title string | paper_decision string | review_1 string | rebuttals_1 string | review_2 string | rebuttals_2 string | review_3 string | rebuttals_3 string | review_4 string | rebuttals_4 string | global_rebuttals string | dataset_source string | conference_year int64 | review_5 string | rebuttals_5 string | review_6 string | rebuttals_6 string | review_7 string | rebuttals_7 string | review_8 string | rebuttals_8 string |
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Locating What You Need: Towards Adapting Diffusion Models to OOD Concepts In-the-Wild | Accept (poster) | Summary: The paper introduces CATOD, a framework designed to enhance the adaptation of text-to-image models for out-of-distribution (OOD) concepts. It addresses the issue of low-quality training data by employing an active learning approach that iteratively improves the training set. The framework utilizes a scoring sy... | Rebuttal 1:
Rebuttal: Thank for the good words! We are happy that you enjoyed the paper!
**Part 1: Why CMMD is a good metric (W1, Q1)**
As reported in many recent works [1,2,3], the most popular image-matching metrics like Inception Score, Precision/Recall, and FID (Frechet Inception Distance) may disagree with human... | Summary: This paper introduces a novel framework called Controllable Adaptor Towards Out-of-Distribution (OOD) Concepts (CATOD). CATOD is designed to adapt text-to-image models to OOD concepts and generate high-quality images of those OOD concepts accordingly. The authors identified the challenge of accurately depictin... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions! We hope that our rebuttal addresses your concerns.
**Part 1: About the diversity evaluation (W1, Q1)**
CATOD maintains the diversity to produce OOD concepts with different angles or poses in generative results. To validate the diversity of our generat... | Summary: This work tackles the challenge of adapting text-to-image diffusion models to out-of-distribution (OOD) concepts. The authors introduce a framework called Controllable Adaptor Towards Out-of-Distribution Concepts (CATOD), which employs an active learning paradigm to iteratively accumulate high-quality training... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions. We have carefully addressed your concerns as follows:
**Part 1: About the key technical contribution (W1)**
The key technical contributions of this paper are twofold: (1) our method for estimating the impact of each sample on the model without trainin... | Summary: This work presents CATOD, a new method for dealing with the challenging scenario of adapting generative models to OOD concepts. In particular, CATOD leverages an active learning setting to update the adaptor to generate better OOD images more broadly. Furthermore, the authors provide both extensive empirical e... | Rebuttal 1:
Rebuttal: Thanks for your comments!
**Part 1: About the number of samples to use for training and validation.**
In brief, since most recent adaptors require only a small number of training samples (usually around 100), our validation set is also set to be of the same scale as the training samples.
As men... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SeTAR: Out-of-Distribution Detection with Selective Low-Rank Approximation | Accept (poster) | Summary: This work proposes SeTAR for CLIP-based OOD detection. SeTAR is based on low-rank approximation. It determines the optimal rank for each weight block with a greedy hyperparameter search using validation samples. While SeTAR itself is training-free, the paper also proposes SeTAR+FT which incorporates LoRA as a ... | Rebuttal 1:
Rebuttal: # 1. Performance Gains
1. **Limited Room for AUC Improvement:**
- The baseline AUC scores are above 90, leaving limited room for significant improvement. Despite this, our method still achieves AUC improvements, demonstrating its effectiveness even in a high-performance context.
2. **Signific... | Summary: The paper introduces SeTAR, a novel training-free out-of-distribution (OOD) detection method that leverages selective low-rank approximation of weight matrices in vision-language and vision-only models. SeTAR enhances OOD detection by post-hoc modifying the model’s weight matrices using a greedy search algorit... | Rebuttal 1:
Rebuttal: # 1. Performance Gains
1. **Limited Room for AUC Improvement:**
- The baseline AUC scores are above 90, leaving limited room for significant improvement. Despite this, our method still achieves AUC improvements, demonstrating its effectiveness even in a high-performance context.
2. **Signific... | Summary: This paper proposes an algorithm for OOD detection along with CLIP models.
It observes that pruning based on SVD decomposition on CLIP models can improve the OOD detection performance.
A greedy search algorithm is developed for searching pruning rations of each weight in CLIP models.
Experiments on regular se... | Rebuttal 1:
Rebuttal: # 1. Applicability to CNN-Models
1. **Will it work on CNN-based ResNets?**
- No. Our method is not applicable to pure CNN models like traditional ResNet50. The loss function (Eq. 12) in our approach includes an OOD loss (Eq. 11), which relies on local features from the attention layer. Since p... | Summary: The paper presents SETAR, a novel method designed to enhance out-of-distribution (OOD) detection without requiring additional training. The proposed method leverages rank reduction techniques applied to the model weights, specifically targeting the minor singular components, while retaining the principal compo... | Rebuttal 1:
Rebuttal: # 1. Performance Disparity on Swin-Base
- The performance boost on Swin-Base is more pronounced because Swin is trained directly on ImageNet, lacking a text-encoder and thus requiring training solely on IN1K. This can lead to overfitting on ID data and poor recognition of OOD images, providing mo... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Exploring Molecular Pretraining Model at Scale | Accept (poster) | Summary: # Summary
This paper presents Uni - Mol2, a molecular pretraining model, and systematically investigates the scaling law within molecular pretraining models.
# Contributions
1. The largest dataset for molecular pretraining: Curated a dataset of approximately 884 million 3D conformations for pretraining.
2. ... | Rebuttal 1:
Rebuttal: We extend our sincere thanks to Reviewer ER2p for the positive evaluation and the thoughtful time invested in reviewing our manuscript. Your encouraging feedback is greatly appreciated, and we have carefully addressed each of your comments in the detailed responses provided below.
**Response to W... | Summary: In this work, the authors propose Uni-Mol2 , a molecular pretraining model that leverages a two-track transformer to integrate features at the atomic level, graph level, and geometry structure level. The authors also investigate the scaling law within molecular pretraining models, characterizing the power-law ... | Rebuttal 1:
Rebuttal: We would like to thank Reviewer XhCf for the detailed and constructive review. Below, we respond to all your comments.
We appreciate the opportunity to clarify a key point at the outset: typically, when referring to LLM models, the term denotes large language models that primarily aim at next-tok... | Summary: This paper studies large scale pretraining for molecule. The authors compose the largest molecular pretraining dataset, Uni-Mol2, and train the largest molecular pretraining model with 1.1B parameters. This paper also fits a scaling law in this domain that can accurately predict losses on a validation set. Dow... | Rebuttal 1:
Rebuttal: We deeply appreciate Reviewer buoE's careful review and thoughtful feedback. Your suggestions have greatly contributed to improving our manuscript. We respond to your comments and questions in detail below.
**Response to Weaknesses**
**1. Performance Improvements**
We acknowledge your concern ... | Summary: This paper studies the pretraining task on molecular domain. The main contribution includes extending the size of pretraining dataset, scaling the model to 1.1B size, investigating pretraining scaling law behavior. The evaluation of the pretrained model is conducted for some molecular property prediction task.... | Rebuttal 1:
Rebuttal: We are grateful to Reviewer V7dw for the thorough review and insightful comments. Below, we have carefully considered your feedback and provided detailed responses to each point.
**Response to Weaknesses:**
1. **Limited Evaluation of the Pretrained Model**
We begin by highlighting the promi... | Rebuttal 1:
Rebuttal: ## General Rebuttal
(R IDs: R1=V7dw, R2=buoE, R3=XhCf, R4=ER2p)
We thank the reviewers for the detailed and helpful reviews. Next, we address the main concerns from reviewers.
1. **Time Complexity and GPU Resources**
We utilized a computational cluster comprising 64 NVIDIA A100 GPUs, each e... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fine-grained Image-to-LiDAR Contrastive Distillation with Visual Foundation Models | Accept (poster) | Summary: This work aims to tackle the image-to-LiDAR contrastive learning problem for LiDAR-based point cloud segmentation. Previous approaches designed the cross-modal contrastive learning objective for model pretraining, using superpixels and superpoints as guidance.
In this work, the authors observe that the superp... | Rebuttal 1:
Rebuttal: Thanks for your time and effort in reviewing our submission and valuable comments. In the following, we will address your concerns and correct the potential misunderstandings.
**Q:** *The weakly-supervised contrastive distillation method has been used in previous literature [R1, R2].*
**A:** We ... | Summary: Annotating point clouds with semantic classes can be expensive and time consuming. The authors of this work propose a new pretraining strategy for weakly supervising point cloud segmentation using image-based supervision (i.e., image-to-LIDAR knowledge transfer). The proposed approach improves upon traditional... | Rebuttal 1:
Rebuttal: Thanks for your time and effort in reviewing our paper, the valuable comments, and the favorable recommendation.
**Q:** *To differentiate this approach from prior works.*
**A:** We agree with you that it's necessary to highlight the shortcomings of previous works and the novelty of OLIVINE in th... | Summary: In this paper, the authors introduced a novel approach for improving 3D representation learning by leveraging VFMs to generate semantic labels for weakly-supervised pixel-to-point contrastive distillation. The proposed method addressed the self-conflict issue in traditional contrastive learning and presented a... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's time and effort in reviewing our paper. Thanks for your valuable comments and recognition of our work. In the following, we will comprehensively address your concerns.
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**Comment:** *The validity of the proposed SSL method. It is not clear whether the red... | Summary: The paper addresses the "self-conflict" issue in contrastive image-to-LiDAR knowledge transfer, where features of semantically similar but unmatched points and pixels are unintentionally dissociated, compromising representation integrity. To solve this, Visual Foundation Models are employed to generate semanti... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's time and effort in reviewing our paper. In the following, we will comprehensively address your concerns.
---
**Comment:** *The overall architecture is similar to Seal, limiting its novelty except for the sampling strategy. Providing more clarification about ... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for your time and constructive comments.
---
We are glad that the reviewers see the value in our work:
1. "_The paper addresses the self-conflict issue in contrastive image-to-LiDAR knowledge transfer ... significantly outperforms traditional methods in various d... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Worst-Case Offline Reinforcement Learning with Arbitrary Data Support | Accept (poster) | Summary: In this submission, the authors propose some new bounds for offline reinforcement learning. Their contribution is twofold. First, they remove the classical data support assumption by solving a relaxed problem, in which any non observed transition is replaced by a transition to an absorbing state. This new MDP ... | Rebuttal 1:
Rebuttal: Thank you very much for your helpful review and feedbacks.
In particular, we agree that the manuscript can be improved by adding the position/contribution summary and improving visual aspects of some notation.
Please find below the answers for your questions.
----
> Are the author aware of corre... | Summary: This paper studies offline reinforcement learning with arbitrary data support. More specifically, they truncate the original environment so that the new environment (called truncated environment) is always covered by the offline data. They further show that the optimal policy of the truncated environment can b... | Rebuttal 1:
Rebuttal: Thank you very much for your helpful review and comments.
Please find below the answers to your questions.
> can we get rid of $\tilde{C}\_\infty$?
Asymptotically, yes. Such extensions are discussed in the appendix, Section E.4.
In summary, we can replace the uniform concentrability $\tilde{C}\_... | Summary: The paper develops a framework for evaluating offline reinforcement learning methods without any data-support conditions. Traditional techniques rely on the concentrability coefficient to ground the difference between the offline data distribution and the induced policy distribution. However, the concentrabili... | Rebuttal 1:
Rebuttal: Thank you very much for your helpful review and comments.
Below, we would like to provide additional discussion on the weakness you mentioned.
> I feel the proposed truncated environment is overly restrictive and ignores information between similar state-action pairs, even if they're not represen... | Summary: This paper studies offline RL with no data support assumptions. To address the data coverage problem, the paper studies a new setting with worst-case policy value. It formulates the RL problem with Lagrangian and shows the instability compared with the regularized Lagrangian. Improved sample complexity is prov... | Rebuttal 1:
Rebuttal: We really appreciate your helpful review and comments.
We hope our response below resolve potential misunderstandings and address your questions.
The response consists of two parts: clarifications and answers.
## Clarifications
> It seems unfair to compare the results in the new framework with pr... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
One-shot Federated Learning via Synthetic Distiller-Distillate Communication | Accept (poster) | Summary: The paper introduces FedSD2C, a new one-shot Federated Learning (FL) framework designed to address challenges in existing methods. Previous approaches have used data-free knowledge distillation to improve one-shot FL, but these methods struggle with data heterogeneity and scalability issues. FedSD2C aims to s... | Rebuttal 1:
Rebuttal: ***Q1 On line 52, the values 4.21 and 2.06 are not clearly explained. I suggest adding a figure or table to provide more specific details about these numbers.***
We would like to thank the Reviewer Bcom for suggestions. We will include a detailed figure to better illustrate this in the final vers... | Summary: This paper proposes a one-shot FL method (FedSD2C), utilizing V-information to select local core set data and server-pretrained autoencoder and Fourier-domain perturbation to ensure privacy preservation for local "distillate" sharing. In comparison to existing works such as DENSE and Co-Boosting, FedSD2C can r... | Rebuttal 1:
Rebuttal: ***Q1 The assumption that the server holds an autoencoder is pretty strong, as the autoencoder must be trained in the clients' data domain to ensure it works.***
Thanks for the detailed comments. The large-scale datasets used to pre-train the autoencoder contain diverse data, which is enough to c... | Summary: The paper presents FedSD2C, a novel one-shot federated learning (FL) framework that aims to improve communication efficiency, privacy preservation, and model performance. The approach addresses issues with data heterogeneity and information loss by synthesizing informative distillates from local data and shari... | Rebuttal 1:
Rebuttal: ***Q1 A critical question on the optimal observer of approximating the V-information is that, the local model is trained locally, which may not indicate good V-information of the global datasets, i.e., all local datasets.***
Thanks for the detailed comments. In the context of one-shot federated l... | Summary: This paper proposes a one-shot federated learning approach designed to enhance privacy protection, communication efficiency, and model performance. Firstly, the authors introduce a Core-Set selection method based on V-information to extract the most informative data from the original dataset. The amplitude spe... | Rebuttal 1:
Rebuttal: ***Q1 Patch size, number of patch and influence of number of patch***
**A1** We apologize for the unclear statement. For each image $x_i$, we employ the `torchvision.transform.RandomResizeCrop` $K$ times to generate a collection of patches. For patch size, we set the `scale=(0.08, 1.0)`, which is... | Rebuttal 1:
Rebuttal: Dear Reviewers and ACs,
We would like to thank the reviewers' insightful reviews and constructive comments on our manuscript. We have carefully considered all the suggestions and made the following changes:
1. We have included an additional datasets. By doing so, we aim to demonstrate our method... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ECLipsE: Efficient Compositional Lipschitz Constant Estimation for Deep Neural Networks | Accept (spotlight) | Summary: This paper presents two novel algorithms for computing the Lipschitz constant of feedforward neural networks (NN). The starting point is a previously-known semi-definite programming (SDP) problem which enables to compute the Lipschitz constant. The paper proposes a decomposition of this SDP in sequential subpr... | Rebuttal 1:
Rebuttal: Thank you for your encouraging evaluation of our work and thoughtful questions. We address all of them in detail as follows.
>**Theoretical results in Section 3.3 are a bit hard to follow, because the section gives the story behind the proposed relaxation, as well as geometric interpretation, but ... | Summary: This paper tackles the problem of computing the Lipschitz constant of a neural network. Since computing the exact Lipschitz constant is NP-hard, efforts have been made to obtain tight upper bounds on the Lipschitz constant. This paper builds on the work of LipSDP [1], which involves solving a large matrix veri... | Rebuttal 1:
Rebuttal: Thank you for your encouraging evaluation of our work and thoughtful questions. We address all of them in detail as follows.
>**It looks like the approach is restricted to a very limited set of neural networks (feedforward neural networks), can the approach be used for convolutional neural network... | Summary: The paper proposes two algorithms, ECLipsE and ECLipsE-Fast, to estimate the Lipschitz constant of a feed-forward neural network. The estimation of the Lipschitzness plays a crucial role in certifying the robustness of neural networks and is known to be an NP-hard problem. The proposed algorithms are based on ... | Rebuttal 1:
Rebuttal: We thank the reviewer for a thorough reading of our manuscript and providing several suggestions to improve the clarity of our presentation (see General Response - point III). We individually address the technical questions raised by the reviewer below, and provide additional experiments benchmark... | Summary: The paper proposes two novel Lipschitz constant estimation algorithms ECLipsE and ECLipsE-Fast. They are supported by a new decomposition theory developed for the Lip-SDP framework, derived by applying an existing theory (Lemma 2 of [31]). Experiments demonstrate the estimation accuracy and acceleration using ... | Rebuttal 1:
Rebuttal: Thank you for your encouraging evaluation of our work and for the insightful questions on our experiments. We address all your questions as follows, and provide additional experiments to show the strength of our method for wide networks.
>**(I). The proposed algorithm is efficient at addressing n... | Rebuttal 1:
Rebuttal: We are extremely grateful to the reviewers for their detailed, thorough, and constructive feedback. We are glad to read that the reviewers found paper to be interesting, novel, practical and well-written. We appreciate the suggestions from Reviewers kbMy, 3iDD on enhancing the clarity of writing, ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors are able to decompose a particular case of LipSDP-neuron exactly into a series of sub-problems leading to the proposed algorithm ECLipSE. In the case of the relaxed LipSDP-layer, it can be shown that each sub-problem can be solved analytically and eliminate the need for solving an SDP. The proposed... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and positive evaluation of our work. We address your concerns as follows.
>**It seems that the approach doesn't yet apply to residual networks or CNN commonly found in state-of-the-art vision models. For this reason, it seems difficult to show improved certi... | null | null | null | null | null | null |
VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time | Accept (oral) | Summary: The paper present VASA, a new method for talking head video generation. The method is build as a diffusion model using Transformer. To increase the performance of the model and allow more control on the generated video the authors decided to use the representation from [1] and learn to disentangle its componen... | Rebuttal 1:
Rebuttal: **Response to W1 (CAPP model sharing):** We will soon release the CAPP model, which we believe fills the missing piece of audio-pose alignment metric in talking face generation research and will be valuable to the community.
**Response to W2 (Voxceleb training data):** We used the entire training... | Summary: The paper presents a method for generating highly realistic talking head avatars that combines the diversity of facial expressions with the real-time generation speed. It provides a practical and commercially valuable approach to the field of talking head generation.
Strengths: 1. The overall structure of the... | Rebuttal 1:
Rebuttal: **Response to W1:** We achieve real-time efficiency because of our framework design, i.e. (diffusion-based) motion generation in latent space + (CNN-based) decoding in image space. Our diffusion transformer works in the *latent space* and is small (only 29M parameters), so it runs very fast. The C... | Summary: This paper introduces a two-stage talking head method that can generate impressive talking faces. It includes 1) A diffusion-based model to generate implicit facial dynamics and head movements from audio and additional conditions. 2) A modified 3D-aided face reenactment decoder for generating faces from latent... | Rebuttal 1:
Rebuttal: **Response to W1:** Thank you for the comment. We will add more details of the 3D-aided face latent model into our main paper and appendix. Our network architecture follows MegaPortraits [18] where details can be found. To achieve disentanglement, we modified the loss functions and incorporated th... | Summary: This paper aims to effectively and efficiently generate high-fidelity audio-driven talking head videos. To improve performance and efficiency, the authors have designed a Diffusion Transformer model within the latent space of motion signals, encompassing facial dynamics and head movements. Additionally, they p... | Rebuttal 1:
Rebuttal: **Response to W1 (contributions):** Thank you for the comment. In response to your question on our technical contributions and relationship with MegaPortraits, we'd like to emphasize two aspects. **First**, our motivation in the first place is to model the human face conversational behavior (facia... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for the valuable comments and suggestions. We are encouraged by the reviewer's acknowledgment that our paper: *"innovatively defining the diffusion model within... which is quite interesting"*; *"convincingly demonstrate the effectiveness.."* (Reviewer WJD9); *"de... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Federated Model Heterogeneous Matryoshka Representation Learning | Accept (poster) | Summary: This paper introduces FedMRL, a method based on distillation to mitigate the model heterogeneity issue in Federated Learning (FL). FedMRL operates by learning a small proxy homogeneous global model in a federated manner and distilling knowledge from it to heterogeneous client models. To enhance representation ... | Rebuttal 1:
Rebuttal: **W1:**
- **W1.a:** Apologize for the confusion. Our comment on the limitations of **existing strategies**, e.g., FedKD and FML, where they communicate the homogeneous proxy model. Although FedMRL falls in this category, it improved communication and computation efficiency by reducing the represe... | Summary: The authors study the model heterogeneity challenge in federated learning using Matryoshka representation learning. It requires that the global model and the local models share one common part inspired by the two key modules: adaptive representation fusion and multi-granularity representation learning. They pr... | Rebuttal 1:
Rebuttal: **W1:** FedGH achieves FL collaboration across clients with heterogeneous local models by sharing a co-training homogeneous prediction header at the server. It can be categorized into the model split branch. However, sharing one part of the heterogeneous client model may result in insufficient gen... | Summary: The paper proposed a FedMRL method for model-heterogeneous FL, which adapted Matryoshka Representation Learning to learn representations from multiple granularities.
Strengths: 1. The proposed method is a new way to tackle the heterogeneous challenge of federated learning.
2. The paper is well-organized and... | Rebuttal 1:
Rebuttal: **W1:** We acknowledge the pointed related work heterogeneous federated learning. However, our approach, FedMRL, introduces significant innovations that differentiate it from existing methods. We appreciate the oppporutnity to highlight our novelty.
**Frist, The referenced works do not fuse rep... | Summary: This paper focus on model heterogeneous federated learning. Existing distillation-based learning results in limited knowledge transfer. In order to mitigate this challenge, the authors propose FedMRL. In FedMRL, each client trains an extra shared global auxiliary homogeneous small model such that the server c... | Rebuttal 1:
Rebuttal: **W1:** We would like to clarify that the additional computational cost in our proposed FedMRL is minimal. Specifically, it involves a small proxy homogeneous model and a one-linear-layer representation projector for the clients. Notably, the parameters of this additional component constitute only... | Rebuttal 1:
Rebuttal: Please see Tables X1-X5 for rebuttal from the attached pdf file.
Pdf: /pdf/16a0e4ccd0335ae1e1440eae19c2db1f79ed68cd.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Atlas3D: Physically Constrained Self-Supporting Text-to-3D for Simulation and Fabrication | Accept (poster) | Summary: This paper proposes to combine physical standability with existing diffusion-based text-to-3D generators in order to synthesize objects that not only follow the text description but also can stand on their own without falling. To this end, the paper proposes a physical loss function and uses it with score dist... | Rebuttal 1:
Rebuttal: We thank you for recognizing the effectiveness and elegance of our pipeline. Below we clarify your questions.
**Q1: First, there are good methods, such as 'make it stand' that could be used as post-processing tools[...]. Why should we go through the paper's approach in the light of these methods?... | Summary: This paper introduces a method to make 3D assets produced by SDS-based generative models stand on their own. On top of the SDS loss, it proposes a “standability" loss that encourages the rigid-body simulation result to be rotation-free, and a “stable” loss to encourage the generated shape to be a “local minimu... | Rebuttal 1:
Rebuttal: We thank you for recognizing the novelty and effectiveness of our work. Indeed, our work is not directly targeting 3D printing, but an automated 3D generation pipeline without manual tuning.
**Q1: No ablation study is provided to prove the necessity of each term. In particular, are both standabil... | Summary: The paper introduces a differentiable simulation-based loss to refine the existing SDS(Score Distillation Sampling)-based text-to-3D frameworks. Concretely, it relies on a differentiable simulator Warp to provide gradients for keeping the rotation of the generated mesh unchanged after a period of time. Besides... | Rebuttal 1:
Rebuttal: We thank you for recognizing the effectiveness and flexibility of our framework. Indeed, our pipeline can work with different 3D generative models and different differentiable physical simulators, allowing potential variants and extensions.
**Q1: The paper lacks enough baselines to show that the... | Summary: This paper addresses the problem of generating 3D models from text that are visually appealing but often physically unstable in simulations or when 3D printed. The authors incorporates a differentiable simulation-based loss function and physically inspired regularization to ensure generated models are stable u... | Rebuttal 1:
Rebuttal: We thank you for recognizing the novelty and effectiveness of our method. To the best of our knowledge, our method is the first to bring standability to large generative models via joint optimization of standability loss and score distillation sampling loss.
**Q1: I am curious about the failure ... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We would like to thank all reviewers for your insightful and constructive feedback. We are encouraged by the recognition that our paper:
- Addresses the interesting problem of physical stability in text-to-3D generation and provides an effective and important solution [Reviewer Nr... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Efficient Large Multi-modal Models via Visual Context Compression | Accept (poster) | Summary: This paper shows that visual tokens are redundant in MLLM and can be compreseed by a large ratio without significantly hurting the model performance. Based on this observation, this paper studied several different approaches to compress visual tokens and identified that the simple average pooling method is the... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive suggestions. We address the concerns raised on a point-by-point basis, including additional benchmarks on the global response PDF. We will include the new results in our revised manuscript.
For weakness 1: "more discussions and investigations on why other... | Summary: The paper presents a novel approach to reducing redundancy in visual tokens within MLLMs by introducing the Visual Context Compressor (VCC) and Stage-wise MLLM Training. The VCC uses simple average pooling to compress visual tokens during training, enhancing efficiency without sacrificing performance. The Stag... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive suggestions. We address the concerns raised on a point-by-point basis, including additional benchmarks on the global response PDF. We will include the new results in our revised manuscript.
To address weakness 1, we follow the reviewer's suggestion, and a... | Summary: The paper presents a compelling study on redundancy of visual tokens in MLLMs and practical approaches to reduce them. The paper first verifies that one could eliminate up to 70% of visual tokens at testing time via simple average pooling with minial performance degradation. Then they experiments with several ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive suggestions. We address the concerns raised on a point-by-point basis, including additional benchmarks on the global response PDF. We will include the new results in our revised manuscript.
For weakness 1, we have included 2D-Conv and C-Abstractor (from H... | null | null | Rebuttal 1:
Rebuttal: We are grateful to the reviewers for their feedback. Reviewer dJy4 commends the paper for presenting "The paper presents a compelling study. The idea is clean and simple". Reviewer ENde notes "the paper addresses an underexplored area in MLLMs", and Reviewer PsS7 appreciates the thorough empirical... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MambaTalk: Efficient Holistic Gesture Synthesis with Selective State Space Models | Accept (poster) | Summary: This paper explores the application of state space models (SSMs) to co-speech gesture generation. The authors identify the computational challenges and jittering issues associated with the direct application of SSMs to gesture synthesis. To address these, they propose a two-stage modeling strategy with discret... | Rebuttal 1:
Rebuttal: **A1: Comparison with baseline methods**
Thanks for your advice.
Comparing with the baseline methods, we not only propose a method that applies a selective scan mechanism on co-speech gesture synthesis with local and global scans (a novel method not found in previous work), but we also consider t... | Summary: This study explores the use of state space models (SSMs) to enhance gesture synthesis, addressing challenges such as diverse movement dynamics and unnatural jittering in generated gestures. Through a two-stage modeling approach and the introduction of MambaTalk with hybrid fusion modules, the study demonstrate... | Rebuttal 1:
Rebuttal: **A1: Computational efficiency**
Thanks for your question. Our computational efficiency is mainly reflected in the inference time, which has been analyzed in section A.2 of the appendix. We leverage the linear computational complexity of Mamba and the sequence compression capability of VQVAE with... | Summary: The paper explores the selective scan mechanism for gesture generation. First, it trains VQVAE to reconstruct faces and body parts using discrete latent space. Then, It uses local and global scanning mechanisms to improve the latent representations of various body parts for the purpose of gesture generation.
... | Rebuttal 1:
Rebuttal: **A1: Related work and Preliminaries section**
We apologize for any inconvenience caused to your reading due to the generic explanation. We will provide a more detailed explanation for related work (e.g., VQVAE, HiPPO, LSSL) in section 3.1, which acts as preliminaries of our work.
For the use of ... | Summary: This paper focuses on the problem of co-speech gesture generation, particularly aiming to address the challenges of jittery movements, long motion sequences, and holistic gesture generation (including both face and body movements). To tackle these issues, the authors present a new method that combines diffusio... | Rebuttal 1:
Rebuttal: **A1: Reasons for choosing BEATX**
Thanks for your kind advice. Our work focuses on holistic co-speech gesture generation. We chose to use this dataset because it includes global movements and the smplx sequences of the entire body (e.g., face, upper body, lower body, and hands). However, most cu... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Transformers on Markov data: Constant depth suffices | Accept (poster) | Summary: This paper attempts to provide a possible explanation the capability of transformer architecture for efficient next token prediction of (stationary) $k$th order Markov data. The main result is a constructive proof that a $3$-layer transformer with a single head per layer can emulate conditional $k$ grams, and ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and constructive criticism. We have fixed the typo pointed out. Below we address the main weaknesses discussed in the review:
### **[W1,W2] Experiments for and learning dynamics under larger state spaces?**
In the attached rebuttal pdf, we ran experiments f... | Summary: This paper studies the learning process and representational power of transformers on the data sequence generated by k-th order Markov processes (or k-gram). Theoretically, this paper proved that (1) an attention-only transformer with $O(\log k)$ layers with one head for each layer can represent the conditiona... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments, and constructive criticism. Below we address the main weakness pointed out, as well as the question asking about experiments for $k > 8$.
