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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Enriching Disentanglement: From Logical Definitions to Quantitative Metrics | Accept (poster) | Summary: This paper investigates relating logical definitions of disentanglement and existing quantitative metrics via formal derivations of novel metrics.
Strengths: The topic is very interesting and it seems the authors were very rigorous in their investigations especially concerning the amount of all of the backgro... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed review of our work!
We'd like to address your concerns as follows.
---
However, the paper is very poorly motivated, structured and written. It is very difficult to follow the authors along what they are trying to achieve (the motivation), what they are do... | Summary: The paper consider the connection between logical definitions and quantitative metrics, proposing a systematic approach to design metrics from logical definitions. Particularly, the paper is focused on the measure of disentanglement. The paper theoretically justifies the correspondence between logical definiti... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of the novelty and soundness of this work!
We'd like to address your concerns as follows.
---
The superiority of the proposed metrics over the the existing ones does not seem fully validated, either empircally or theoretically. The evidence provided does not sh... | Summary: The paper proposed to establish a connection between logical definitions of disentanglement and quantitative metrics from the perspective of typos theory and category theory. It then propose a metrics for disentanglement with stronger theoretical guarantees and compared it with some state of the art metrics.
... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the innovation of our work!
We will answer your questions as follows.
---
... the advanced mathematical concepts makes the paper very hard to follow and limit its accessibility.
---
Thank you for pointing this out.
We developed the theory with the help of these ma... | Summary: This study introduces a systematic approach to quantify properties of representation learning models. By translating logical definitions into quantitative metrics, the paper evaluates two key properties: modularity and informativeness. Two sets of metrics are derived for each property, one based on approximati... | Rebuttal 1:
Rebuttal: Thanks again for your kind evaluation of our work!
Regarding your concerns, our answers are as follows.
---
It might be difficult for people with less prior knowledges to read.
---
Thank you for pointing this out.
In the main body of this paper, we tried our best to avoid using abstract cat... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DisCEdit: Model Editing by Identifying Discriminative Components | Accept (poster) | Summary: This paper applies model editing to address two active areas of research, Structured Pruning and Selective Class Forgetting.
Specifically, it adopts a distributional approach to identify important components useful to the model's predictions.
With the witness function-based lower bounds on the TV distance, it ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and useful suggestions.
### Weaknesses:
**The code provided by the authors redirects to an empty GitHub repository, lacking reproducibility.**
We wish to immediately amend the issue with the empty github and apologize for the mistake. We prov... | Summary: In this work, the authors propose to tackle two problems at once, class unlearning and structured pruning. To do so, they propose a novel way to compute a lower bound on the total variation distance between distributions of features.
In practice, this distance is approximated using the first and second order m... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive appraisal of our work, and
### Questions:
**(main point from weaknesses) can the author provide results on ImageNet without fine-tuning to highlight the better selection of filters from their method in the main paper?**
We thank the reviewer for the insigh... | Summary: The paper addresses the task of model editing that focuses on modifying critical components within neural networks to improve performance. One of the cornerstone steps is to first identify these components. The authors adopt an approach based on recently proposed discriminative filters hypothesis. Instead of u... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive appraisal of our work, and we address the concerns raised below.
### Weaknesses:
**It is quite challenging to evaluate the performance of the propose solution due to the absence of comparison to other methods. While it is possible to see that the method is ... | Summary: This paper proposes a method for model editing of convolutional classifier networks. The proposed approach assess the class-discriminative ability of convolutional filters by looking at the distribution of their produced feature maps. Specifically, by comparing the class conditional feature distribution to the... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive appraisal of our work, and especially the detailed feedback and insightful questions. We address the reviewers concerns below.
## Weaknesses
**Numerical results both in the main paper and the appendix do not report the actual accuracies of any of the models... | Rebuttal 1:
Rebuttal: We thank the readers for their appreciation of our work. In particular, we thank reviewers for noting:
- The importance of the problem addressed by our work - that is, model editing with a view toward structured pruning and classwise unlearning/
- The efficient and simple nature of our solution to... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unveiling the Tapestry of Consistency in Large Vision-Language Models | Accept (poster) | Summary: This paper introduces a multimodal Consistency Benchmark (ConBench) to systematically evaluate the capabilities of LVLMs via diverse question formats.
ConBench has a total of 4k questions on 1k images and corresponding 3k discriminative ground truths, as well as two special metrics to evaluate the consistency... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the efforts in reviewing our paper and the positive evaluation. Our responses according to the reviewer's comments are summarized as follows.
---
> 1. In addition to ConScore[C], how does TDR affect LVLMs' performance on comprehensive multimodal benchmarks?
O... | Summary: This paper presents ConBench, a multi-modal benchmark to intuitively analyze how LVLMs perform when different prompts are used for one model around a knowledge point. Based on the proposed benchmark, several interesting findings are pointed out, such as the relationships between the prompt space and the answer... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer aYRC for the efforts in reviewing our paper and positive evaluation. Our responses according to the reviewer's comments are summarized as follows.
---
> 1. Explain the definition of the discriminative and generative domains clearly.
(1) Discriminative questions p... | Summary: The paper presented a comprehensive study LVLMs on their inconsistent answers given different prompt solution spaces (true/false, multiple choice, and limited QA). Specifically, the authors introduced the ConBench benchmark to evaluate the performance of various models. They investigated the relationship betwe... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer AnYL for the efforts in reviewing our paper. Our responses according to the reviewer's comments are summarized as follows.
---
> 1. It would be beneficial to increase the diversity and scale of ConBench.
We greatly appreciate your attention to the scale and diver... | null | null | Rebuttal 1:
Rebuttal: Dear ACs and Reviewers,
We thank all the reviewers for their valuable comments and efforts in reviewing our paper.
We are delighted that Reviewer AnYL, aYRC, and wv4L stated that our findings are interesting and the benchmark is comprehensive; Reviewer AnYL and wv4L acknowledged that our method ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Revisiting the Integration of Convolution and Attention for Vision Backbone | Accept (poster) | Summary: This paper addresses the scalability issue in vision transformers by integrating convolutions (Convs) and multi-head self-attentions (MHSAs) at different granularity levels, rather than at the finest pixel level. The authors propose using Convs for fine-grained per-pixel feature extraction and MHSAs for coarse... | Rebuttal 1:
Rebuttal: W1: Introducing the soft clustering and dispatching modules adds complexity to the implementation, which might pose challenges for practical deployment.
The clustering and dispatching modules involve only standard and widely used operators such as Matrix Multiplication and SoftMax, which are supp... | Summary: The authors propose to leverage the strengths of convolution layers and MHSA block to improve the performance of vision transformers. They propose to apply them in parallel at different granularity, such that the convolution layers are applied to the grid of local features and MHSA to slots for global features... | Rebuttal 1:
Rebuttal: W1: Please comment on the effectiveness of GLNet against SMT.
* For classification, the throughput/efficiency of SMT is not as good as its parameters and FLOPs indicate. This is because its core design, scale-aware modulation (SAM), relies heavily on depthwise convolutions (DWConvs), which cannot... | Summary: The paper discusses the use of Convolutions (Convs) and multi-head self-attentions (MHSAs) in vision backbones. Traditionally considered alternatives, the authors question the need for both to operate at the finest pixel granularity, particularly highlighting the scalability issues this causes in vision transf... | Rebuttal 1:
Rebuttal: W1: IN-1k size dataset cannot evaluate "scalability." It is advisable to revise this argument.
We thank this reviewer for the suggestion. However, in Line 6, we actually refer to the scalability w.r.t. the input size, instead of the dataset size. This can be reflected by the FLOPs-performance tra... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive comments and suggestions. We are glad to see that reviewers consider our work novel/interesting/innovative (Reviewers fhiH/CazW/txiT), appreciate the semantic grouping effect brought by our design (Reviewers fhiH/CazW/txiT), and highlight the extensive... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
AirSketch: Generative Motion to Sketch | Accept (poster) | Summary: The authors present an interesting application of multiple vision components merged together. They introduce AirSketch to generate sketches directly from hand gestures. They present a self-supervised training procedure and augmentations to with an image diffusion model to generate the realistic sketches. They ... | Rebuttal 1:
Rebuttal: #### **Limited description of the hand tracker**
We thank the reviewer for pointing this out! While we did discuss hand trackers in the appendix (A.3), we will add a more explicit description to the main body of the paper.
