networks. LoQT factorize the weight matrices of the neural network into low-rank components P and B. During training, only
B is updated while P and weights are freezed. Similar to ReLoRA, the... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback and for highlighting the strengths of our work.
## 1. More fine-tuning experiments
Good suggestion. We have included fine-tuning experiments of a 7B model in the **common response**. We adopt a Llama model for a new low-resource language and a com... | Summary: LoQT is a method for low-rank training of high-rank quantized weights, using gradient-based tensor factorization to initialize low-rank trainable weight matrices. LoQT is suitable for both pretraining and fine-tuning models, significantly enhancing its applicability. Despite the 24G GPU memory limitation, LoQT... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and recognition of LoQT’s memory efficiency and its practical applicability. % for both pretraining and fine-tuning through gradient-based tensor factorization.
## 1. Size of models
We agree that training larger models than 1B would strengthen our work. In p... | Summary: The authors propose LoQT, a novel method to use low-rank LoRA adaptors directly during pretraining. LoRA adaptors have been used for downstream finetuning before, but not for full pretraining.
LoQT works by first quantization a weight matrix $W$ into $W_q$. Then the quantization error is decomposed into low-r... | Rebuttal 1:
Rebuttal: ## Generalization to other architectures and models
We agree that LoQT should work with any type of DNN using linear layers, such as vision transformers or state space models. To narrow the scope of our work and provide a more detailed analysis, however, we choose to focus on a well-studied auto... | Rebuttal 1:
Rebuttal: We appreciate the constructive and positive reviews of our work. All reviewers point out the significance of the memory reduction of LoQT in pre-training, and that our experiments show that "quantization does not lead to significant degradation" **CPTS** while "significantly reducing GPU memory re... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SemCoder: Training Code Language Models with Comprehensive Semantics Reasoning | Accept (poster) | Summary: This paper proposes SemCoder, a code LLM with code semantic-aware pre-training. The authors first use OSS-instruct to create a synthetic dataset PYX for model training, and a PYX-R dataset for training LLMs for debugging and repair. Then the authors train the SemCoder model with NL to code, forward monologue, ... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful comments!
## Seed Snippets
PyX only uses parsable code snippets as seeds (Section 3.1). This is a small improvement over OSS-Instruct which randomly samples consecutive lines from code files, based on the observation that parsable seeds are more likely to yield syntact... | Summary: Recent advancements in Code Large Language Models have primarily focused on code completion, yet these models often lack the ability to comprehend deeper semantics, such as the execution effects and dynamic states of code. This paper presents a pioneering approach aimed at enhancing Code LLMs' understanding of... | Rebuttal 1:
Rebuttal: Thanks for your supportive feedback! We are glad that you like our idea of training code LMs with comprehensive semantics. Here, we would like to clarify the scope of SemCoder discussed in the paper and its potential for more software engineering applications.
## SemCoder’s Scope
SemCoder extend... | Summary: This paper addresses the limitations of Code Large Language Models in understanding deeper semantics such as execution effects and dynamic states. It introduces a novel training strategy to enhance Code LLMs with comprehensive semantics, including high-level functional descriptions, local execution effects, an... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s detailed comments and feedback!
## Baselines in Section 6.2
We apologize for the confusion. We provide an example of baseline trace formats in Appendix H. Given that neither scratchpad nor NeXT provides replication packages, we re-implement the approach following the... | Summary: The paper proposes PyX, a training dataset focusing on preserving the semantics of sample programs, and SemCoder, a coding language model trained with PyX with better ability to capture program semantics. The construction of PyX dataset comprises three phases to ensure the code samples involved are correct and... | Rebuttal 1:
Rebuttal: We appreciate the detailed reviews and questions!
## Self-contained Programs with Built-in Types
The primary goal of SemCoder is to propose a recipe for learning comprehensive semantics of code with LLMs. To achieve this, we initially focused on collecting self-contained code snippets with buil... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Shielding Regular Safety Properties in Reinforcement Learning | Reject | Summary: The paper proposes an online shielding approach for safe RL that does not assume prior knowledge of environment dynamics and utilizes finite-horizon model checking with learned approximations of the environment dynamics. It specifically focuses on RL with regular safety properties provided as a PCTL formula. T... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time in constructing their review.
Re weaknesses: we point the reviewer to the experiments involving property 3 on our colour grid-world environment, in this instance there is a direct conflict between optimal reward and constraint satisfaction, this is reflected b... | Summary: The authors present a new safe RL approach, building on safety shields.
The idea is to leverage model-checking techniques during the RL training to block actions that are identified as unsafe in the shield and use a learned backup policy if this is the case. In contrast to previous approaches, the "meta-algori... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for taking the time to construct such an insightful review.
We will clear up the questions you have, and I think this will deal with some of the weaknesses you have highlighted.
Re questions:
- When we start using neural networks or deep learning architectur... | Summary: This paper studies RL with 'regular' safety properties. The constraint of safe RL is based on the satisfaction of a logic formula in probability. The action from the 'backup' policy will proactively override the potentially unsafe action from RL to ensure/optimize safety, a typical shielding mechanism in forma... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time in constructing their review.