### **[W1] Evidence that ADAM/GD converges to theoretical construction**
This is a great question. In Fig. 3 ... | Summary: This paper studies the ability of transformers to learn $k^{th}$ order Markov chains. They first conduct experiments showing that transformers with 2 layers and 1 head can learn Markov chains of up to order $k=4$. Similarly, with 3 layers, they can learn Markov chains of order $k=8$. Based on these observation... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments.
### **[W1] Thorough discussion for why contradictions arise in past work**
As discussed in the common rebuttal, the main reason for the differences in observations compared to [1] and [2] is the fact that the models were not trained for sufficient... | Summary: This paper investigates the representation capability of transformer with different number of layers or heads when learning k-th order Markov Process. They provide theoretical results demonstrating that attention-only transformers with O(log2(k)) layers can represent the in-context conditional k-th order Marko... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments. Below we address the main questions and weaknesses:
### **[W1] Error bounds**
We are happy to include a longer discussion of error bounds in the paper. To expand, suppose all the weights in the transformer model are upper bounded by $1$. When th... | Rebuttal 1:
Rebuttal: ## **Common Rebuttal**
We thank all the reviewers for taking the time to go through our paper and suggest constructive criticism. Please find attached a pdf containing additional plots.
Below we address some common points raised by multiple reviewers.
### **Long training reveals contradictions ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper studies the representation capacity of transformers in in-context learning of order-$k$ Markov chains. First, the authors theoretically show that $O(\log (k))$ layers are sufficient to represent $k$-th order induction heads in attention-only transformers. The paper also demonstrates the benefit of no... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and questions about the paper. Below we address the main questions and weaknesses raised:
### **[W1] Comparing single vs. multi-head**
In the attached material, please refer to Fig. 1, which we will add into the paper in the subsequent version (as the new F... | null | null | null | null | null | null |
Language models scale reliably with over-training and on downstream tasks | Reject | Summary: While existing scaling law studies look at compute-optimal pretraining, this paper considers scaling laws in the context of both pretraining and downstream performance. They perform scaling experiments and find that performance is predictable even in overtraining, and average downstream performance is also pre... | Rebuttal 1:
Rebuttal: Thank you for the attention to our work! Please see below for responses to your review. We are happy to provide more clarification or results should it be helpful!
**Over-training novelty.**
Thank you for pointing out that Chinchilla Approach 3 implies that over-trained model behavior is predicta... | Summary: The authors propose a scaling law for the “Chinchilla over-trained” regime where models are trained on many more tokens (in this paper, up to 30x) than Chinchilla-optimal. They motivate a scaling law relating pre-training compute and “over-training” to validation loss. They empirically demonstrate that the pro... | Rebuttal 1:
Rebuttal: **Comparison to Kaplan et al.**
Thank you for mentioning this important prior work. Unfortunately, the methodology in Kaplan et al., which utilizes early-stopped models and a different learning rate schedule, is not compatible with our scaling testbed, which is similar to the more contemporary Hof... | Summary: This paper investigates the power laws (scaling laws) of neural language models, particularly from the perspective of over-training and the relationship between validation loss (perplexity) and NLP downstream tasks. The authors define over-training as the situation where runs consume large amounts of computati... | Rebuttal 1:
Rebuttal: Thank you for the attention to our work! Please see below for responses to your review. We are happy to provide more clarification or results should it be helpful!
**Many open source models are larger than 7B parameters.**
Thank you for pointing this out––we agree that verifying scaling trends is... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their attention to our work, constructive comments, and positive feedback. Specifically, we are grateful for reviewers highlighting our empirical efforts and strengths of our methodology (RC8r, CG6A). We also appreciate their mentioning the relevance of our scaling study... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Supervised Kernel Thinning | Accept (poster) | Summary: This paper applies the kernel thinning approach to non-parametric regression, with two kinds of estimators (NW and KRR) considered. This approach speeds up the computational efficiency by using carefully chosen coresets for approximation. Theoretical results on the approximation error are established. Numerica... | Rebuttal 1:
Rebuttal: Thank you for the time you’ve spent reviewing our work and for your thoughtful feedback. We now address each of your questions and concerns in turn.
$\blacktriangleright$ $\textbf{Comparisons to existing methods for efficient non-parametric regression}$
- _"The application are only to toy example... | Summary: The authors speed up two non-parametric regression estimators, Nadaraya-Watson (NW) and Kernel Ridge Regression (KRR), by using a kernel thinning (KT) technique to compress the input data in a way that preserves important statistical properties. They include a theoretical analysis proving that that KT-based re... | Rebuttal 1:
Rebuttal: Thank you for the time you’ve spent reviewing our work and for your thoughtful feedback. We now address each of your questions and concerns in turn.
$\blacktriangleright$ $\textbf{Comparison with large-scale kernel methods}$
- _"The comparison with other large-scale kernel methods is limited to R... | Summary: After the rebuttal, I have updated my score from 3 to 5.
----
This paper provides a meta-algorithm based on Kernel Thinning for non-parametric regression, in particular, the Nadaraya-Watson (NW) regression, and the Kernel Ridge Regression (KRR).
The idea is to run NW or KRR on a thinned coreset by KT. The ... | Rebuttal 1:
Rebuttal: Thank you for the time you’ve spent reviewing our work and for your thoughtful feedback. We address each of your concerns below.
$\blacktriangleright$ $\textbf{Comparison with RPCholesky}$
- _"The empirical advantage..."_
- _"KT-KRR does not demonstrate..."_
Thank you for raising these points. T... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their helpful and detailed feedback on our work. We summarize our additions as follows:
$\blacktriangleright$ $\textbf{New experiment on SUSY dataset}$
- In Table 1 of the attached PDF, we have added results on the SUSY dataset ($d=18,N=5\times 10^6$). We use $4\tim... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LION: Linear Group RNN for 3D Object Detection in Point Clouds | Accept (poster) | Summary: This paper proposes to leverage linear networks such as RWKV and Mamba to capture long-range dependencies in LiDAR-based outdoor 3D object detection, leading to relatively larger group sizes of the voxel partition. The proposed techniques include voxel merging/expanding and voxel generation. Experiments are co... | Rebuttal 1:
Rebuttal: **W1**: Some claims are obscure and not well supported by evidence ...
Before feeding voxel features into linear RNN, we need to flatten 3D voxel features into 1D sequence features. Unlike the common 3D sparse convolution operation that directly deals with 3D voxel features in 3D space, the linea... | Summary: This paper proposes a linear Group RNN-based backbone for 3D object detection tasks. It can achieve a larger window size compared to previous transformer-based methods. A 3D spatial feature descriptor is also introduced to capture 3D spatial information. Furthermore, to address the sparsity of point clouds, th... | Rebuttal 1:
Rebuttal: **W1-1**: In this paper, the author has transformed the irregular point cloud into regular voxel representation. However, L172-175, the author claim max or average-pooling operations is not suitable to achieve downsampling or upsampling operations. This is conflicted.
Sorry for this misleading! S... | Summary: This paper targets the problem of long-range feature interactions for point cloud detection. It proposes a window-based 3D backbone based on linear group RNN and sparse convolution. In contrast to existing Transformers methods, this work increases the group size by leveraging the linear complexity of recent Ma... | Rebuttal 1:
Rebuttal: Thanks for your careful review and valuable suggestions. Here, we further address our contributions in this paper:
1) **Linear RNN-based 3D detection framework**: support kinds of linear RNNs (e.g., Mamba, RWKV, RetNet) to allow long-range feature interaction.
2) **3D spatial feature descriptor... | Summary: This paper presents the LION block, a neural component that builds up a backbone to extract 3D features with linear group RNNs for 3D object detection. The authors introduce a 3D spatial feature descriptor to extract point features, and a novel auto-regressive voxel generation method to density the foreground ... | Rebuttal 1:
Rebuttal: **W1**: Figure 3 (a) is confusing. Since the 3D Spatial Feature Descriptors are neural layers with learnable parameters, it would be better to represent these layers with blocks like “LION Layer”.
Thanks for your nice suggestion! We will revise "3D Spatial Feature Descriptors" as "3D Spatial Desc... | Rebuttal 1:
Rebuttal: We are grateful for the valuable suggestion and feedback of all reviewers, which will greatly improve the quality of our paper. We will carefully revise our paper according to your suggestions.
To Reviewer ehGN and wvps: We provide the visualization of long-range dependencies in the uploaded PDF... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ZipCache: Accurate and Efficient KV Cache Quantization with Salient Token Identification | Accept (poster) | Summary: The paper presents an adaptive, mixed-precision quantization method for compressing KV cache in LLMs. It proposes a channel-separable tokenwise quantization scheme to establish a robust quantization baseline, reducing the memory overhead of quantization parameters. A saliency metric, based on normalized attent... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for the valuable comments.
**Q1: Evaluating ZipCache on other generation or comprehending tasks.**
As shown in Table C in the rebuttal PDF, we evaluate the performance of ZipCache on LongBench. The results show that ZipCache outperforms the previous state-of-the-art metho... | Summary: The paper introduces ZipCache, an adaptive KV cache compression method for LLMs by accurately identifying salient tokens. It first presents a channel-separable tokenwise quantization scheme that reduces the overhead of quantization parameters. Next, it proposes a metric for identifying salient tokens based on ... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for the valuable comments.
**Q1: Evaluate ZipCache over LongBench.**
As shown in Table C in the rebuttal PDF, we evaluate the performance of ZipCache on LongBench. The results show that ZipCache outperforms the previous state-of-the-art method, KIVI. Due to the limited tim... | Summary: This paper proposes a post training quantization framework named ZipCache for quantizing the key and value cache of LLMs. The authors introduce a channel-separable token-wise quantization scheme, which consumes less memory than the group quantization in terms of the quantization parameters. They also select sa... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for the valuable comments.
**Q1: The idea of channel-separable quantization is not novel.**
Please refer to General Response Q1.
**Q2: Performing the mean operation may cause a prefer to the latest tokens.**
Indeed, there might be a prefer to the latest tokens since the ... | Summary: The paper introduces a channel-separable quantization scheme that decouples the quantization along channel and token dimensions. This method significantly reduces the quantization overhead without compromising performance. To accurately recognize salient tokens, the paper introduces a new token saliency metric... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for the valuable comments.
**Q1: The writing can be more native.**
Thanks for your valuable comment. We will carefully proofread the paper in the final version.
**Q2: The idea of group quantization (different for different K and V) is not new.**
There might be some misun... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable feedback. Overall, our work has been well recognized as
- "It is novel and well-motivated" (Reviewers S3gk and E63Z)
- "It is well-integrated into FlashAttention" (All Reviewers)
- "It is clear and easy to follow" (Reviewers Nc8R, S3gk, and E63Z)
- "It dem... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Perceiving Longer Sequences With Bi-Directional Cross-Attention Transformers | Accept (poster) | Summary: The paper proposes Bi-Directional Cross-Attention Transformers, a new architecture that aims to reduce the computational complexity of self-attention in transformers. The authors claim linear complexity with respect to sequence lengths. This is achieved by replacing the query embeddings in self-attention with ... | Rebuttal 1:
Rebuttal: We thank you for your helpful suggestions, and address your points in the following.
**[P1] - Focus on 'short' sequences**:
We would like to point out that the arguably most-common sequence lengths in vision tasks like classification are around *197 tokens* (224x224 w/ patch size 16x16 + cls to... | Summary: The paper presents a novel Transformer architecture called BiXT (Bi-directional Cross-Attention Transformers) that efficiently processes longer sequences like point clouds, text, or images while maintaining competitive performance across various tasks. The BiXT model is inspired by the Perceiver architecture b... | Rebuttal 1:
Rebuttal: We thank you for your helpful review and address your questions in the following:
**[Q1] - Improvement over iterative method**:
As we outline in Section 3.2 and in more detail in Appendix A.4, a big improvement in performance comes due to *'unblocking'* the bottleneck that exists in iterative a... | Summary: This research paper presents an enhancement to the Perceiver architecture in terms of accuracy and efficiency. The key innovation is a bidirectional cross-attention module designed to iteratively stack query-to-token and token-to-query cross-attention modules, revealing a symmetry between these two attention m... | Rebuttal 1:
Rebuttal: We thank you for the helpful suggestions and address your points individually in the following.
**[P1] - Presentation & architectural details**:
- **Visuals**:
We have included into the document attached to the global response:
1) Transition from iterative to sequential and then bidirecti... | null | null | Rebuttal 1:
Rebuttal: Dear reviewers and AC,
We want to genuinely thank you for your valuable time and effort spent reviewing our manuscript, and are grateful for the detailed and constructive remarks that have helped us to further improve the quality of our paper.
We individually address each reviewer's comments as... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Algorithmic progress in language models | Accept (poster) | Summary: The authors of this paper examine the performance improvements of language models over the past decade, and investigate how much of it can be attributed to algorithmic improvements of language models.
Strengths: I note here that, given that this paper proposes a method to evaluate the historical progress of l... | Rebuttal 1:
Rebuttal: Thank you for the feedback – we address your questions and concerns below.
We fully agree with you that there is substantial uncertainty about how our results could extrapolate into the future. Indeed, we have mentioned this point in the limitations section, and pointed out that our core focus h... | Summary: This paper investigates the rate of algorithmic progress on the task of language modeling, using a dataset of over 200 LM evaluations on WikiText and Penn Treebank between 2012-2023. The authors fit an augmented scaling law to the data and show that the models are requiring 2x less compute roughly every eight ... | Rebuttal 1:
Rebuttal: Thank you for the feedback on our paper.
We agree that we make several assumptions in our work quantifying algorithmic progress, and that this introduces uncertainty into our conclusions. However, we do not believe that these assumptions undermine the core results of our paper. We have performed... | Summary: This paper presents an analysis of the relative contribution of algorithmic progress to overall LM performance gains over the window of 2012-2023. The authors evaluate a large number of potential equation variants for modeling algorithmic progress using leave-one-out CV. By making use of defined notions of eff... | null | Summary: This paper breaks down the driving force behind language models into two factors: scaling up the amount of compute used to train the models and algorithmic innovations. A statistical model, similar to the scaling law, is built and fitted to analyze the contributions of these two factors. The paper claims that ... | Rebuttal 1:
Title: Is there a mix up?
Comment: Dear reviewer,
is this the review for the write paper? The authors and the area chair suspect that this review is for a different paper. Could you kindly update your review?
Thanks,
Your Area Chair
---
Rebuttal Comment 1.1:
Title: Appologise for mix up
Comment: Yes, I ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Scalable and Effective Arithmetic Tree Generation for Adder and Multiplier Designs | Accept (spotlight) | Summary: This paper presents a new method for designing arithmetic modules by modeling tasks as single-player tree generation games, using reinforcement learning techniques. This approach combines prefix and compressor tree modules to find optimal multipliers. Experiments show that the developed 128-bit adders and mult... | Rebuttal 1:
Rebuttal: Thank you so much for your positive comments.
**Weakness 1 [Innovation]**
- Circuit design is a broad field encompassing various tasks. Our work focuses on arithmetic unit design (adders and multipliers), which differs significantly from graph placement in search space and evaluation metrics. Gr... | Summary: The paper introduces a novel approach to optimizing arithmetic module designs, particularly adders and multipliers. By modeling design tasks as single-player tree generation games and employing RL techniques (MCTS and PPO), the authors effectively explore the design space to uncover superior structures. Their ... | Rebuttal 1:
Rebuttal: Thank you very much for your positive comments.
**Weakness 1 [Focus on Adders and Multipliers]**
- While our current results focus on adders and multipliers, these operations are among the most time-consuming on GPUs, making their optimization highly significant.
- We would like to clarify that... | Summary: This paper discusses the application of reinforcement learning to optimize the design of arithmetic circuits, specifically adders, and multipliers. Two single-player tree generation games, AddGame and MultGame, are designed to formulate adder and multiplier design problems. AddGame re-designs the search method... | Rebuttal 1:
Rebuttal: Thank you very much for your positive comments.
**Weakness 1 [Add Detailed Introduction in Main Text]**
Thank you for the suggestion. In our revised paper, we will include the introduction of the states and actions for both AddGame and MultGame, and the tree representations in the Appendix, to t... | Summary: This paper aims to leverage reinforcement learning (RL) techniques for automatic arithmetic circuit design. The authors propose to cast the design tasks as single-player tree generation games, and leverage reinforcement learning techniques to optimize these arithmetic tree structures. For adder circuits, the p... | Rebuttal 1:
Rebuttal: Thank you very much for your positive feedback.
**Weakness 1 [PDK Selection]**
Although the 45nm and 7nm PDKs employed in our study are open-source and academically oriented, they are designed to closely mimic corresponding industry nodes and are widely used for benchmarking purposes within the ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper presents a reinforcement-learning-based approach to optimizing arithmetic units, specifically adders and multipliers, to enhance computational efficiency and reduce area consumption. The key idea is to frame the design as tree generation games. The evaluation shows that the proposed method can gener... | Rebuttal 1:
Rebuttal: Thank you so much for your constructive comments.
**Weakness 1 [Application in Modern LLM]**
- We appreciate this observation regarding the diminishing improvements of our PPO-based method as the bit width decreases. Indeed, the paper highlights the significance of multipliers and adders for lar... | Summary: A reinforcement learning-based method is proposed to design adders and multipliers within a framework of single-player tree generation games, named AddGame and MultGame. The method leads to the discovery of superior designs for 128-bit adders and multipliers, achieving significant improvements in delay and siz... | Rebuttal 1:
Rebuttal: Thank you very much for your constructive feedback.
**Weakness 1 [Distinction and Improvement over PrefixRL [17]]**
Both our approach and PrefixRL [17] apply an RL framework to circuit design. However, our method incorporates several critical enhancements that we would like to clarify:
- **Advan... | null | null | null | null |
Verifiably Robust Conformal Prediction | Accept (poster) | Summary: The authors propose a novel method to recover coverage guarantees for conformal predictions in the presence of adversarial attacks. Unlike previously proposed approaches, the authors directly leverage verifiable methods for NN to compute prediction scores. Through empirical tests, the authors prove the benefit... | Rebuttal 1:
Rebuttal: > 1. In the paper, the authors focus on PDG. However, the literature offers more advanced and sophisticated attacks. It would be beneficial to assess the robustness of the proposed approach against other type of attacks.
With regard to the chosen attack methods, we evaluate PGD in the case of cla... | Summary: The paper provides a verifiably robust conformal prediction method via neural network verification. It considers two paradigms for considering the perturbation either at the calibration stage or the inference stage. They also consider the validity of method for both classification and regression.
Strengths: 1... | Rebuttal 1:
Rebuttal: > 1. Novelty: I understand that it is a new angle that combines NN verification and conformal prediction to provide a certifiably robust conformal prediction framework, but I wonder technically, can we do more about the combination. For example, can we have new conformity score adapted to NN verif... | Summary: This submission proposes VRCP, a framework for verifiably robust conformal prediction under Lp-bounded adversarial attacks. The framework integrates existing bound propagation tools for verification of conformal predictions. Two variants, VRCP-I that triggers bound computation at inference time and VRCP-C that... | Rebuttal 1:
Rebuttal: > 1. The framework is relatively straightforward. The foundation is some probability relaxations that can be intuitively derived. I would not view this intuitiveness as a weakness. However, it would be great if the authors could discuss some extensions and optimizations, e.g., proof sharing for VR... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their useful comments.
Here we respond to the common feedback amongst all the reviews.
---
## Part 1. Verification-Friendly Non-Conformity Score Functions
We agree that investigating verification-friendly score functions is a great idea for future work. We use $1-f_... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CLODE: Continuous Exposure Learning for Low-Light Image Enhancement using Neural ODEs | Reject | Summary: This paper formulates the higher-order curve estimation problem as a NODE problem, enabling effective and accurate solutions with standard ODE solvers.
Strengths: This paper is well-written and structurally organised.
Weaknesses: Reference formats are not consistent.
Technical Quality: 3
Clarity: 3
Questi... | Rebuttal 1:
Rebuttal: We are glad to hear that you found the paper to be well-written and structurally organized. We have diligently examined your comments and concerns as a reviewer, and have prepared responses addressing the raised concerns.
**W1: Reference format**
- Thanks to your thorough suggestion, we believe ... | Summary: This paper mainly addressed the problem of insufficient data for low-light enhancement. Specifically, it proposed CLODE , which employs Neural Ordinary Differential Equations to learn the continuous dynamics of the latent image for the first time. The experiments demonstrate the CLODE performs better than othe... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for recognizing our contribution and pointing out constructive concerns. We have made efforts to address the reviewer’s concerns as follows.
**Q1, W7: More explanation of CLODE**
Due to character limitation we placed explanation of this question in global rebu... | Summary: This manuscript introduces CLODE, which learns low-light image enhancement using neural ordinary differential equations (NODE). The key innovation lies in formulating the higher-order curve estimation problem as a NODE problem. Experimental results show that the proposed approach outperforms state-of-the-art u... | Rebuttal 1:
Rebuttal: Thank you for providing a thoughtful review. For enhancing our paper, we have diligently reviewed the weaknesses and questions raised regarding our paper and have prepared additional experiments and answers.
**W1: Novelty**
- We cautiously wish to assert the novelty of our approach. In contrast ... | Summary: This paper This paper proposes an ODE-based method to tackle low-light image enhancement problems. The motivation of the paper is inspired by the observation that the conventional discrete iterative approaches set fixed update steps. It does not only miss the optimal solution and also does not guarantee the c... | Rebuttal 1:
Rebuttal: We are glad to hear that you found the paper to be strong and solid. We have diligently examined your comments and concerns as a reviewer, and have prepared responses addressing the raised concerns.
**W1: Highlight region**
- We understand the reviewer’s concern in **Fig. 4**. As a reminder, C... | Rebuttal 1:
Rebuttal: To the reviewers.
First and foremost, we appreciate the reviewers' efforts. We have prepared responses to all comments, along with figures and tables in the attached PDF that show additional experiments to enhance our explanations. Below are brief explanations for each figure and table:
There ar... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes a Neural ODE method for curve-adjustment-based low light image enhancement methods to achieve better results which are often sub-optimal for fixed discrete step methods. Specifically, the proposed method reformulates the curve-adjustment-based from the discrete version into the ODE problem ... | Rebuttal 1:
Rebuttal: Thank you very much for thoroughly reviewing our paper. We appreciate your feedback. To address the concerns you raised, we are providing several experimental results and our perspectives.
**W1, Q1: Color cast**
**[What leads to the color cast?]**
- CLODE enhances the image based on the color s... | null | null | null | null | null | null |
Unconditional stability of a recurrent neural circuit implementing divisive normalization | Accept (poster) | Summary: Stability is a critical notion in the understanding of dynamical systems as well as for learning them. This paper studies the stability of a biologically plausible recurrent cortical model, ORGaNICs, that implements divisive normalization. More precisely:
- it demonstrates the local stability of two specific s... | Rebuttal 1:
Rebuttal: **Weaknesses**:
- “the introduction of the dynamics … why they are important”. **Answer:** We thank the reviewer for the insightful feedback. We will add more intuition about the dynamics in the model description and compare ORGaNICs to LSTM, an RNN architecture similar to ORGaNICs. We will also... | Summary: This work is based on ORGaNICs, a particular type of RNN architecture that implements divisive normalization (from neuroscience). The authors explore whether ORGaNICs are stable enough to be meaningfully (and stably) trained by gradient descent. Due to their stability, which the authors have proven theoretical... | Rebuttal 1:
Rebuttal: **Weaknesses:**
- “Fundamentally, ORGaNIC … one particular effect found in V1”. **Answer:** DN is observed in numerous cortical areas beyond V1 and across different species (Carandini & Heeger, 2012). It explains a wide range of experimental phenomena in various neural systems and cognitive pro... | Summary: This paper studies the stability properties of a model of cortical circuits which was introduced in 2019 (and I wasn't yet aware of): the ORGaNIC model by Heeger & Mackey. This LSTM-like model uses a simple set of differential equations that unify several phenomena observed in cortex, including normalization (... | Rebuttal 1:
Rebuttal: **Weaknesses:**
- “Overall this is a … uncertainty rating)”. **Answer:**
We argue that the potential impact is high. LSTMs and GRUs have had a huge impact on ML/AI applications even though they are not always stable and hence require ad hoc techniques for training (e.g., gradient clipping/scalin... | Summary: The paper discusses the development and analysis of "Oscillatory Recurrent Gated Neural Integrator Circuits" (ORGaNICs), a biologically plausible model of recurrent neural networks that implements divisive normalization. The authors prove the unconditional local stability of ORGaNICs with an identity recurrent... | Rebuttal 1:
Rebuttal: **Weaknesses:**
- “Generalization: The paper ... the tested benchmarks”. **Answer:** We expect ORGaNICs to perform well on other ML benchmarks, especially those concerning sequential data. This will be done in a future study, therefore we refrain from making any specific claims about performance ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for dedicating their time and providing valuable feedback on our submission. Your insights and comments are greatly appreciated and will help improve the quality of our work.
In response to the reviewers' comments, we have conducted additional experiments and analyses. ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: Recurrent neural networks are widely applied in both solving machine learning tasks and modeling neural recordings. However, they can be difficult to train due to exploding/vanishing gradients and other sources of instability, often requiring hyperparameter optimization. In this paper, the authors argue that... | Rebuttal 1:
Rebuttal: **Weaknesses:**
- “The main … raise my score”. **Answer:** Our proof for the stability of the high-dimensional model (using the indirect method of Lyapunov) relies on the existence of analytical expressions for the fixed point. Such an expression exists when $\mathbf{W}_r=\mathbf{I}$, but does not... | null | null | null | null | null | null |
Reinforcement Learning Gradients as Vitamin for Online Finetuning Decision Transformers | Accept (spotlight) | Summary: The authors consider the problem of online fine-tuning decision transformers. Current approaches for this problem do not work well when the offline data is low-quality. The authors analyze the online DT algorithm theoretically and show why DT-induced policy update would not work if the RTG used for conditionin... | Rebuttal 1:
Rebuttal: Thanks for appreciating our work. Here are our responses:
**Q1. Results are noisy, and a more careful approach to statistical significance is needed.**
Thanks for pointing this out! We understand the importance of reliable evaluation and statistical significance. For this we reported standard de... | Summary: This paper presents a method for fine-tuning decision transformers (DT) online. The proposal is to integrate TD3 with DT such that the gradients from TD3 can help the online policy to explore highly rewarding trajectories, hence further improving the agent performance. It can be seen as a variant of TD3+BC wit... | Rebuttal 1:
Rebuttal: Thanks for appreciating our work. Below are responses to questions:
**Q1. TD3 gradient for policy update is not clear.**
Thanks for pointing this out! There is a typo in Eq. (2) which should read as follows:
$$\min\_{\mu^{\text{DT}}}\mathbb{E}\_{\tau\sim D}\Bigg[\frac{1}{T\_{\text{train}}}\sum\... | Summary: The authors introduce a novel framework for improving the performance of Online Decision Transformer through adding TD3 gradients to the fine-tuning ODT objective. This is motivated by a theoretical analysis of ODT, that highlights an issue with low-reward, sub-optimal pretraining. The authors also provide an ... | Rebuttal 1:
Rebuttal: Thanks for appreciating our work. Below are responses to questions:
**Q1. Overclaim of “boosting performance”, and the need to refine claims with properties where TD3+ODT should outperform ODT.**
Thanks for pointing this out. While TD3+ODT is generally better than ODT, we are happy to rephrase o... | Summary: This paper addresses the challenge of online finetuning of online decision transformers (ODT). Theoretical results are provided to show the target return-to-go can hamper finetune. The authors propose to have TD3 gradients added to ODT finetune and improve its performance especially when pretrained with low-re... | Rebuttal 1:
Rebuttal: Thanks for appreciating our work. Below are responses to questions:
**Q1. test ODT baselines that tackle online finetuning with alternative methods, e.g., better exploration policy and gradually increasing target RTG.**
Great suggestions! Currently, ODT explores due to the stochasticity of its p... | Rebuttal 1:
Rebuttal: We thank the reviewers, ACs and SACs for valuable feedback. We are delighted that our idea was appreciated as novel (AdzZ, 1gFB), well-motivated (1gFB), valuable (NWdD, gVzs, 1gFB) and backed by theoretical foundations (AdzZ, NWdD), the literature review was appreciated as well-described (NWdD), a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Efficiency of ERM in Feature Learning | Accept (poster) | Summary: This paper considers a "feature learning" setting equivalent to structural risk minimization over a set of hypotheses of the form $\langle w, \phi_t(x)\rangle$. It demonstrate that the statistical error of this procedure converges to a quantity that depends on a natural empirical process defined *only* in term... | Rebuttal 1:
Rebuttal: We thank the reviewer for engaging with our paper and for their feedback. We address some of their concerns below.
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***I apologize for the directness, but I do not find the qualitative finding particularly surprising. We know that ERM localizes, and as a consequence, if I have a predictor cla... | Summary: This paper investigates the learning theory of empirical risk minimization (ERM) with feature learning. Under the setting where the optimal finite-sample feature is selected by minimizing the empirical risk over a class of features, the authors show that ERM with feature learning implies convergence of the exc... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our paper. We address some of their concerns below.