#### **Why only Quick, Draw! was used as the starting point?**
1. When dra... | Summary: The paper presents a technique for generating raster handwritten sketches based on the tracking of the hand (from egocentric video), with the target application scenario of sketching in AR/VR.
The training is done mostly based on Quick, Draw! dataset of sketches combined with generated hand videos using Unit... | Rebuttal 1:
Rebuttal: #### **Weakness 1.1: Finetuning samples leaking**
*-- “While the evaluation is done on a held-out set of Quick, Draw classes (appendix, line 521), it seems that the tuning could allow leakage of the set of classes to the model generation - suggesting that instead of the ability to generalize to un... | Summary: This paper addresses a new task: sketch generation from marker-less air drawing. The authors trained a spatially conditioned diffusion model to generate sketches from noisy hand tracking results and text prompts. During training, they devised an augmentation-based training procedure.
Strengths: 1. Good paper ... | Rebuttal 1:
Rebuttal: #### **Model overfitting on Quick, Draw! Dataset**
In Figure[3] in the rebuttal PDF we show inference results on unseen classes during both augmentation-based training and LoRA finetuning, where our method still maintains its performance.
On the other hand, we agree with the reviewer that due to ... | Summary: The authors investigate the problem of sketch generation from the finger's motion trajectory, which is interesting. To make it, the authors adopt a standard ControlNet pipeline, while highlighting the importance of data augmentation (adding noise to clean sketches to mimic the hand-tracking image). Hence the m... | Rebuttal 1:
Rebuttal: #### **Concern about the novelty of the paper**
Thank you for your feedback. While augmentation is a well-known method, it has never been applied for sketch generation, and our finding that it can be used as an effective self-supervised pre-training task for airsketch is in our opinion a non-trivi... | Rebuttal 1:
Rebuttal: We thank all reviewers for spending time reading our paper and providing insightful feedback. We appreciate reviewers finding our proposed task and approach novel, interesting, and useful (V9Hw, gzU1, Gs1n), and the experiments being extensive and convincing (V9Hw, Gs1n, THqa). We address each of ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Leveraging Tumor Heterogeneity: Heterogeneous Graph Representation Learning for Cancer Survival Prediction in Whole Slide Images | Accept (poster) | Summary: The authors present ProtoSurv, a model that leverages heterogneous graph representation learning to predict survival risk. Their model can be decoupled into two basic submodules: 1)Structrure view module whish is responsible for injectiing the topological ingormation of the wsi into the model and the 2) histol... | Rebuttal 1:
Rebuttal: Thanks for your constructive comment. Here are the responses to Weaknesses (W), Questions (Q) and Limitations(L).
**W. The prototyes that are used in histology view are not learnt,
potentially hindering the adaptability of the model since preselected prototypes may not capture the full variabili... | Summary: This paper analyzes the limitations of the existing MIL method in survival prediction with WSIs 1) overfitting, 2) numerous redundant and irrelevant instances, and 3) insufficient exploration of the interaction between local, regional features and global contextual features in WSI. To address these issues, the... | Rebuttal 1:
Title: update review
Comment: I'm sorry for the reviews submitted for another paper. I update the reviews here.
This paper proposes ProtoSurv, a heterogeneous graph model for WSI survival prediction. ProtoSurv is driven by data and incorporates pathological domain knowledge. Specifically, ProtoSurv consi... | Summary: The authors proposed ProtoSurv, a graph model for WSI survival prediction. The key contribution is learning different prototypes for each node type, and aggregating nodes using cross attention and learned prototypes.
Strengths: - Outcome prediction for cancer patients is a very relevant and important problem ... | Rebuttal 1:
Rebuttal: Thanks for your constructive comment. Here are the responses to Weaknesses (W).
**W1. Having a fixed set of hand crafted node types and a fixed number of multi-prototypes is limiting,
and restricts the model’s ability to learn from previously unknown factors / phenotypes that may contribute to p... | Summary: This paper introduces ProtoSurv, an algorithm which performs survival prediction for Whole Slide Images (WSIs) of tumour samples, by taking into account the interaction between different tissue types and tumour heterogeneity. ProtoSurv proposes leveraging prior tissue knowledge by constructing a graph where th... | Rebuttal 1:
Rebuttal: Thanks for your constructive comment. Here are the responses to Weaknesses (W), Questions (Q) and Limitations(L).
Due to word limit, we omit many details.
If you have any further questions, we would be willing to response.
**W1. This is not a heterogeneous graph neural network.**
We describe ou... | Rebuttal 1:
Rebuttal: We thank all reviewers for the valuable feedbacks and constructive comments.
We have responded to each reviewer's comments.
We attach a PDF containing an expanded interpretability figure.
From the interpretability figure,
we observed that the attention preferences of multi-prototypes from a cate... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Get Rid of Isolation: A Continuous Multi-task Spatio-Temporal Learning Framework | Accept (oral) | Summary: This work proposed a novel spatiotemporal learning framework CMuST. In CMuST, MSTI is devised to dissect complex multi-dimension data correlations, to reveal disentangled patterns. And RoAda is proposed to extract the task-wise consistency and task-specific diversity. In addition, this paper introduce a benchm... | Rebuttal 1:
Rebuttal: Dear Reviewer xkVJ,
Thank you for your meticulous review and insightful comments, which are invaluable in refining our approach and enhancing the quality of our manuscript.
**W1. Data preprocessing.** Thank you for your reminder. We have included the detailed data processing code in our anonymou... | Summary: This paper proposes a Continuous Multi-task Spatio-Temporal Learning Framework CMuST to facilitate the task-level cooperation in spatiotemporal predictions (mainly for traffic related tasks). The model is composed of three components . Data representation and integration module processes and standardizes diver... | Rebuttal 1:
Rebuttal: Dear Reviewer GaXf,
Thank you for your thoughtful review and acknowledging the potential and contribution of our work. We appreciate your insightful comments, which have provided us with an opportunity to refine our manuscript and address critical aspects that will enhance the clarity and impact ... | Summary: This paper proposes a Continuous Multi-task Spatio-Temporal learning framework (CMuST) to enhance urban intelligence. CMuST introduces a Multi-dimensional Spatio-Temporal Interaction network (MSTI) for capturing complex data interactions and a Rolling Adaptation training scheme (RoAda) to iteratively update th... | Rebuttal 1:
Rebuttal: Dear Reviewer SHU9,
Thank you for your valuable feedback and for recognizing the contributions of our work. Your insights are greatly appreciated and will help us further improve the quality of our manuscript.
**W1&Q1. Avoiding catastrophic forgetting.** Actually, to avoid catastrophic forgettin... | Summary: This work proposes a multi-task spatiotemporal learning framework that helps the model understand the relationships between multiple tasks. The specific contributions lie in proposing MSTI to model the multidimensional spatiotemporal data and RoAda to capture the commonality and personalization among multiple ... | Rebuttal 1:
Rebuttal: Dear Reviewer YiFX,
Thank you for your detailed and insightful feedback. We have carefully addressed each concern below.
**W1. Concept, definition and scope of Multi-task learning.** Various domains correspond to different urban elements in a given city, and the concept of multi-task is to forec... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thanks to all reviewers for your meticulous review and valuable feedback. We collated several common questions that identified multiple reviewers and have compiled detailed explanations and responses to these concerns as follows:
**Common issue 1.(Reviewer YiFX, GaXf)** **The co... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
TransAgent: Transfer Vision-Language Foundation Models with Heterogeneous Agent Collaboration | Accept (poster) | Summary: This paper proposes the TransAgent framework, which unifies and transports knowledge from isolated agents to guide CLIP in generalizing through multi-source knowledge distillation. This framework allows flexible collaboration with 11 heterogeneous agents to enhance vision-language foundation models. Importantl... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments. We provide our feedbacks as follows.
**Q1: The paper mentions that “it is the first unified transfer framework for generalizing vision-language foundation models with heterogeneous agent collaboration.” However, CaFo [67] had done similar things by adopting ... | Summary: The paper introduces TransAgent, a novel framework designed to enhance vision-language foundation models like CLIP through the integration of knowledge from diverse, pre-trained expert models. These experts, which include vision, language, and multi-modal models, possess rich knowledge acquired from different ... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments. We provide our feedbacks as follows.