Re weakness 1.: Indeed, similar chance constraints have been introduced and studied in several prior works including [1] and [2]. We thank the reviewer for bringing these papers to our attention and I believe paper [2] is cited in... | Summary: This paper presents an approach to online shielding for reinforcement learning agents. Namely, safety is formulated in probabilistic temporal logic with a parametric threshold as an indicator for reachability of the goal state. The proposed algorithm checks the reachability probability threshold in each state ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time in constructing their review.
Re summary: one minor inconsistency, the backup policy is not necessarily pre-trained, in our experiments the backup policy is trained online with RL to minimise cost, although our framework is flexible enough to allow for a pre-t... | Rebuttal 1:
Rebuttal: There is some apparent confusion between the claims made in our paper and the different model checking paradigms that we present. The main goal of our paper is to present a flexible framework for shielding RL agents w.r.t arbitrary regular safety properties (i.e. those properties that can be expre... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper addresses safety and constraint compliance in deploying reinforcement learning (RL) systems. Such issues have triggered a vast body of research in the area of safe RL over the last few years. The paper introduces a safe RL framework for so-called regular safety properties, focusing on satisfying the... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their extensive review.
Re weakness 1.: In our paper we consider the most permissive setting where no prior knowledge about the transition probabilities is required. We only require that there is a labelling function defined over the state space of the MDP ... | null | null | null | null | null | null |
Interpretable Mesomorphic Networks for Tabular Data | Accept (poster) | Summary: This work explores interpretable models with a focus on tabular data and introduces a novel method that is locally linear while retaining a non-linear global decision boundary. The authors achieve this by expressing their model as a linear model that takes the input features and produces a dot product with wei... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing valuable feedback. Below we address the concerns raised by the reviewer:
- Regarding: **“Weakness 1**
Supposing a single input example $x$ with $d$ features, one would need to project each of the $d$ features to a fixed dimensionality to represent tokens, i... | Summary: The paper presents a hypernetwork approach to build an interpretable neural network for tabular data. Deep hypernetwork takes an input and returrns the weights for a linear model, which classify a given point. The example-baed interpretability follows from the interpretability of a linear models. The model is ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. Below we will address the concerns raised by the reviewer:
- Regarding: **“The authors restrict their baseline to interpretable classifiers. There are a lot of recent studies on tabular data that authors should compare with.”**
The main focus o... | Summary: This paper introduces a new neural network architecture called Interpretable Mesomorphic Networks (IMN) for handling tabular data. IMN combines the high accuracy of deep learning models with the interpretability of linear models by generating instance-specific linear models through deep hypernetworks, achievin... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the valuable feedback. Below we will address the concerns raised by the reviewer:
- Regarding: **W1**
**Implementation:**
We would like to point out to the reviewer that we reuse simple feed-forward backbones from previous work [1]. The only extr... | Summary: The paper introduces a new class of neural networks designed to be both deep and interpretable. These networks, referred to as Interpretable Mesomorphic Networks (IMN), utilize deep hypernetworks to generate linear models on a per-instance basis. This approach aims to retain the high accuracy of traditional bl... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the valuable feedback. Below we will address the concerns of the reviewer:
- Regarding: **“However, the author overlooked DANet [1], a model that outperforms TabNet and provides a better interpretable framework. This could be included for comparison.”:**
... | Rebuttal 1:
Rebuttal: We thank all the reviewers for providing valuable feedback regarding our work. Below we will summarize the main concerns raised by the reviewers:
- Reviewer csKM: **“The authors overlooked DANet”**
While DANet is an interpretable method by design, compared to IMN/TabNet, it does not provide l... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Policy Gradient with Tree Expansion | Reject | Summary: Disclaimer: I do not have the mathematical background required to check all proofs. It is also my first time reviewing for NeurIPS. However, I have contributions in classical (deep) RL and finishing my PhD.
In this paper, authors use a novel policy parametrization for RL. Namely, the SoftTreeMax policy that r... | Rebuttal 1:
Rebuttal: Thank you for your response. Please find our answers below.
**Question 1 - Do you learn the forward model and if not how can you use learned models?**
In the experiments, the forward model is not learned but given. In cases where access to a forward model is not given, it should be learned and r... | Summary: The paper introduces a model-based online planning method called SoftTreeMax. The method acts as an extension of the softmax, by replacing the logit in softmax with a n-step return. Based on SoftTreeMax, the paper proposes two learning algorithms, C-SoftTreeMax and E-SoftTreeMax. The work includes a mathematic... | Rebuttal 1:
Rebuttal: We appreciate your feedback on the method's soundness and reproducibility. We hope the following clarifications and new content address your concerns.