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***The authors have reviewed many statistical theory papers. However, in terms of feature learning or representation learning, they didn't conduct a thorough literature review. Specificall... | Summary: This paper consider the problem of regression over the linear classes induced by a collection of feature maps. They study both the asymptotic and the non-asymptotic behavior of the empirical risk minimizer. Surprisingly, although the linear classes has a complexity much higher than that of only one linear map,... | Rebuttal 1:
Rebuttal: We thank the reviewer for reading our paper and for their positive comments. We address their question and comments below.
***It will be good if more case studies are provided.***
As mentioned in another comment, one additional example we wanted to include of some practical relevance is the foll... | Summary: This paper studies the novel setting where we are give a collection of predefined feature maps (indexed by a set T) and we choose one of these feature maps and then, learn a linear predictor on top of the chosen feature map. The authors derive upper bounds on the excess risk that depend on the number (size) o... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read our paper and for their positive evaluation. We address their comments below.
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***The analysis ignores the role of the learning algorithm and looks at the problem from a purely statistical perspective. The role of implicit bias of the algorith... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper considers a linear regression problem where an ERM learner tries to learn a linear predictor and a feature map (over a countable set of feature maps) under some specific assumptions on the set of feature maps and the property of minimizers. The paper analyzes both asymptotic and non-asymptotic behavi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive evaluation and detailed feedback. We will make sure to fix the typos for the final version of the paper. We address the reviewer's main concern below.
---
***The setting is narrow: when I think about feature learning, I imagine we first learn a feature ma... | null | null | null | null | null | null |
Text2CAD: Generating Sequential CAD Designs from Beginner-to-Expert Level Text Prompts | Accept (spotlight) | Summary: This paper investigates an interesting task (Text2CAD) in CAD automated applications, which achieves generating parametric CAD models from text prompts. Specifically, the authors first introduce a data annotation pipeline to generate suitable text prompts for CAD models in public DeepCAD dataset (including abo... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's careful consideration of our work and the thoughtful responses. Below are our responses to the specific points you raised.
# Response to Weaknesses
**(W1) Reviewer:** *Some typos need to be corrected ...*
We have corrected the typo.
**(W2) Reviewer:** *In... | Summary: The paper proposes Text2CAD, a framework designed to expedite the prototyping of complex computer-aided design (CAD) models. The proposes method uses designer-friendly text instructions to generate parametric CAD models, making it accessible for all skill levels. To facilitate this, a data annotation pipeline ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's careful consideration of our work and the thoughtful responses. Below are our responses to the specific points you raised.
# Response to Weaknesses and Questions
**(W1) Reviewer:** *The authors assert that the proposed method Text2CAD is the first AI framewor... | Summary: This paper proposes Text2CAD, the first approach for generating parametric CAD models from different levels of designer-friendly language instructions. The critical challenge of generating CAD models from text is the lack of high-quality paired data. The paper's main contribution lies in a well-designed LLM-as... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's careful consideration of our work and the thoughtful responses. Below are our responses to the specific points you raised.
# Response to Weaknesses and Questions
**(W1) Reviewer:** *The main paper and the appendix provide only a small number of qualitative ... | Summary: This paper proposes the first text2cad framework. This includes a CAD model annotation pipeline using VLM + LLM. And an autoregressive Transformer for generating sketch and extrude. Authors extended DeepCAD dataset with different level of text description using their method and demonstrate promising results fo... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's careful consideration of our work and the thoughtful responses. Below are our responses to the specific points you raised.
# Response to Weaknesses and Questions
**(Q1) Reviewer:** *What is the quality of the annotated text? Can authors provide some way to ... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their thorough evaluation and insightful comments. We are delighted that the reviewers recognize the significance of our work.
1. Text2CAD is acknowledged as the *first framework* (**ovUL**) for generating parametric 3D CAD models *from different levels of des... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
HYSYNTH: Context-Free LLM Approximation for Guiding Program Synthesis | Accept (poster) | Summary: This paper presents a hybrid program synthesis approach that strengthens the classic bottom-up search with an LLM-guided prior distribution, given as a probabilistic context-free grammar (PCFG). The focus of this paper is to solve complex PBE tasks where programs in unfamiliar DSL are difficult for LLMs to gen... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and thoughtful comments. Please note our top-level comment with additional experimental results. Below we address specific comments and questions.
**Q: The datasets are small and domain specific, and requires implementing a synthesizer. Can HySynth scale to more... | Summary: The paper introduces a new approach for solving structured prediction and reasoning tasks by leveraging the programming and planning capabilities of LLMs to enhance bottom-up search in program synthesis.
Specifically, HYSYNTH initially generates preliminary solutions directly from the LLM. Since direct LLM sa... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and thoughtful comments. Please note our top-level comment with additional experimental results.
**Q: The experiments show that HySynth solves more problems within the same time limit. Does the time reported include the time taken for sampling from the LLM and t... | Summary: This paper introduces HySynth, an approach to program synthesis that combines (1) sampling programs from a large language model (LLM) to train a probabilistic context free grammar (PCFG) with (2) applying bottom-up enumerative search guided by the PCFG to solve programming by example tasks. It applies this app... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and thoughtful comments. Please note our top-level comment with additional experimental results. Below we address specific comments and questions.
**Q: For the direct sampling baseline, clarification about how many samples are drawn from the LLM in the direct sa... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their time, valuable comments, and encouraging feedback! In the global part of the response, we answer shared questions and provide a list of changes we plan to make in a revised version of the paper.
**Q1 (Reviewers FZiE and XXbQ): Does the time reported in Fig 4 inclu... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Adversarial Robustness of Benjamini Hochberg | Accept (poster) | Summary: Benjamini-Hochberg provides a means for controlling the false detection rate (FDR) in multiple hypothesis testing. This paper explores how the FDR region can be perturbed by an adversary. Specifically, the author(s) propose two adversarial attacks *Increase-C* and *MOVE-1* to efficiently perturb *c* scores a... | Rebuttal 1:
Rebuttal: Addressing the *``Weaknesses"* $\ldots$
*1. Very Little Analysis of Section 5's Empirical Results*
*Sections 5.1 and 5.2 are essentially just an overview of the two experimental setups. There is no real analysis or discussion of what the experimental results show or why it's important. While ... | Summary: This paper studies the adversarial robustness of the Benjamini-Hochberg (BH) procedure, introducing simple adversarial test-perturbation algorithms. The experiments show that BH's control can be significantly compromised with minimal perturbations. The analysis uses a combinatorial perspective and generalized ... | Rebuttal 1:
Rebuttal: Addressing the Weakness comment $\ldots$
*This paper would benefit from a discussion of potential mitigation strategies that could enhance the robustness of the Benjamini Hochberg procedure against attacks such as INCREASE-c.*
**Rebuttal:**
**Thanks for asking about mitigation strategies, which... | Summary: In this paper, the authors have tested the Benjamini-Hochberg (BH) 's adversarial robustness, as this procedure is deployed in critical applications such as drug discovery, forensics, and anomaly detection. Specifically, the authors develop a class of simple and easily implementable adversarial test-perturbati... | Rebuttal 1:
Rebuttal: # Q1 Re:
Indeed, $z$ scores are classically $z_i := \frac{\sum_{j=1}^n x_{ij}/n - 0}{s/\sqrt{n}}$, so that perturbations to the "samples" $x_{ij}$ translate to perturbation of the z-score $z_i$, and ultimately the p-value $p_i:= 1 - \Phi(z_i)$. Since p-values are ultimately the input processed by... | Summary: The paper explores the adversarial robustness of the Benjamini-Hochberg procedure. In particular, the authors theoretically show that it is possible to perturb test scores to cause the BH procedure to not be robust to adversarial attacks. BH is reframed as a "balls into bins" problem, and the authors propose a... | Rebuttal 1:
Rebuttal: **Addressing Weaknesses**
1. *In the introduction, the paper (correctly) highlights the importance of hypothesis testing $\ldots$ However, the experimental section does not $\ldots$ consider an application such as OOD detection \ldots*
**Rebuttal:**
We did in fact experiment on an ``OOD-classifi... | Rebuttal 1:
Rebuttal: We thank the reviewers for their questions and feedback. We have provided individual responses to each and welcome the opportunity to engage further in the discussion period. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models | Accept (spotlight) | Summary: The paper proposes token-level generalization bounds for large language models (LLMs), such as LLaMA2-70B, using less restrictive compression techniques like Monarch matrices, Kronecker factorizations, and post-training quantization. The authors argue that traditional document-level bounds are vacuous at this ... | Rebuttal 1:
Rebuttal: Many thanks for your encouraging and thoughtful feedback! We respond to your questions below.
**An intuitive application of our bounds to downstream tasks:**
Inspired by your comments, we provide another intuitive application of our bounds. In this case, to a downstream scientific task. Our tok... | Summary: This paper develops nonvacuous generalization bounds for modern language models. Specifically, this paper proves a token-level generalization bound, and applies different techniques (LoRA, 2 Kronecker Product, Monarch Matrices, and post-training quantization) to control the capacity of model class.
Strengths:... | Rebuttal 1:
Rebuttal: We really value your thoughtful and supportive feedback! We provide several additional results inspired by your comments.
**An intuitive application of our bounds to downstream tasks:**
Inspired by your comments, we provide another intuitive application of our bounds. In this case, to a downstr... | Summary: This paper presents a novel approach that computes non-vacuous compression-based generalization bounds for LLMs at the billion-parameter scale. Prior works could only achieve vacuous bounds for these large-scale models and rely on the assumption of IID documents. By leveraging the vast number of tokens in LLM ... | Rebuttal 1:
Rebuttal: We thank you for your thoughtful and supportive feedback! We respond to your questions below.
**Effect of finetuning on downstream task performance:**
As per your suggestion, we run additional experiments where we finetune the GPT2 large model (774M parameters) – pretrained on the WebText datas... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their very supportive and helpful feedback. Inspired by the reviewers’ comments, we now report additional results that highlight the following contributions: (i) we complement our other understanding-oriented experiments through an antibody design setting where the t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Peri-midFormer: Periodic Pyramid Transformer for Time Series Analysis | Accept (spotlight) | Summary: This paper introduces Peri-midFormer to capture multi-periodicity in time series data. Specifically, it designs a pyramid structure and attention mechanisms to effectively model complex temporal variations. The proposed methods demonstrates great performance in several time series analysis tasks in the author'... | Rebuttal 1:
Rebuttal: # Response to Reviewer ECSx
Thank you for your detailed review and questions. Please find our answers below.
### **Q1: Were the results of DLinear and PatchTST obtained with a lookback of 96?**
We have reviewed the results for DLinear and PatchTST in Table 12 and confirmed that you are correct—... | Summary: In this paper, the authors proposed a new method Peri-midFormer, which uses the multi-periodicity of time series and modeling the periodic part of a time series in a pyramid way. They further proposed an attention mechanism to use the neighborhood relation in the pyramid. Extensive experiments on different tas... | Rebuttal 1:
Rebuttal: # Response to Reviewer jAZw
Thanks for your valuable comments. We will explain your concerns point by point.
### **Q1: Some important related works are missing**
Thank you for your reminder. We have carefully read the paper you provided and found them very helpful for improving the background o... | Summary: The paper introduces Peri-midFormer, a novel transformer-based architecture designed for time series analysis.
By leveraging the multi-periodicity inherent in time series data, the model constructs a Periodic Pyramid structure that decouples complex periodic variations into inclusion and overlap relationships... | Rebuttal 1:
Rebuttal: # Response to Reviewer GBMh
Thanks for your valuable comments. We will explain your concerns point by point.
### **Q1: (cr. W1). Add in the main body of the paper a discussion about the training and inference time, as well as some critical results and main conclusions.**
Thank you very much for... | Summary: The abstract succinctly introduces the Peri-midFormer, a novel approach designed for time series analysis, acknowledging the challenges posed by the discrete nature of time series data and the complexity of capturing periodic variations directly. It proposes a method to address these challenges by decomposing ... | Rebuttal 1:
Rebuttal: # Response to Reviewer W65V
Thank you for your detailed review and questions. Please find our answers below.
### **Q1: Complexity and Scalability**
We have adopted the suggestions from you and two other reviewers to include additional experiments, covering the complexity, time, and memory effici... | Rebuttal 1:
Rebuttal: # General Responses
We thank the Reviewers for the insightful comments and detailed feedback. Here's the global reply.
### **1. Validation of Computational Complexity and Scalability**
Following the suggestions of several reviewers, we have supplemented our experiments with tests on computationa... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Testably Learning Polynomial Threshold Functions | Accept (poster) | Summary: This paper studies testably learning an n-dimensional Polynomial Threshold function using a reduction proved in a previous work called fooling. The authors give an analysis of a construction of fooling multilinear PTF and then further for fooling arbitrary PTF.
The paper completes itself with proof that push-... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for the kind feedback. We appreciate that the reviewer thinks of testable learning of PTFs as a natural problem to consider.
The primary motivation for including the impossibility result for proving testable learning guarantees via the push-forward is to show that a ... | Summary: Background: Agnostic learning is a well-studied framework that models learning when no function is some hypothesis class F describes the data perfectly. Specifically, the agnostic learning framework requires the learning algorithm to give a hypothesis whose classification error is at most opt+$\epsilon$, where... | Rebuttal 1:
Rebuttal: We wish to thank the anonymous reviewer for their kind feedback. We are encouraged that the reviewer believes the problem we study is well-motivated and naturally extends previous work and that the reviewer appreciates that our work qualitatively matches existing lower bounds even for the agnostic... | Summary: This paper studied the problem of testably learning polynomial threshold functions (PTFs). The authors aimed to answer the question of whether PTFs are qualitatively harder to learn in the testably learning model, compared to the agnostic learning model. The authors answered the question in the negative, showi... | Rebuttal 1:
Rebuttal: First, we would like to thank the reviewer for their kind feedback. We are encouraged by the fact they appreciate that we give the first result on testably learning PTFs.
We agree with the reviewer that it would be interesting future work to try to improve the runtime, potentially to $n^{\mathrm{... | Summary: The authors study the problem of testing polynomial threshold functions in the agnostic setting in the testable learning paradigm. They present a testable learning algorithm that matches with the asymptotic bound (in terms of n) known for agnostic learning
Strengths: Testing of polynomial threshold functions ... | Rebuttal 1:
Rebuttal: We wish to thank the anonymous reviewer for their feedback and questions. We are happy that the reviewer finds testable learning, particularly of PTFs, to be a very natural topic.
The primary concern of the reviewer appears to be the dependence of our results on the error $\varepsilon$ and the de... | Rebuttal 1:
Rebuttal: First and foremost, we would like to thank the reviewers for their time and valuable feedback. We appreciate that many of the reviewers consider testably learning PTFs as a natural and well-motivated problem.
Several reviewers correctly pointed out that the dependence of our runtime on $d$ (the d... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper investigates testable learning of polynomial threshold functions (PTFs) with respect to the standard Gaussian distribution. The authors extend previous work on testable learning of halfspaces to show that PTFs of arbitrary constant degree can be testably learned up to excess error \epsilon in time n... | Rebuttal 1:
Rebuttal: We thank the reviewer for their kind feedback and insightful questions. We appreciate that the reviewer found our paper easy to follow, and that they believe we make significant progress on an open problem in learning theory.
The main concerns of the reviewer appear to be the relative importance ... | null | null | null | null | null | null |
Categorical Flow Matching on Statistical Manifolds | Accept (poster) | Summary: The paper extends Flow Matching to discrete ("categorical") variables, similarly to a whole flourish of other papers that appeared in the last months, all of which work on the "probability simplex", i.e. they pass from a discrete alphabet A to the finite dimensional probability space P(A), which can be identif... | Rebuttal 1:
Rebuttal: We thank you for your high recognition of our work's novel geometric insight and clear delivery. We will address your questions and concerns as follows.
## Q1 Advantage of Riemannian structure
Unlike LinearFM which assumes a flat simplex and straight flows, SFM considers the Riemannian structure... | Summary: The paper proposes an approach to generative modelling of discrete data based on the Riemannian Flow Matching [1] algorithm where the authors use the Fisher metric to define the geometry of this space.
In detail, the authors define the generative modelling from the discrete data as sampling from the empirical... | Rebuttal 1:
Rebuttal: Thank you for your high recognition of our work's significance in the realm of discrete generative modeling and for raising interesting questions and suggestions. We will fix the typo and citation in the revision. Our responses to your questions are as follows:
## Q1 Motivations
### Sampling *ov... | Summary: This work introduces statistical flow matching, an improved method for discrete generation on the simplex by utilizing the geometry induced by the Fisher information metric. By mapping points on the simplex to points on the sphere, flows wrt the Fisher metric can be efficiently computed. SFM is tested on toy e... | Rebuttal 1:
Rebuttal: We appreciate your recognition of our work's clear presentation and novel geometric perspective for discrete generation. We'd like to clarify that Text8 results of MultiFlow were added on Jun 5 after the NeurIPS ddl, thus we were unable to compare them although it was cited.
We wish to emphasize ... | Summary: This paper proposes flow matching on the manifold of discrete distributions, where each point represents a probability mass function (pmf). In essence, this is done by parameterizing a vector of size n for each n-class categorical distribution. Instead of using Euclidean geodesic,s the authors discuss the use ... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our work's clear presentation as a natural and timely extension of Riemannian FM on discrete data. We thank your thoughtful suggestions and will fix typos and clarify minibatch-OT in the revision. We'd like to address your comments as follows.
## Q1 Regarding li... | Rebuttal 1:
Rebuttal: Dear all Reviewers,
We sincerely appreciate your reviews that help make our work more concrete and comprehensive. We thank you for recognizing our novel geometric perspective of discrete generation and our paper's clear presentation. Here we address some of the common questions.
## More detail... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Imitating Language via Scalable Inverse Reinforcement Learning | Accept (poster) | Summary: This paper investigates the possibility of applying inverse reinforcement learning for language imitation learning problem. Specifically, the paper reformulates IQLearn as a temporal difference regularized extension of MLE. This basically bridges inverse reinforcement learning with MLE with a coefficient $\lam... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback. By addressing these helpful comments and questions, the paper has improved and gained in clarity. Please mention further questions or clarifications and we will work to address them ASAP.
As the review mentions, our small performance gains are consistent a... | Summary: The paper introduces a RL-centric perspective to imitation for LLMs, with an novel imitation learning algorithm for fine tuning LLMs, that is derived from IQLearn, forming a dynamics dependent temporal difference regularized variant of MLE. The authors provide an extensive analysis of other potential inverse R... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback. By addressing these helpful comments and questions, the paper has improved and gained in clarity. Please mention further questions or clarifications and we will work to address them ASAP.
Uncertainty estimates and error bars: We always plot the standard er... | Summary: This paper investigates using inverse reinforcement learning (IRL) to directly optimize sequences for fine-tuning large language models. Moreover, this work reformulates inverse soft-Q-learning as a temporal difference regularized extension of maximum likelihood estimation (MLE), which bridges IRL and MLE in s... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback. By addressing these helpful comments and questions, the paper has improved and gained in clarity. Please mention further questions or clarifications and we will work to address them ASAP.
Thank you for pointing out computational requirements. We crucially ... | Summary: In this paper the authors aim to cast fine-tuning LM as inverse RL from the perspective of distribution matching. In order to avoid online generation in existing IRL algorithms such as GAIL, the authors leverage an offline IRL algorithm, IQL, and reformulate it as a temporal difference regularized extension of... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback. By addressing these helpful comments and questions, the paper has improved and gained in clarity. Please mention further questions or clarifications and we will work to address them ASAP.
One of the review’s key points regards limited performance improveme... | Rebuttal 1:
Rebuttal: We would like to thank our reviewers for their valuable feedback. The paper has already considerably improved and we will work hard to address further comments and questions to conclude the rebuttal process to everyone’s satisfaction.
The reviews generally appreciate the discussion of inverse RL ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper looks at the existing IQLearn algorithm and makes some connections with standard maximum likelihood learning in the context of sequence based language models.
On the empirical front, results are given showing different model performance along diversity (measured via self-Bleu) and some accuracy mea... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback. By addressing these helpful comments and questions, the paper has improved and gained in clarity. Please mention further questions or clarifications and we will work to address them ASAP.
The included experiments show that IRL methods (GAIL/IQLearn) improv... | null | null | null | null | null | null |
Position Coupling: Improving Length Generalization of Arithmetic Transformers Using Task Structure | Accept (poster) | Summary: This paper proposes a method called "position coupling" to enhance the length generalization of Transformer models, specifically targeting arithmetic tasks such as integer addition. The authors claim both empirical success and theoretical guarantees for their approach. The method involves assigning the same po... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer’s effort and constructive feedback. Below we summarize your feedback/questions and address these one by one.
> **W1. Scope of Application: the generalizability of position coupling to broader and more complex tasks is not convincingly shown.**
- We agree that the... | Summary: This paper proposes a new way to bake in the positional structure of problems for transformers. The authors also analyze the potential for models with and without their proposed positional coupling to solve problems of arbitrary size. They also show empirically that their method helps a small transformer learn... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer's valuable questions and rich comments, and we hope our response relevantly addresses all points raised in the review.
> **W1. Some of the contributions are limited to 1-layer Transformers.**
- We first highlight that our theoretical construction (Thm 5.1) is no... | Summary: This paper proposes "position coupling", a novel technique to improve the length generalization ability of decoder-only Transformers. Unlike standard positional embeddings, position coupling assigns the same position ID to semantically related tokens across the input sequence, directly embedding task structure... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments. We provide our response to the reviewer's concerns.
> **W1. Theoretically, why do deeper models perform worse despite their expressivity?**
- For a broader answer to the question, see our General Response.
- We hypothesize that the performance ... | Summary: This work considers the problem of length generalization of Transformers and proposes injecting the task structure through positional embeddings for improving length generalization. Task structures are known and therefore, the authors come up with a (relatively) general heuristic to leverage this structure.
T... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review and valuable comments. Below, we address the reviewer’s concern.
> **W1. Position coupling is geared towards specific tasks in length generalization.**
First, as mentioned in the conclusion of our paper, we focus on certain tasks with a handy structu... | Rebuttal 1:
Rebuttal: We deeply appreciate all reviewers for their insightful and detailed reviews, questions, and comments on our work. We assure the reviewers that all the answers and discussions will be incorporated into our final manuscript.
We are encouraged to see that the reviewers recognized that our method i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SIRIUS : Contexual Sparisty with Correction for Efficient LLMs | Accept (poster) | Summary: The paper introduces a sparse LLM correction mechanism (SIRIUS) designed to improve the inference efficiency of large language models through contextual sparsity. Contextual sparsity reduces computational cost by pruning parameters that are less relevant based on the input context. However, this approach degra... | Rebuttal 1:
Rebuttal: Thank you so much for your time and attention on the paper. We are very grateful for you to be interested in the KV Cache correction. We will try our best to answer your questions.
1. Full and Sparse of the same architecture
Contextual Sparsity is the focus of Sirius. Contextually Sparse (CS) ... | Summary: The paper introduces SIRIUS, a novel method designed to enhance the efficiency and accuracy of sparse Large Language Models in reasoning and deduction tasks. SIRIUS employs contextual sparsity to reduce computational costs and incorporates an efficient correction mechanism that recovers sparse model performanc... | Rebuttal 1:
Rebuttal: Thank you for the time taken to read the paper and your insightful comments. We are thankful that you suggested we add more experiment data to evaluate Sirius. We will try our best to address your concern. If you feel that your concern has been taken into account, please consider raising your scor... | Summary: This paper aims to improve contextual sparsity (CS) approaches: LLM sparsification / parameter pruning methods where the sparsification strategy is conditioned on the input sequence / prompt itself. The paper begins by reproducing contextual sparsity baselines, but applied to modern LLMs: LLama 2 and 3 (7B & 8... | Rebuttal 1:
Rebuttal: Thank you for the time taken to read the paper and your insightful comments. We are thankful that you point out that the comparison against speculative decoding is missing and we lack of convincing real efficiency metrics. We will try our best to address your concern. If you feel that your concer... | Summary: This paper focuses on enhancing the inference efficiency of large language models (LLMs) through contextual sparsity. While it identifies that contextual sparsity reduces hallucination, it also notes a significant impact on reasoning and deduction performance. To address these drawbacks, the paper introduces S... | Rebuttal 1:
Rebuttal: Thank you so much for your attention and time taken to read the paper and your insightful comments. We are very thankful for you to point out the inadequacy of our method presentation and raising concerns on the diversity of the evaluation. We will try our best to address your concern. If you feel... | Rebuttal 1:
Rebuttal: We thanked all the reviewers [R1(a8uY), R2(rbBk), R3(ArEw), R4(BB9w)] for their attention and time put into reviewing the paper and also for the thoughtful and supportive comments. We are glad to see that the reviewers find the work interesting [R4(BB9w)] and effective [R1(a8uY)], consider the pro... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
PANORAMIA: Privacy Auditing of Machine Learning Models without Retraining | Accept (poster) | Summary: The paper proposes a method to estimate a lower bound on the (pure) Differential Privacy parameter $\epsilon$ for already trained machine learning models using a post-hoc empirical evaluation based on conducting Membership Inference Attacks. Building on the analysis by Steinke et al., 2023, for auditing DP wit... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review.
**How is the helper model used in the baseline trained? (Q1)**
The helper model has the same classification task and architecture as the target model. To train it, we generate separate sets of training and validation data using our generator. For image data, ... | Summary: This paper proposes a novel privacy auditing procedure called PANORAMIA. The method works with a single model that uses all available training data, with no modifications to the training procedure, and with only access to a subset of the training data. This is achieved by synthetically generating non-member sa... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review. Hereafter, we start with answers to the questions, before addressing other weaknesses listed that we believe stem from a miscommunication.
**Questions:**
**Q1: Might there be a way to use the membership classifier itself as a baseline, e.g., by somehow averag... | Summary: The paper proposes a novel way for privacy auditing of machine learning models through MIAs. The framework they propose, panoramia, aims to audit the privacy of a ML model post-hoc (so with no control over the training procedure), and with access to a known member subdataset. The method first consists of train... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review.
**Q1: What happens when you develop a generator that is perfect (c=0) which just samples randomly from the known member subset.**
In that case (a highly overfitted generator), the baseline and MIA will output $c _{lb} = \\{\epsilon+c\\} _{lb} = 0$ (though te... | Summary: The authors propose a privacy auditing technique that utilizes partial access to member data to generate synthetic non-member data, which in-turn is used to train a meta-classifier that can be used to empirically measure privacy leakage relating to record membership. The authors evaluate their technique on mod... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review. We start with answers to the two explicit questions, before addressing two of the weaknesses listed that we believe stem from a miscommunication or misunderstanding.
**Questions:**
**Q1 - Figure 3a suggests an ordering of 100 > 20 > 50 in terms of leakage for... | Rebuttal 1:
Rebuttal: Thank you for the thought-provoking reviews and suggestions! In our answers, we focus on key misconceptions regarding our paper and misunderstandings in the reviews. Hereafter, we summarize the most important points addressed:
- Potential baseline weakness (reviewer CsXP): this is an issue due to... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Hypothesis Testing the Circuit Hypothesis in LLMs | Accept (poster) | Summary: This paper considers the problem of formalizing and evaluating the circuit hypothesis. This hypothesis posits that specific subnetworks within a Transformer model are responsible for specific model behaviors. Although there are multiple examples of such circuits that have been manually discovered in the litera... | Rebuttal 1:
Rebuttal: ## Weaknesses
**First, the hypothesis test for judging equivalence (Equation 2) feels somewhat unintuitive. Consider the scenario where the circuit $C^*$ significantly outperforms the model $M$ on half the inputs and vice versa. Intuitively, one would not consider such a circuit equivalent to the... | Summary: This paper proposes a set of tests to evaluate how well a "circuit" meets its desired properties.
- Here a circuit refers to a subnetwork, which could be either synthetic (e.g. constructed according to RASP) or discovered in a trained model.
- The desired properties considered in this paper are:
- *faithfuln... | Rebuttal 1:
Rebuttal: ## Weaknesses
**I'm concerned about the practical applicability of the tests (...) can be sensitive to the choice of the reference distribution.**
Thank you for raising this important point! We believe that the main objections stem from a small but important misunderstanding of the paper's main ... | Summary: The paper operates in the framework of mechanistic interpretability of transformer models, where it is assumed that subgraphs (i.e. circuits) of the computational graph determined by the model implement specific capabilities of the latter. It defines a set of properties that an ideal circuit should have, namel... | Rebuttal 1:
Rebuttal: ## Weaknesses
**I believe it would have been nice to perform further tests to understand whether the idealized tests have some applicability on discovered circuits. (or if they always lead to the null being rejected), potentially by also altering the discovered ones.**
**Would have found it inter... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful reviews and support of the paper.
We are pleased to see that the reviewers find the paper well-explained, with original and well-considered hypothesis tests (Reviewer wEmx); that it is clearly written, offers a quantitative approach to evaluating the co... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Toxicity Detection for Free | Accept (spotlight) | Summary: This work proposes to leverage logits of the first token in LLM responses to identify toxic prompts. The experiments against LLaMAGuard and multiple open-sourced LLMs show satisfactory performance in ToxicChat and LMSYS-Chat-1M datasets.
Strengths: - The proposed method is simple and easy to implement.
- The... | Rebuttal 1:
Rebuttal: We thank the review for raising valuable questions. However, it seems to us that there might potentially be some misunderstandings from the reviewer regarding the implementation of MULI and our conceptual contribution. Here we offer our response to the reviewer's questions.