**Q1: After changing the expert agent selection, the model needs to be retrained, and this structure is not plug-and-play.**
**A1:** We would like to clarify that the training effort required by our framework is minimal.... | Summary: The paper focuses on the challenge of vision-language foundation models (e.g., CLIP) struggling to generalize to diverse target domain data in downstream tasks. It highlights the potential of using expert models, which are pre-trained on various modalities, tasks, networks, and datasets, to improve generalizat... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments. We provide our feedbacks as follows.
**Q1: Almost all the baselines to be compared do not rely on external models. Hence, I think the majority of comparisons (e.g., Table 1 and Figure 4) may be unfair.**
**A1:** Thank you for raising the concern.
(1) Table... | Summary: This paper aims to handle heterogeneous foundation model combination across different pretrained backbones. Instead of using vanilla ensemble, it proposes to use a distillation process to transfer knowledges from different agents. More specifically, it uses a learnable gate module to integrate different knowle... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments. We provide our feedbacks as follows.
**Q1: The proposed method is more like a jointly distillation technique, instead of so-called agent collaboration, which may lead to certain misunderstanding.**
**A1:** We would like to clarify this misunderstanding. Joi... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their constructive comments.
We are delighted to receive positive feedback such as
"the idea is intriguing" (**DUuV**),
"valuable research direction" (**gY7M**),
"can be extented flexibly" (**QaNB**),
"unique contribution" (**A3Ab**),
"of practical im... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper introduces a TransAgent framework, which guides CLIP to generalize with multi-source knowledge distillation. The framework contains three kinds of collaboration, including vision models, language models and muti-modal models. A Mixture-of-Agents (MoA) gating mechanism is proposed to adaptively integ... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments. We provide our feedbacks as follows.
**Q1: The method applies multi-teacher distillation in clip with prompts, and popular large models are exploited as teachers. Thus, the novelty is limited.**
**A1:** To be noted, how to perform multi-source distillation ... | null | null | null | null | null | null |
Evaluation of Text-to-Video Generation Models: A Dynamics Perspective | Accept (poster) | Summary: This paper proposes a new evaluation metric for the text-to-video model, and this metric in particular focus on the dynamics on the generated video. Their metric is based on three sub-scores: inter-frame dynamics score, inter-segment dynamics score, and video-level dynamics. They did
Strengths: It explores a ... | Rebuttal 1:
Rebuttal: #### **Q1: Compare to existing metrics, such as Motion Quality in EvalCrafter. Consider to use the average of three motion quality metrics in EvalCrafter (after normalization) and show which one is aligned with human evaluation better?**
The differences between our method and the existing metrics,... | Summary: Effective evaluation protocols are essential for developing advanced text-to-video (T2V) generation models. Current protocols primarily address temporal consistency and content continuity but often neglect the dynamics of video content, which are crucial for visual vividness and fidelity to text prompts. This ... | Rebuttal 1:
Rebuttal: #### **Q1: Demonstrations of Dynamics Score of Real Videos (weakness1) and Fake Videos from Different T2V Methods (weakness2).**
Thank you for your valuable suggestion. We have collected some real videos and fake videos from different T2V models with dynamic scores for demonstration. However, due ... | Summary: This paper proposes DEVIL, an evaluation suite of metrics for evaluating text-to-video generation, focusing on dynamics (the authors note that many previous works on video generation focus on other aspects but ignore dynamics). Proposed metrics are computed using a number of automatically extracted values base... | Rebuttal 1:
Rebuttal: #### **Q1: Concern about segmenting videos based on length relative to total number of frames.**
1. **Why using relative length.** Segmenting videos by relative length enables standardized comparison no matter how long the video is. The following table shows consistently high correlation values wi... | Summary: The paper presents a comprehensive study on the evaluation of Text-to-Video (T2V) generation models, with a particular focus on the dynamics of video content. The authors introduce a novel evaluation protocol named DEVIL, which aims to address the often overlooked aspect of dynamics in existing evaluation metr... | Rebuttal 1:
Rebuttal: #### **Q1: How to differentiate dynamic video from videos with low-quality motions?**
We aim to motivate models to produce videos with a wide range of dynamics while maintaining high quality. We recognize that some videos may exhibit high dynamics but poor quality.
To address, we have enhanced qua... | Rebuttal 1:
Rebuttal: Thanks to all reviewers and ACs for the valuable comments and suggestions. In the original review, all the reviewers acknowledged the contributions of the proposed evaluation protocol(DEVIL). The strengths are summarized as follows:
1. Reviewer R-7oNg: "The paper introduces an **innovative** T2V ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Adaptive Variance Reduction for Stochastic Optimization under Weaker Assumptions | Accept (poster) | Summary: This paper introduces novel adaptive variance reduction methods for stochastic optimization, building on the STORM technique. The proposed Ada-STORM method closes the $O(\log T)$ gap and achieves an optimal convergence rate of $O(T^{-1/3})$ for non-convex functions under assumptions weaker than previous approa... | Rebuttal 1:
Rebuttal: Thank you very much for your constructive comments!
---
**Q1:** In Theorem 4, while the authors clearly state the convergence rate of the proposed method in terms of $T$, $\Delta_F$, and $L$, they only present the convergence of previous adaptive finite-sum methods in terms of $T$. A comparison ... | Summary: This paper proposes a novel adaptive STORM method that achieves an optimal convergence rate of $O(T^{-3})$ for nonconvex stochastic optimization, which requires weaker assumptions and attains the optimal convergence rate without the additional $O(\log T)$ term.
Strengths: For stochastic non-convex optimizatio... | Rebuttal 1:
Rebuttal: Thank you very much for your constructive comments and suggestions.
---
**Q1:** The experimental part is relatively limited. The proposed algorithm is parameter-free, so it is best to provide some experimental results to demonstrate whether these compared algorithms are sensitive to parameter ch... | Summary: This paper studies non-convex stochastic optimization under the assumption of mean-square smoothness. It introduces, Ada-STORM, a variant of the STORM algorithm, which achieves the optimal rate $O(T^{-1/3})$. Unlike vanilla STORM, Ada-STORM eliminates the $O(\log T)$ factor and does not require the Lipschitz a... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments, and we will revise our paper accordingly! We have addressed the major concerns about the proof as outlined below. We sincerely hope that the reviewer can examine them and reevaluate our results.
---
**Q1:** One major concern pertains to the technical correc... | Summary: This paper studies adaptive variants of STORM, a variance reduction technique proposed by Cutkosky and Orabona (2019), for nonconvex stochastic minimization problems. Through introducing a novel adaptive parameter and step size tuning method, the authors aim to remove the bounded gradients and bounded function... | Rebuttal 1:
Rebuttal: Thank you very much for the constructive review!
---
**Q1:** My understanding of the original non-adaptive STORM proposed by Cutkosky and Orabona (2019) does not have a log factor in their convergence rate when noise is present. Specifically their convergence rates in expectation is $O(\log T / ... | Rebuttal 1:
Rebuttal: ## **Global Response** ##
---
In response to the request of reviewers, we provide additional experimental results in this part.
**Figure 1:** According to the suggestion of Reviewer 2239, we provide the performance of the STORM method with different initial learning rates. Specifically, we test... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Dynamic Conditional Optimal Transport through Simulation-Free Flows | Accept (poster) | Summary: This paper introduces COT-FM, a generalization of the Flow Matching model for conditional generation. Specifically, this paper investigates the Conditional Wasserstein Space, a space of joint probability measures on $Y \times U$ with fixed $Y$-mariginals $\mu$. This paper proves that an absolutely continuous p... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for their constructive feedback and suggestions.
> I would like to clarify the connection between Section (4, 5) and Section 6 [...] Section (4,5) justify the triangular parametrization of the standard Flow Matching model within the Conditional Wasserstein Space. I... | Summary: This paper provides a theory for conditional optimal transport (as defined by the authors), followed by numerical simulations. Among their contributions, the authors put forth theory for the geometry of the conditional Wasserstein space (where analogous quantities of e.g., the McCann interpolation, hold). This... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback and positive review! We have updated our paper to incorporate the various suggested references and stylistic edits.
> Is there a clear way to choose the $\epsilon$ parameter for the COT Flow Matching? Any heuristics whatsoever?
This is an interes... | Summary: This paper characterizes dynamical conditional optimal transport (COT). It generalizes the Benamou-Brenier theorem to dynamical COT. The authors then propose conditional flow matching and apply it to synthetic data.