**Weakness 1 - Difference between SoftTreeMax and n-step return**
1. Thank you for this insightful point. SoftTreeMax may seem similar to n-step... | Summary: This paper proposes a new policy parameterization called SoftTreeMax, which can reduce the variance of the stochastic policy gradient. The authors consider finite state and action spaces throughout the paper. They start by taking a softmax tabular policy and replacing the logit $\theta(s,a)$ with a score from ... | Rebuttal 1:
Rebuttal: Thank you for supporting the paper and for your detailed and insightful response. We are delighted that you found the concept intriguing and innovative. Please find our answers below.
**Weakness 1 - “Not a weakness”: I expect to see improvement to any policy gradient method**
Indeed, our SoftTr... | Summary: This paper proposes a model based policy gradient (PG) algorithm that tries to decrease the variance of the gradient updates in PG methods and thus (hopefully) improving their sample complexity. The proposed approach works by modifying the softmax policy to work not just not with simple score functions (mappin... | Rebuttal 1:
Rebuttal: **Weakness 1 - Implementation details**
We've added pseudo-code for SoftTreeMax-PPO (https://ibb.co/s2CRMp8), a new GPU tree expansion diagram (https://ibb.co/5RPRkzF), and open-source code (supplementary). We used hyper-parameters from the original PPO paper (Appendix B.1) and defaults from stab... | Rebuttal 1:
Rebuttal: We thank the reviewers for their dedicated and comprehensive efforts. We are encouraged
that three reviewers found our paper to be technically solid with possibly high impact and recommended to accept. We are also pleased the reviewers found the paper novel [dZVs, mmxv, d2zq], innovative and well... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ENAT: Rethinking Spatial-temporal Interactions in Token-based Image Synthesis | Accept (poster) | Summary: The paper analyzes the information flow in Non-autoregressive Transformers image generation methods (NAT) to improve their efficiency.
In particular, they find that in a forward pass, having the observed tokens attend to masked token is unnecessary and proposes to change the architecture accordingly.
Moreover,... | Rebuttal 1:
Rebuttal: > **W1: Relationship with MAE, CrossMAE**
Thanks for your valuable suggestion. We are happy to include more discussions in our revision.
**Firstly**, we kindly note that our approach includes *two* key aspects: a *disentangled architecture* and a *computation reuse* mechanism. To the best of our... | Summary: This manuscript reveals NATs' progressive token revelation, with asymmetric intra-step interactions where [MASK] tokens gather info and visible tokens offer it, focusing on key updates amidst repetition across steps. It then presents ENAT, which isolates [MASK]/visible token computations, prioritizes crucial u... | Rebuttal 1:
Rebuttal: > **W1: How the Projection Module $f$ is Trained?**
As discussed in line 221-224, we execute the "reuse mode forward" (Eq. (5)) with some probability during training, thus involving the parameters of $f$ in the computation graph. More specifically, the "reuse mode forward" during training is achi... | Summary: The paper deals with improving the computational efficiency of image
generation non-autoregressive transformers (NATs) (e.g. MASKGIT). NATs
are usually trained by a fill-in-the-blanks objective and sample by
iteratively predicting missing tokens. The approach is based on
separately encoding observed and missin... | Rebuttal 1:
Rebuttal: >**W1: Relationships with MAE and MAGE**
**Firstly**, we kindly clarify that our approach includes *two* key aspects: a *disentangled architecture* and a *computation reuse* mechanism. To the best of our knowledge, the *computation reuse* mechanism is a novel contribution that has not been explor... | Summary: This paper proposes an improved Efficient MaskGIT-like non-autoregressive image generation method. Its core component is to (1) spatially disentange visible tokens and mask tokens in attention, and prioritize compute for the visible tokens; (2) temporally reuse the visible tokens from previous decoding step. T... | Rebuttal 1:
Rebuttal: > **W1: Questions on MSCOCO Results**
**Firstly**, we kindly clarify that the two models, U-ViT and ENAT-B, may *not* be viewed as being "on the same level."
Our primary objective is to improve inference efficiency.
Therefore, FLOPs, as an important indicator of practical efficiency (see Fig. 6 o... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful comments and suggestions.
We are encouraged that our work was found to be **well-motivated/written** (all reviewers), with our findings **offering fresh new perspectives** (Reviewer bqgC) and our method being **simple** (Reviewer GVb9) and **effecti... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendation | Accept (poster) | Summary: This paper examines the limitations of softmax loss in recommender systems, focusing on its weak correlation with ranking metrics like DCG and sensitivity to false negatives. The authors propose pairwise softmax loss (PSL), which replaces the exponential function in softmax loss with activation functions such ... | Rebuttal 1:
Rebuttal: # Response to Reviewer `m2Qo` (1/2)
Dear Reviewer `m2Qo`,
Much thanks for your detailed comments. In the following, we provide responses to the concerns you have raised:
> **[C1] While the paper tests on a few models, the results might not hold for other models or architectures, limiting the ge... | Summary: From the unique characteristics of recommendation systems (RS), this paper highlights the incompatibility of the exponential function in the softmax loss when applied to RS. To address these issues, the authors propose the PSL loss, introducing two main modifications: the replacement of the activation function... | Rebuttal 1:
Rebuttal: # Response to Reviewer `ufck` (1/2)
Dear Reviewer `ufck`,
We greatly appreciate your acknowledgement of our contributions and your insightful comments. In what follows, we provide responses to the Weaknesses (**W**) and Questions (**Q**) you have raised:
> **[W1] The proposed PSL loss is derive... | Summary: The authors re-examine the connection between the Softmax Loss (SL) and the evaluation metric Discounted Cumulative Gain (DCG),
highlighting the inadequate tightness of SL as a surrogate loss for DCG.