**Q1. I don't think t... | Summary: This work proposes a toxicity detection method for LLMs that incurs negligible additional inference cost, and shows superior performance compared with two existing methods. The main observation of this work is that the logits of the first generated token (after prompt) are informative about toxicity of the pro... | Rebuttal 1:
Rebuttal: Thank you for identifying the originality, quality, clarity and significance of our method, as well as raising valuable questions.
Here are the responses to your questions:
**Q1. One question that immediately came to my mind while reading is what happens beyond the first token logits. My intu... | Summary: The paper introduces Moderation Using LLM Introspection (MULI), which leverages the LLM's first token logits for toxicity detection. This is a novel approach compared to traditional methods that require an additional LLM for toxicity detection, thereby reducing computational costs and latency.
Strengths: - Ef... | Rebuttal 1:
Rebuttal: We thank the reviewer for identifying multiple strengths of our method, as well as raising valuable questions. Here are our responses to the questions.
**Q1. Line 212: is it should be “a tolerance of 0.1% FPR” instead of TPR?**
R: Yes, it should be FPR instead of TPR. We will correct this in t... | Summary: This paper introduces a novel approach to detecting toxic prompts in LLMs using a method called Moderation Using LLM Introspection (MULI). The authors highlight the limitations of SOTA toxicity detectors, which often have low true positive rates (TPRs) at low false positive rates (FPRs) and incur high computat... | Rebuttal 1:
Rebuttal: Thank you for identifying multiple strengths of our method, as well as raising valuable questions.
Here are the responses to your questions:
**Q1. One weakness of this work is its limited scope of evaluation. While the paper evaluates MULI on specific datasets (ToxicChat and LMSYS-Chat-1M), it ... | Rebuttal 1:
Rebuttal: We appreciate the reviewers for their valuable time. Based on the questions and suggestions in the reviews, we plan to make the following adjustments to our paper:
1. We will provide a qualitative overview on common failure modes, with examples of toxic prompts that are uncaught by MULI.
2. We w... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors of this paper propose an approach for detecting toxicity of prompts from strongly aligned models (that are trained to refuse toxic prompts) using their Moderation Using LLM Introspection (MULI). Key to this approach is the observation that even though LLMs may not refuse toxic prompts always at ver... | Rebuttal 1:
Rebuttal: Thank you for the encouraging words about our idea and methodology, as well as the valuable questions.
Here are the responses to your questions:
**Q1. Need to mention somewhere that this will work only for current LLMs that are safety aligned in a certain way and using specific refusal respons... | null | null | null | null | null | null |
Deep Support Vectors | Accept (poster) | Summary: This paper introduces the concept of Deep Support Vectors (DSVs) as an adaptation of support vectors from Support Vector Machines (SVMs) to deep learning models. The authors propose the DeepKKT condition, which generalizes the traditional Karush-Kuhn-Tucker (KKT) conditions of SVMs to handle the high-dimension... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and constructive feedback. We appreciate the acknowledgement of our contribution - Originality of our DeepKKT condition and its practical application.
**Mathematical Derivation of DeepKKT Condition:**
While we acknowledge that a rigorous mathematical derivatio... | Summary: This paper introduces a novel Deep Support Vectors (DSVs) framework, which can be used to reconstruct data and serve as latent generative models using logits as latent. By adapting the traditional Karush-Kuhn-Tucker (KKT) condition for deep learning models, the authors introduce the DeepKKT one and show that g... | Rebuttal 1:
Rebuttal: Thank you for your feedback and valuable insights, and for acknowledging our work. We are especially grateful for your recognition of the originality and practical applicability of the DeepKKT condition. However, we would like to clarify that we do not deal with DeepSVM. Specifically, we do not ai... | Summary: The paper describes rethinking a classification through support vector in a way similar to SVMs. It provides additional benefits such as:
- few-shot dataset distillation
- using the similarity of the proposed formulation to the diffusion models, the model can be transformed into a generative model
Strengths:... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback. Your insightful feedback and objective perspective greatly helped our research. Also, we are particularly grateful for your acknowledgement upon our originality and intention of the paper.
**Clarity on Mathematical Notation:**
We appreciate your suggesti... | Summary: This paper introduces the DeepKKT conditions for deep svm models, which correspond to the KKT condition in traditional linear SVMs. By either selecting deep support vectors (DSVs) from training data or generating them from already trained deep learning models, The authors show DSVs can play a similar role to c... | Rebuttal 1:
Rebuttal: Thank you very much for your feedback; we really appreciate the time and effort you have invested in reviewing our paper. However, it appears that there may be some misunderstandings regarding key aspects of our work, and we believe certain elements of your review may be misleading.
**First of a... | Rebuttal 1:
Rebuttal: **Summary**
In this paper, we propose a method to identify deep support vectors (DSVs) for pre-trained deep models without access to the original dataset. Our work does not involve training or constructing a Deep Support Vector Machine (DeepSVM). Instead, our original contribution is introducing ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper introduces Deep Support Vectors (DSVs), an adaptation of support vector concepts to deep learning models. The authors propose a DeepKKT condition, analogous to the KKT conditions in SVMs, to identify or generate DSVs in trained deep models. They demonstrate that DSVs exhibit properties similar to tr... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and constructive feedback. We appreciate the opportunity to address the points you raised.
**Experiments with Transformer Architectures:**
In our global rebuttal (see pdf file), we included experiments on Transformer architectures. Specifically, we demonstrat... | null | null | null | null | null | null |
Efficient Centroid-Linkage Clustering | Accept (poster) | Summary: The authors give an algorithm that approximates a centroid linkage clustering. Their algorithm is fast both in theory and in practice. The algorithm is based on a novel fully dynamic data structure for nearest neighbors which is of independent interest.
Strengths: The proposed algorithm is a significant stren... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and suggestions. We respond now to the specific questions of the reviewer.
> The techniques are very much geared towards the centroid linkage function. It could be interesting to mention how much of it could potentially generalize to other linkage function... | Summary: The paper deals with centroid-linkage agglomerative clustering (centroid HAC), where the distance between two clusters is the distance between their centers. It presents a subquadratic time algorithm (approximate centroid HAC) that, instead of requiring the two closest clusters to be merged at each step, it al... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and suggestions. We respond now to the specific questions of the reviewer.
> Q1/W1: Coherence and clarifying why we use a different ANNS algorithm in theory and in practice.
We thank the reviewer again for raising this question, which we agree is importan... | Summary: This paper considers the design of approximate versions of the centroid linkage method for hierarchical clustering. The exact version of centroid linkage requires $\Theta(n^2)$ time, but it could be possible to do better if some relaxation is allowed. This paper shows how to get a $c$-approximate centroid li... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and suggestions. We respond now to the specific questions of the reviewer.
> W1: The experimental evaluation isn't for the algorithm that is proposed theoretically, but instead for a variant with several practical modifications (e.g., using graph-based ANN... | Summary: Paper studied Hierarchical clustering algorithm. HAC is popular method where n points are initially singleton clusters (leaves). At each step, two clusters are merged, and the process is continued until one giant cluster of all n points (root) remains. THe merging process gives a hierarchy or nested clustering... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and suggestions. We respond now to the specific questions of the reviewer.
> “Datasets chosen for evaluation can be more diverse.”
To address this point we have assembled several new clustering datasets with ground truth from existing sources (which we wi... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their thoughtful comments and suggestions. We reply to the questions and comments of each reviewer individually in the corresponding rebuttal fields.
# Coherence:
First, we would like to clarify a point raised by the reviewers regarding the coherence of our theoret... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Conditional Density Estimation with Histogram Trees | Accept (poster) | Summary: This paper proposes a tree-based conditional density estimation model. Previously, conditional density estimation involved: 1) kernel density estimation-based method (requires expensive bandwidth tuning); 2) neural network (black-box approach); 3) a tree-based model which focuses on a Gaussian distribution for... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thanks for the constructive advices! I will now reply to each point in the Weaknesses and Questions sections.
# Regarding Weaknesses in the review:
## 1. Rebuttal to Weakness 1: In comparison to previous research for CDE, the datasets we used are NOT smaller.
- We included 14 d... | Summary: This paper addresses the problem of conditional density estimation, i.e. given a conditioning variable x estimate the whole distribution of y, with special emphasis on interpretability.
For this interpretability requirement, the authors resort to classical decision trees allowing to partition the conditioning ... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments and advice.
We now address **each paragraph in the "Weakness" section** of your review.
## Factual Error in Paragraph 2:
- The statement in the review that "no hyperparameter tuning is needed" is incorrect. Our paper highlights that the advantage of using MD... | Summary: This paper proposed to use decision tree with leaves formed by histogram models for conditional density estimation to gain interpretability. Characteristics of the proposed method, along with the density estimation accuracy and run time, are evaluated with numerical experiments.
Strengths: Extensive experimen... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Although I understand that the judgment about novelty can be a subjective matter, I would appreciate it if more information is provided regarding the motivation for this judgment, which would be helpful for me to improve the paper.
Regarding your two questions:
1. I am not sure... | Summary: This paper proposes a new conditional density estimation algorithm based on histogram trees.
The base model corresponds to a full binary tree, where each internal node is associated with a split of the feature space in one coordinate, and each leaf node is associated with a histogram density estimator for the... | Rebuttal 1:
Rebuttal: Thank you so much for the detailed, constructive, and very helpful comments on my writing!
## Presentational issues fixed
I have fixed the presentational issues and typos you mentioned. Among others, one extremely helpful comment is that "lines 108-110 are confusing": in these lines I wrote "..... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Hierarchical and Density-based Causal Clustering | Accept (poster) | Summary: The paper aims to understand treatment effect heterogeneity and identify and evaluate subgroup effects. It addresses the challenge of typically unknown subgroup structures by proposing a solution based on causal k-means clustering to assess effect heterogeneity. The approach is improved by integrating hierarch... | Rebuttal 1:
Rebuttal: We appreciate your valuable comments and suggestions. We address each of
them below.
1. **\[Experimental comparison\]** Yes, we completely agree that our
work could be strengthened by experimentally comparing with other
clustering methods, especially if other methods require any special
... | Summary: This work solves the task of clustering treatment-effect data based on their conditional treatment effect with a discrete set of treatments and continuous (possibly multivariate) effects. More specifically, they extend the framework of previous work on causal k-means which achieves the same, albeit which is no... | Rebuttal 1:
Rebuttal: We appreciate your valuable input and insights. We address each of your
comments below.
1. **\[Clarification on contributions\]** Thank you for bringing our
attention to this. The main contributions of our work is that we
have proved that the two appealing off-the-shelf cluster-analysis
... | Summary: The authors propose an extension of existing causal (i.e., treatment effect heterogeneity) k-means clustering techniques to hierarchical and density-based clustering, including novel estimators and convergence guarantees.
**Edit**: increased rating from 3 to 7 (with the understanding that more qualified peopl... | Rebuttal 1:
Rebuttal: Thank you for your time and thorough feedback. We have addressed each of your concerns as outlined below.
1. **[Originality and related work]** Thank you for highlighting the connection between our research and other works in the literature on causal discovery or structural causal models. To our... | Summary: The paper deals with problems arising in understanding treatment response/effects and in particular evaluating subgroup effects building on recent work using causal k-means clustering. The main contribution of the paper is to circumvent the k-means approach and suggest a hierarchical and also a density-based c... | Rebuttal 1:
Rebuttal: Thank you for your time and valuable feedback. We would like to address each of your major concerns in the following.
1. **[Novelty of the problem]** Thank you for pointing this out. First, we want to emphasize that the problem of causal clustering is novel; clustering on counterfactual outcomes... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Transfer Q-star : Principled Decoding for LLM Alignment | Accept (poster) | Summary: This paper introduces 'Transfer Q*', which is a decoding strategy to align language models with target reward models without any fine-tuning. The main contribution of this paper is the estimation of the optimal Q* function, which is often unavailable in practice and is necessary for approximating the RL optima... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback on our paper.
>**Weakness 1:** Equation 5.... introduced.
**Response to Weakness 1:** We will add the derivation for the closed-form solution in Equation 4 (also shown in CD [B]) as a Lemma in the appendix for better clarity.
>**Weakness 2:** The introd... | Summary: This paper addresses decoding for aligning large language models, which is a process of inference-time, token-level optimisation without updating the parameters of the LLM. Two scenarios are considered: 1. where a baseline policy is given and is aligned with the target trajectory-level reward. 2. where the bas... | Rebuttal 1:
Rebuttal: Thank you for your comments.
>**Weakness 1:** Morally speaking, it is not ...... empirically, though.
**Response to Weakness 1:** We believe the reviewer wants to understand the advantage of directly generating the response using $\rho_{\text{BL}}$ over TQ*. We explain in detail below.
***Ou... | Summary: The paper Transfer Q⋆: Principled Decoding for LLM Alignment proposes a novel approach to aligning large language models by leveraging a principled decoding strategy. The authors propose a method to estimate the optimal Q-function for decoding using an existing aligned policy, addressing limitations in previou... | Rebuttal 1:
Rebuttal: **Response to Reviewer Summary:** We thank the reviewer for the encouraging remarks and recommending acceptance of our work. We provided detailed responses to other comments one by one as follows.
>**Weakness 1:** Comparison with Baselines: The comparisons with existing baselines like DPO are ... | Summary: The paper proposes a new estimation of Q function for controlled decoding. Instead of using a Q function derived from SFT model, the paper propose to use Q function derived from separate aligned models. Evaluation shows the proposed method can achieve higher reward in benchmarks.
Strengths: 1. The idea of usi... | Rebuttal 1:
Rebuttal: **Response to Reviewer Summary:** We sincerely thank the reviewer for the thoughtful review of our work, highlighting the strength and novelty in both our formulation and experimental design in leveraging available aligned models for principled decoding. We address all other comments one by one a... | Rebuttal 1:
Rebuttal: ## General Response
We want to thank all the reviewers for their time and effort in providing detailed comments to our work. We are encouraged that the reviewers found our proposed approach
- ***novel*** (Reviewer ypg9, Reviewer xz5J) & ***theoretically rigorous*** (Reviewer hjEu, Reviewer 1x8S)... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
TOPA: Extending Large Language Models for Video Understanding via Text-Only Pre-Alignment | Accept (spotlight) | Summary: This paper introduces Text-Only Pre-Alignment (TOPA), a framework designed to extend LLMs for video understanding, without the need for real video data. TOPA leverages textual videos (Tideo), generated by capable LLMs, to mimic real videos. Aided by CLIP's aligned cross-modal space, this framework despite tra... | Rebuttal 1:
Rebuttal: > Weakness1: The paper could benefit from providing a more detailed comparison between Tideo and real video. For instance, the TextVid dataset likely covers more diverse domains, due to the use of varied prompts during Tideo generation. It could be analyzed in depth.
Ans: Thank you for your sugge... | Summary: The paper presents Text-Only Pre-Alignment (TOPA), a method to extend LLMs for video understanding without training on real video data. TOPA generates "Textual Videos" using LLMs to simulate real video data, then pre-aligns the LLMs with video modalities using the CLIP model for feature extraction. This approa... | Rebuttal 1:
Rebuttal: Overall, thank you for your careful reading, detailed feedback, and valuable suggestions. We will revise our paper based on the discussion.
> Weakness1: L42: The authors describe subtitles as "frame-level descriptions," but subtitles are actually spoken speech extracted from videos and have intri... | Summary: The authors introduce Text-Only Pre-Alignment (TOPA), a novel approach that extends large language models (LLMs) for video understanding without pre-training on real video data. TOPA generates Textual Videos, comprising continuous textual frames and annotations to simulate video-text data, and uses these to pr... | Rebuttal 1:
Rebuttal: > **Weaknesses:** In Table 1, the author did not include key papers such as LifelongMemory[1], Video-Agent: A Memory-Augmented Multimodal Agent for Video Understanding[2], LangRepo[3], and MVU[4]. LifelongMemory achieves 68% and 62.1% on the subset and the full set of EgoSchema, respectively, and ... | null | null | Rebuttal 1:
Rebuttal: # General Response:
We greatly appreciate the reviewers' careful reading and insightful feedback on our work. It's encouraging to receive comments recognizing our work's **novel idea, good motivation, thorough experiments and promising results**. We have respond the concerns raised and are open t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Private Attribute Inference from Images with Vision-Language Models | Accept (poster) | Summary: The paper presents a timely and important investigation into the privacy implications of Visual Language Models (VLMs) by evaluating their ability to predict sensitive personal information from images found online. The authors introduce a new dataset, which consists of:
1. Images sourced from posts on selecte... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their time and effort spent reviewing our paper, for their insightful and detailed feedback, and for their positive overall assessment of our work. We especially appreciate the reviewer’s acknowledgment of the criticality of the studied privacy issue and for... | Summary: The authors focus on the privacy risks of multimodal LLMs by demonstrating the personal information can be extracted from publicly accessible images, and leveraged by LLMs to infer sensitive details.
Strengths: The intersection of privacy and multimodal AI is interesting, and the evaluation is strong, using b... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their time and effort spent reviewing our paper. We also appreciate the reviewer's acknowledgment of the studied problem being interesting, our evaluation and presentation. We hope our answers below address the reviewer's concerns.
**Q1: Does the accurate, scal... | Summary: The paper performs a novel analysis privacy-leakage due to modern multimodal VLMs. Specifically, they show that modern VLMs, when correctly promoted, can infer sensitive information from seemingly innocuous images. They curate a dataset of images containing clues to sensitive information and query a few SOTA m... | Rebuttal 1:
Rebuttal: First, we would like to sincerely thank the reviewer for their time and effort spent reviewing, their insightful comments, and for their highly favorable assessment of our work. We are especially thankful for the reviewer’s acknowledgement of the relevance and timeliness of the presented privacy t... | Summary: The paper proposes a new privacy attack with VLMs where the model is queried with an image and the goal is to predict the private attributes such as place and gender of the person not shown in the image.
Strengths: - Good experimental evaluations
- Clear problem formulation
- Practical automated attack using ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and efforts spent reviewing and for their insightful feedback. We are especially appreciative of the reviewer’s recognition of our attack’s novelty and our extensive experimental evaluation. Below, we address the reviewer’s comments and questions.
**Q1: Would ... | Rebuttal 1:
Rebuttal: First of all, we would like to thank all reviewers for their time and efforts spent on reviewing our paper, and for their insightful, constructive, and valuable comments. We are especially appreciative of several reviewers’ acknowledgement of the relevance, practicality, and cruciality of the exam... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
General Compression Framework for Efficient Transformer Object Tracking | Reject | Summary: The authors proposed a novel compression strategy of transformer based trackers. Unlike previous works, it divides the teacher network into multiple segments, each segment corresponds a single transformer layer of student network, then train each student layer separately. It also introduced some training strat... | Rebuttal 1:
Rebuttal: Thank you for recognizing the efficiency and value of our work. We also appreciate your insightful advice and comments. We value your support for our work!
## Q1: Stage Division
Yes, our framework is indeed really simple yet effective. When the teacher model and student model share the same stru... | Summary: This paper aims to distill knowledge from larger teacher models into more compact student trackers. Three techniques are proposed: A stage division strategy that segments the transformer layers of the teacher model. Replacement training technique. Prediction guidance and stage-wise feature mimicking. Experime... | Rebuttal 1:
Rebuttal: Thank you for recognizing efficiency and value of our work. We also appreciate your insightful advice and comments. We would be grateful if you could support our work and reconsider your rating.
## Q1: Inherent Consistency
Thank you for your insightful advice. CompressTracker is a unified and ge... | Summary: The paper introduces CompressTracker, a novel general model compression framework that enhances the efficiency of transformer-based object tracking models. It innovatively segments transformer layers into stages, enabling a more effective emulation of complex teacher models by lightweight student models. The f... | Rebuttal 1:
Rebuttal: Thank you for your support and insights. We appreciate your deep understanding of the innovation and effectiveness of our work. We are grateful for your support for our work!
## Q1: Difference from BEVDistill
Our CompressTracker is quite different from BEVDistill. (1) Purpose and Scope. CompressT... | Summary: In this paper, the authors proposed a general model compression framework for efficient Transformer object tracking, named CompressTracker. The method adopts a novel stage partitioning strategy to divide the Transformer layers of the teacher model into different stages, enabling the student model to more effec... | Rebuttal 1:
Rebuttal: Thanks for your insightful advice. We sincerely appreciate your valuable comments and recognition of the novelty of our work. We will carefully review and modify our manisctipt based on your suggestion to improve its presentation. We greatly value your support for our work!
## Q1: Font Size in Pi... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper introduces CompressTracker, a general model compression framework for efficient transformer-based object tracking. CompressTracker divides the teacher model into stages corresponding to student model layers and randomly replaces student stages with teacher stages during training. It also aligns the ... | Rebuttal 1:
Rebuttal: We greatly appreciate your recognition of the efficiency and value of our work, as well as your insightful advice and comments. Other reviewers, such as Reviewer M8LK, Reviewer 8Frr, and Reviewer 2g8j, have also acknowledged the novelty and effectiveness of our approach. Our CompressTracker achiev... | null | null | null | null | null | null |
AED: Adaptable Error Detection for Few-shot Imitation Policy | Accept (poster) | Summary: The paper aims to address the adaptable error detection problem, i.e., detecting the error behavior of a robot policy in an unseen environment. Solving the problem ensures that the robot is stopped before performing behavior that causes disruptions to the surroundings.
The AED problem introduces three challen... | Rebuttal 1:
Rebuttal: Dear Reviewer SywQ,
Thank you for your insightful comments and high recognition of our work! Our responses below are dedicated to addressing your questions and concerns.
---
**[W1] The authors may need a real-world environment that contains some disruption scenarios and test the method in such ... | Summary: This paper introduces Adaptable Error Detection (**AED**) within Few-Shot Imitation (**FSI**) tasks, a critical yet underexplored area in robotics and AI. The authors establish a novel benchmark for assessing AED methods and present **PrObe**, designed specifically for this task. Through comprehensive evaluati... | Rebuttal 1:
Rebuttal: Dear Reviewer 3VqP,
Thank you for your insightful comments and high recognition of our work! Our responses below are dedicated to addressing your questions and concerns.
---
**[W1] Incorporating mathematical notation would enhance clarity and comprehension.**
- Thank you for your suggestion. W... | Summary: This paper proposed a task Adaptable Error Detection (AED) that attempts to perform online behavior error detection for FSI policies, it advocate three main challenges comparing to FSAD: novel environment, no notable change for behavior error that AED tries to detect, and it has to be conduct simultaneously wi... | Rebuttal 1:
Rebuttal: Dear Reviewer qV6D,
Thank you for your insightful comments and high recognition of our work! Our responses below are dedicated to addressing your questions and concerns.
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**[W1] If frame-level label is not available, how come the measurement is averaging over actions?**
- We try to make Eq.... | null | null | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their constructive and insightful comments on our work.
We are excited that our work possesses several strengths recognized by the reviewers, as summarized below:
- Our proposed AED task is **important** (all reviewers) with **unique challenges** (3VgP).
... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
e-COP : Episodic Constrained Optimization of Policies | Accept (poster) | Summary: In this paper, the authors propose a policy optimization algorithm for constrained Reinforcement Learning (RL) problem in a finite horizon setting. First, the authors develop a policy difference lemma for the finite horizon MDP problem. Following that, the authors combine a set of ideas to propose the e-COP al... | Rebuttal 1:
Rebuttal: We thank the reviewer for comments on exposition and correctness. Responses below:
We disagree with the reviewer on the weakness mentioned:
(i) Calling introduction of quadratic penalty as purely heuristic minimizes the utility of such ideas that have proven to be central in making optimization ... | Summary: The paper proposes a new algorithm for finite-horizon constrained RL problems. The algorithm is based on three ideas: PPO-like updates for the finite-horizon setting, P3O-like treatment of constraints with adaptive penalty coefficient, and quadratic damping penalty. The proposed algorithm outperformed previous... | Rebuttal 1:
Rebuttal: We thank the reviewer for comments on the novelty of the theory and strong empirical performance. Responses below:
First please note that as far as we know this is the first policy optimization algorithm for the constrained or unconstrained setting as far as we know. Second, all policy optimizati... | Summary: The authors introduce a policy optimization algorithm for episodic constrained RL problems, e-COP. In general, Lagrangian formulation is used for constrained optimization problems, however, the constraints are not always satisfied in real world applications. The solution approach avoids Hessian matrix inversio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments on originality, and well-written exposition. We will fix any grammatical issues and typos. We do want to point out that while the algorithm does not perform the best on Humanoid, AntReach, and Grid, it still performs the second best on these three while per... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning Graph | Accept (poster) | Summary: The paper introduces a framework, Dynamic Evaluation of Large Language Models via Adaptive Reasoning Graphs (DARG), which dynamically extends benchmarks by generating evaluation data with controlled complexity. This framework addresses key limitations of static benchmarks, such as data contamination and a lack... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful feedback and recognition of our work's key strengths. We are encouraged by your highlight which **addresses the critical limitations of static benchmarks** with **high-quality generated data**, our **comprehensive experiments with thorough analysis and valua... | Summary: This paper proposed a dynamic evaluation of LLMs --- DARG. The authors first generate a reasoning graph of the problem, then perturb the problem's complexity along various "dimensions", then convert the more complicated graph back to natural language questions. The authors evaluate several LLMs on 4 perturbed ... | Rebuttal 1:
Rebuttal: We greatly value your insightful feedback and are encouraged by your recognition of the **importance of the problem** we investigated. We would like to provide the following clarifications:
- W1: DARG can only applied to tasks that have a clear reasoning graph and is not clear on how to apply to ... | Summary: The paper proposes a new framework, DARG, to extend current reasoning benchmarks with controlled and diversity dynamically. Authors evaluate multiple LLMs on those generated output from DARG and concluded that the proposed method is useful for evaluating LLMs in a
dynamic and adaptive way.
Strengths: 1. Th... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful feedback. We are encouraged by your recognition of our **novelty, the comprehensive empirical evaluation, well-motivation, and the generality** of our proposal. We would like to address your concerns and offer the following clarifications:
- W1: Lack of dif... | Summary: This paper introduces DARG (Dynamic Evaluation of LLMs via Adaptive Reasoning Graph), a framework for dynamically generating evaluation data for large language models (LLMs) with controlled complexity. The DARG framework constructs reasoning graphs from existing benchmarks, perturbs these graphs to generate mo... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful feedback on our DARG framework. We're pleased that you recognize the **novelty of our approach**, particularly the **use of code generation for filtering examples**, as well as our **comprehensive evaluation and analysis** and the **potential for improving m... | Rebuttal 1:
Rebuttal: **Global Response:**
- 1. Only use GPT-4-Turbo for graph construction and graph-to-text decoding and lack exploration of different LLM choices especially open-source models:
We appreciate the importance of generalizing the DARG framework to different LLMs, especially open-source models. To a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MG-Net: Learn to Customize QAOA with Circuit Depth Awareness | Accept (poster) | Summary: The paper proposes a deep learning approach called Mixer Generator Network (MG-Net) to enhance the performance of Quantum Approximate Optimization Algorithm (QAOA) by dynamically designing optimal mixer Hamiltonians for a given class of problem and a circuit depth constraint. This is done by parameter grouping... | Rebuttal 1:
Rebuttal: We sincerely appreciate your detailed evaluation and thoughtful feedback on our manuscript. We are pleased to hear that you found MG-Net to be a novel, innovative, and sound contribution, with strong theoretical backing and comprehensive performance evaluation. In the following, we separately addr... | Summary: This paper provides a theoretical analysis on parameter grouping strategy in QAOA circuits. And the authors proposed MG-Net to design the mixer Hamiltonian, leading to the advantage over conventional methods and other QAOA classes.
Strengths: 1. MG-Net reduces the cost of labelling and training by utilizing t... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough evaluation and thoughtful feedback on our manuscript. We are pleased that you recognized the strengths of MG-Net, including its cost-effective two-stage training strategy and the extensive numerical results that highlight its advantages, enhancing the practica... | Summary: In this paper, the authors propose a model named MG-Net to automatically generate QAOA ansatz. It is able to generate the mixer layer in QAOA and decide which gates to share parameters. By sharing parameters, the ansatz generated by the proposed model can potentially achieve better trainability as well as expr... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our manuscript. We value your insights and address your concerns as follows:
**Q1: It is too simple ... candidate gate set.**
**A1:** We respectfully disagree with the reviewer's perspective that designing the mixer Hamiltonian is too simple. As mentioned ... | Summary: This paper studies the convergence behavior of QAOA, a variational quantum algorithm for combinatorial optimization. The authors prove a convergence result showing how the use of parameter grouping affects the expressibility (i.e. effective dimension) and training time. Furthermore, they design a deep learning... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the potential of the proposed method to enhance QAOA with practical utility. Your feedback is invaluable, and we address each of your concerns as follows:
**Q1: In Section 3, there are several references mentioned that also study the convergence of VQAs, but... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We thank all reviewers for their efforts, insightful comments and constructive suggestions. We appreciate the opportunity to clarify the main motivation, contributions and impact of our work.
**Motivation.** A promising approach to tackle combinatorial optimization problems (COPs... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FIDE: Frequency-Inflated Conditional Diffusion Model for Extreme-Aware Time Series Generation | Accept (poster) | Summary: The problem statement, solution and its mathematical formulation is nicely presented in the draft.