Strengths: - The paper successfully extends the Benamou-Brenier theorem to the context of dyna... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback!
> The paper appears to be extremely similar to [1] [...] I would like to ask authors to discuss the difference with result in [1].
We first would like to remind the reviewer of the [NeurIPS concurrent work policy](https://neurips.cc/Conferences/2024/Pape... | Summary: This work first extends conditional optimal transport theory to the dynamical setting. Then a flow-matching model is proposed to approximate these flows with a simulation free training objective. This is then applied in several conditional generation tasks including two Bayesian inverse problems. Triangular op... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback!
> It would be good to know empirically how far from optimal the learned transport maps are, as this is a known limitation of mi[n]ibatch-based approaches.
We thank the reviewer for pointing this out. Please see our global response for a discussi... | Rebuttal 1:
Rebuttal: # Summary
We would like to thank the reviewers for their detailed and valuable feedback. We are encouraged to hear that the reviewers found our theoretical contributions to be a strength (QX6V hqSP, i1c7, yVF4) and that the submission is well-written (QX6v, hqSP, yVF4, i1c7). We are also glad the ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflection | Accept (poster) | Summary: This paper introduces a time series forecasting framework, where LLM-based agents are employed to sift out relevant news to time series of interests and the news are utilized to enhance the accuracy of time series forecasting models.
Strengths: 1. The idea of filtering and utilizing news to enhance time serie... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments and for recognizing the innovation of our idea.
```Q1```: **''Time consumption''**
```A1```: Thanks for the question. The time cost of our method can be divided into training time and inference time. For a dataset with 3,500 time series, training... | Summary: This paper proposes a new framework for time series forecasting. This framework fine-tunes a generative large language model (LLM) to improve forecasting accuracy by integrating news and supplementary information with numerical data and introducing iterative self-evaluation through LLM-based agents.
Strengths... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for the insightful feedback and for highlighting the originality of our work.
```Q1```: **"Statistical analysis on random events compared with normal events"**
```A1```: Thanks for your question. To address it, let me first define random and normal events. Random events ar... | Summary: This paper introduces a novel approach to enhance time series forecasting using Large Language Models (LLMs) and Generative Agents. By integrating news content with time series data, the method aims to align social events with fluctuations in time series to provide enriched insights. The approach involves filt... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments and for recognizing the potential of our work.
```Q1```: **''Inaccurate or irrelevant news can degrade the accuracy.''**
```A1```: Thanks for the question. Inaccurate or irrelevant news does reduce prediction accuracy, as demonstrated in our pape... | Summary: The paper proposes a novel method to integrate event news as external information into the time series forecasting system.
Strengths: 1. An important problem is studied in this paper.
2. An innovative idea of an automatic relevant news extraction mechanism is proposed.
3. Overall, the presentation is clear an... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's insightful comments and for recognizing the novelty in our work.
```Q1```: **"Demonstrate the datasets used"**
```A1```: Thanks for your question. In Appendix A.4, we presented the source details of datasets, and they may answer part of the question. Our news d... | Rebuttal 1:
Rebuttal: We thank all reviewers and area chairs for their valuable comments. We are pleased that all reviewers have responded positively to our paper. They acknowledge that our work addresses an important problem (Reviewers QJWf, UwQn), introduces an innovative idea (Reviewers QJWf, UwQn), and demonstrates... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
EigenVI: score-based variational inference with orthogonal function expansions | Accept (spotlight) | Summary: This work proposes a new variational family based on orthogonal function expansions from the quantum mechanics literature. Furthermore, the paper proposes to find the basis coefficients by solving a score matching problem, where the problem cleverly reduces to an eigenvalue problem. The overall approach, calle... | Rebuttal 1:
Rebuttal: Thanks for your feedback on the paper. We discuss several of the points below.
> 1. It isn't clear if subsampling results in an unbiased objective
Any unbiased estimate of the gradient to our objective results in an unbiased estimate of the objective. And so EigenVI can scale to large n with su... | Summary: The authors propose EigenVI, a new method for black-box variational inference (BBVI). The method uses the Fisher divergence (rather than the more typical reverse KL divergence) and a variational family based on orthogonal function expansions (which I've never seen used before). Advantages of the approach inc... | Rebuttal 1:
Rebuttal: Thanks for your feedback; in the revised manuscript, we will add or expand our discussion to address several of the points you bring up, as detailed below and in the main rebuttal comment.
> 1. A practitioner would want to know how MUCH additional compute is required to obtain any advantages.
W... | Summary: This paper proposes EigenVI, which uses orthonormal distribution functions as the basis of the variational distribution family $q$. Then, minimizing the difference between $q$ and the target distribution $p$ via minimizing the Fisher divergence (2-norm score distance) is turned into an eigen-decomposition prob... | Rebuttal 1:
Rebuttal: Thanks for your feedback on the paper. In the revised manuscript, we will work on clarifying the following points.
> 1. Should Equation 9 be dz?
We could alternatively write $q(z) dz$ here (with the appropriate adjustments of $q(z)$ and $p(z)$).
> 2. Line 130: the score of the target is not equ... | Summary: The paper proposes Eigen-VI, a black-box variational inference method that uses orthogonal function expansions to parameterize the variational distribution and uses Fisher divergence as an objective. The method does not require gradient-based optimization method and behaves well in a set of synthetic and real ... | Rebuttal 1:
Rebuttal: Thanks for your feedback on the paper. We discuss several of the comments and questions below.
> 1. Compared to other BBVI methods, Eigen-VI restricts the variational families to be 𝐾th-order variational family(defined in eq. 2). When generalize to other variational families, gradient-free optim... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and engagement – we are pleased that they found the work to be highly original, well-written, and of interest to the variational inference community.
In this work, we propose a novel variational family and an efficient algorithm to fit it via score matching.... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors introduce a novel family of variational distributions based on orthogonal function expansions which is optimized by minimizing a Fisher divergence. They show that an unbiased importance sampling estimate of the divergence results in a quadratic form, which can be minimized by finding its smallest e... | Rebuttal 1:
Rebuttal: Thanks for your interest in the paper. We will revise the final version of the paper to make the following points more clear.
> 1. The authors describe a procedure to sample from the variational approximation, so I am not sure why importance sampling is needed to estimate the Fisher divergence? I... | null | null | null | null | null | null |
Prediction with Action: Visual Policy Learning via Joint Denoising Process | Accept (poster) | Summary: The submission aims to enhance policy learning through co-training with image prediction. The intuition is that image prediction and robot action are highly correlated since they share the same underlying physical dynamics. Thus, a diffusion model that generates both future images and actions may generalize be... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and efforts in reviewing our paper! Based on your review, we added a detailed discussion and additional experiments. **Updated Figures can be found in the PDF attached to the global response.**
---
**Q1: About the contribution and novelty: the implementation pa... | Summary: - This work proposes a new method for language conditional imitation learning for robotics. The core idea is to combine image diffusion and policy diffusion to simultaneously predict future image observations and actions using a latent diffusion transformer and DDPM.
- The diffusion process is conditioned on t... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and efforts in reviewing our paper! Based on your review, we added a detailed discussion and additional experiments.
---
**Q1: Discussion and comparisons with DBC paper which learn a diffusion model over state-action pairs as auxiliary loss**
ANS: Thank you fo... | Summary: This submission presents a new learning framework called PAD, which utilizes a diffusion transformer (DiT) to jointly denoise both future image frames (RGB/Depth) and generate actions together. This joint learning process yields a scalable model that achieves higher success rates compared to various other robo... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and efforts in reviewing our paper! Based on your review, we added a detailed discussion and additional experiments.
---
**Q1: How well does PAD predict future frames? how far PAD can predict in some sense tasks where there is a lot more ambiguity (e.g. objects... | Summary: The paper presents a novel framework called PAD. This framework unifies image prediction and robot action within a joint denoising process, leveraging Diffusion Transformers to integrate images and robot states. PAD supports co-training on both robotic demonstrations and large-scale video datasets and can be e... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and efforts in reviewing our paper! Based on your review, we added a detailed discussion and additional experiments.
---
**Q1: The performance improvement on real data is somewhat marginal. Is it caused by limited model size and pre-trained data compared to RT-... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and efforts of all reviewers and the AC in evaluating our paper. We are grateful for the insightful and constructive suggestions, which have helped us improve our work. Below, we summarize our contributions and updates. (Updated Figures are in the attached PDF.)
*... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning to Reason via Program Generation, Emulation, and Search | Accept (poster) | Summary: In this paper, the authors present a fine-tuned LLM that reasons by generating pythonic code and executes it through the model rather than passing it to an external interpreter (CoGEX). They also use the model to generate a set of candidate programs from a training set, among which the top-k performing program... | Rebuttal 1:
Rebuttal: We greatly appreciate your feedback on our draft. We are excited that you have found our approach useful and flexible. We aim to address your concerns below:
> W1: The ideas do not strike me as particularly novel. As noted in the paper, using LLMs to generate code is not new, and neither is usin... | Summary: The proposed approach, Code Generation and Emulated Execution (COGEX), involves training LMs to generate pseudo-programs with some undefined functions and then emulate the execution of these programs. This allows the LM's knowledge to fill in the gaps while maintaining a program like reasoning framework. The C... | Rebuttal 1:
Rebuttal: Thank you for your elaborate review and feedback on our submission. We aim to address your concerns below:
> Motivation: While in general I like the idea of pseudo-programs, but I don’t think it is very well motivated or explained in the paper. For this relatively new concept to be introduced, I... | Summary: This paper proposes a means of training language models to perform semi-programmatic reasoning, along with an adaptation method based on program search to specialize the resulting models for particular downstream tasks without updating their parameters.
The authors use GPT-4 to generate programmatic reasoning... | Rebuttal 1:
Rebuttal: We sincerely thank you for the constructive feedback on our submission. We are glad that you found our approach neat, our ablations useful, and our writing clear. We aim to address your concerns below.
> W1: The authors don't report statistical significance (e.g. through bootstrapping) or varian... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Reimagining Mutual Information for Enhanced Defense against Data Leakage in Collaborative Inference | Accept (poster) | Summary: The collaborative inference enables resource-constrained IoT devices to support deep learning applications without sharing raw data with the cloud server, but previous research has revealed that this approach still leaks input and prediction information from edge devices. To address this vulnerability, the aut... | Rebuttal 1:
Rebuttal: Thanks for your review and constructive suggestions. We are delighted to answer the questions and address the concerns.
## W1.
The novelty of our theoretical analysis is that we analyze why we should choose CLUB rather than other approximations to approximate the upper bound of mutual informatio... | Summary: This paper considers a setting where two parties (Cloud Server and Edge Device) are collaboratively training a deep model. The threat model considers both inputs and outputs to be private data of the Edge Device that should be protected from the Cloud Server. The authors propose a learning algorithm that is th... | Rebuttal 1:
Rebuttal: Thanks for your review and constructive suggestions. We are delighted to answer the questions and address the concerns.
## W1.
In real life, there are some applications and settings where the classifier or other head models are deployed on the edge. For example, in some medical applications, the... | Summary: This paper provides InfoScissors, a learning algorithm that regularizes the model during the training phase. This paper also compares their method with VIB-based methods and evaluates it with multiple attacks.
Strengths: The paper provides the theoretical analysis for the defense method and also compares it w... | Rebuttal 1:
Rebuttal: Thanks for the constructive advice. To evaluate performance on large-scale datasets with higher dimensions, we conducted more experiments on the mini-ImageNet dataset, which is a subset of ImageNet. We also conducted experiments on the Vision Transformer (ViT-B/16). Due to the time limit, we only ... | null | null | Rebuttal 1:
Rebuttal: Here are the results against PMC attacks on mini-ImageNet with different model architectures.
Pdf: /pdf/37ab59fd41f611d7c3a70fe023d79366bb1eb43e.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
KALM: Knowledgeable Agents by Offline Reinforcement Learning from Large Language Model Rollouts | Accept (poster) | Summary: This paper introduces a novel approach, KALM (Knowledgeable Agents from Language Model Rollouts). KALM extracts knowledge from LLMs in the form of imaginary rollouts, which agents can learn through offline RL. KALM fine-tunes the LLM to bridge the semantic gap between LLMs and RL agents. The paper demonstrates... | Rebuttal 1:
Rebuttal: Thank you for your time and valuable comments. Below, we address each of your questions and provide detailed responses to your concerns.
> Q1: Constrained by the limited context length, generating a complete trajectory seems unreliable for more complex tasks with longer trajectories. Moreover, th... | Summary: This paper presents KALM, a novel approach adopting LLMs to generate imiginary rollouts to augment the offline dataset for offline RL. Structure of LLMs is altered to handle the numeric states and actions in decision-making environments. Experiments demonstrate the imaginary rollouts benefits the offline polic... | Rebuttal 1:
Rebuttal: We appreciate your insightful comments and finding our idea interesting. To address your concern, we have devoted effort to include discussion and experiment on model-based RL. Please find our response below.
> Q1: Model-based offline RL methods like MOPO, MOReL and COMBO demonstrate strong perfo... | Summary: This paper provides KALM (Knowledgeable Agents from Language Model Rollouts) that fine-tunes a LLM (e.g., Llama-2-7B-Chat) with offline RL (e.g., CQL) for robotic manipulation tasks (e.g., CLEVR-Robot and Meta-world).
KALM mainly consists of three steps: (1) LLM grounding, (2) rollout generation, and (3) offl... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and time in reviewing this paper. It appears there may be some confusion regarding the fairness of the experimental comparison. Below, we provide a comprehensive response to address these concerns.
> Q1: Concerns about the fairness of comparison.
**Comment 1:... | Summary: To bridge the gap between agents that can act and the vast prior knowledge that language models contain, the authors propose KALM (Knolwedgable Agents from Language Model rollouts), a method for training action-taking agents from language models. KALM is a finetuning method that enables a language model to tra... | Rebuttal 1:
Rebuttal: We appreciate your highlight of the method novelty and the significance of the problem setting. Please find our response to each comment as follow.
> Q1: While the method seems to work in these simple simulated settings, the method itself seems susceptible to language model hallucinations. It wou... | Rebuttal 1:
Rebuttal: We express our sincere thanks to the reviewers and chairs for their valuable time and constructive feedback on this paper. We are encouraged to note that reviewers acknowledge this paper's novelty (Reviewer rv4h,JwPT,bRuq), its contribution to community (Reviewer rv4h,JwPT,bRuq) and the sufficienc... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
UPS: Unified Projection Sharing for Lightweight Single-Image Super-resolution and Beyond | Accept (poster) | Summary: This paper introduces Unified Projection Sharing (UPS), a novel algorithm for lightweight single-image super-resolution (SISR) that decouples feature extraction from similarity modeling by employing a unified projection space. This approach achieves state-of-the-art performance across various benchmarks while ... | Rebuttal 1:
Rebuttal: **Q1. Conduct experiments outside of the lightweight model...**
Thanks for your thoughtful comments. As you appreciated the potential generalization of UPS on more image restoration tasks, e.g., JPEG compression removal and image de-noising tasks, we explore the potential application of UPS for c... | Summary: This work introduces a novel unified projection sharing algorithm that decouples feature extraction and similarity modeling. A unified projection space defined by a learnable projection matrix is created for similarity computation across all self-concerned layers. Extensive experiments demonstrate that the pro... | Rebuttal 1:
Rebuttal: **Q1. Discussion and analysis with attention sharing...**
Thanks for your suggestion. ShareFormer [1] presents a local similarity map-sharing scheme between neighboring attention layers for lower latency. Thus, ShareFormer shares a static similarity map for neighboring attention layers while UPS ... | Summary: The paper proposes an effective lightweight decoupled SISR algorithm that simultaneously performs layer-specific optimization for deep feature extraction and similarity modeling. Specifically, the proposed method casts the deep feature extraction as per-layer optimization, while the similarity modeling is achi... | Rebuttal 1:
Rebuttal: We sincerely thank you for your appreciation of the novelty and effectiveness of our UPS for lightweight SISR.
**Q1. A typo: Line 49...**
We appreciate your kind comment. We will carefully revise our paper to fix the typo.