They propose minimal yet effective modifications (PSL-tanh/relu/atan) based on the pair-wise formulation of ... | Rebuttal 1:
Rebuttal: # Response to Reviewer `kuk6`
Dear Reviewer `kuk6`,
We sincerely appreciate your recognition of our theoretical contributions and experimental setup. Below are our detailed responses to the Weaknesses (**W**) and Questions (**Q**) you have raised:
> **[W1] In the IID setting, PSL demonstrates o... | Summary: This paper aims to investigate the effectiveness of softmax loss in the recommendation model. To overcome the limitation of SL, the authors propose a new pairwise softmax loss (PSL). Based on the analysis, the authors argue that replacing exponential function with other active functions can benefit the ranking... | Rebuttal 1:
Rebuttal: # Response to Reviewer `VFmg`
Dear Reviewer `VFmg`,
We sincerely appreciate your recognition of our work. Your detailed comments are highly valued, and the questions you raised are both interesting and practical. Below are our detailed responses to the Weaknesses (**W**) and Questions (**Q**):
... | Rebuttal 1:
Rebuttal: # Overall Rebuttal
Dear Reviewers `VFmg`, `kuk6`, `ufck`, and `m2Qo`,
We thank all reviewers for taking the time to review our paper and for providing valuable and insightful feedback. We are delighted to see that our work has been recognized for its contributions and inspiration to the recommen... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Provable Posterior Sampling with Denoising Oracles via Tilted Transport | Accept (poster) | Summary: This work studies posterior sampling using unconditional diffusion models, or denoising oracles. The authors propose a "tilted transport" technique to combine a linear inverse problem and denoising oracles into a "boosted" posterior sampling problem. The authors study the feasibility of "boosted" posterior sam... | Rebuttal 1:
Rebuttal: Thank you for reviewing our submission. We regret that you have a rather negative view of the paper. It seems to us that there are some fundamental misunderstandings of our tilted transport technique. We hope that our clarifications below, as well as the other reviews, can help resolve these misun... | Summary: The paper presents a theory regarding posterior sampling in linear inverse problems with additive Gaussian noise. It provides two major contributions:
* A proof that no general algorithm exists for posterior sampling of Ising models when the degradation operator is ill-conditioned.
* A transport equation for ... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in reviewing our submission. We will do our best below to address your concerns and queries.
> Figure 1 and its description are not clear. I would expect it to clearly summarize the essence of the work, particularly highlighting the second contribution.
Our tec... | Summary: This paper proposes a novel posterior sampling algorithm specialized to linear inverse problems. The proposed algorithm relies on a sequence of intermediate and **tractable** posterior distributions $\nu_t$ that satisfy a transport equation up to some time $T^*$. The algorithm then operates as follows; first d... | Rebuttal 1:
Rebuttal: We thank the reviewer very much for the time and effort spent in reviewing our paper, and for the positive evaluation. | Summary: The paper "Provable Posterior Sampling with Denoising Oracles through Tilted Transport" analyses the possibility of posterior sampling utilizing denoising oracles, such as score-based diffusion models. To demonstrate the possibilities the authors focus on linear inverse problems and propose the tilted transpor... | Rebuttal 1:
Rebuttal: We thank the reviewer very much for the time and effort spent in reviewing our paper, and for the positive evaluation. Below we address your specific question.
> What is the reason for the peak with large percentiles for 10^-3 SNR in Figure 3?
The peak observed with large percentiles for an SN... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their time and effort in reviewing our paper and for providing valuable suggestions that imrpove the draft. We address most points raised by individual reviewers in separate rebuttals. **Here we attach a line-by-line description of our tilted transport algo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On Sampling Strategies for Spectral Model Sharding | Accept (poster) | Summary: This paper addresses the problem of heterogeneous clients in federated learning, where each client may have different constraints and capabilities. The authors propose two sampling strategies for spectral model sharding, which involves partitioning the model parameters into low-rank matrices based on singular ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort invested in our work. We are happy to take the feedback into account and improve our presentation accordingly. Below we address the questions raised in the review.
**Comparison with FedHM.** According to the paper [1], FedHM is a method also based on ... | Summary: To handle system heterogeneity in federated learning, spectral model sharding partitions model parameters into low-rank matrices using singular value decomposition (SVD). As done in prior work, e.g., PRiSM, SVD of the weight matrices of affine layers are done and only randomly sampled terms from this decomposi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort invested in our work. We are happy to take the feedback into account and improve our presentation accordingly. Below we address the questions raised in the review.