Authors have identified a problem in the time series generation process which is under explored and they propose a new algorithm Frequency-Inflated Conditional Diffusion Model (FiDE) to address this. The proble... | Rebuttal 1:
Rebuttal: **Response to Question 1:**
The choice of time window for defining block maxima is domain-dependent and application-specific, driven by the temporal characteristics of the data and the phenomena of interest. For climate or energy data, monthly block maxima (30-day or 90-day windows) are often rel... | Summary: This article discusses the shortcomings of the insufficient ability to focus on maximum values when applying DDPM in the field of time series generation, and proposes a new framework to overcome this problem by introducing a high-frequency expansion strategy in the frequency domain to ensure the emphasis on hi... | Rebuttal 1:
Rebuttal: **Response to Question 1:**
We initially evaluated linear, sqrt, and sigmoid noise schedulers. We chose the linear scheduler as it provides a more gradual perturbation compared to the sqrt and sigmoid, which alter data more rapidly in initial iterations. We appreciate the reviewer's suggestion r... | Summary: The article introduces FIDE (Frequency Inflated Diffusion Estimation), which is geared towards better capturing
extreme values when generating time series through diffusion models, which the authors stress as crucial in domains like climate
science and disaster preparedness.
The approach involves inflating hi... | Rebuttal 1:
Rebuttal: **Response to Questions:**
We thank the reviewer for the question. While Assumption 1 holds for numerous real-world time series, it may not be applicable to slowly varying time series without abrupt changes, where block maxima build up smoothly.
In such instances, a customized diffusion model l... | Summary: The paper presents a novel generative model designed to better capture extreme values in time series data. FIDE introduces a high-frequency inflation strategy to prevent the loss of extreme values, integrates conditional diffusion modeling to condition on block maxima, and incorporates the Generalized Extreme ... | Rebuttal 1:
Rebuttal: **Response to Question 1:**
We appreciate the reviewer's concern regarding the fidelity of generated time series. We have investigated this issue in our experiments. First, Figure 5 demonstrates the tradeoff between preserving the overall distribution and the distribution of block maxima. The res... | Rebuttal 1:
Rebuttal: Thank you all for your careful and valuable suggestions. In response to your insightful feedback, we have expanded our comparative analysis to include the latest Diffusion-TS model and two additional flow-based baselines (Fourier-Flows and RealNVP) as recommended by the reviewers. This ensures a m... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Task-recency bias strikes back: Adapting covariances in Exemplar-Free Class Incremental Learning | Accept (poster) | Summary: This paper addresses the Exemplar-Free Class Incremental Learning (EFCIL) challenge. It identifies two critical issues that undermine the effectiveness of existing methodologies and proposes a novel approach, AdaGauss, which adapts covariance matrices from task to task and mitigates task-recency bias.
Strengt... | Rebuttal 1:
Rebuttal: We appreciate the feedback provided by the Reviewer. We will now address the specific weaknesses indicated:
**W1: Novelty of explanation of the task-recency bias**
We agree that task recency bias was well explored in CIL literature. However, in most works [1-4], the focus is on the bias in t... | Summary: Existing methods use Gaussian distributions to represent classes in the feature extractor's latent space, but face unchanged covariance matrices and task-recency bias. This paper introduces AdaGauss, an approach that adapts covariance matrices and mitigates the bias through an anti-collapse loss function. AdaG... | Rebuttal 1:
Rebuttal: We thank the Reviewer for providing important references, constructive feedback, and insightful comments. Below we respond to the weaknesses mentioned.
**W1: Incomplete literature and comparison to ACIL and DS-AL**
Thank you for providing relevant literature - we have added it to the Related... | Summary: The paper addresses the problem of Exemplar-Free Class Incremental Learning (EFCIL), which involves training a model on sequential tasks without access to past data. Current methods represent classes as Gaussian distributions in the feature space, enabling Bayes classification or pseudo-feature replay for clas... | Rebuttal 1:
Rebuttal: We express our gratitude to the Reviewer for the feedback and insightful remarks. We shall begin by addressing the specific weaknesses pointed out:
**W1: Innovation of knowledge distillation through a projector**
In this work, we focus on knowledge distillation in EFCIL, which is different ... | Summary: This paper analyzes the impact of dimensionality collapse in EFCIL and examines the distribution shift of the mean and covariance matrix. Based on these findings, the paper proposes the AdaGauss method, which adapts the covariance and mean, and designs a loss term to prevent the dimensionality collapse of the ... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the insightful comments and providing relevant works. We begin by responding to the weaknesses:
**W1: Writing improvements**
We have revisited the whole Section 3 and applied the Reviewer's suggestions in the new version of the manuscript.
**W1: Meaning of $a_{i}$... | Rebuttal 1:
Rebuttal: We want to express our gratitude to all the Reviewers and Chairs for their dedication and effort. The majority of Reviewers consider accepting the work, all agree on comprehensive experimental analysis, and three Reviewers highlight the motivation behind the method (5j6M, VMJn, 6bmT). We have thor... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards Exact Gradient-based Training on Analog In-memory Computing | Accept (poster) | Summary: This is a theoretical paper about training analog systems based on resistive memories. The paper tackles specifically the problem of weight update asymmetry in such resistive devices. The paper develops a model for the weight dynamics under “Analog SGD”, taking into account weight update asymmetry, and it show... | Rebuttal 1:
Rebuttal: We really appreciate your recognizing the importance of our work and for helpful suggestions. Please find our point-by-point reply to your comments below.
> W1. There is no discussion about which models are accurate and widely accepted.
As the reviewer correctly pointed out, adding a discussion ab... | Summary: The paper introduces the training on the analog in-memory accelerators with SGD. The traditional analog SGD algorithm suffers from inexact convergence due to asymmetric updates/gradient noise. The paper shows theoretical foundations for gradient-based training on analog devices. The authors propose Tiki-Taka, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the merits of our work. Our point-to-point response to your comments and suggestions follow next.
> W1. It'd be great if authors present how computations (ops) can be realized on the analog devices.
The workload of forward and backward computation can be s... | Summary: This paper presents a theoretical framework for gradient-based training on analog devices. This work first identifies the non-convergence problem of Analog SGD, which stems from asymptotic errors due to asymmetric updates and gradient noise and then presents a convergence analysis of Tiki-Taka, demonstrating i... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time spent in reviewing our paper and for the valuable comments.
The weaknesses identified by the reviewer mainly focus on the datasets and the model archtecture used in the experimental results as follows.
> - The experimental results were obtained on rather sma... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their constructive comments. Comments from all the reviewers were really helpful, which we believe have been fully addressed in detail in our rebuttal.
The attached PDF is the illustration to explain how analog devices implement MVM operations (see the re... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Minimizing UCB: a Better Local Search Strategy in Local Bayesian Optimization | Accept (poster) | Summary: The paper presents a novel method for local Bayesian Optimisation. Instead of relying on estimates of the gradient of the black-box functions like earlier methods, the algorithm proposed in the paper minimises an upper bound on the objective function. The paper shows a connection between the proposed method an... | Rebuttal 1:
Rebuttal: **Weakness 1**: I belive Theorem 1 could use a proof sketch...
**Response**: We apologize for not including a proof sketch in the paper. The core tool we use in the paper is actually to prove the Lipshitz properties of various functions in Gaussian processes, i.e. the mean function $\mu(x)$, and ... | Summary: This paper proposes and analysis a new local Bayesian optimisation scheme. The main idea is to replace the update of the current solution by minimising the upper confidence bound of the GP estimate (in the minimisation setting). The strategy is shown to obtain similar convergence rates as previous works. In ad... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback. The suggestions and questions are briefly responded below.
**Weakness 1 and Question 1**: The minimization of the UCB score in the step update appears to be global in nature... The range of min /argmin operations is never specified... What does... | Summary: The author proposed a local Bayesian optimization method using UCB to drive its iterates. The UCB step replaces the gradient descent step that is typically in such methods.
Strengths: The comparison of gradient-descent to UCB is very interesting. The author included analysis as well so there is enough content... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback. The suggestions and questions are briefly responded below.
**Weakness** : The biggest concern I have is that the algorithm is too similar to Bayesian optimization using UCB. The author tried to add more technical development such as the look-ah... | Summary: The paper presents an extension of a local Bayesian optimization strategy, specifically targeting methods based on gradient information (GIBO). GIBO-type algorithms operate through two stages: an exploitation step and an exploration step. The paper introduces two novel algorithms within this framework.
In GIB... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback. The suggestions and questions are briefly responded below.
**Weakness 1**: Locality: I hypothesize that the "exploitation" step of MinUCB will explore globally until it finds a "promising region"... switch between local and global exploration
... | Rebuttal 1:
Rebuttal: We thank the reviewers for their constructive comments and suggestions, which have brought great help to improve our paper. We would like to provide a clearer explanation of two aspects: the local behaviour and why our algorithms are local.
In this paper, we would like to emphasize that the term ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Grammar-Aligned Decoding | Accept (poster) | Summary: This paper focuses on the constrained decoding scenario where LLMs are expected to produce high-quality and grammatically correct outputs. It presents the adaptive sampling with approximate expected futures (ASAp) method, which is designed to enhance the quality of the output by adjusting the conditional proba... | Rebuttal 1:
Rebuttal: > The algorithm heavily relies on prior samples to estimate grammatical probabilities, which can be computationally expensive and difficult to manage… a fair comparison of decoding speed between ASAp and GCD methods is required, as…conducting sampling on LLMs is very time-consuming.
Indeed, ASAp ... | Summary: This paper proposes adaptive sampling with approximate expected futures (ASAp) for grammar-aligned decoding for LLMs. The main objective of this method is to match the conditional probability of the LLM’s distribution conditioned on the given grammar. The evaluation is performed on code generation and structur... | Rebuttal 1:
Rebuttal: > I suggest maybe some improvement in Section 2. Restructuring it maybe and providing slightly more context on CFG (or giving a concrete example of how it works as a reminder, I found Fig 1 not illustrative enough), I'm sure I won't be the only reader who doesn't have recent experience with CFG.
... | Summary: This paper points out that prior methods on grammar-constrained decoding (GCD) distorts the language model's learned distribution over sequences. At the heart of this exposition is the notion of _expected future grammaticality_ (EFG), where prior GCD methods can be cast an an upper-bound approximation to the E... | Rebuttal 1:
Rebuttal: > The proposed method seems very slow to run, requiring many iterations. Additionally, it requires storing a table of all seen prefixes and their future grammatically, which would be very large if the grammar itself is large. It seems to me that the ASAp method only depends on a grammar -- it does... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their feedback, which will greatly improve the paper.
We have addressed each reviewer’s comment in their corresponding answer.
We summarize the edits we plan to do to improve the paper based on the feedback we received:
Reviewers cHBv and gp9U proposed ways to potenti... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
VLM Agents Generate Their Own Memories: Distilling Experience into Embodied Programs of Thought | Accept (spotlight) | Summary: This work proposed ICAL, aims to improve decision-making in large language and vision-language models by generating optimized trajectories and language annotations from noisy demonstrations and human feedback. ICAL abstracts noisy trajectories into optimized sequences with language comments, refined through hu... | Rebuttal 1:
Rebuttal: >**ICAL's effectiveness is constrained by the capabilities of VLMs like GPT-4V. The VLM-driven Abstraction Generation relies heavily on GPT-4V. Is there an ablation study for its replacement?**
We appreciate the comment regarding the reliance on GPT-4V for the VLM-driven Abstraction Generation co... | Summary: The paper proposes a pipeline for Large Language and Vision-language models (LLMs and VLMs) to digest and learn from sub-optimal demonstrations and human feedback. The LLM/VLM, given sub-optimal task demonstrations, is prompted to produce abstractions of the trajectory (including task and causal abstractions, ... | Rebuttal 1:
Rebuttal: >**Efficiency of ICAL method? For simple tasks in the TEACh benchmark the method needs ~100 trajectories to perform well according to Figure 5. Scaling capability of the human-in-the-loop phase?**
Actually, these roughly 100 trajectories are used for all tasks in TEACh combined. In fact, each t... | Summary: The paper proposes ICAL, In-Context Abstraction Learning, which builds a memory of suboptimal experiences that are abstracted into states and plans, as well as correction and reflection from human feedback. The approach is based on extensive prompting to elicit structured representations from past experiences,... | Rebuttal 1:
Rebuttal: >**Clarifying the novel contributions and advancements beyond existing methods, such as CoT, ReACT, Socratic Model, or RAG.**
While our framework incorporates chain of thought prompting from CoT, interleaves reasoning and acting as seen in ReACT, predicts plans over pretrained modules similar to ... | Summary: The goal of this paper is to teach VLMs novel tasks by prompting VLMs to create multimodal abstractions for unfamiliar domains. Given instructions paired with noisy demonstration trajectories, this paper proposes a method to encapsulate the information from these into examples consisting of optimized trajector... | Rebuttal 1:
Rebuttal: >**While the appendix goes a long way towards reducing this, it is likely that the paper might still be difficult to follow for readers unfamiliar with the datasets and prior methods referenced in this paper.**
Thank you for your feedback. We acknowledge that the paper may still be challenging f... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' positive feedback. Reviewer VRBk commended our innovative combination of foundation models with case-based learning, robust generalizability across benchmarks, and transparency in results. Reviewer ih5w highlighted ICAL's improved success rates across domains, and our ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Computerized Adaptive Testing via Collaborative Ranking | Accept (poster) | Summary: In this paper, the authors first discovered the inconsistency issue in existing Computerized Adaptive Testing (CAT) solutions for estimating the latent abilities of students, that is the higher accuracy of ability estimation (with a lower MSE) does not necessarily guarantee the ranking consistency of students’... | Rebuttal 1:
Rebuttal: Thank you for your feedback on our manuscript. We sincerely appreciate your time and effort in evaluating our work, and we appreciate you for the opportunity to explain and articulate our work.
> **Q1:** It is not easy to illustrate the main ideas of CAT as it contains both the Ability Estimation... | Summary: This paper addresses a real-world problem in AI education: improving the accuracy of student rankings by selecting different questions during the exam process. It proposes a question selection method based on collaborative students and provides theoretical guarantees. The experimental results have demonstrated... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback! Regarding the questions you raised, we have carefully considered each point and have made following responses:
> **Q1:** Can you clarify the detail of $\theta^T_c$? Furthermore, since the true abilities of "collaborative students" are known, why use the abil... | Summary: The paper proposes an algorithm for performing computerized adaptive testing that handles and accounts for student rankings in the item recommendaiton.
Strengths: - I like the idea of incorporating additional information in the collaborative filtering approach (while I don't quite understand why you need to i... | Rebuttal 1:
Rebuttal: Thank you for your feedback on our manuscript. We sincerely appreciate your effort in evaluating our work. Below, we address each of your comments in detail:
> **Q1**: My main critique of the paper is the work's motivation for connecting ranking and CAT: Ranking seems like a completely separate t... | Summary: This paper proposes a new perspective on Computerized Adaptive Testing (CAT) by framing it as a task of ranking students. The authors define CAT as a ranking problem and present a feasible optimization algorithm to address this. Extensive experimental results demonstrate that this method significantly improves... | Rebuttal 1:
Rebuttal: We appreciate your comments! To address your concerns, below we prudently justify the details of our proposed method and experiments.
> **Q1:** This paper has rich and convincing experiments, but I want to know why Table 1 only shows the results of BOBCAT in the Table 1(a). Furthermore, this pape... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The study deals with CAT( Computer Adaptive Testing) via Collaborative Ranking.
Strengths: The proposed CCAT algorithm demonstrates superior performance in ranking 259 consistency across two public datasets. Particularly, CCAT shows more significant improvement 260 when fewer questions are tested, outperformi... | Rebuttal 1:
Rebuttal: > **Q1:** The study does not discuss in sufficient detail the following: limitations, generalization possibility and the application of the algorithm as part of instructional design.
Thank you for your valuable feedback. We apologize that we have shown in the experimental and appendix sections, r... | null | null | null | null | null | null |
Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning | Accept (poster) | Summary: The paper introduces Federated Behavioural Planes (FBPs), a method designed to track FL clients' behavior by means of examining the their representations in two behavioural planes with the aid of a server-owned dataset. The Error Behavior Plane (EBP) and Counterfactual Behavioural Plane (CBP) correspond to two... | Rebuttal 1:
Rebuttal: _Privacy concerns of transmitting the plaintext of all local models_
__Our method can integrate Local Differential Privacy (LDP) or Homomorphic Encryption (HE) to enhance privacy, ensuring that sensitive information remains protected while maintaining robustness.__
Our framework, like traditional... | Summary: This paper introduces a novel method called Federated Behavioural Planes (FBPs) for analyzing, visualizing, and explaining the dynamics of Federated Learning (FL) systems. FBPs are consist of Error Behavioural Plane (EBP), reflecting the model’s predictive performance, and Counterfactual Behavioural Plane (CBP... | Rebuttal 1:
Rebuttal: _The paper heavily relies on visual representations to explain client behaviors, infeasible for large FL systems._
__Our method is scalable and automatically extracts and analyzes statistics from behavioral planes (BPs) to identify and mitigate malicious clients without relying on visual inspecti... | Summary: This paper introduces a novel method called Federated Behavioural Planes (FBPs) to analyze, visualize, and explain the dynamics of client behavior in Federated Learning (FL) systems. The primary contributions of the paper are as follows:
* 1\. Introduction of Federated Behavioural Planes (FBPs): FBPs consist ... | Rebuttal 1:
Rebuttal: _Computational Overhead and Evaluation Metrics, the paper could benefit from a detailed analysis of the computational costs._
As suggested, __we performed a detailed analysis of the computational overhead of our framework, examining all its components__: Local Computation, Communication Overhead,... | null | null | Rebuttal 1:
Rebuttal: # Answer to reviewers and ACs
We thank the reviewers for their insightful feedback. We are encouraged by their recognition of the novelty in our ideas and proposed methods for analyzing and visualizing client behaviors (eMJr, ULpn, aREF), and the significance of this work in the FL field (eMJr). W... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Robust Gaussian Processes via Relevance Pursuit | Accept (poster) | Summary: This work proposes a new way to perform heteroskedastic regression using Gaussian processes via data-point-specific noise levels. These noise levels are inferred using a sequential selection procedure maximizing the log-marginal likelihood. The authors show that under a specific parametrization the log-margina... | Rebuttal 1:
Rebuttal: > “While correct as far as I can tell, I question the value of the theoretical result, if in practice you optimize the hyperparameters of the covariance function and ρ jointly. Is there reason to believe, that the convexity in ρ for fixed hyperparameters is beneficial given this choice?” “It would... | Summary: The paper proposes a robust Gaussian Process regression by inferring data-point-specific noise levels with a sequential selection procedure maximising the log marginal likelihood. The authors show the good performance of their method in a mix of synthetic and real-world regression tasks, including Bayesian opt... | Rebuttal 1:
Rebuttal: > “The weakest part of the paper is the regression problems in the experiment section”
As part of our rebuttal, we have produced a significant number of additional results on UCI benchmarks, the benchmarks and real-world Twitter crash example of Altamirano et al. 2024, and included an additional ... | Summary: The authors propose a robust Gaussian process model in the sparse heteroscedastic noise setting. Orthogonal Matching Pursuit- like algorithm is proposed to infer data point specific noise levels. The negative log marginal likelihood function to be optimized is claimed to be strongly convex by reparametrizing t... | Rebuttal 1:
Rebuttal: > Kersting et. al. 2007:
This is indeed related work, and we will discuss it in the updated manuscript. The main difference to our method is that their approach is unlikely to work well with outliers. In particular, while they do account for heteroskedastic observation noise, they model the logar... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their detailed comments and valuable suggestions. We are glad to see that the reviewers found our "novel approach” to be “clearly motivated and presented” and of “broad interest”, and that our work “provides a nice addition to the existing toolbox of methods”.
While re... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Surprising Effectiveness of Attention Transfer for Vision Transformers | Accept (poster) | Summary: In this work, the authors demonstrate that a large part of the benefits of pre-training in ViT models actually comes not from the pre-trained features but the pre-trained knowledge of attention maps. Specifically, the authors propose an alternative to fine-tuning call Attention Transfer, and they use this meth... | Rebuttal 1:
Rebuttal: We’re glad that you found our results interesting and our analysis comprehensive. We address your questions below:
> I’m not sure if it will make its way into practical use, for either the Attention Copy or Attention Distillation variants.
We agree that the attention transfer methods are not cu... | Summary: The paper introduces attention transfer as an alternative to fine-tuning in Vision Transformers (ViT), separating intra-token and inter-token operations to enhance feature extraction and combination. Attention transfer contains attention copy and attention distillation. Attention distillation matches the perfo... | Rebuttal 1:
Rebuttal: Thank you for the feedback on our work! Below, we respond to your questions and comments:
> In Table 1, transfer Q is better than transfer Q,K (which is equivalent to the attention map), does it mean distillation on features would achieve better performance?
See the main rebuttal above for a det... | Summary: The authors propose a novel perspective on the utility of pretraining vision transformers by demonstrating that the actual features and representations learned during pre-training are not crucial. Instead, they find that simply re-using the self attention from pre-training specifically, the way information flo... | Rebuttal 1:
Rebuttal: We’re really glad that you liked the motivation, analysis, and writing of our paper! We respond to your comments below:
> Fig. 5: In attn-distill from layer 20-24, the correlation increases much more than the pretrained model, which is not the case with attn-copy. Do the authors have any intuitio... | Summary: This paper investigates how transferring attention patterns from a pre-trained ViT to a student affect the student's downstream performance. By applying the attention copy strategy, the paper shows that when the pre-trained dataset and downstream dataset are the same, the trained student may achieve performanc... | Rebuttal 1:
Rebuttal: We’re glad that you found our work well-written, with extensive experiments and interesting findings. Below, we respond to your questions and comments:
> The findings is somewhat similar to previous works which apply attention transfer in ConvNets. The only different things is in ViT, attention c... | Rebuttal 1:
Rebuttal: We thank reviewers for their time, effort, and feedback for our paper. To recap, reviewers appreciated various strengths of our work:
- **Significance**: “well motivated” (7AbG), “very interesting implications for ViT pretraining and finetuning” (9FPV), “potential to boost the performance of sel... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MeMo: Meaningful, Modular Controllers via Noise Injection | Accept (poster) | Summary: This manuscript presents a hierarchical controller for reinforcement learning-based control. In particular, given a robot that can be decoupled into a high-level controller and a few low level (joint level) controllers, the two modules are learned jointly via a behavior cloning objective. The proposed method f... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review. Below we address the reviewer’s concerns and questions.
**Under "Weaknesses":**
> Opposing strength 2, especially in simulation, many existing works randomize over many morphologies to enable efficient transfer between them or to an unseen morphology. It is... | Summary: The MeMo framework presented in this paper proposes an innovative approach for enhancing the transferability of control systems across robots with varied morphologies by utilizing pre-trained, modular controllers. This method facilitates rapid adaptation to new robot designs by leveraging previously trained mo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and are glad that they find our results intriguing. Below we address the reviewer’s questions.
**Under "Questions":**
> The comparative analysis raises some questions regarding the fairness of the evaluation. Specifically, were the control modules of the... | Summary: This paper introduces a new framework designed to create modular controllers allowing for quicker adaptation of control strategies when building new robots with similar methodology. The MeMo framework employs a novel modularity objective optimized alongside standard behavior cloning loss through noise injectio... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review and are glad that they find our method easy to understand and empirical evaluation thorough. Below we address the reviewer’s concerns and questions.
**Under "Weaknesses":**
> The tasks evaluated are relatively simple, only basic locomotion tasks and quite si... | Summary: This paper presents a method for learning modular controller that can be transferred and adapted to different morphology of robots and tasks. The high level idea is to decompose control of each motor through distilling a learned hierarchical RL policy and then uses these primitive policies as building blocks t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review and are glad that they find our problem novel and our proposed framework intuitive. Below we address the reviewer’s concerns and questions.
**Under "Weaknesses":**
> The proposed method only trains the master policy at adaptation stage, which assumes the cha... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Energy Rank Alignment: Using Preference Optimization to Search Chemical Space at Scale | Reject | Summary: The paper proposes a new method, called Energy Rank Alignment (ERA) to finetune large language models (LLMs) for molecular generation in a similar fashion to Reinforcement Learning from Human Feedback (RLHF). The paper first introduces how the alignment task in LLMs is very similar to creating property-condit... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback and insightful questions, which we address below.
## Discussion of related work
We compare to state-of-the-art methods and widely used methods such as REINVENT on two benchmark tasks that focus on small-molecule drug design and find that we are ab... | Summary: The authors introduce “Energy Rank Alignment”, a novel alternative to PPO and DPO for policy optimization when an explicit reward model is available. ERA is shown to work for enriching chemical libraries for proxy objectives that are fast and easy to compute, and has clear benefits in the simplicity of tuning ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback and insightful questions, which we address below.
## Evaluation of lead optimization
We agree that this is a weakness of the paper and have carried out additional experiments to design small-molecules with high activity against biological targets ... | Summary: The authors study an important problem about searching through chemical space, where the number of possible molecules grows combinatorially with the number of atoms. They focus on aligning large autoregressive models trained on chemical compound databases to generate molecules. The energy rank alignment (ERA) ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and suggestions and for pointing us to additional works.
## Diversity Novelty and Uniqueness
We investigate ERA on two tasks that mimic a drug-discovery effort and find that we are able to **efficiently** generate **novel, diverse, and unique compounds** t... | null | null | Rebuttal 1:
Rebuttal: We appreciate the careful reviews and detailed feedback of our paper and address shared concerns and suggestions in this response.
## Simulated lead optimization and comparison to competitive approaches
The reviewers raised concerns about the complexity of the benchmarks included in the paper.
... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unsupervised Modality Adaptation with Text-to-Image Diffusion Models for Semantic Segmentation | Accept (poster) | Summary: The authors extend the traditional image domain adaptation to modality adaptation for unsupervised semantic segmentation in real-world multimodal scenarios. They use text-to-image diffusion model with strong generalization capabilities and proposed Diffusion-based Pseudo-Label Generation (DPLG) and Label Palet... | Rebuttal 1:
Rebuttal: **To Reviewer Qg6E**
Thank you for the insightful and very positive comments. In the following, we provide our point-by-point response and hope our response helps address your concerns. We also look forward to the subsequent discussion which further helps to solve the current issues.
>**Q1**: *A... | Summary: This paper introduced text-to-image diffusion models to enhance the generalization across different modalities. The proposed MADM includes two key components: Label Palette and Latent Regression (LPLR) and Diffusion-based Pseudo-Label Generation (DPLG). This method alleviates issues related to pseudo-labeling ... | Rebuttal 1:
Rebuttal: **To Reviewer 4TqZ**
Thank you for the insightful and positive comments. In the following, we provide our point-by-point response and hope our response helps address your concerns. We also look forward to the subsequent discussion which further helps to solve the current issues.
>**Q1**: *The pr... | Summary: This paper proposes Modality Adaptation with text-to-image Diffusion Models (MADM). MADM leverages pre-trained text-to-image diffusion models to enhance cross-modality capabilities, comprising two main components: diffusion-based pseudo-label generation to improve label accuracy and a label palette with latent... | Rebuttal 1:
Rebuttal: **To Reviewer uNLv**
Thank you for the insightful and positive comments. In the following, we provide our point-by-point response and hope our response helps address your concerns. We also look forward to the subsequent discussion which further helps to solve the current issues.
>**Q1**: *Discus... | Summary: This paper proposes an interesting task that adapting image segmentation knowledge to other input modalities, such as depth, infra, and event. This is beneficial for applications at nighttime.
Strengths: 1. The task is promising for applications at nighttime.
2. The label palette is novel and can be generaliz... | Rebuttal 1:
Rebuttal: **To Reviewer x1By**
Thank you for the insightful and positive comments. In the following, we provide our point-by-point response and hope our response helps address your concerns. We also look forward to the subsequent discussion which further helps to solve the current issues.
>**Q1**: *The me... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude to all of the four reviewers for the valuable and constructive feedback provided on our manuscript. Your insights have been instrumental in enhancing the quality and clarity of our work.
In the attached PDF, we include additional visualizations as re... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CausalStock: Deep End-to-end Causal Discovery for News-driven Multi-stock Movement Prediction | Accept (poster) | Summary: The paper presents a novel framework, termed CausalStock, for news-driven multi-stock movement prediction. The authors address two key issues in existing methods: the unidirectional nature of stock relations and the substantial noise in news data. CausalStock introduces a lag-dependent temporal causal discover... | Rebuttal 1:
Rebuttal: ---
Thank you very much for your valuable suggestions! We will try our best to tackle the concerns one by one.
(1) Comparision with the traditional causal discovery method and the correlation matrix
- We greatly appreciate your insightful comments! Following your suggestion, we compare our discov... | Summary: This work predicts stock movements by inferring the causal relation between stocks and news. News are encoded to structured representation through LLMs to filter out noises. Causal relation is modeled as directional graph and is inferred through variational approach.
Strengths: 1. The news denoising module is... | Rebuttal 1:
Rebuttal: ---
Thank you very much for the insightful and constructive comments! We sincerely appreciate the suggestions to improve our submission. We will address all raised concerns one by one.