**Q2. Figure 2: the misplacement of $S_1$ and $S_i$**
Thanks, we wil... | Summary: This paper presents a novel algorithm named UPS designed to enhance the performance of Transformer-based frameworks in single-image super-resolution (SISR), particularly under lightweight scenarios. The authors identify the challenge posed by the simultaneous layer-specific optimization required for deep image... | Rebuttal 1:
Rebuttal: **Q1. In line 30, "Fig. 1a.(1-3) below shows over 0.95% (0.99%, 0.95%, 0.96%) ..."**
Thanks a lot for pointing out this typo, actually it's a writing error. It should be "Fig. 1a.(1-3) below shows over **0.95 (0.99, 0.95, 0.96)** ...". We will revise our manuscript to avoid misunderstanding.
**... | Rebuttal 1:
Rebuttal: Dear AC and Reviewers,
We sincerely thank all the reviewers for their constructive comments and consistent appreciation of the novelty and effectiveness of our UPS for lightweight SISR. Reviewer fbd3, Reviewer RnSV, and Reviewer HE6k have raised concerns about inference efficiency, and both Revie... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
When to Act and When to Ask: Policy Learning With Deferral Under Hidden Confounding | Accept (poster) | Summary: The paper proposes a learning to defer method for policy learning problem with binary actions under unobserved confounding. The proposed method uses MSM to bound the confounding strength and compare the pessimistic bound between Y1, Y0 and identifiable human reward. The final optimization uses the surrogate lo... | Rebuttal 1:
Rebuttal: **Related work:**
Thank you for pointing this paper out! As this very relevant paper has also been brought up by another reviewer, we choose to comment on it in detail in the “general” comment at the top. We should definitely have caught on to it, and we will refer to it, revise our claims, and di... | Summary: This paper studies deferral policy learning when there is unmeasured hidden confounding observed by human experts but not recorded in data. The paper formulates it as a cost-minimization problem and derives a feasible surrogate loss. The method is shown to achieve better policy value on synthetic and semi-synt... | Rebuttal 1:
Rebuttal: **Cost function alternatives - conservative vs. optimistic costs:** This is an excellent question. Under the validity assumption, the conservative approach is guaranteed to be correct whenever the expert is correct, while the optimistic costs do not have this guarantee. Thus, intuitively the conse... | Summary: This work learns a policy that can abstain from predicting an action in the case where actions are binary. Their idea follows previous work in the learning to defer literature for supervised learning. Their proposed surrogate can recover the optimal policy, for which they design the cost functions in a way to ... | Rebuttal 1:
Rebuttal: **References on Learning to Abstain:**
Thank you for bringing this paper to our attention. While our paper has been inspired by the work of Mozannar & Sontag on deferral which adopts an end-to-end approach, this work presents an alternative, post-hoc approach to the problem of (non-causal) deferra... | Summary: The paper combines two frameworks: optimizing surrogate losses for learning to defer, and bounds on CATE under unobserved confounding under the marginal sensitivity model. The challenge with applying the surrogate loss from Mazumdar and Sontag to the causal setting is that the costs of various classification o... | Rebuttal 1:
Rebuttal: **Implicit assumption on the confounded behavior policy:**
We would be grateful for a further clarification of this comment, as it seems to have been cut short.
**Relationship between implicit assumptions on regimes where this approach "does well":**
This is a fascinating connection, as both our... | Rebuttal 1:
Rebuttal: We thank all of our reviewers for their insightful comments and constructive feedback, and we are encouraged by your support. Your comments have helped us clarify and strengthen this work, and we are grateful for them. We will address here a major comment regarding the paper of Gao & Yin, and addr... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper extends the work of [Mozannar & Sontag 2020] on "learning to defer" to the causal inference setting with (bounded) hidden confounding. Compared to the original supervised learning setting, the causal setting does not have ground truth labels. Therefore this paper proposes to use estimated bounds on ... | Rebuttal 1:
Rebuttal: **Baselines Presenting:**
Thank you for this useful suggestion, we will make sure to better explain the baselines and justify the merits of our approach See also our reply to the major question.
**Clarification of baseline and differentiation from proposed method:**
Thank you for this important... | null | null | null | null | null | null |
Transductive Active Learning: Theory and Applications | Accept (poster) | Summary: This paper considers active learning with Gaussian Process in a transductive setting where the learner wants to optimize the model performance on a target space A while it can only sample from a sample space S. It proposes a few algorithms that sequentially choose the example that minimizes a few variations of... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper! Please find our detailed responses to the concerns raised.
Please let us know if you have any further concerns or suggestions.
## Concerns
> The main idea of the proposed query strategy is to minimize posterior uncertainty. This is a well-known method in active... | Summary: The paper investigates the generalization of active learning to scenarios with specific prediction targets and limited information due to constrained sample spaces, offering a flexible framework adaptable to various domains such as recommender systems, molecular design, and robotics. It introduces novel genera... | Rebuttal 1:
Title: Ask for Clarification
Comment: We thank the reviewer for their review. To address the points raised in our rebuttal effectively, we kindly ask for some clarification and additional references.
> For instance, there are more state-of-the-art baselines than badge, typicluster and probcover, for AL dom... | Summary: This paper introduces and considers approaches for the generalized (transductive) active learning problem, where the space of prediction targets and samples are not necessarily the same. The authors propose methods ITL and VTL to select samples in order to minimize the posterior uncertainty about the target fu... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper! Please find our response to your question.
Please let us know if you have any further
concerns or suggestions.
> - The paper is well-written and organized, with ample thorough discussions.
> - The problem of transductive active learning is novel to the best of m... | Summary: This paper introduces transductive active learning based on the uncertainty of GP when the labeling space and target space can be different. They have assumptions such as submodularity of information gain and information capacity’s sub-linearity in GP, resulting in a bound for variance of GP’s posterior with t... | Rebuttal 1:
Rebuttal: Thank you for your review and detailed comments! You can find below our response to some of your questions. Please let us know if you have any further concerns or suggestions.
> Among many advantages, the theoretical aspect is better because it can provide the criteria for the use of kernels that... | Rebuttal 1:
Rebuttal: We thank all reviewers for their feedback, and would like to emphasize the novel contributions of our work.
1. We introduce a new problem setting generalizing classical inductive active learning. All reviewers appear to agree that this new problem setting is relevant and unlocks several new appli... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Latent Representation Matters: Human-like Sketches in One-shot Drawing Tasks | Accept (poster) | Summary: This paper investigates how different regularization techniques, applied to the latent space of Latent Diffusion Models (LDMs), impact their performance on one-shot drawing tasks. The Authors explore many regularization methods: KL divergence, vector quantization, classification, prototype-based, SimCLR, and B... | Rebuttal 1:
Rebuttal: We thank the reviewer YDjR for the positive feedback as well as the relevant comments. We especially appreciated that the reviewer highlighted the interdisciplinary approach, which was at the heart of the article. Unfortunately, interdisciplinarity has its own limitations, especially when it comes... | Summary: The authors propose to explore how different regularizers impact the performance of LDM on one-shot sketch generation, with a specific focus on evaluating the similarity between the generated sketches and real ones. It reveals that prototype- and Barlow-based regularizers are among the best, and claims the gap... | Rebuttal 1:
Rebuttal: We thank the reviewer 1Pi8 for the meaningful comments. Please find below a point-by-point response that addresses the reviewer’s concerns:
* **About the incremental work compared to [30]** : We agree with the reviewer that our article builds on the comparison framework and some ideas introduced ... | Summary: i)This paper uncovers the impact of representational inductive biases on Latent Diffusion Models through one-shot tasks, particularly in the realm of human-like sketching. It compares three distinct groups of regularizers: a standard baseline, supervised methods, and a third group consisting of self-supervised... | Rebuttal 1:
Rebuttal: We thank the reviewer ZReU for the detailed comments. Here is our point-by-point answer:
* **About the lack of analysis of the different regularizer components**: We agree with the reviewer that we did not discuss enough the reasons for such differences. As this issue was shared with other review... | Summary: This paper investigates how different representational inductive biases in Latent Diffusion Models affect their performance on one-shot drawing tasks, aiming to close the gap with human abilities. The authors explore six regularization techniques: KL divergence, vector quantization, classification, prototype-b... | Rebuttal 1:
Rebuttal: We thank the reviewer vmMb for the relevant and valuable comments. Please find below a point-by-point to answer that answer the main concerns of the reviewer :
* **About the lack of analysis on the impact of the different regularizers on the performance**: In Fig 2, the comparison between the regu... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time, their valuable comments and reviews. While the reviewers have acknowledged our “rigorous academic attitude… that strictly adhere to scientific methods” (Reviewer ZReU), as well as the novelty (Reviewers vmMb, YdjR) and significant practical impact of our arti... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference | Accept (poster) | Summary: This paper proposes an efficient framework for unlearning in Large Language Models (LLMs) called "Unlearning from Logit Difference" (ULD). Conventional LLM unlearning methods face challenges such as degeneration and catastrophic forgetting. ULD introduces an assistant LLM with reversed learning objectives—reme... | Rebuttal 1:
Rebuttal: We thank reviewer uFqn for the valuable feedback. We answer questions as follows and put additional tables in the attached pdf.