**Post-client update.** We appreciate this valuable suggestion! To analyze the post-gra... | Summary: he study addresses the challenge of heterogeneous clients in federated learning by introducing two sampling strategies for spectral model sharding. These strategies, derived from specific optimization problems, either produce unbiased estimators of the original weights or minimize the squared approximation err... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort in evaluating our work. We are happy to take the feedback into account and improve our presentation accordingly. Below, we address the questions raised in the review.
**Q1 and Q2.** While using a model with fewer parameters is a straightforward solu... | null | null | Rebuttal 1:
Rebuttal: Dear reviewers, please find the results of requested experiments attached.
The comments on these results are provided in the individual replies.
Pdf: /pdf/e379fae19ddce3344561e6dedfb4aa551734a556.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Algebraic Positional Encodings | Accept (spotlight) | Summary: The paper presents a groundbreaking positional encoding strategy named Algebraic Positional Encodings for Transformer-style models, The paper offers a versatile mapping technique that translates the algebraic specifications of a domain into orthogonal operators. By doing so, it preserves the fundamental algeb... | Rebuttal 1:
Rebuttal: Warm greetings, and thank you for your review.
---
> `The paper is relatively hard to understand.`
We are very sorry to hear. To summarize, what we we are basically saying is that:
1. The properties of several common I/O structures in ML literature can be perfectly described using elementary gr... | Summary: Current techniques for specifying relative position encodings are often ad-hoc or clearly fit to specific problems. This paper attempts to resolve this by creating theoretically grounded positional encodings via describing the underlying algebraic structure of the positions in the input as a group and then fin... | Rebuttal 1:
Rebuttal: Greetings, and thank you very much for your review.
---
We appreciate your feedback and are glad you saw merit in our approach. We will try to address your concerns below.
> `wanting to multiply elements ... remains highly unmotivated`
We hear your criticism. The multiplicative approach is th... | Summary: This study proposes a generalized positional encoding for sequential, tree, and grid-structured data. The high-level view of encoding is algebraically framed, while the implementation of encodings remains simple; roughly, positions are embedded in linear subspaces spanned by the column space of orthogonal matr... | Rebuttal 1:
Rebuttal: Greetings, and many thanks for an insightful review.
---
We are pleased that you appreciated the clarity of the presentation and the simplicity of the implementation.
Your questions and remarks raise valid points, but we believe there may be a few minor but crucial misunderstandings. We will ad... | Summary: This paper provides a group-theoretic framework to generate positional encodings which reflect and preserve the underlying algebraic structure of the data by instantiating homomorphisms between a syntactic denotation of the data structure and a semantic interpretation using orthogonal matrices. The authors de... | Rebuttal 1:
Rebuttal: Thank you so much for your feedback.
---
We are thrilled that you found our work insightful and original, and that you consider it an important contribution to the field; this means a lot.
You’re absolutely right that we did not provide an in-depth explanation of the findings in Table 2. We p... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper presents a framework to derive positional encodings based on algebraic specifications for various structures such as sequences, grids, trees, and their compositions. The approach is evaluated on multiple training tasks involving sequence, image, and tree inputs, demonstrating performance that is com... | Rebuttal 1:
Rebuttal: Many thanks for your thoughtful and constructive review.
---
We are very glad you found our framework interesting and the results promising, and that you appreciated the potential for future directions.
We acknowledge your remark about (more complex) compositional structures. This is something ... | null | null | null | null | null | null |
Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers | Accept (spotlight) | Summary: The paper introduces HPT a large-scale transformer model pretrained on multi embodiment robotic data. The main idea is to split the policy into three parts: a dataset specific token encoder for images and proprioceptive information called Stem. A shared Trunk, that processes all latent tokens and a dataset spe... | Rebuttal 1:
Rebuttal: Thank you so much for the extensive feedback and suggestions. We have revised the manuscript and **conducted new experiments in the attached PDF** and will address your questions below.
> No adequate baselines in the real world .... I expect a comparison against Octo [1] as another generalist p... | Summary: This paper presents Heterogeneous Pre-trained Transformers (HPT), a method for training robotic models that addresses the challenge of heterogeneity across different robot embodiments and tasks. HPT pre-trains a shared neural network trunk to create a universal representation, which is then fine-tuned for spec... | Rebuttal 1:
Rebuttal: Thank you so much for the extensive feedback and suggestions. We have revised the manuscript and **conducted new experiments in the attached PDF** and will address your questions below.
> Despite the difficulty of large-scale evaluation, there is no inevitable relationship between the loss value... | Summary: This paper aims to pretrain a policy representation across a variety of simulated and real robotics datasets such that it can be quickly adapted to new downstream problem instances (embodiment, environment, task). The key technical contribution is the design of a Transformer architecture that can consume a var... | Rebuttal 1:
Rebuttal: Thank you so much for the extensive feedback and suggestions. We have revised the manuscript and **conducted new experiments in the attached PDF** and will address your questions below.