(1) About the performance without graph module
- Following your suggestion, we ablate the graph module and comp... | Summary: This paper proposes a news-driven multi-stock movement prediction model called CausalStock. The paper introduces a Denoised News Encoder, which utilizes LLMs to evaluate news text and obtain denoised representations. It also presents a Lag-dependent temporal causal discovery module to discover causal relations... | Rebuttal 1:
Rebuttal: ---
Thank you very much for your valuable suggestions and your encouraging comments! We will try our best to tackle the concerns.
(1) If sparseness constraints used in the loss function?
- Thank you for the question. We use sparseness constraints in the loss function. These constraints are inclu... | Summary: This paper proposes a stock price prediction system based on the history of stock price features and news features, with a model which models the causal relationships between different stacks throughout window of time. The model is based on a temporal causal graph that determines whether the features of stock ... | Rebuttal 1:
Rebuttal: ---
Thank you very much for your valuable suggestions! We will try our best to tackle the concerns one by one.
(1) About the choice reason of Bernoulli distribution
- Thank you for the question. The essence of causal relationships is determining whether a change in one variable directly causes a... | Rebuttal 1:
Rebuttal: The more detailed model structure.
Pdf: /pdf/f41c0fb5a41c57ee286727e6bb42a8a73666103d.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
TARSS-Net: Temporal-Aware Radar Semantic Segmentation Network | Accept (poster) | Summary: This paper proposes a novel framework designed to integrate temporal information into radar-based semantic segmentation tasks. To achieve more effective temporal information integration, the framework introduces two key modules: the Target-History Temporal Relation Encoder (TH-TRE), which analyzes the relation... | Rebuttal 1:
Rebuttal: # **Questions**
**[Q1. COMPUTATIONAL TIME COMPARISON.]** There is real-time performance comparison **in `Sec. E.2 of Appendix`**, which shows the comparison between TARSS-Net and some other models, involving**model size**, **calculation amount**, **real-time performance** and other information of... | Summary: In this work, the authors created a network called TARSS-Net for radar semantic segmentation. Compared to traditional methods, the authors emphasized the superiority of their approach in the clever utilization of historical information. Specifically, TARSS-Net incorporates a module called TRAM, which is design... | Rebuttal 1:
Rebuttal: ## Weaknesses
**[W1. LACK OF CLEAR HYPOTHESES AND REASONING.]** Sorry for the confusing, and you are right that clear hypotheses and reasoning are important. However, at present, the data-driven learning training of AI models allows researchers to conceptuate and design algorithms at a higher lev... | Summary: This paper primarily introduces a network model called TARSS-Net, designed for the task of radar semantic segmentation. It effectively utilizes the temporal information of radar signals by introducing a novel temporal modeling mechanism, enhancing radar semantic segmentation performance. Specifically, the pape... | Rebuttal 1:
Rebuttal: ## Weaknesses
**[W1. FEW INNOVATIONS.]** Thank you for recognizing the rationality of our approach. However, beyond reasonability, the novelty of TARSS-Net is also guaranteed. We feel sorry that we failed to make you see the novelty, as well as the careful thought and extensive experimental valida... | Summary: This paper proposed a temporal-aware framework, TARSS-Net, to enhance Radar semantic segmentation. The key idea is to propose a Temporal relation attentive module, TRAM (consists of Target-History Temporal Relation Encoding [TH-TRE] and Temporal Relation-Aware Pooling [TRAP] ), to capture the relations between... | Rebuttal 1:
Rebuttal: ## Weaknesses
**[W1. LIMITED TECNICAL CONTRIBUTION.]** Sorry to have caused the reviewer such concerns. The core point of this paper is to **redesign a better spatio-temporal modeling method for radar data from the perspective of temporal information utilization**. Indeed, there are many works in... | Rebuttal 1:
Rebuttal: We would like to express our respect to all reviewers and AC. Thanks for your time and hard work. Based on your professional opinions, we have carefully replied all the high-value questions and supplemented the content accordingly.
Pdf: /pdf/8fee510df92fd698c7cc6a3c4be2739378aa624a.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Relating Hopfield Networks to Episodic Control | Accept (poster) | Summary: The paper shows that the Differentiable Neural Dictionary (DND) used in the context of reinforcement learning is mathematically equivalent to an Hopfield network with heteroassociative memories. Based on this observation, the paper generalises DND using the formulation of Universal Hopfield Networks (UHN) and ... | Rebuttal 1:
Rebuttal: We thank Reviewer w6Cb for the constructive feedback and for acknowledging the strengths of our paper, including the solid contribution and soundness of our approach. We appreciate the recognition of our work in characterizing Differentiable Neural Dictionaries (DNDs) as associative memories and i... | Summary: This paper introduces a formulation of an energy function which involves retrieving top-k memories while making a connection to both Neural Episodic Control and Associative Memory. The novel energy function utilized in this work demonstrates superior performance in the retrieval setting which involves image in... | Rebuttal 1:
Rebuttal: [concise because hitting char lim]
Thank you for your constructive feedback and insights on our work.
1. Comment: "Although the experiments are good, many of the experiments are ablation studies of the introduced energy function, while there is one experiment section contrasting the proposed ene... | Summary: This paper establishes a direct connection between two important frameworks: neural episodic control and Universal Hopfield Network. It further derives Lyapunov functions for the dynamics and explores the ability of Neural Episodic Control to function as a memory system.
Strengths: The idea of the paper is or... | Rebuttal 1:
Rebuttal: We thank Reviewer 9C8q for the constructive feedback and positive remarks on our work. We appreciate the recognition of the originality and clarity of our manuscript, as well as the significance of the theoretical connections made. Below, we address the specific questions and points raised.
1. Co... | Summary: The paper introduces the differentiable neural dictionary, which uses template-based memory storage, relating it mathematically to Hopfield Networks within the UHN framework. This novel model is shown to be capable of storing memories through operations such as similarity, separation, and projection, thus demo... | Rebuttal 1:
Rebuttal: We thank Reviewer UfDJ for the constructive feedback and positive remarks on our work. We appreciate the recognition of our novel approach and detailed experiments. Below, we address the specific concerns raised.
1. Comment: "The article starts from the Hopfield Network framework, and my concern ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their detailed and constructive feedback on our submission. We are pleased that the reviewers recognize our significant theoretical contribution of linking Differentiable Neural Dictionaries (DNDs) to the Universal Hopfield Network (UHN) framework (R.4Gfy) and the extens... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper establishes a novel connection between differentiable neural dictionaries (DNDs) used in episodic control for reinforcement learning and Hopfield networks used as associative memory models. The authors show that DNDs can be formulated within the Universal Hopfield Network (UHN) framework, derive ene... | Rebuttal 1:
Rebuttal: We would like to thank Reviewer 4Gfy for the detailed and constructive feedback on our submission. We appreciate the recognition of our significant theoretical contribution and extensive empirical evaluation. Below, we address each of your comments and suggestions in detail.
1. Comment: "While th... | null | null | null | null | null | null |
Adaptable Logical Control for Large Language Models | Accept (poster) | Summary: This paper proposes an approach called Ctrl-G to control LLM generation, specifically, constraining LLM’s output to deterministically follow certain logical constraints, such as maintaining a certain keyword in the generated text.
The approach has two main parts, the first is a Hidden Markov Model (HMM) that s... | Rebuttal 1:
Rebuttal: Thank you for your feedback and questions.
We would like to clarify that Ctrl-G does not use DFAs to post-check the generated text; instead, the generated text is always guaranteed to satisfy the constraint. Specifically, Ctrl-G achieves constrained generation by generating from
$p_{ctrlg}(x_{t}... | Summary: This paper proposes Ctrl-G to enable flexible lexically constrained generation with high accuracy and efficiency. Ctrl-G first distills an HMM from an unconditioned language model and then formulates the lexical/logical constraints with deterministic finite automata (DFAs). The inference algorithm takes both t... | Rebuttal 1:
Rebuttal: Thank you for your feedback and questions.
- For the CommonGen benchmark, we did not include NADO because they only published results on the dev set and their released code is not reproducible due to lack of documentation; In the revised paper, we have added the results from NADO and GeLaTo on bo... | Summary: It is a challenge to control LLMs' generation to adhere to logical constraints. Ctrl-G integrates LLMs with a Hidden Markov Model (HMM) and deterministic finite automata (DFAs) for flexible and tractable control, such as keyword and length constraints. Ctrl-G combined with TULU-2-7B model outperforms GPT-3.5 a... | Rebuttal 1:
Rebuttal: Thank you for your feedback and questions.
- We believe that better control in general will substantially benefit creative tasks. For example, according to the experience of our collaborators, when LLMs are asked to generate generic lyrics, they often tend to generate lyrics for love songs even w... | Summary: The paper tackles the problem of generating sequences from llms while following a constraint \alpha, providing a solution for the case where \alpha can be represented as a regular language (e.g., a constraint on containing certain substrings). The method allows application of any regular-language constraint, ... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback. Following some prior years’ tradition, our appendix was mistakenly uploaded as the supplementary material and we apologize for the confusion. We have greatly improved the presentation of the paper and would love to share the revision upon AC’s approval.
- We c... | Rebuttal 1:
Rebuttal: Thank you all for your feedback. Please refer to the attached PDF for some of our main evaluation results as well as an additional runtime comparison: (1) Evaluation results on CommonGen (dev & test) for FUDGE, NADO, NeuroLogic A\*esque, GeLaTo and Ctrl-G. (2) Runtime analysis on CommonGen (dev) f... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment | Accept (poster) | Summary: This paper proposed Cal-DPO, a variation of DPO to address the issue of decreasing rewards of chosen answers. In addition to DPO loss, Cal-DPO add a pair of calibration terms, which aim to match the rewards induced by language model with some absolute ground truth reward value. Theoretical analysis shows that,... | Rebuttal 1:
Rebuttal: **Dear reviewer yAWB, we appreciate your great summarization and recognition of our contributions and your positive comments on our work: "interesting," "solid theoretical analysis," and "motivations, method and analysis are clearly presented ." Please find our responses to your comments below:**
... | Summary: This paper proposes a simple yet effective change to the DPO objective that acts as a regularizer on the implicit reward, $\beta \log \frac{\pi(y \mid x)}{\pi_\mathrm{ref}(y \mid x)} + \beta \log Z(x)$, that is maximized by the LM. Specifically, the implicit reward is encouraged to be appropriately scaled rela... | Rebuttal 1:
Rebuttal: **Dear reviewer f8MD, we appreciate the reviewer's perception of our contributions to both empirical and theoretical analysis, and we thank the reviewer for their insightful questions. Please find our detailed responses below:**
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**Q1. My main difficulty is in the presentation of the motivatio... | Summary: The authors propose Cal-DPO, a preference-tuning algorithm that modifies the DPO loss by adding two MSE terms that aim to "calibrate" the log-likelihood ratios of y_w and y_l to their respective reward values. They claim that this is advantageous because the DPO loss only maximizes the reward ratio, and does n... | Rebuttal 1:
Rebuttal: **Dear reviewer s9Bq, we appreciate the reviewer's perception of our contributions to both empirical and theoretical analysis. We believe that there are some important misunderstandings**
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**Q1. Unfair comparisons: The authors show in Figure 4 that the best $\beta$ value for Cal-DPO is 0.001,... | Summary: This paper proposes a simple yet effective method called calibrated direct preference optimization (Cal-DPO), which addresses the limitation of ignoring the actual values of implicit rewards. The authors demonstrate the theoretical advantages of Cal-DPO over existing approaches and show the effectiveness on a ... | Rebuttal 1:
Rebuttal: **Dear reviewer EJ4r, we appreciate your efforts and detailed comments very much! However, we believe that there are some misunderstandings. Therefore, we would like to provide a point-by-point response to your comments.**
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**Q1. It is still not intuitive for me why the scale of the reward's a... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their insightful comments and helpful suggestions. We deeply appreciate the numerous positive comments on our work, such as describing it as "simple and effective," "solid motivations," and "solid theoretical and empirical analysis".
We have made our grea... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning | Accept (poster) | Summary: This paper provides a theoretical analysis of in-context learning (ICL) in transformer models. The authors prove exponential convergence of the 0-1 loss for a three-layer transformer (attention + MLP) trained on a concept-specific prompt distribution. They also demonstrate how the model can leverage multi-conc... | Rebuttal 1:
Rebuttal: Thank you for the insightful review. We greatly appreciate your acknowledgment of our theoretical advancements, rigorous analysis, well-grounded connections to empirical observations, and fair assessment of the limitations we address.
**Q: Limited training setup & challenges of training all weigh... | Summary: This paper tries to understand the mechanisms that explain the emergence of in-context learning.The starting point of their approach is the observation that the embeddings have geometric properties to encode within-concept and cross-concept relationships. Their goal is to connect this geometric properties with... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and recognition of the strength of our theoretical results, particularly Proposition 1 which demonstrates how transformers can leverage their multi-concept semantic knowledge to perform effective unseen ICL tasks. We appreciate your insightful assessment of the... | Summary: This paper performs an in-depth analysis of the optimization dynamics of a simplified Transformer on a sparse coding prompt model. It manages to show the exponential 0-1 loss convergence on this non-convex loss. Experiments on synthetic data verify the convergence.
Strengths: 1. This paper is very original an... | Rebuttal 1:
Rebuttal: We are immensely grateful for your thoughtful and insightful feedback on our work. Your recognition of the originality, soundness, and excellent contributions of our paper is deeply encouraging. We found your comments to be highly professional and constructive, and we would like to respond as foll... | Summary: The paper investigates how transformer-based large language models (LLMs) leverage their in-context learning (ICL) capabilities through the lens of multi-concept semantics. The authors cited limitations in existing theoretical work, which uses oversimplified models and unrealistic loss functions, leading to on... | Rebuttal 1:
Rebuttal: Thank you for acknowledging our work for linking the multi-concept word semantics to transformer’s ICL capability.
**Q: More concise and clearer presentation, especially the theoretical content, would be helpful.**
**A**: We very much appreciate your suggestion. We will provide more explanation... | Rebuttal 1:
Rebuttal: Dear ACs and Reviewers,
Thank you again for all of your positive and constructive feedback! We are truly encouraged to see so many well-recognized comments on our work, such as the **innovative angle** (Reviewer qjJG, Reviewer dQGe), **advanced techniques** (Reviewer dQGe, Reviewer Bq3t), **signi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Molecule Generation with Fragment Retrieval Augmentation | Accept (poster) | Summary: The paper introduces a novel fragment-based molecule generation framework called Fragment Retrieval-Augmented Generation (f-RAG). This framework aims to address the limitations of existing fragment-based molecule generation methods, which often struggle to explore beyond the existing fragments in their databas... | Rebuttal 1:
Rebuttal: We sincerely thank you for your comments. We appreciate your positive comments that our paper proposes an effective strategy to enhance the quality, diversity and novelty of molecules. We address your concerns and questions below.
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**Comment 1**
The improvement of f-RAG compared to Genetic G... | Summary: The paper proposed a fragment retrieval-augmented generation for molecule discovery, namely f-RAG. f-RAG retrieves two types of fragments, i.e., hard fragments and soft fragments. Hard fragments serve as build blocks that are explicitly included in the newly generated molecules, while soft fragments guide the ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your comments. We appreciate your positive comments that our paper is well-written and the experiments show the effectiveness of our method. We address your concerns below.
---
**Comment 1**
The novelty of the work is limited compared to [1,2,3]. In [1], retrieved exe... | Summary: The paper introduces f-RAG, a novel framework for fragment-based molecular generation that integrates hard and soft fragment retrieval and genetic fragment modification. It aims to improve the exploration-exploitation trade-off in drug discovery by leveraging existing molecular fragments and exploring beyond t... | Rebuttal 1:
Rebuttal: We sincerely thank you for your comments. We appreciate your positive comments that our dynamic vocabulary update strategy expands the explorable chemical space and that our retrieval-augmented generation strategy provides chemical explainability. We address your concerns and questions below.
---... | Summary: This study proposes a fragment retrieval-augmented generation framework for molecular designs based on language models. The arm and linker vocabulary are constructed by fragments that have top average contribution to the given property. The hard fragments and a pool of soft fragments are retrieved from the voc... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive comments that our paper demonstrates an effective strategy for balancing exploration and exploitation, provides a comprehensive evaluation in many drug discovery tasks simulating real-world scenarios, and is overall solid. We address your questions below.
---... | Rebuttal 1:
Rebuttal: Dear reviewers, we sincerely appreciate your constructive comments. There were a number of comments that will help us strengthen our paper, and we will be sure to incorporate them into the revision.
We have had a few questions about the difference of our proposed $f$-RAG compared to RetMol [1], s... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: Fragment-based drug discovery methods are limited in their exploration beyond existing database fragments, as they primarily reassemble or slightly modify the given fragments. This paper introduces a new approach, fragment retrieval-augmented generation (f-RAG), which retrieves two types of fragments—hard frag... | Rebuttal 1:
Rebuttal: We sincerely thank you for your comments. We appreciate your positive comments that our paper introduces a novel molecular generative framework that combines fragment-based drug discovery (FBDD) and retrieval-augmented generation (RAG). We address your concerns and questions below.
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**Comment... | null | null | null | null | null | null |
Graph-enhanced Optimizers for Structure-aware Recommendation Embedding Evolution | Accept (poster) | Summary: In this paper, the authors propose a novel optimization algorithm that is talored for recommender systems. It incorporates graph structural information into the optimization process, aleviating the burden of performing GNN for RS. The convergence of the algorithm is theoretically demonstrated. Besides, it coul... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding our work innovative.
Each comment (presented in *italics*) is followed by its corresponding response.
> *W1: The technique in Section 2.4 is a little too specific for AdamW.*
Thank you for your feedback.
AdamW was chosen as an example because it is the most wide... | Summary: This paper proposes Structure-aware Embedding Evolution (SEvo) to improve recommender systems by directly integrating graph structural information into embeddings. Unlike traditional methods, authors propose guide embedding update momentum with graph smoothing regularization. The proposed method can be integra... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding our work intriguing.
Each comment (presented in *italics*) is followed by its corresponding response.
> *W1: The paper is difficult to follow. It would be nice to see a simple summary of the SEvo pipeline in the form of a scheme or algorithm.*
Thank you for your... | Summary: The paper introduces Structure-aware Embedding Evolution (SEvo), a novel embedding update mechanism for recommender systems. SEvo directly integrates graph structural information into embeddings, ensuring that related nodes evolve similarly with minimal computational overhead. This approach differs from tradit... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding our work novel.
Each comment (presented in *italics*) is followed by its corresponding response.
> *W1/Q1: Uncleared relationship related to recommender system. It is more suitable to study it under more general graph datasets. Recommendation task has no relation... | Summary: This paper proposes SEvo, an embedding updating mechanism that directly injects the graph information into the optimization process. This paper points out two critical criteria for directly injecting graph structure information into the embedding updating process for recommendation. Based on the proposed two c... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding our work well-motivated and well-organized.
Each comment (presented in *italics*) is followed by its corresponding response.
> *W1: The reason behind the success of SEvo on large-scale datasets remains unclear. I am extremely curious about this. The improvement i... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for reviewing our paper and providing us with their insightful comments.
We are excited that the reviewers found our work novel and well-written, and pleased that they were satisfied with both our theoretical analysis and experimental results.
We do our best to... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
T2V-Turbo: Breaking the Quality Bottleneck of Video Consistency Model with Mixed Reward Feedback | Accept (poster) | Summary: The paper presents T2V-Turbo, a training strategy where additional reward models are introduced during the consistency distillation process to enhance the T2V consistency model's quality. With such enhancement, the trained model achieves favorable results in both VBench and human evaluations.
Strengths: 1. Th... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback!
> Regarding the technical contribution, the proposed method seems like a straightforward extension of the video consistency model. The idea of adding direct supervision on the clean samples in consistency distillation has also been explored in ... | Summary: This paper presents a distillation method for text-to-video models. In short, it builds upon latent consistency models (more specifically, adapting the paper "Reward Guided Latent Consistency Distillation" from image to video models). The method involves the usual consistency model objective, in addition to th... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's positive feedback on our work. Please find our detailed response below.
> I would really like the authors to point out if I have missed out details in their contributions or any other novel aspects of their work.
We appreciate the opportunity to clarify our con... | Summary: This paper aims to achieve a video consistency model with both fast and high-quality generation. Specifically, the authors introduce T2V-Turbo, which integrates feedback from a mixture of differentiable reward models into the consistency distillation (CD) process of a pre-trained T2V model. The differentiable ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments!
>The paper seems to simple combine the video consistency model (VCM) and the differentiable reward models
We emphasize that our method is NOT a simple combination of VCM and differentiable RM. Previous works focus on aligning a pretrained DM t... | Summary: The paper introduces T2V-Turbo to enhance the quality of video consistency models in text-to-video generation. The authors address the slow sampling speed of diffusion-based T2V models and the low quality of generated video by integrating feedback from a mixture of differentiable reward models into the consist... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback on our work! Please find our detailed response below.
> Q1: The proposed approach is simply a mixture of previous works, i.e., consistency distillation and reward feedback learning
We emphasize that our method is NOT simply combining the video cons... | Rebuttal 1:
Rebuttal: We appreciate the reviewers for their time and constructive feedback on our work. We have responded to individual reviews below and would like to highlight additional qualitative results in the attached PDF. **Please download and open it with Adobe Acrobat** to click and play the videos.
The atta... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes T2V-Turbo, a model aiming to achieve both fast and high-quality text-to-video generation by breaking the quality bottleneck of a video consistency model (VCM). It integrates mixed reward feedback from one image and one video reward model into the consistency distillation process of a teacher... | Rebuttal 1:
Title: Rebuttal by Authors
Comment: We thank the reviewer for the positive feedback on our work! Please find our detailed feedback below.
> The method mainly combines the consistency distillation in the Video Consistency Model (VCM) with multiple reward models. Although it has achieved good results in solv... | null | null | null | null | null | null |
Improving Context-Aware Preference Modeling for Language Models | Accept (poster) | Summary: The paper focuses on fine-tuning LLMs to improve their ability to handle context-aware preference modeling. The authors address the challenge of the underspecified and ambiguous nature of natural language preferences by introducing a two-step modeling process. This includes selecting a context and evaluating p... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and feedback. Please find our responses below.
> The paper could benefit from testing the proposed method across a broader range of general benchmarks, such as MMLU and AGI-Eval, to assess how fine-tuning for context-aware preferences might affect the L... | Summary: This paper divides preference modeling into two steps: first estimating the user's intent then evaluate the generated text within the context of this intent. The paper makes the Reasonable Preference Reversal Datasets, which encompass criteria and scenarios for preference data. Experiments find that models can... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and feedback. Please find our responses below.
> citations / Li et al. [1]
Thank you for bringing Li et al. to our attention. We agree it is closely related and will add it to the related work, and we will revisit our literature search for other rece... | Summary: This paper points out that the preference label can be reversed by inserting additional context into the prompt. Based on this observation, the authors build a paired preference dataset. The authors also try to provide some theoretical analysis.
Strengths: * This paper studies a specific and interesting probl... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and feedback. Please find our responses below.
> The current experiments do not demonstrate the general advantage of using a paired dataset with reversed preference labels.
Thank you. This is a good criticism. We realized this post submission and have ... | Summary: The motivation behind this paper is to address the critical challenges of finetuning language models (LMs) from pairwise preferences due to the underspecified nature of natural language. Direct preference feedback is often uninterpretable, inconsistent, and difficult to provide, especially when multidimensiona... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and feedback. Please find our responses below.
> Under-supported claims / Assumption about Cardinality
The framing of this in the discussion is not as “claims” but rather a “conjecture” (L182) and a “hypothesis” (L188,L193). As noted in the text, we do... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time, consideration, and numerous comments that will help us improve the manuscript. We have responded to each reviewer individually. If you find our rebuttal to be responsive to your concerns, we kindly ask you to consider recommending “accept” --- we believe cont... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Bootstrapping Top-down Information for Self-modulating Slot Attention | Accept (poster) | Summary: This paper proposes a method that improves the performance of object-centric learning by modulating Slot Attention with semantic and location information obtained based on the output slots and attention maps of Slot Attention. For a given output slot, the semantic information is chosen as the vector closest to... | Rebuttal 1:
Rebuttal: ## Sensitivity to codebook size
We acknowledge the performance dependence on the codebook size, as noted in our limitations section.
However, we would like to emphasize that the optimal codebook size was determined automatically using the training set only (without the validation set). To choose ... | Summary: Slot Attention is a popular component for object-centric learning methods. In this
paper, the authors propose an extension of Slot Attention named "top-down pathway".
After the last iteration of Slot Attention, the slots are mapped to a discrete, learnable
codebook. Jointly with the final attention map, this i... | Rebuttal 1:
Rebuttal: ## Clarification on term “top-down pathway” / modulating encoder
We appreciate the reviewer's comment on our use of the terminology, "top-down pathway." We respectfully explain that our terminology is appropriate for the following reasons:
In our context, "top-down" refers to using higher-level ta... | Summary: This paper proposes a modification to Slot Attention incorporating top-down information into the algorithm. After an iteration of Slot Attention, the slots are quantized into a learned codebook. The quantized slots and attention maps are then used in another iteration of Slot Attention, refining the representa... | Rebuttal 1:
Rebuttal: ## Experiment on models other than DINOSAUR
We appreciate your suggestion for additional experiments. We have implemented our self-modulation technique with the original slot attention setting [28], which includes training encoders from scratch and using an image reconstruction objective. The foll... | Summary: This work proposes a novel unsupervised object-centric learning method. In particular, the proposed approach enhances the slot-attention mechanism by incorporating a "top-down pathway" that highlights features relevant to objects in the image.
The method first employs a standard self-attention mechanism to e... | Rebuttal 1:
Rebuttal: ## Validity of mIoU scores in MOVI-C/E
Thank you for your astute feedback. We sincerely apologize for the mistake in the mIoU calculations on the MOVI datasets and any confusion it may have caused. As you correctly pointed out, the mBO should be greater than or equal to the mIoU. The reported mIoU... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for the constructive and insightful comments. Our work introduces a novel top-down pathway for object-centric learning that consistently improves performance across multiple benchmarks. Reviewers highlighted several strengths of our work, including the clarity ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DiTFastAttn: Attention Compression for Diffusion Transformer Models | Accept (poster) | Summary: In this paper, the authors introduce a novel post-training model compression method aimed at accelerating Diffusion Transformers (DiT) used for image and video generation tasks. They identify the spatial, temporal, and conditional redundancy in attention blocks and propose the corresponding method to tackle th... | Rebuttal 1:
Rebuttal: We truly appreciate the reviewer for the insightful and constructive comments.
> W1. The authors sample 5K images to evaluate the generation quality while the standard setting is sampling 50K images (following original DiT).
Thanks. Following your suggestion, we have increased the evaluation si... | Summary: The authors observe computational redundancies across the three main dimensions of the generation process in DiTs -- space, sampling time, and conditional vs. unconditional forward passes due to CFG. Based on these observations, the authors introduce a set of approaches leveraging them to enable more efficient... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the insightful and constructive comments.
> W1. the evaluation of the effect of parts of the method & design choices seems lacking: there are missing ablations regarding different variants (e.g., naive approaches) of leveraging specific redundancies, and especially... | Summary: The paper presents a post-training model compression method aimed at reducing the computational complexity of Diffusion Transformers. The authors identify three key redundancies in the attention computation. To address these, they propose three techniques. The proposed methods compress the model FLOPs and ena... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the insightful and constructive comments.
> W1 & Q1. How do the authors ensure the generalizability of their method across different datasets and tasks? A more rigorous theoretical analysis could help understand the limitations and potential extensions of the propos... | Summary: This paper proposes a combination of novel techniques to reduce the self-attention computation in diffusion transformers (DiTs), without requiring finetuning. This methods leverage locality in attention scores, similarity in timesteps, and similarity with classifier free guidance. Overall, the proposed method ... | Rebuttal 1:
Rebuttal: We truly appreciate the reviewer for the insightful and constructive comments.
> Q1. You should cite Sora in the introduction and not OpenSora.
Thanks. We have added the citation of Sora in the introduction.
> Q2. How is your windowed attention method different from neighborhood attention (Nat... | Rebuttal 1:
Rebuttal: Thanks all the reviewers for the time and effort taken to provide valuable insights and comments on our work. Here we provide experiment results and answers for some common questions and comments:
> 1. The use of 5k image samples for evaluation is not enough.
We have increased the evaluation siz... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Gaussian Process Bandits for Top-k Recommendations | Accept (poster) | Summary: This paper addresses the problem of top-k recommendation with bandit feedback, where the goal is to recommend a list of k items and receive feedback only on the overall quality of the list, not individual items. This captures the notion that user satisfaction depends on the overall quality of the recommendatio... | Rebuttal 1:
Rebuttal: **Dear Reviewer N1XB,**
We are thankful for your continued positive outlook on this work and encouraged to find recognition of our contributions. Thanks for identifying the typos; we will fix them. Next, we respond to your questions and weaknesses below.
> clarify the technical contribution (n... | Summary: The paper introduces a contextual bandit algorithm for top-k recommendations, leveraging Gaussian processes with a Kendall kernel to model the reward function. The proposed method utilizes full-bandit feedback without assumptions such as semi-bandit feedback or cascade browsing. Theoretical analysis demonstrat... | Rebuttal 1:
Rebuttal: **Dear Reviewer DrUM,**
We appreciate your positive feedback on multiple aspects, including writing, results, theoretical analysis, and novel improvements in computational efficiency and memory requirements. We respond to your questions below.