> **W1: ULD inference latency**
Although our assistant model involves additional inference cost, the computation can be parallelized. More importantly, our assistant mo... | Summary: This paper provides a new formulation of machine unlearning approach ULD that is claimed to be free of two problems: (i) unbounded forget loss, and (ii) forgetting of the general knowledge due to the under-representativeness of the retain knowledge data. Specifically, the paper proposes to adopt an extra "assi... | Rebuttal 1:
Rebuttal: We thank reviewer 6JsY for the valuable feedback. We answer questions as follows and put additional tables in pdf.
> **W1: Minimize CE loss w.r.t. uniform distribution to avoid unbounded loss**
We agree that the unbounded loss can be solved by minimizing the CE loss w.r.t. a target distribution,... | Summary: This paper proposes a new unlearning method. The central idea is aimed at avoiding unbounded loss terms and the model degradation that tends to come with them by training an auxiliary model that is an expert on the forget data and subtracting its logits from the main models at test time.
Strengths: 1. This me... | Rebuttal 1:
Rebuttal: We thank reviewer Y4ow for the valuable feedback. Regarding the questions:
> **W1: Experiment result table hard to parse**
Thank you for the suggestion. To improve the presentation of experiment results, we include a scatter plot in Figure 1 of the attached pdf to show the main performance compa... | Summary: This paper looks into two challenges in LLM unlearning: 1. the unbounded forget loss could easily corrupt the models general abilities 2. the retain loss is usually computed on a relatively small set of data. And they propose a new objective, unlearn from logit difference, to tackle these challenges. Specifica... | Rebuttal 1:
Comment: We thank reviewer F8Xo for the feedback. Regarding the questions:
> **W1: Assistant model requires additional augmented retain data.**
We would like to clarify that the fact that our method requires an augmented retain set **does not contradict** our claim that our method is insensitive to the re... | Rebuttal 1:
Rebuttal: We would like to thank all ACs and reviewers for handling our submission. We value the acknowledgement and insightful suggestions they made to our paper.
We are pleased to see that all reviewers acknowledged various aspects of our paper:
* Novel and creative method (Reviewer Y4ow, uFqn)
* Exten... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improving Equivariant Model Training via Constraint Relaxation | Accept (poster) | Summary: The paper proposes to relax the equivariance constraints in equivariance networks by adding a non-equivariant residual in the network weights. The methodology can be applied to different equivariant architectures, e.g. vector neurons, SE(3)-equivariant GNNs, Equiformers. Besides these strictly equivariant netw... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments as well as raising many points for clarification. We try to address the points raised below.
## Citation Format
We apologize for the incorrect formatting of citations and appreciate the reviewer for pointing it out. We will correct this in the fina... | Summary: The paper proposes relaxing the equivariance constraint on an equivariant network during training. This is done by adding free weights to equivariant linear layers but setting the free weights to zero after training. Further, two regularizations are introduced to stabilize the training: a Lie derivative term e... | Rebuttal 1:
Rebuttal: Thank you for the positive assessment of our work. For the questions you raise, we attempt to address them below. Please let us know if we can provide further clarifications.
## Theoretical motivation
Within the equivariant NNs community, especially amongst practitioners using such networks in s... | Summary: The work proposes a method for improving generalization by relaxing hard equivariance and minimizing the equivariance error as additional regularizer during training.
Strengths: Symmetries play an important role in machine learning and deep learning specifically. There has been recent attention to relaxed for... | Rebuttal 1:
Rebuttal: We thank the reviewer for sharing constructive feedback. We agree that we can improve attribution to related work to emphasize the differences from our work. It is certainly true that there are many works on relaxed equivariance, some of which also employ regularization terms that penalize relaxat... | Summary: Starting from the consideration that equivariant neural networks, though effective for tasks with known data symmetries, are hard to optimize and require careful hyperparameter tuning, this study proposes a framework to improve their optimization process. This is done by temporarily relaxing the equivariance c... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive assessment of our paper and for the comments. Below we address all of the points the reviewer has raised.
## Comparison with works on Equivariant Adaptation/Fine-tuning of pre-trained models
Thank you for raising this point. While it is true that both our m... | Rebuttal 1:
Rebuttal: We thank all of the reviewers for their comments and constructive feedback on our paper. Here we would like to provide an overview of some of the main points of our individual responses to the reviewers and also take the opportunity to highlight the main contributions of our work:
In this work, ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SyllableLM: Learning Coarse Semantic Units for Speech Language Models | Reject | Summary: This paper proposes a two-stage speech language model with semantic tokens and acoustic tokens similar to AudioLM ([Borsos et al., 2022]).
- The semantic tokens come from a speech tokenizer that can group a variable number of frames into a single token. To train such a speech tokenizer,
1. This paper fi... | Rebuttal 1:
Rebuttal: We would like to start by thanking kvPb for their clear and significant commitment to understanding our paper in detail. In our rebuttal below, we try to demonstrate that the presentation clarity errors pointed out only need minor changes to be fixed.
**Regarding "Confusing equation":**
> * Th... | Summary: This paper first introduces an algorithm named LossPred that generates syllable-level speech segmentation without any training or supervision. The algorithm works by analyzing the prediction loss of speech tokens under different mask positions.
With the initial boundaries proposed by LossPred, the paper propo... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their feedback. We hope to clarify any additional concerns they had below:
**Weaknesses**
> 1. As pointed out by the authors, the proposed LossPred and SylBoost methods seem to be restricted to speech representation learning. It might be difficult to apply the... | Summary: This paper studies learning low bitrate speech units that preserves semantic information. As presented in the paper, the proposed approaches achieve SoTA performance on tasks like ASR and ZeroSpeech. The proposed approach also shows benefits in terms of compute resources — as claimed by the authors, 30x faster... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and for stating the proposed approach shows clear benefits in terms of performance and efficiency. We respond to each of the reviewer’s concerns below.
> Demonstrating efficiency: As efficiency is also one selling point of the paper, it would be g... | Summary: This paper proposes an approach for extracting syllable-like units from speech SSL models for use in a transformer-based language model. The motivation is that, compared to baseline acoustic units, which tend to mimic phonetic units in their time resolution, syllable-like units have lower time resolution, whic... | Rebuttal 1:
Rebuttal: Thank you for the review. Below, we will respond to several questions you raised about our work.
> line 189 -> Superior compared to what?
This is answered in lines 189-190: “Superior performance in high difficulty settings **compared to other Neural Codec Lanaugae Models like VALL-E [54]**”
> T... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision | Accept (spotlight) | Summary: FLASH ATTENTION sped up attention on GPUs but achieved only 35% utilization on H100 GPUs. To address this, FLASH ATTENTION-3 introduces three techniques for Hopper GPUs: warp-specialization to overlap computation and data movement by assigning warps to producer and consumer roles; interleaving operations to co... | Rebuttal 1:
Rebuttal: We thank the reviewers for the support and appreciate the thoughtful questions.
1. Other hardware: please refer to the common response. We are collaborating with other researchers and engineers on FA3 for AMD cards, Google TPUs, and Nvidia Ada cards (e.g. 4090). As suggested by the reviewer, this ... | Summary: The paper builds on the existing work on FlashAttention, and introduces an advanced method to accelerate the attention mechanism in Transformer model. It specifically targets the newer GPU architectures, like NVIDIA H100, by exploiting asynchrony in Tensor Cores and Tensor Memory Accelerators. There are three ... | Rebuttal 1:
Rebuttal: We are very happy that the reviewer appreciates the impact of the paper.
1. Generality: we are excited about generalizing our techniques for other hardwares (AMD GPUs, Google TPUs, and Nvidia Ada GPUs). Please refer to the common response for more details.