> Since the authors conduct transfer learning experiments in simulation (in addition to real experiments), I ... | Summary: This paper presents an approach for training large transformer behavior models on diverse heterogenous data. This involves separate encoders for proprioception and image observations for each embodiment, as well as different action decoding heads. Experiments include validation loss comparisons.
Strengths: Wh... | Rebuttal 1:
Rebuttal: Thank you so much for the extensive feedback and suggestions. We have revised the manuscript and **conducted new experiments in the attached PDF** and will address your questions below.
> Evaluation Metrics in the experiments. Validation loss is not perfectly correlated with downstream robot per... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable and constructive comments. Overall, we appreciate that most reviewers acknowledged the contributions, **“important problem of relevance to the community.”** (from Reviewer Vi4L), **“The proposed architecture is fairly simple and intuitive.”** (from Rev... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices | Accept (oral) | Summary: This paper proposes DapperFL, a novel framework for domain adaptive federated learning designed specifically for heterogeneous edge devices. It addresses the challenge of system heterogeneity and domain shifts in FL by integrating a Model Fusion Pruning (MFP) module and a Domain Adaptive Regularization (DAR) m... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive and helpful comments. Below we address your concerns in order.
## Response to Weakness 1:
**Clarification on System Heterogeneity:**
In our paper, we emphasize that low-capability clients "fail to complete" local training rather than being "unable to... | Summary: The authors propose a new FL scenario that faces challenges of both system heterogeneity and domain shifts. This situation means that the clients have varying capabilities and their data domains also differ. The authors claim that they propose three novel modules to tackle the challenges: Model Fusion Pruning ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive and helpful comments. Below we address your concerns in order.
## Response to Weakness 1:
We would like to emphasize that, our proposed framework is **NOT** a combination of existing approaches.
While our work builds on established concepts, it intr... | Summary: This paper proposes DapperFL, an innovative FL framework designed to enhance model performance across multiple domains within heterogeneous FL environments. DapperFL addresses the system heterogeneity challenge through the deployment of a dedicated Model Fusion Pruning (MFP) module. Additionally, a Domain Adap... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive and helpful comments. Below we address your concerns in order.
## Response to Weakness 1:
We utilize one epoch of local training to determine the pruned models for the following reasons:
1) Additional local epochs do not significantly enhance the mod... | Summary: The paper introduces a novel federated learning framework to address system heterogeneity and domain shifts in edge computing environments. The framework employs a Model Fusion Pruning (MFP) module to generate personalized compact local models and a Domain Adaptive Regularization (DAR) module to enhance perfor... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive and helpful comments. Below we address your concerns in order.
## Response to Weakness 1 \& Question 1:
Thanks for your suggestion to provide default values or an automatic selection mechanism for hyper-parameters. We acknowledge that hyper-parameter s... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their time and appreciate all the detailed reviews and constructive feedback. Extra experimental results and illustrations are presented in the rebuttal PDF file to address some common concerns raised by reviewers. In addition, each review will be replied to in... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
What Makes CLIP More Robust to Long-Tailed Pre-Training Data? A Controlled Study for Transferable Insights | Accept (poster) | Summary: This author empirically studies the mechanisms behind CLIP's generalization ability from multiple aspects. Based on the findings, they further transfer the merits of CLIP to other scenarios like supervised learning and self-supervised learning.
Strengths: 1. The proposed problem is worth exploring.
2. The exp... | Rebuttal 1:
Rebuttal: Dear reviewer WXxS, thanks for helping improve our paper, and your concerns are responded to as follows:
> L1: No code is provided at this stage
We have organized the code and provided it to the AC with a separate comment according to the rebuttal policy, please contact the AC for access. The co... | Summary: This paper explores the remarkable robustness of CLIP training compared to supervised training when dealing with real-world imbalanced datasets. This advantage can be attributed to dynamic classification using a subset of classes. Furthermore, the paper shows that robustness and discriminability improve with d... | Rebuttal 1:
Rebuttal: Dear reviewer iwVX, your questions are answered below:
> W1: The factors studied are primarily from a data perspective and known to enhance robust performance
1. To the best of our knowledge, most existing works study CLIP’s robustness to OOD evaluation data, while we are the first to study CLIP... | Summary: Motivated by the novel observation on CLIP's robustness against data imbalance, this paper provides extensive analysis on factors that determine such robustness, including language supervision, classification templates, data scaling, distribution, and open-world concepts. The paper shows that such factors are ... | Rebuttal 1:
Rebuttal: Dear reviewer XjUF,
Thank you so much for your thorough review and kind suggestions! We answer your questions as follows:
> W1&Q2: Given large-scale data, how ineffective are other factors studied in the paper; Fig. 2 on large-scale datasets should be included
Nice suggestion! We agree that sca... | Summary: CLIP models are known to exhibit good robustness and high quality representations compared to supervised models. In this paper, the authors study CLIP models through the lens of the distribution of concepts in the underlying training datasets. The authors find that CLIP appears to demonstrate better robustness... | Rebuttal 1:
Rebuttal: Dear reviewer tULz,
Thank you for your time and efforts in helping us improve our paper! We hereby answer your questions and resolve your concerns as follows:
> W1: Frequency of classes in LAIONet may not be accurately measured
1. Thanks for pointing this out! This is why we tried to study as m... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time, valuable comments, and kind suggestions. We also appreciate that our work is recognized to have a "novel" observation [tULz, XjUF, iwVX], study a "critical" problem [XjUF, iwVX, WXxS], design "sound" [tULz] and "comprehensive" [WXxS] experiments, and present ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Elo Uncovered: Robustness and Best Practices in Language Model Evaluation | Accept (poster) | Summary: This paper conducts an empirical study on the reliability and transitivity of the Elo rating system when evaluating Large Language Models. By conducting experiments on both simulated data and real-world scenarios, this paper suggests that certain parameter setting guidelines should be followed to ensure the st... | Rebuttal 1:
Rebuttal: We thank Reviewer [ hqxB] for their positive feedback including observing our **“detailed”** and **“appropriate”** experiment setup as well as noting the breadth of ablation including **“sequence arrangements, the number of games, the K value, and win rates”**. We appreciate the comment that the r... | Summary: Updated after response: I think this paper could provide some valuable contributions to the community, but has real presentational issues. The authors fixed some of them that had an easy fix (which I pointed out) during the rebuttal. However, I believe the presentational standards (and therefore the understan... | Rebuttal 1:
Rebuttal: We thank Reviewer [9SNU] for their helpful feedback and their observations noting that our study is **“important”** and the experiments are **“extensive”**. We also hugely appreciate the reviewer’s note that this paper provides **“several interesting lessons for using Elo ratings for LLMs”** that ... | Summary: Evaluation plays an important role in LLM research. Previous works have assessed the performance using the Elo rating system, which is designed for ranking players in games. This paper shows that Elo rating does not always satisfy two critical axioms, reliability and transitivity. So the rankings of the models... | Rebuttal 1:
Rebuttal: We thank Reviewer [ NZSD] for their helpful feedback and observations, noting that our study is **“an interesting”** and **“meaningful research topic”** and find our axiom definitions **“reasonable and useful”**. We take this opportunity to address R NZSD concerns and feedback below.
> The writin... | Summary: This paper examines how well the Elo rating system works for evaluating LLMs based on human feedback. The authors first check how stable and reliable Elo ratings are under different settings and point out key factors that affect these ratings. They study how changing the order of matches and adjusting setting... | Rebuttal 1:
Rebuttal: We thank Reviewer [ jqvp] for their helpful feedback and observations, noting that our study **“offering valuable insights for future Elo rating applications”**. We thank R jqvp for noting the comprehensive nature of the evaluation, **“on both synthetic and real human feedback”**, and involving *... | Rebuttal 1:
Rebuttal: We appreciate the thoughtful and positive feedback from the reviewers.
We are encouraged that the reviewers found the paper comprehensive and helpful and the experimental setup clear and detailed [jqvp, hqxB] **“This paper provides a comprehensive investigation, helping us better understand and ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition | Accept (poster) | Summary: The paper proposes a novel method to address the long-tail problem in semi-supervised learning by leveraging continuous contrastive learning on both labeled and unlabeled samples to improve model performance, particularly for minority classes.
Strengths: - Novelty: The paper introduces a novel approach to int... | Rebuttal 1:
Rebuttal: Dear Reviewer JsM2,
We sincerely appreciate your thoughtful feedback. We will address each of your concerns in detail.
**Weakness #1: Why selecting pseudo-labels using energy score can ensure better model calibration**
Since the confirmation bias is induced by **self-training**, using **confide... | Summary: The author proposes a probabilistic framework that unifies many recent proposals in long-tail learning. Specifically, for long-tailed semi-supervised learning, a continuous contrastive learning method based on reliable and smoothed pseudo-labels to address confirmation bias and improve the quality of learned ... | Rebuttal 1:
Rebuttal: Dear Reviewer WTLw,
We sincerely appreciate the reviewer for thoughtful feedback. We address your concerns one by one.
**Weakness #1: How the proposed method solves the unlabelled data for diverse label distributions of unlabeled data**
Having an accurate estimation of unlabeled data prior $\wi... | Summary: This paper proposes a novel contrastive learning method for long-tailed semi-supervised learning (LTSSL). The method is motivated by variational information bottleneck for learning good representations and extends to unlabeled data using continuous pseudo-labels. This paper showcases strong empirical results o... | Rebuttal 1:
Rebuttal: Dear Reviewer biXa,
We sincerely appreciate the reviewer's thoughtful feedback. We will address your concerns individually.
**Weakness #1: How does the proposed method address the issue of having mini-batches with no samples from certain classes during training**
For our proposed method CCL, to... | Summary: This paper tackles the long-tailed semi-supervised learning problem. It first reviews recent works with a novel probabilistic framework. Based on this, it proposes a continuous contrastive learning method, CCL, to extend the framework to unlabeled data with pseudo-labels. Experiments show that it outperforms a... | Rebuttal 1:
Rebuttal: Dear Reviewer 1xLE,
We sincerely appreciate your thoughtful feedback. We will respond to each of your concerns individually.