> Are Gaussian processes restrictive for modeling... | Summary: The authors propose a new bandit algorithm for top-k recommendations that utilizes a Gaussian Process to model the reward of the exponentially large set of arms, and provide regret bounds which improve upon the naive approach of modeling each arm independently. The authors also present a new kernel and show th... | Rebuttal 1:
Rebuttal: **Dear Reviewer 9Xg2,**
We appreciate your positive feedback on the readability and clarity of the proofs. We respond to your comments below. Thanks for pointing out typos and flipped values of \lambda parameter, i.e., when we wrote \lambda = 0.25, we meant to write \lambda = 0.75.
> new algor... | Summary: This paper considers the slate recommendation problem where k items are simultaneously recommended to a user at the same time (in a banner or "slate"). In order to solve this problem the authors adapt existing Gaussian process methods to the top-k setting by modifying Kendall kernels to the top-k setting. Th... | Rebuttal 1:
Rebuttal: **Dear Reviewer tLdn,**
We appreciate your positive feedback on the readability and design of our experiments. We address your comments in the order they were presented.
> On the cascade model, utilize click information.
We acknowledge that exploiting click information can be valuable. Stil... | Rebuttal 1:
Rebuttal: **Dear Program Chairs, Area Chairs, and Reviewers,**
We appreciate the constructive feedback on our work. It is encouraging that all reviewers affirm this work's soundness, presentation, and contributions. Below, we address the questions and concerns raised by each reviewer. We provide additional... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Exploring Low-Dimensional Subspace in Diffusion Models for Controllable Image Editing | Accept (poster) | Summary: Even though there have been many research papers on conditional Diffusion Models, sampling-based disentangling method remains a challenge. The authors empirically/theoretically show that PMP is locally linear and the singular vectors of the gradient of PMP are in low-rank space. Based this, the authors propos... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive feedback that helps improve the quality of our work. Below, we address the reviewer's major concerns and clarify potential misunderstandings in some parts of our work. And we will incooperate those valuable points into our final version.
>**Q1:
>Literature... | Summary: This paper proposes an diffusion image editing framework called LOw-rank COntrollable edit (LOCO Edit) based on two observations: 1) the learned posterior mean predictor (PMP) is locally linear during the middle timesteps during denoising, and 2) the singular vectors of the PMP's Jacobian lie in low-dimension... | Rebuttal 1:
Rebuttal: We thank the reviewer for constructive feedback and interesting questions. During rebuttal, we address the reviewer’s concerns on the experiments and other questions as follows.
> **Q1: The qualitative resultes are not quite the best... up-to-date with the current state of image editing.**
**A1:... | Summary: The paper examines the use of low-dimensional subspaces in diffusion models for precise and disentangled image editing. The paper observes that the Posterior Mean Predictor (PMP) in diffusion models shows local linearity across various noise levels, and the singular vectors of its Jacobian exist in low-dimensi... | Rebuttal 1:
Rebuttal: We thank the reviewer for constructive feedback. During rebuttal, we address the reviewer’s concerns on the experiments and other questions as follows.
>**Q1. Limited experimental evidence and quantitative and qualitative comparisons.**
**A1:** Thanks to the reviewer's suggestions, we added more ... | Summary: The paper presents a method for steering the generation in a diffusion model, without any further training. The proposed method is based on two insights about the Posterior Mean Predictor (PMP) and its Jacobian -- that is, the former being locally linear and the latter having singular vectors lying on low-dime... | Rebuttal 1:
Rebuttal: We thank the reviewer’s constructive feedback, and in the following we address the reviewer’s concerns one-by-one.
>**Q1: Not enough qualitative and quantitative results and comparisons with existing work.**
Thanks to the reviewer's suggestions, we conduct more qualitative and quantitativ... | Rebuttal 1:
Rebuttal: We thank all reviewers for carefully reviewing our work with constructive and positive feedback. Most reviewers find our empirical observation “interesting”, “well-validated”, “important” (eLFT, h6gb, 9wnQ), our theoretical analysis “useful”, “strong”, (h6gb, pgJR), our edit method “novel”, “quite... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper made an interesting observation on local linearity of the diffusion model's denoiser. Based on the observation, the author introduces a novel method of performing one-step closed-forrm operation to achieve semantic image editing. Empirical and numerical results are given to demonstrate the effective... | Rebuttal 1:
Rebuttal: We thank the reviewer’s constructive feedback, and in the following we address the reviewer’s concerns one-by-one.
>**Q1: The computation time of the method is still a bit long (taking around 70s if I understand correctly)**
**A1:**
1. Compared with the global edit method Pullback [25], the ad... | null | null | null | null | null | null |
Motion Graph Unleashed: A Novel Approach to Video Prediction | Accept (poster) | Summary: The paper proposes the motion graph method in predicting the video frames by exploring the spatio-temporal relations among frames from the limited data.
Strengths: It is significant to propose methods of few-shot prediction techniques in video inputs.
Weaknesses: Please provide more details about the setup o... | Rebuttal 1:
Rebuttal: Question 1: More details about the setup of the datasets such as KITTI and Cityscapes that are not originally used for testing video prediction.
Answer 1: We follow the experiment setup from previous works which originated from *Wu, Yue, et al. "Future video synthesis with object motion predicti... | Summary: The paper introduces a graph-based methods to predict video frames through motion prediction. The proposed motion graph captures complex spatial-temporal relationships by transforming video frame patches into interconnected graph nodes. This method improves performance and reduces computational costs compared ... | Rebuttal 1:
Rebuttal: Question 1: Do the authors see a future where such work might be useful for improving video generative models?
Answer 1: Yes. This work uses video prediction task as an example to validate the efficiency and effectiveness of motion graph as a comprehensive video motion representation. In the futu... | Summary: This paper proposes a motion-based method for video prediction. They design a new motion representation named motion graph that transforms patches of video frames into interconnected graph nodes. The proposed video prediction pipeline, empowered by the motion graph, exhibits substantial performance improvement... | Rebuttal 1:
Rebuttal: Q1. Motivation and occlusion / out-of-view cases.
A1: As a non-generative model, our proposed video prediction system may face challenges with occlusions that require the generation of unseen objects. However, it excels in scenarios with scenarios involving occlusions of known objects, as showcas... | Summary: The authors propose a Motion Graph for predicting future video pixels. To achieve this, they introduce three modules: 1. Motion Graph Node Construction; 2. Edge Construction; 3. Graph Interaction Module; 4. Video Prediction Model. The first three modules encode patches and their interactions in a frame, while ... | Rebuttal 1:
Rebuttal: Question 1: Which backbone are used to extract features during motion graph construction. Does the method robust to different backbones?
Answer 1: ResNet was used as the backbone of our image encoder to extract features during motion graph construction. In the manuscript submission, Figure 7 of A... | Rebuttal 1:
Rebuttal: We are grateful for the thoughtful suggestions and comments from the reviewers.
In response, we have implemented several enhancements to the manuscript, including, but not limited to:
a) Enhancing the visual presentation throughout, such as adding an overview figure;
b) Adjusting the text fon... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
NoMAD-Attention: Efficient LLM Inference on CPUs Through Multiply-add-free Attention | Accept (poster) | Summary: The paper presents NoMAD-Attention, using SIMD registers in CPU, to speedup LLM inference in CPU.
Strengths: (1) Important problem with interesting solution.
(2) The idea is straightforward, and the figure is very clear.
(3) Good system speedup and maintaining the original performance of attention.
Weakness... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's careful review and valuable suggestions. We address their comments as follows.
**[W1] Evaluations on harder tasks.**
To further assess NoMAD-Attention, we conducted additional evaluations on the more challenging MMLU, GPQA, and MGSM (English) benchmarks. Our ... | Summary: This paper proposes using SIMD instructions on CPUs to speed up Transformers by removing multiply-add instructions. The paper replaces the attention operation a lookup-table based alternative. The paper motivates the application well for Transformer inference on CPUs.
Strengths: Strengths:
* Important problem... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their support of our paper and the thoughtful suggestions. We address the reviewer's concerns as follows.
**[W1] Evaluation on additional and more recent open models.**
- We conducted additional experiments on the LLaMA-3-8b model with NoMAD-Attention across a... | Summary: This paper utilizes product quantization (PQ) to replace dot product operations in the matrix multiplications involved in the attention mechanism of transformers with memory lookup operations, showcased on language models. To my knowledge, this technique has been first introduced by Blalok et al (reference [5]... | Rebuttal 1:
Rebuttal: We are grateful for the reviewers' careful review and insightful comments. We have addressed their feedback in detail below.
**[W1] d_sub is very confusing to me. What is the length or your codeword? if d_sub = 1, is the size of the codeword = 1? how is that product quantization? How does it resu... | Summary: This paper proposes an algorithm to compute vector inner product efficiently on CPU by exploiting the fast access speed of in-register memory for fast self-attention computation on CPU for model inference. Instead of use multiply and add to compute the inner product between query and key vectors, the authors p... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers' careful consideration of our paper and their valuable feedback. We have addressed each of their comments below.
**[W1] It would be better to also give some data about the latency of GPU decoding so that we know how far CPU is behind the GPU in Figure 2.**
-... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers' careful evaluation of our paper and their valuable feedback. In the following section, we address common concerns raised by multiple reviewers. We are happy to provide further clarification during the discussion period.
**1. Regarding $d_{sub}$**
NoMAD-Atte... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LCGen: Mining in Low-Certainty Generation for View-consistent Text-to-3D | Accept (poster) | Summary: The paper attempts to address the Janus Problem in SDS-based text-to-3D methods. It first analyzes the cause of the Janus Problem in SDS-based approaches, identifying that discrete view encoding and shared priors in 2D lifting are the primary causes. To address this, it proposes the LCGen method, which guides ... | Rebuttal 1:
Rebuttal: Thank you very much for your effort in reviewing our work! Our responses are as follows:
## 1. Diversified Examples
In *Rebuttal File*, we demonstrate how our method alleviates the Janus Problem in various other types of examples.
In **Fig. C and D**, we tested on "a sunflower" and "a piano" t... | Summary: This paper presents a simple and effective method to address the Janus Problem for Score Distillation Sampling (SDS)-based text-to-3D methods. This paper argues view consistency is related to that the 3D model tends to learn content with higher certainty from each perspective, and using different priors with d... | Rebuttal 1:
Rebuttal: Thank you very much for your effort in reviewing our work! Our responses are as follows:
## 1. Quantitative Comparison with Other Methods Dealing with the Janus Problem
We have provided the quantitative comparison results with other methods in the table in *Rebuttal File* (as well as in the tabl... | Summary: This paper presents a method to tackle the issue of the Janus Problem in text-to-3D content generation method. A method named LCGen has been proposed that focuses on low certainty regions to generate view-consistent generation.
Strengths: Some causes of the Janus Problem have been analysed visually. We then i... | Rebuttal 1:
Rebuttal: Thank you very much for your efforts in reviewing our paper! Below is our response. For Figure and Table, please see *Rebuttal File*.
## 1. Diverse Examples
The main purpose of our method is to help address Janus Problem appearing on a single object in text-to-3D baselines. In the **limitations ... | null | null | Rebuttal 1:
Rebuttal: Thank you to all the reviewers for their hard work! We are very honored that our work has been recognized for: 1) **significant contribution to addressing key challenges**, 2) **being well-motivated by a detailed analysis of root causes**, 3) **simplicity and effectiveness**, and 4) **good experim... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Continual Learning with Global Alignment | Accept (poster) | Summary: This paper tackles continual learning by addressing interference between tasks. For the interference between different tasks, the authors propose a method called ‘global alignment’ to align the data representations using task-specific compositions of pre-trained token representations. Then the authors conduct ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and feedback. We address your concerns as follows.
___
**W1. The analysis of interference in Section 3.2 does not consider the activation function between the two layers of the network.**
* We consider the ReLU activation which is widely used in neural networ... | Summary: In Continual Learning (CL), the interference caused by the constant modification of the representation is the leading cause of catastrophic forgetting. Motivated by this and the idea that gradients in opposite directions are one cause of this interference, the authors proposed new ways of adding knowledge to a... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and feedback. We address your concerns as follows.
___
**W1. What is the actual contribution of applying wiring or C-LoRA?**
* As shown in Eq. 1, the interference depends on two factors: (1). **Correlation between representations.** The wiring and C-LoRA model... | Summary: This paper addresses the problem of Task-Incremental-Learning (TIL) with pre-trained transformer in the context of NLP. The author extended their experiments in the Class-Incremental Learning (CIL) scenario. The authors identify potential forgetting causes as (1) negative correlation between data representatio... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and feedback. We address your concerns as follows.
___
**W1. Comparison to more recent prompt-learning techniques and the application to CV tasks.**
* Comparison to CODA: we show CODA's average accuracy on Task-IL below. Since CODA's hyperparameters are set fo... | Summary: This paper studies the cause of cross-task interference in class-incremental learning of transformer-based language models. The authors disentangle the cause into the correlation (i) between data representations and (ii) between class vectors in the linear classifier. To tackle (i), the authors propose three w... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and feedback. We address your concerns as follows.
___
**W1. While the main goal of the paper is reducing cross-task interference, the main results are using the Task-IL setup.**
We focus on Task-IL because:
* Cross-task interference is a fundamental problem... | Rebuttal 1:
Rebuttal: We thank all reviewers for their thoughtful reviews and feedback. We address common questions as follows.
___
**1. The intuition behind only replacing the key matrix but not the query and value matrices in wiring models.**
* **Why not replace value matrices**: when replacing the value matrices wit... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
When are dynamical systems learned from time series data statistically accurate? | Accept (poster) | Summary: The present manuscript concerns the use of neural networks to fit physical dynamical systems of generic kind (including and focusing on chaotic maps). It is shown that adding the information of the Jacobian of the dynamical map in the supervised learning process leads to better performances.
Strengths: Origin... | Rebuttal 1:
Rebuttal: ### Results require full Jacobian but it can be approximated with available derivative information in practice
You are absolutely right in that the Jacobian is computationally hard to calculate (through AD or finite difference) in high dimensions; thank you and Reviewer vAp3 for pointing this out... | Summary: The paper addresses the problem that classical ERM training of dynamical systems models often fails to capture invariant measures of the observed dynamics, even when test errors are low. The authors use ergodic theory to explain this failure from a theoretical viewpoint. They further demonstrate that incorpora... | Rebuttal 1:
Rebuttal: ### Related work on invariant measures
Thank you for sharing these references -- we have added them to Related Work, which has spilled into the Appendix! Since the focus is on deriving dynamics-aware generalization bounds, we consider a minimal regression setup for one-step dynamics, which does n... | Summary: This paper extends generalization results to models trained on dynamical data, especially Neural ODEs. The paper shows and then attempts to explain why Neural ODE trained without a Jacobian matching term fail to capture physical behaviour even when they have low generalization error. Under a generalization ass... | Rebuttal 1:
Rebuttal: ### When Assumption 1 is expected to hold
A necessary condition for Assumption 1 ($\mathcal{C}^1$ strong generalization) is that the optimization problem with the Jacobian loss is solved "well" -- resulting in low generalization errors. But, as you have carefully observed, this is insufficient to... | Summary: The authors focus on analyzing why MSE loss fails to capture the physical behavior of dynamical systems. Narrowing their analysis to invariant ergodic systems, they provide theoretical insights on when generalization implies statistical accuracy. They propose that for models to be statistically accurate, they ... | Rebuttal 1:
Rebuttal: ### Implications for training with Sobolev norm
It is indeed interesting to consider errors in the learned dynamics as distributions in a Sobolev space, $W^{k,p}$, as you point out. You are absolutely correct that our MSE loss is a special case for $k = 0, p =2$ and the Jacobian loss for $k= 1, p... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Gradient-based Discrete Sampling with Automatic Cyclical Scheduling | Accept (poster) | Summary: The paper presents a novel gradient-based algorithm to sample from complex multimodal discrete distributions based on differentiable energy functions. Overall, the method is based on locally balanced proposals, previously introduced, and instantiates it with parametrized functions and a cyclical schedule for t... | Rebuttal 1:
Rebuttal: Thank you for your supportive comments. We include our responses to your points below:
# Q1: Insufficient Content in Main Body regarding Tuning Algorithm
In section 4.4, we first provide an intuition for our algorithm under “Main idea” and then present our algorithm by separating it into three se... | Summary: The paper proposes a solution to the challenge of sampling from high-dimensional discrete spaces, where conventional discrete samplers often get trapped in local modes. To address this, the authors introduce a discrete Langevin sampler with automatic cyclical scheduling. This method comprises three components:... | Rebuttal 1:
Rebuttal: Thank you for your supportive and valuable comments. We will address the issues you raise below.
# Q1: Complexity of Tuning Algorithm
In total, the automatic tuning algorithm takes 500 sampling steps as a budget at maximum, which is much smaller when compared to the 5,000 sampling steps we use f... | Summary: The paper introduces a novel method for sampling from multimodal discrete distributions, which presents an innovative approach to address the challenge of local modes trapping in gradient-based discrete sampling, together with non-asymptotic convergence guarantee and empirical validation of the proposed method... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. We answer the questions below.
# Q1: Error Bars Fig 1, Table 1
Figure 1 corresponds to density estimation, where error bars would not make sense. If you are referring to Figure 3, it should be noted that the shaded area represents the range within 1 standar... | Summary: This paper proposes a new discrete sampling method called ACS that addresses a common problem for existing gradient-based approach where they are susceptible to becoming trapped in local modes. ACS combines local-balancing proposals with a cyclic step size to balance local exploitation and global exploration;... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review. We answer your questions below.
# Q1: Missing Error Bars, Figure 3 and Table 1
Thank you for pointing this out. For the log MMD curves for the RBM experiments in Figure 3, it should be noted that the filled in area corresponds to values within one standard erro... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their constructive reviews. Multiple reviewers have pointed out that our results for sampling from Energy Based Models in Figure 3 and the Annealed Importance Sampling results in Table 1 do not have error bars. Below, we have provided the updated result... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Regularized Q-Learning | Accept (poster) | Summary: This paper provides Q-learning convergence (asymptotic) in linear architectures with regularization power. Their algorithm is tested on mountain car example.
Strengths: Q-learning convergence in linear architectures is an important problem in RL.
Weaknesses: - The analysis follows ODE style analysis from Bo... | Rebuttal 1:
Rebuttal: **Q1** *The analysis follows ODE style analysis from Borkar and Meyn. So it is asymptotic. However, non-asymptotic guarantees (rate of convergence) can be provided when assuming non-zero stationary distribution (like Assumption 2.1). For e.g. see Chen et al, 22. There are research on extension to ... | Summary: This paper introduces a novel approach, RegQ, which is a framework for dealing with linear approximation of Q-function. Compared to the instability of the traditional Q-learning with function approximator, which is known as the deadly triad, RegQ addresses this problem by regularization term, making the algori... | Rebuttal 1:
Rebuttal: **Q1.** *Most of the concerns arose from the lack of experiment scope, prior work comparison, and implementation. Authors claim about the strengths of the RegQ algorithms, but lots of claims are not confirmed by experiments. Also, the claims are given with the comparison of the prior work, but exp... | Summary: This paper proposes a new Q-learning variant with linear function approximation called RegQ, and proves that its ODE form converges (even when associating it with linear function approximations).
Q-learning is famously known to be affected by the 'deadly triad': it tends to diverge in practice when combined t... | Rebuttal 1:
Rebuttal: **Q1** *In the related work, the paper lists several existing works proving the convergence of Q-learning under linear function approximation with some theoretical assumptions. The assumptions made in the present paper are weaker than most of the ones of existing works, which include restrictions ... | Summary: The paper introduces a new regularized Q-learning algorithm "RegQ" suitable for linear function approximation, which essentially adds an $\ell^2$ regularization term to the TD error in semi-gradient Q-learning. The authors prove that this addition ensures convergence of the algorithm and analyze the error with... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback, and the time and effort for reviewing our paper. Following the reviewer's comments, we have added the related discussion in the revised manuscript:
**Q1** *The biggest weakness of this paper is the limited experiment ... . The two experiments th... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers’ constructive comments for our manuscript. The comments are valuable for improving the quality of our paper and provide important guidance for our research.
In the following, we address the concerns commonly raised by the reviewers.
**G1** Reviewers yA4E an... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: Q-learning is a popular RL algorithm. With function approximation, though, it is known that this algorithm can diverge. This issue is attributed to the `deadly triad': off-policy learning, bootstrapping, and function approximation. This work addresses this issue in the context of linear function approximation.... | Rebuttal 1:
Rebuttal: **Q1** *The work studies only the case of a fixed behavior policy. This approach is extremely restrictive and practically not very useful .... Specifically, as stated by Melo, Meyn, and Ribeiro (2008), ..., the behavior policy would need to be close to the optimal policy, ...., the policy estimate... | null | null | null | null | null | null |
VFIMamba: Video Frame Interpolation with State Space Models | Accept (poster) | Summary: Based on the popular S6 model's advantages of linear computational complexity and data-independent modelling capability, this paper applies it to VFI. Specifically, this paper proposes a token rearrangement strategy to learn the information of adjust frames, in addition to introducing a curriculum learning str... | Rebuttal 1:
Rebuttal: We are grateful for your recognition and feedback on our work. We would like to respond to your concerns as follows:
**Q.1** Visualization of error maps
**R.1** Thank you for your suggestion. Visualizing the error maps can indeed provide a more intuitive demonstration of the accuracy of the inte... | Summary: This paper introduces a novel video frame interpolation (VFI) method called VFIMamba. VFIMamba is the first method that combines the State-Space Model Mamba with VFI architectures and therefore, it has the advantage of a linearly growing complexity w.r.t. the resolution while maintaining the ability to utilize... | Rebuttal 1:
Rebuttal: Thank you for your positive and constructive suggestions. We have the following responses to your concerns:
**Q.1** *How is 8x interpolation performed?*
**R.1** As mentioned in line 497, we followed the testing procedure of FILM [1] and used an iterative approach for frame interpolation. Specifi... | Summary: The paper presents a novel approach for video frame interpolation using Selective State Space Models (S6). The authors introduce VFIMamba, a method designed to efficiently and dynamically model inter-frame information. This method features the Mixed-SSM Block (MSB), which rearranges tokens from adjacent frames... | Rebuttal 1:
Rebuttal: We sincerely thank you for the recognition and suggestions regarding our work, and our responses to your questions are as follows:
**Q.1** *What specific optimizations could be applied to VFIMamba to make it suitable for real-time applications? Are there trade-offs between speed and accuracy that... | Summary: The paper introduces Mamba-based video frame interpolation. To fully incorporate the power of Mamba, the paper proposes an interleaving rearrangement method. Using this method, the SSM scans the same location tokens of 2 frames together instead of processing each frame separately. The paper also proposes curri... | Rebuttal 1:
Rebuttal: We sincerely appreciate your feedback on the experimental and implementation details of our work. We would like to provide the following responses:
**Q.1** *Regarding which models are trained only on Vimeo90K or Vimeo90K+X-TRAIN in the tables.*
**R.1** Thank you for your suggestion. We would lik... | Rebuttal 1:
Rebuttal: We sincerely appreciate all reviewers' efforts in reviewing our paper and giving insightful comments as well as valuable suggestions. We are glad to find that the reviewers generally acknowledge the following contributions of our work.
* **Framework.** We are the first to adapt the S6 model to th... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FNP: Fourier Neural Processes for Arbitrary-Resolution Data Assimilation | Accept (poster) | Summary: This paper proposes an innovative Fourier Neural Process (FNP) model designed to address the limitations of existing data assimilation methods when handling observational data of varying resolutions. The FNP model combines the characteristics of neural processes and Fourier transforms to effectively assimilate... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you very much for your thorough review, highly constructive comments, and feedback! We sincerely appreciate your recognition of the effectiveness and significance of our method. Below, we will address each of your questions and concerns in sequence.
> The ERA5 dataset is glo... | Summary: The paper introduces a new approach (Fourier Neural Processes, FNP) for weather data assimilation using data from different resolutions. The authors show that the new approach improves the results over similar data assimilation networks from earlier papers.
Strengths: The paper demonstrates well the advantage... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you very much for your detailed review, thoughtful comments, and feedback! We sincerely appreciate your recognition of the effectiveness of our method and its relevance to the research field. At the same time, we deeply regret and apologize for any confusion our writing may h... | Summary: This paper proposes a new variant of neural processes called Fourier Neural Processes (FNPs) to solve the data assimilation problem with arbitrary solution, which is an important component in modern weather forecast system. The proposed method based on FNP has better computational efficiency, and achieves stat... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you very much for your thorough review, highly constructive comments, and feedback! We greatly appreciate your positive reception of our work and recognition of its practical value. At the same time, we deeply regret any confusion or difficulties you may have encountered rega... | Summary: The authors propose a method for arbitrary-resolution data assimilation, called Fourier Neural Processes (FNP). This approach improves generalization by addressing resolution limitations in existing methods. Key features include unified coordinate transformation, spatial-variable functional representation, and... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you very much for your detailed review, thoughtful comments, and feedback! We appreciate your recognition of the effectiveness of our method, while deeply regretting and apologizing for any confusion or concerns we may have caused you regarding the motivation and design of ou... | Rebuttal 1:
Rebuttal: Dear reviewers and meta-reviewers,
We greatly appreciate the considerable time you have dedicated to providing us with constructive comments and feedback to further enhance our paper. It is gratifying to see that all reviewers acknowledge the effectiveness and contribution of our method. We have ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Do Finetti: On Causal Effects for Exchangeable Data | Accept (oral) | Summary: The paper generalizes the traditional iid settings in casual inference to exchangeability settings by de Finetti theorem, and proposes a new model named the casual Polya urn model to illustrate the new scheme and to catch more relationship. The experiments show when the number of environment is less than 5000,... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time and address the questions below.
> Aldous 1985 shows many (not all) conclusions in iid can be naturally transformed into those under exchangeability. So the theoretical improvement seems not much.
Indeed, many i.i.d. results transfer to the exchangeable c... | Summary: The paper studies causal effect identification and estimation in exchangeable data. The main result here is theorem 1, which shows that causal effects are identifiable in ICM generative processes.
Strengths: - The paper provides a great framework to think about interventions in exchangeable data. Starting fro... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and their appreciation of our work. We hope to clarify their questions below:
> The latter parts of the paper is rushed and left me confused. For example, it is unclear how causal de Finetti theorems apply to the Causal Pólya Urn Model, Theorem 2, and the ent... | Summary: The paper formalizes the observational and interventional distribution under the ICM generative process, of which iid is the special case. It provides an identifiability result for the causal effect given that the causal graph is known. Then, it shows that both the causal graph and the causal effect can be ide... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and their recognition of the importance of relaxing the i.i.d. assumption, a general problem in the ML community. Please see below answers to the questions:
> Definition 3: We should also break the edge from the de-finnetti parameters to the intervened varia... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Seek Commonality but Preserve Differences: Dissected Dynamics Modeling for Multi-modal Visual RL | Accept (poster) | Summary: The paper proposed a method, namely Dissected Dynamics Modeling (DDM), for multi-modal environment dynamics modeling in visual RL. The core idea is to adopt additional modules to extract separate modality-consistent and modality-inconsistent features in each modality stream with designated losses as the regula... | Rebuttal 1:
Rebuttal: We are truly grateful for your thoughtful remarks and experimental suggestions. These remarks shed light on what we can improve and are crucial for refining our work. We address your main concerns as follows:
> The method is only tested on visual modalities, limiting its generalizability and cont... | Summary: The paper presents a solution for better multimodal dynamic modeling in visual RL. The paper claims that existing works only emphasize consistent (aligned) information across modalities, leaving out the opportunity for the model to benefit from the inconsistent features. The work introduces a new consistency l... | Rebuttal 1:
Rebuttal: Thank you so much for the time and effort invested in reviewing our work. The positive remarks are truly appreciated, and we feel encouraged by the feedback. We address the points raised in the comments as follows:
> The authors report the episode to return and driving distance but do not report ... | Summary: The paper proposes Dissected Dynamics Modeling (DDM), a dynamics modeling framework for learning latent features in multi-modal visual RL. The methodology focuses on capturing both the shared and distinct information contained across input modalities. The paper presents a multi-modal architecture and training ... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude for the insightful comments. We also deeply appreciate the suggestions regarding presentation and experimentation. Below are our responses to your concerns:
> [W1] Some implementation and experiment details are not clear.
Please refer to our respons... | null | null | Rebuttal 1:
Rebuttal: We sincerely appreciate all reviewers for the valuable time and effort dedicated to reviewing our work. The comments have been highly constructive, and the positive evaluations from all reviewers are immensely encouraging. We have carefully considered each remark and have responded accordingly. Fo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Alleviate Anchor-Shift: Explore Blind Spots with Cross-View Reconstruction for Incomplete Multi-View Clustering | Accept (poster) | Summary: The paper proposes a cross-view reconstruction-based multi-view clustering algorithm to address the issue of anchor shift in missing data scenarios. Specifically, The method guides anchor learning by reconstructing the missing parts of the data. It uses an affine combination-based reconstruction strategy, rath... | Rebuttal 1:
Rebuttal: **1. Inaccurate statements**
Thanks for the comment. Due to the additional introduction of regularization constraints on the entire graph, such as manifold constraints, most existing matrix decomposition methods exhibit an $O(n^2)$ complexity [1][2]. We will revise this statement in the final ver... | Summary: By employing cross-view anchor learning and affine combination-based reconstruction，the authors propose an incomplete multi-view clustering method to alleviate the anchor-shift problem. Besides, the authors theoretically analysis the advantages of affine combination-based reconstruction, which help to explore ... | Rebuttal 1:
Rebuttal: **1. Definition of symbols**
Thanks for the comment. $n_p$ represents the number of samples in the $p$-th view. In the global rebuttal file, we have provided a notation table that explains the main symbols used in the paper. Please refer to the Table 2 in the global rebuttal file.