**Weakness #1: Misunderstanding between $\widehat{\mathcal{L}}_{\mathrm{cls}}$ in Eq. (22) and $\widehat{\mathbb{P}}^{\mathrm{cls}}\left(Y=y \mid \boldsymbol{x}^u\right)$ ... | Rebuttal 1:
Rebuttal: We extend our gratitude to the reviewers for their constructive and valuable feedback.
We are encouraged by the reviewers' acknowledgment of the novelty of our continuous contrastive learning framework (R2, R4), its significant contribution (R1, R2, R3, R4), and its soundness (R1, R2, R3, R4). Re... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
KnowGPT: Knowledge Graph based Prompting for Large Language Models | Accept (poster) | Summary: This work presents KnowGPT, a novel KG-based Prompting framework designed to integrate domain knowledge into LLMs effectively. KnowGPT addresses the key challenges of previous methods, such as large search spaces, high API costs, and labor-intensive prompt engineering. The framework includes a knowledge extrac... | Rebuttal 1:
Rebuttal: Dear Reviewer t5Eo,
Thanks a lot for your detailed feedback. We really appreciate your time and effort in pointing out the potential concerns related to our paper, and also, thanks a lot for the opportunity to further clarify the motivation, contribution, and technical details of our framework.
... | Summary: The paper proposes KnowGPT, a novel KG prompting enhanced LLM framework that leverages deep reinforcement learning (RL) to extract knowledge and Multi-Armed Bandit (MAB) to generate effective prompts for domain-specific queries. Empirical evidence on QA benchmarks shows KnowGPT’s superiority over other methods... | Rebuttal 1:
Rebuttal: Dear Reviewer vKLu,
We really appreciate your recognition of our work, and thanks a lot for providing insightful suggestions that can help further polish our paper.
> ### **Regarding W1 (Q1,Q2): Efficiency Analysis**
A: Thank you for the constructive comments. To make it more clear, we will fi... | Summary: The paper presents a novel framework, KnowGPT, for incorporating Knowledge Graphs into LLM-prompting-based question answering. It breaks down the problem into two main parts: identifying the concise, rich, and relevant subgraph for the question and using the Multi-Armed Bandits framework to select the best pro... | Rebuttal 1:
Rebuttal: Dear reviewer 4L5K,
Thanks a lot for your detailed feedback. We really appreciate your recognition of our work and also appreciate your time and effort in providing insightful suggestions that can help further polish our paper. Below are detailed responses to your comments and suggestions:
> ##... | null | null | Rebuttal 1:
Rebuttal: In this paper, we propose a knowledge injection framework called KnowGPT, which injects knowledge from KGs into LLMs to assist the LLM in accurately answering domain-specific questions. Overall, we primarily focus on two research questions:
* Given a domain-specific question and a large-scale KG, ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Hollowed Net for On-Device Personalization of Text-to-Image Diffusion Models | Accept (poster) | Summary: The authors propose an efficient personalization method using LoRA for on-device text-to-image diffusion models. The method relies on removing non-essential layers in the U-net, reducing GPU usage during fine-tuning.
Strengths: - The paper is well written.
- Discarding a few layers in the U-net copy architect... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. Please note our top-level comment with additional experimental results. Below we address specific questions.
> 1. Clarification on assumption and experimental details
The experiment depicted in Figure 2 of the manuscript, as detailed in Section 4.1, was co... | Summary: This paper proposes a novel framework named “Hollowed Net” for memory-efficient LoRA fine-tuning on the personalization task, showing its potential for on-device personalization tasks with limited resources. With the hollowed UNet structure, the training memory requirement is largely reduced while the performa... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. Please note our top-level comment with additional experimental results. Below we address specific questions.
> 1. Experimental evaluation with user studies
Based on you suggestion, we conducted user studies with 40 participants, each completing a set of 25... | Summary: This work proposes Hollowed Net, a parameter-pruning method for training subject-driven synthesis models under limited computing resources.
Strengths: According to the experimental results, applying Hollowed Net will reduce the memory costs during training stages, while keeping similar performance.
Weaknesse... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. Please note our top-level comment with additional experimental results. Below we address specific questions.
| Methods | LoRA | | Hollowed Net | |
|---------------|------------------|-------------|----------------|--... | Summary: The paper introduces Hollowed Net, a novel approach for on-device personalization with T2I LDMs. The model gives better memory efficiency by modifying the U-Net to remove less-essential deep layers. This helps with the on-device memory constraints. It reduces memory usage during fine-tuning while maintaining p... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. Please note our top-level comment with additional experimental results. Below we address specific questions.
>1. 1.$\sim$GB memory usage save, while beneficial, is still marginal
In this paper, we are assuming an extremely resource-constrained environment ... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We sincerely appreciate the reviewers for dedicating their time and effort to review our work. We have conducted multiple sets of experiments and studies to address all feedback and questions from the reviewers. Please refer to the attached document for a one-page analysis.
In ord... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.