**2. Inconsist... | Summary: This paper proposes an anchor-based incomplete multi-view clustering with cross-view reconstruction (AIMC-CVR). To tackle the anchor-shift induced by incomplete multi-view data, AIMC-CVR reconstructs missing samples with learned anchors. The traditional convex combination is replaced with affine combination fo... | Rebuttal 1:
Rebuttal: **1. Incomplete data construction**
Thanks for the comment. For the datasets mentioned in our method, we remove some instances on each view randomly to get their incomplete versions. Specifically, with the principle that each instance is present in at least one view, we generate missing datasets ... | Summary: This paper proposes a novel anchor-based IMVC method called AIMC-CVR to address the anchor-shift caused by missing data. AIMC-CVR consists of two modules: cross-view anchor learning and affine combination-based reconstruction. The former helps in learning a complete anchor graph, while the latter aims to recov... | Rebuttal 1:
Rebuttal: **1. The necessity of the proposed modules:**
Thanks for the comment. In AIMC-CVR, both modules are essential for alleviating the anchor-shift problem caused by incomplete data. The cross-view anchor learning module mitigates such problem by leveraging available data across views to learn complet... | Rebuttal 1:
Rebuttal: We thank the SAC, AC, and PCs for their efforts and constructive comments, which are helpful in further improving the quality of our manuscript. We respond to your questions carefully one by one carefully, and we hope our responses can address your concerns.
Note that there are two tables and two... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Key-Grid: Unsupervised 3D Keypoints Detection using Grid Heatmap Features | Accept (poster) | Summary: This paper presents a novel unsupervised network for keypoint detection. The method can be applied to handle both rigid and deformable objects. It follows an autoencoder framework where the encoder predicts keypoints and the decoder utilizes the generated keypoints to reconstruct the objects. The main contribu... | Rebuttal 1:
Rebuttal: Thanks for your great suggestion. In the rebuttal phase, we address the reviewers' concerns about the effectiveness of our method on partial point clouds, such as those obtained from back-projecting depth map sand point clouds containing outliers. We also provide a detailed explanation of the conv... | Summary: This paper presents Key-Grid, an unsupervised keypoint detection network designed for 3D point clouds. Unlike previous methods that emphasize leveraging various priors on 3D structures, this paper converts keypoints into a grid heatmap. This heatmap forms a continuous feature landscape across the entire 3D spa... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments. We address your concerns as follows: We explain the rationale of the grid heatmap from a theoretical perspective and highlight its advantages over the skeleton structure proposed by SM. Subsequently, we demonstrate that the grid heatmap is more effective for k... | Summary: The authors propose a novel unsupervised method to detect 3D point clouds key points by producing an intermediary heatmap based on a grid and points distances from the skeleton and connected key points. They achieve state-of-the-art accuracy and semantic consistency and easily achieve Se-(3) invariance with mi... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We address your concerns as follows: we provide demonstrations of our method on some less successful objects and investigate whether keypoints maintain semantic consistency across various deformations of objects.
$\color{Indigo} Q1$：A small neat-picking addit... | Summary: Novel PointNet-based autoencoder method called KeyGrid, that predicts semantic keypoints on objects, even when objects are subject to deformations. Similar to previous approaches, keypoints are produced as a linear combination of inputs points, according to a learned weight matrix.
The key novelty over relat... | Rebuttal 1:
Rebuttal: Thank you for providing thoughtful and detailed feedback, which greatly enhances the quality of our article. Based on your comments regarding related work, clarity, and grammar & style nits, we will revise our paper in the came ready section.
Regarding your inquiries about experimental results an... | Rebuttal 1:
Rebuttal: Thanks to the esteemed reviewers for your insightful feedback, which has significantly enhanced the quality of our paper. Based on your suggestions, we provide the corresponding visual results in the new submission material.
**Figure 1(a)**: In response to Reviewer YtUf and Reviewer LZF7's inquir... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ArkVale: Efficient Generative LLM Inference with Recallable Key-Value Eviction | Accept (poster) | Summary: This paper proposes a paged-based KV cache manager that identifies and recalls important tokens for LLM inference, termed ArkVale. Results show that ArkVale achieves 2.2x latency and 4.6x throughput improvement on various long context tasks.
Strengths: 1. The paper is easy to follow, with clear writing and pr... | Rebuttal 1:
Rebuttal: >How does the page size affect the memory consumption for the KV cache? Would smaller page size lead to potential fragmentation issues?
We allocate pages from a pre-allocated memory pool, and all pages have the same page-size, thus avoiding the issue of memory fragmentation. However, a smaller pa... | Summary: ARKVALE presents a page-based key-value (KV) cache manager designed to address the challenges associated with long-context processing in large language models. The main contribution is its ability to recognize and recall important tokens that were previously evicted, thereby optimizing memory usage and improvi... | Rebuttal 1:
Rebuttal: >What is the performance of ARKVALE on Mistral-7B-Instruct-v0.2 and LLaMA-3-8B-Instruct?
We conduct experiments on our method adapted to Mistral-7B and Llama-3-8B, as detailed in Global Response.
---
>How does the performance (accuracy, latency/throughput) of ARKVALE compare to other methods
I... | Summary: The paper proposed a method to minimize the risk of KV cache eviction by efficiently and soundly offloading some of them into external memory, which is realized by page organization, page digest, and digest ranking/scoring. The method gets much better performance in context retrieval tasks compared to other KV... | Rebuttal 1:
Rebuttal: > It would be interesting to see how the page size affect the methods, as vLLM's default page size is 16. Is there any reason to scale to 32?
We briefly discuss the impact of different page-sizes on accuracy and latency in Global Response. A page-size of 32 is a compromise between these two aspec... | Summary: The paper introduces ARKVALE, a novel page-based key-value (KV) cache management approach designed to optimize the performance of Large Language Models (LLMs) when dealing with long context lengths. As the demand for higher context lengths in tasks such as multi-turn chats and content generation increases, the... | Rebuttal 1:
Rebuttal: >Clarification on Model Choice: The paper primarily utilizes the LongChat-7b-v1.5-32k model for evaluating ARKVALE. Can the authors provide specific reasons for choosing this model over others? Additionally, how do the authors anticipate ARKVALE would perform with other, potentially larger or more... | Rebuttal 1:
Rebuttal: ## Adaption to other models
We adapt ArkVale to both `MaziyarPanahi/Llama-3-8B-Instruct-64k` (with 64k context-length extended from Meta Llama-3-8B) and `mistralai/Mistral-7B-Instruct-v0.3` (with a 32k context-length) in hugginggface, and test them on the datasets used in our paper, with the res... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Rethinking Weight Decay for Robust Fine-Tuning of Foundation Models | Accept (poster) | Summary: The paper proposes a method for robust fine-tuning of deep networks. The proposed method is a variant of L2-SP. The original formulation of L2-SP applied a uniform L2 penalty on the difference between the weights of the pre-trained model and the fine-tuned model. The proposed method (L2-SPD) aims to make this ... | Rebuttal 1:
Comment: We appreciate the reviewer's taking the time to examine the paper and the provided code closely. We clarified your concerns and added Infograph experiments as requested. If anything is not clear, we are happy to discuss it during the rebuttal period.
**The Himmelbau example.**
We appreciate the ... | Summary: This paper proposes a new weight decay technique to adapt foundation models to target tasks, focusing on fitting the target data while maintaining the pre-trained knowledge. Specifically, the method, Selective Projection Decay (SPD), selectively imposes a strong penalty on certain layers while allowing others ... | Rebuttal 1:
Comment: We thank the reviewer for the positive comments and pointing our our typos. We clarified your concerns and added L2-SP experiments as requested. If anything is unclear, we are happy to discuss it during the rebuttal period.
**Why is L2-SP not compared in the experiments shown in Tables 1 and 5? ... | Summary: The authors propose Selective Projection Decay, a modification on the AdamW optimizer where a regularization penalty is applied that controls for an overall drift from the prior weights such that as long as the overall shift is controlled for but individual layers can potentially adapt differently. The authors... | Rebuttal 1:
Rebuttal: We really appreciate the thoughtful comments, especially on suggesting a layer-specific hyper-parameter configuration. This was what we thought would be interesting as well. In the response, we provided clarifications to your concerns and a new experiment. If anything is not clear, we are happy to... | Summary: This paper proposes a novel weight decay strategy called Selective Projection Decay (SPD).
SPD selectively imposes a stronger penalty on certain layers, and is designed to improve both in-distribution (ID) and out-of-distribution (OOD) performance.
The paper demonstrates the effectiveness of SPD through experi... | Rebuttal 1:
Comment: We really appreciate the positive comments and suggestions for ensemble methods. In the response, we clarified your concerns and included new experiments. If anything is not clear, we are happy to discuss it during the rebuttal period.
**Overall, I think this is a strong paper, and its strengths ... | Rebuttal 1:
Rebuttal: We thank all of the reviewers for their positive comments on this work's adaptability (dDi9), performance (Ex5N, CVio, Jrpf, 33UR), and insights (Ex5N, 33UR). We aim to provide concrete responses to your questions and clarify your confusion.
The rebuttal PDF includes new experiments and studies r... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This work proposes a new regularization scheme called Selective Projection Decay (SPD), which selectively imposes penalties on layers where the current progress direction does not align with the vanilla update direction. This method is compatible with PEFT methods, including LoRA-type algorithms, making it pra... | Rebuttal 1:
Comment: We thank the reviewer for the positive comments and for suggesting insightful new related works. In the response, we clarified all your concerns. If anything is not clear, we are happy to discuss it during the rebuttal period.
**How does the performance compare when using EWC regularization for f... | null | null | null | null | null | null |
Magnet: We Never Know How Text-to-Image Diffusion Models Work, Until We Learn How Vision-Language Models Function | Accept (poster) | Summary: The paper investigates the text embedding representation of text-to-image models in the context of stable diffusion. In particular, the authors find that object features often bind to their commonly associated attributes and propose the Magnet approach that interpolates object features with their designated at... | Rebuttal 1:
Rebuttal: Firstly, we would like to express our sincere gratitude for reviewing our manuscript and providing valuable feedback. Below are our responses to the weaknesses (W) and Questions (Q).
**W1**: We acknowledge that attribute binding, as our main focus, is part of compositional generation. However, si... | Summary: - This work studies how CLIP text embeddings commonly used in text-to-image diffusion models affect attributes in generated images, and how attributes can be bound to the correct objects during generation.
- There is an analysis of the (a) CLIP text encoder and how it interacts with the padding used during T2I... | Rebuttal 1:
Rebuttal: Your positive comment on the contribution of this work is much appreciated. We apologize for any difficulty you may have experienced in following this paper. Hope the following example and the illustration in Fig. 3 in the attached PDF will help you to understand these $\mathcal{P}$-terms and our ... | Summary: The authors propose _Magnet_ to solve the attribute binding problem. (1) Initially, they specifically analyze how the improper binding problem occurs in text embeddings. By comparing embeddings for each token, they demonstrate the attribute bias phenomenon where attributes do not bind well to the object token ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough review of our manuscript and the insightful comments provided. Here are our detailed responses to the identified Weaknesses (W) and Questions (Q).
**W1**: Our comparison experiment has been designed to evaluate whether each method achieved binding. In Tab. 1,... | Summary: The paper analyzes and improves upon the “(attribute) binding problem” in VLMs such as CLIP, and the focus is primarily on the text encoder side.
First the authors analyze how the individual text embeddings behave in a diagnostic setting when encoding a two-word text “COLOR OBJECT”. With these insights they pr... | Rebuttal 1:
Rebuttal: We sincerely appreciate your careful review and the valuable suggestions provided. Our responses to the Weaknesses (W) and Questions (Q) are outlined below.
**W1**: Magnet is fully automatic during evaluation and does not require manual definition of these positive/negative attributes. We have in... | Rebuttal 1:
Rebuttal: In the attached PDF file, we provide a new perspective on how the word and padding embeddings affect generation (see Fig. 1), additional examples applying Magnet to different T2I models and other techniques (see Fig. 2), and a new Magnet pipeline figure for ease of understanding (see Fig. 3).
We ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Motion Forecasting in Continuous Driving | Accept (spotlight) | Summary: This paper proposes a model for motion forecasting that models the continuous stream of world state over sequential timesteps. This is in contrast to most/all other works which do an indepedent prediction of the future for each timestep based on a fixed window of history, without context of previous model bel... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review as well as the suggestions for improvement. Our response to the reviewer’s comments is below:
Apologies for these unclear explanations.
+ **Data reorganization:**
As shown in Fig. 2(b), the dashed part refers to historical time steps (50 historical s... | Summary: This submission tackles the task of trajectory forecasting in autonomous driving.
In particular, it proposes improvements in two aspects:
1/ Data reorganization: Current datasets are artificially split into non-overlapping segments. The authors propose to reorganize the data to have overlapping windows.
2/ ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review as well as the suggestions for improvement. Our response to the reviewer’s comments is below:
**Q1: Drastic improvement on $ADE_1/FDE_1$.**
By comparing QCNet and other methods (such as ProphNet), it can be seen this phenomenon is common. This is due... | Summary: This paper proposes a framework named "RealMotion" highlighting the importance of continuous motion forecasting in autonomous driving. From the formulation perspective, RealMotion investigates predicting trajectories in a continuous sequence of timestamps instead of previous independent predictions. From the m... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review as well as the suggestions for improvement. Our response to the reviewer’s comments is below:
**Q1: Missing related work.**
Thanks. We will add them.
**Q2: Comparison with QCNet.**
To ensure efficiency, our method adopts an agent-centric design dif... | Summary: The paper introduces an approach to iteratively process temporal scene context for the purposes of agent motion prediction. This differs from traditional approaches that ingest the whole context directly.
The scene is processed in temporal chunks, where in each chunk contains agent-centric context consisting... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review as well as the suggestions for improvement. Our response to the reviewer’s comments is below:
**Q1: Some of the framing to be a bit misleading.**
Apologies for clarity issue in abstract and introduction. We intent to stress the significance of stream... | Rebuttal 1:
Rebuttal: We provide additional figures in the PDF files for more intuitive demonstration.
Pdf: /pdf/26653ba2e4ff6a0bb5db293d1817ed78e33b874c.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Is Cross-validation the Gold Standard to Estimate Out-of-sample Model Performance? | Accept (poster) | Summary: This is a primarily theoretical paper describing the bias and confidence intervals formed from different methods of assessing the true out-of-sample error of various models. In particular, this paper compares cross-validation (CV) to the "plug-in" estimator (i.e., the training loss). The authors assess the met... | Rebuttal 1:
Rebuttal: We sincerely thank you for recognizing our contributions both on the theoretical and practical fronts, and also for the detailed and very helpful suggestions. We address your comments point-by-point as follows.
**Claimed strength of results**: We would follow your suggestion and change that claim... | Summary: The paper establishes asymptotic results for bias and coverage of three different kinds of validation schemes, namely k-fold cross-validation, plug-in validation, and leave-one-out cross-validation. The setup is general and includes both parametric and non-parametric models. There are experimental results that... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the significance of our results and the helpful suggestions. We address the reviewers' main concerns as follows.
**Our choice of interval estimate, and not use the interval suggested in [8]**: The reviewer raises the valid point that our interval is one parti... | Summary: This paper considers model evaluation using plug-in method, CV and leave-one-out CV. The main contribution of this paper lies in the asymptotic bias and coverage performance analysis of these methods. The result is that in most cases, it turns out that the plug-in method is no worse than CV or leave-one-out CV... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for recognizing our contributions and clarity, and also for raising the list of very helpful questions.
Regarding "Weaknesses":
**Difference with literature investigating roles of CV**: First, the reviewer mentions that [8] says CV estimates the risk of the algori... | Summary: The paper argues that Cross-Validation (CV), commonly used to evaluate machine learning models, may not be as statistically beneficial, especially in challenging nonparametric regimes. The paper shows that plug-in is always no worse than K-fold CV for models with any convergence rate. While leave-one-out CV ca... | Rebuttal 1:
Rebuttal: We sincerely thank you for your recognition of the importance and clarity of our paper, and also for your very helpful comments. To respond to your main suggestions, we have run additional experiments on model selection and real-world data set. These experimental results will be incorporated into ... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their helpful suggestions. In this Global Response, we provide additional discussion on several aspects that address the reviewers' comments: practical guidance from our results, differences with existing literature regarding roles of CV, high-dimensional p... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Goal Conditioned Reinforcement Learning for Photo Finishing Tuning | Accept (poster) | Summary: This paper proposes a goal-conditioned reinforcement learning framework for photo finishing tuning. They introduce a novel state representation and treat the image processing pipeline as a black box, avoiding the need for differentiable proxies. The method can efficiently tune parameters to match various goals... | Rebuttal 1:
Rebuttal: # To Reviewer WirW
We sincerely thank you for reviewing our work. After reading your comments carefully, we summarize the following questions.
## Q1: Cross-Dataset Generalization
***
Please refer to Q1 in the **To All** section for a detailed answer to this question. To summarize, we conducted... | Summary: This paper presents a method by which RL is used to drive the optimization of ISP hyperparameters for two tasks: (1) recovering photo finishing parameters and (2) mimicking reference style photo characteristics.
Strengths: Clearly written. Effective application of RL to photo finishing and stylization. Quan... | Rebuttal 1:
Rebuttal: # To Reviewer kK87
Q1 addresses the implementation details of three baselines. Q2 Q3 explain certain behavior (hue shift, green image) of baselines. Q4 extends extra details on baseline implementation and proves our baselines were implemented correctly.
## Q1: Full Details on Baseline Implementat... | Summary: Proposed Goal-Conditioned Reinforcement Learning for Photo Finishing Tuning. Specifically, the authors introduce a novel goal-conditioned reinforcement learning framework for parameter tuning in photo processing pipelines. Unlike existing methods, the proposed approach operates without relying on proxies and t... | Rebuttal 1:
Rebuttal: # To reviewer syDC
We sincerely thank you for reviewing our work. After reading your comments carefully, we summarize the following questions.
## Q1: Choice of RL Algorithm
***
In our task, the choice of RL algorithm is not the primary factor driving performance; instead, our proposed state repre... | Summary: This paper applies goal-conditioned reinforcement learning (RL) to photo finishing tuning. With only 10 queries, it demonstrates that goal-conditioned RL can achieve better performance than zeroth-order approaches that require 500 queries. Additionally, this method is non-differentiable, which might make it mo... | Rebuttal 1:
Rebuttal: # To Reviewer ASBs
We sincerely thank you for reviewing our work. After reading your comments carefully, we summarize the following questions.
## Q1: Details on Baselines
***
In our main paper, we compare three baselines: (1) CMAES: a zeroth order optimization method, that does not need traini... | Rebuttal 1:
Rebuttal: # To ALL
We sincerely thank all reviewers for your comments. We summarize the following questions and add more results to the rebuttal PDF file.
## **Q1. Evaluation on more datasets (Reviewer ASBs syDC WirW)**
***
We test our RL-based framework directly on an extra dataset (HDR+ dataset). The r... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Poisson-Gamma Dynamical Systems with Non-Stationary Transition Dynamics | Reject | Summary: This work extends Poisson-Gamma Dynamical Systems (PGDSs) by considering non-stationary transition dynamics to effectively capture the evolving dynamics of observed count sequences.
The authors propose a model where the underlying transition matrices evolve over time, based on three (gradually more complex an... | Rebuttal 1:
Rebuttal: Thanks for the reviewer's constructive comments, our answers for the questions are as follows:
1. In practice, users can leverage the prior knowledge about the specific task to set the length of sub-intervals, or treat the length of sub-interval as a hyper-parameter, tuning it with part of the... | Summary: Existing PGDS models struggle with capturing the time-varying transition dynamics seen in real-world data. To address this, the submission proposed a non-stationary PGDS, allowing the transition matrices to evolve over time, modeled by Dirichlet Markov chains. Using Dirichlet-Multinomial-Beta data augmentation... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's valuable time, positive comments for our manuscript. | Summary: The work extends Poisson-Gamma Dynamical systems (PGDS) to model non-stationary dynamics by replacing the constant transition matrix $\Pi$ with a time dependent one $\Pi^{(t)}$ and the original Dirichlet prior on the columns with three different Dirichlet Markov chain constructions. The manuscript describes a... | Rebuttal 1:
Rebuttal: We thank the reviewer's constructive comments. We clarify that in contrast to the reviewer's concern, our proposed methods outperform PGDS not only in data smoothing task. As shown in Table 1 in our paper, the proposed three models outperform the baselines in most tasks, even if we only consider N... | Summary: This paper introduces non-stationary Poisson-Gamma dynamical systems, an extension of Poisson Gamma dynamical systems with a dynamic transition matrix. Decomposing the time steps into equally spaced subintervals, the transition matrices evolve between sub-intervals, remaining static within sub-intervals. The a... | Rebuttal 1:
Rebuttal: The authors thank the reviewer's valuable feedback. The main contributions of this paper are: (i) We extend state-of-the-art PGDS model such that the transition matrix of PGDS can evolve over time and thus better fit non-stationary environment. (ii) In order to model the time-varying transition ma... | Rebuttal 1:
Rebuttal: We would like to extend our sincere gratitude to the reviewers for dedicating their time and expertise to evaluate our work. The main concerns of the reviewers are (i) the equally-spaced sub-intervals and the possibility for constructing time-varying transition kernels of other types, (ii) the exp... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improving Robustness of 3D Point Cloud Recognition from a Fourier Perspective | Accept (poster) | Summary: This work introduces a method called Frequency Adversarial Training (FAT) to improve the robustness of 3D point cloud recognition models and examines the robustness of models under 3D point cloud corruptions, including analysis on the power of different corruption effects in the frequency domain. FAT generate... | Rebuttal 1:
Rebuttal: Thank you for appreciating our new contributions as well as providing the valuable feedback. Below we address the detailed comments, and hope that you can find our response satisfactory.
***Question 1: There is confusion in the caption of Figure 3, which suggests the Jacobian matrix for an input ... | Summary: This paper introduces Frequency Adversarial Training (FAT), leveraging Graph Fourier Transform (GFT) to enhance robustness against point cloud corruptions by training models with frequency-domain adversarial examples, demonstrating significant improvements in robustness across various architectures through ext... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the novelty of our paper as well as providing the valuable feedback. Below we address the detailed comments, and hope that you can find our response satisfactory.
***Question 1: Complexity of Implementation might hinder the adoption of the method in practical scenarios... | Summary: This paper studies how to enhance the robustness of 3D point cloud recognition. The authors propose Frequency Adversarial Training (FAT) to improve the corruption robustness of 3D point cloud recognition models. FAT trains a model with adversarial examples that add perturbations to the frequency-domain represe... | Rebuttal 1:
Rebuttal: Thank you for appreciating our new contributions as well as providing the valuable feedback. Below we address the detailed comments, and hope that you can find our response satisfactory.
***Question 1: The paper would be more convincing by using real-world data from LiDAR sensors, such as KITTI d... | Summary: This paper introduces a novel approach to improving the robustness of 3D point cloud recognition models by introducing Frequency Adversarial Training (FAT). By analyzing the frequency space of point clouds through the graph Fourier transform, the authors found that models are sensitive against different freque... | Rebuttal 1:
Rebuttal: Thank you for appreciating our new contributions as well as providing the valuable feedback. Below we address the detailed comments, and hope that you can find our response satisfactory.
***Question 1: The paper would be more convincing by using real-world data from LiDAR sensors, such as KITTI d... | Rebuttal 1:
Rebuttal: We deeply appreciate all the reviewers for their insightful and constructive reviews of our manuscript. Delightfully, we are glad that the reviewers found that:
- ***The presentation of our paper is polished and easy to understand.*** (Reviewers kjym, wa2k, ELyT)
- ***The problem studied in our ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-Learning | Accept (poster) | Summary: This work presents a novel approach for universal anomaly segmentation (AS) by framing AS as Change segmentation. This change of perspective motivates them to create a synthetic training set based on change detection (e.g. by stitching additional objects from a different dataset). With this synthetic dataset t... | Rebuttal 1:
Rebuttal: + **bbUz-Q1**: What are the differences between MetaUAS, MetaUAS*, and MetaUAS*+?
We apologize for the confusion about MetaUAS, MetaUAS*, and MetaUAS*+. In fact, we have given some details of our MetaUAS and its two variants (MetaUAS* and MetaUAS*+) in the Sec. B of Supplementary Materials. Here,... | Summary: The authors introduce a method for anomaly segmentation that relies solely on visual information and does not require any anomaly training data or language guidance. Their method is based on change segmentation, which allows for the synthesis of large-scale image pairs for training the anomaly segmentation mod... | Rebuttal 1:
Rebuttal: + **sm3a-Q1**: The effects of the number of training data for the anomaly segmentation performance.
Thanks for your interest in the effects of training scale. We are also very concerned about this issue, which has been analyzed in our ablation studies (Sec. 4.3). The corresponding experimental re... | Summary: The proposed method reformulates the one-shot anomaly detection task as a change detection task. The proposed method is trained with a synthesitzed change dataset, where objects are added, deleted or exchanged. Additionally, local changes are generated by pasting out-of-distribution textures on images in rando... | Rebuttal 1:
Rebuttal: + **gb5F-Q1**: A Transformer backbone in the ablation in Table 3b would be nice.
Thanks for your suggestion. As pointed out by DDkp and sm3a, our method is compatible with various pre-trained models, including Convolutional and Transformer architecture networks. Considering the efficiency, we emp... | Summary: This paper considers the anomaly segmentation task as a change segmentation task. Then the large-scale image pairs with object-level and local region changes are synthesized to train a universal anomaly segmentation framework MetaUAS. This only needs one normal image as the prompt. The soft feature alignmen... | Rebuttal 1:
Rebuttal: + **DDkp-Q1**: How these features are used in the decoder needs to be explained in more detail.
Our method implements the decoder using standard UNet. Furthermore, we also compare the UNet with the FPN decoder in Table 3(c). In our paper, we omit the details of UNet and FPN because they are two p... | Rebuttal 1:
Rebuttal: We thank all reviewers (DDkp, gb5F, sm3a and bbUz) for your insightful comments. The reviewers believe that the proposed universal anomaly segmentation framework is **novel and interesting** (DDkp, gb5F and sm3a), **simple, effective and efficient** (DDkp and sm3a) and **compatible** (DDkp), the a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ZOPP: A Framework of Zero-shot Offboard Panoptic Perception for Autonomous Driving | Accept (poster) | Summary: The paper introduces a novel framework, termed Multi-modal Zero-shot Offboard Panoptic Perception (ZOPP), specifically designed for autonomous driving applications. This innovative approach integrates zero-shot recognition capabilities with 3D representations generated from point cloud data, enhancing the mode... | Rebuttal 1:
Rebuttal: ## Response 4.1 Data labeling issue. (Weakness 1 and Question 1)
Previous literatures focus on generating high-quality 3D detection results as auto labels with offboard perception fashion. However, they still need high-quality human labels of the AD dataset as a prerequisite for training the whol... | Summary: Offboard perception creates 3D labels for autonomous driving scenes. Current methods are limited and don't match human recognition levels. The authors developed a new framework called Zero-shot Offboard Panoptic Perception (ZOPP), which combines advanced recognition technologies with 3D point cloud data. ZOPP ... | Rebuttal 1:
Rebuttal: We are grateful to you for recognizing our efforts in addressing your concerns during the reviewing process.
## Response 3.1 Lack of novelty (Weakness 1)
Perception and understanding play a vital role in current data-driven autonomous driving. Previous literatures focus on alleviating the burdens... | Summary: ZOPP proposes an offboard auto-annotation method to achieve lidar 3D detection as well as the occupancy label without any annotation data. The whole pipeline ensembles several models including the SAM-track and point cloud completion model. By using some post-processing to complete the
Strengths: 1. The idea ... | Rebuttal 1:
Rebuttal: We are thankful to you for raising such important concerns and questions about our work, we highly appreciate your efforts during review process.
## Response 2.1 Performance of [0, 30]m on Waymo dataset (Weakness 1)
We follow the experiment setting in prior zero-shot 3D detection work [1] to ensu... | Summary: This paper introduces ZOPP, a framework for zero-shot panoptic perception of autonomous driving scenes. Leveraging image foundation models, ZOPP is able to perform zero-shot 3D object detection, 3D semantic segmentation, 3D panoptic segmentation, and 3D occupancy prediction, the first zero-shot model of its ki... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive acknowledgment of our work. We are pleased to provide the supplemental responses.
## Response 1.1 Performance gap between our method and fully supervised methods (Weakness 1)
Yes. Indeed, our zero-shot method still exhibits a notable gap compared to fully sup... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their valuable comments and suggestions. We are sincerely grateful to the reviewers for dedicating their time and effort to review our work. We are delighted to see reviewers commenting on our paper with "significant novelty", "significant impact", "extensive a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
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