For Ampere cards (e.g. A100, 3090) and Ad... | Summary: This is a system paper that introduces FlashAttention-3, an optimized version of FlashAttention for NVIDIA's SM90+ GPUs. The key contributions (summarized by the authors) are:
- Taking advantage of warp specialization in NVIDIA SM90+ and designing a producer-consumer computation paradigm to allow better intra... | Rebuttal 1:
Rebuttal: Thank you for the thorough review and helpful suggestions.
1. Existing literature: The Colfax arXiv paper discusses use of WGMMA and TMA for FA2, but not more sophisticated techniques with asynchrony. This is similar to the Triton implementation that we compared to. We will cite and include this ... | Summary: This paper presents FlashAttention-3, which speedup the commonly-used attention operator on Hopper GPUs. The paper proposes to leverage the asynchronous execution of the Tensor Cores and Tensor Memory Acceleator to better utilize the GPU hardware. Specifically, the paper proposes three techniques: (1) It overl... | Rebuttal 1:
Rebuttal: We appreciate the enthusiastic support from the reviewer.
1. Setting of hyperparameters: We select tile size and stage count hyperparameters as a function of the head dimension (64, 128, 256) and datatype (16 or 8 bit) based on available register and smem budget. This is similar to matrix multipl... | Rebuttal 1:
Rebuttal: We thank the reviewers for their enthusiastic support, their careful read of the paper, and their thoughtful questions and suggestions. We are very happy that the reviewers find the paper “has immediate impact to the community”, the ideas “novel and insightful”, and the writing “clear and easy to ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Deep Submodular Peripteral Networks | Accept (spotlight) | Summary: The authors identify two open problems in machine learning and provide novel solutions to them. The first problem is that even though the submodular functions show up in numerous applications, learning submodular functions through DNNs remain unpractical. Their proposed solution to this issue is their new arch... | Rebuttal 1:
Rebuttal: Firstly, thank you for your review and your comments. We will attempt in the next version of the paper to address all of them. Detailed comments follow.
**Organization**
- We agree that Figure 13 ideally belongs in the main body. If the paper is accepted, for the final camera ready we are allott... | Summary: This paper proposes a new framework to learn submodular functions. Specifically, the paper proposes a new parametric family of submodular functions using neural networks. Then, the paper proposes training such networks with a new loss function that is based on graded pairwise comparisons.
Strengths: I think ... | Rebuttal 1:
Rebuttal: We nonetheless wish to thank you for your review and time. We are glad that you found the paper well-written and easy to follow.
---
Rebuttal Comment 1.1:
Title: Response to Rebuttal
Comment: Thank you for the response. | Summary: The paper introduces Deep Submodular Peripteral Networks (DSPNs), a novel parametric family of submodular functions designed to address practical learning methods for submodular functions and graded pairwise comparisons (GPC). To learn DSPNs, the paper also introduces a new GPC-style “peripteral” loss, which l... | Rebuttal 1:
Rebuttal: Thank you for your review and time. We address your questions below.
**Theoretical guarantee for the proposed peripteral loss**
- In our submission, we have included theoretical results regarding the guarantee that submodularity is retained by the use of the permutation invariant stage of a DSPN... | Summary: The paper introduces deep submodular peripteral networks (DSPNs) and a graded pairwise preferences (GPC)-style peripteral loss. It shows that DSPNs are effective in learning submodularity from a target function.
Strengths: - The construction of the submodular function, i.e. DSPNs are interesting as they assur... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and questions, and are glad that they found our work to be convincing. We address each question in turn.
**More definitions in the main body**
- Yes, in the next version of the paper we will add more such definitions in the main body. According to the Neur... | Rebuttal 1:
Rebuttal: We wish to thank all of the reviewers for their reviews and comments. We are quite happy that the reviewers all fairly unanimously found our work to be interesting and worthwhile. We address each of the reviewers questions and comments in the below. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Reinforced Cross-Domain Knowledge Distillation on Time Series Data | Accept (poster) | Summary: The authors motivate the work by identifying limitations in existing approaches that integrate knowledge distillation into domain adaptation frameworks. Specifically, they note that coarsely aligning outputs over all source and target samples neglects the network capacity gap between teacher and student models... | Rebuttal 1:
Rebuttal: Thanks to Reviewer nLZW for all the comments.
**Response to Weak-1 (Theoretical Foundation):** We appreciate the suggestion to include a stronger theoretical foundation or analysis to further strengthen the contribution. While we acknowledge the value that theoretical insights and guarantees cou... | Summary: This paper proposes a knowledge distillation method for unsupervised domain adaptation models in time series classification. After pre-training the teacher model with existing domain adaptation methods, the proposed Reinforced Cross-Domain Knowledge Distillation (RCD-KD) method selects suitable target domain s... | Rebuttal 1:
Rebuttal: Thanks to Reviewer bzJ9 for all the comments.
**Response to Weak-1-1 (Simple Combination):** We clarify that our solution is not a simple two-stage combination of DA and KD. Particularly, we optimize DA loss $L_{DC}$ and KD loss $L_{RKD}$ together, addressing domain shift and model complexity si... | Summary: This paper proposes a Reinforced Cross-Domain Knowledge Distillation (RCD-KD) framework for time series data, aiming to effectively transfer knowledge from a cumbersome teacher model to a compact student model across different domains. The RCD-KD framework leverages an adversarial domain discriminator to learn... | Rebuttal 1:
Rebuttal: Thanks to Reviewer E4vw for all the comments.
**Response to Weak-1 (Feature Knowledge for Transferability Assessment):** Although we have discussed above weakness as one of our limitations in manuscript, we conducted some preliminary experiments as suggested. We investigate several feature-based... | Summary: This paper introduces a reinforcement learning-based active learning method designed to dynamically select target data for knowledge-transfer, whose goal is to bridge the network capacity gap between teacher and student networks within a domain adaptation framework incorporating knowledge distillation. Specifi... | Rebuttal 1:
Rebuttal: Thanks to Reviewer Ltvb for all the comments.
**Response to Major Weak-1 (Comparison with Active Learning):** We fully agree that our method is closely related to AL, particularly in selecting critical samples with uncertainty. We are also willing to include the acknowledge of AL in our updated ... | Rebuttal 1:
Rebuttal: ## Summary
We sincerely thank all the reviewers for their insightful and valuable feedback. We are pleased that the reviewers recognized the novelty of our work and appreciated our motivation for designing a reinforcement learning-based sample selection approach for cross-domain knowledge transfe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SyncVIS: Synchronized Video Instance Segmentation | Accept (poster) | Summary: The paper argues that existing methods for video instance segmentation use asynchronous designs, leading to difficulties in handling complex video scenarios. To address this problem, the paper proposes a SyncVIS method for synchronized modeling, achieving state-of-the-art results on several benchmarks.
Streng... | Rebuttal 1:
Rebuttal: In the beginning, we want to thank you for the detailed, insightful, and constructive comments.
### Novelty
To sum up, as other reviewers have mentioned, our method provides an architectural design that is **intuitive and effective** (Reviewer 3JCg) and **innovative** (Reviewer 2sLS), **demonstr... | Summary: This paper focuses on improving synchronization between frame and video queries in video instance segmentation for better long-range video analysis. The authors propose encoding frame and video queries separately, then using confidence scores to select Nk queries. These queries are updated through mutual infor... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thorough review and valuable input. Your feedback has been instrumental in helping us enhance the quality and clarity of our work.
### Analysis of baseline method
The analysis of CTVIS and VITA are included in the Introduction and Related Works (L. 37, L. 104). We ch... | Summary: This paper concentrates on the video instance segmentation task. To address the problem of motion information loss when existing methods use frame queries, and the optimization issue of bipartite matching across multiple frames, the authors propose a synchronized video-frame modeling paradigm. This paradigm al... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for taking the time to provide such detailed and constructive criticism. Your suggestions have been invaluable in strengthening our paper.
### Object motion modeling
- **Synchronous design for robust modeling**
Our proposed SyncVIS employs image encoding as well ... | Summary: This paper proposes SyncVIS, an approach for Video Instance Segmentation (VIS), which tries to jointly model frame-level and video-level embeddings thus can capture both semantics and movement of instances. The new architecture design is intuitive and generic enough to be applied to various VIS models. Experim... | Rebuttal 1:
Rebuttal: In the beginning, we want to thank you for the detailed, insightful, and constructive comments.
### Qualitative results
In the Appendix, we provide many illustrations comparing previous base models with our SyncVIS. We select some cases under different scenarios, which include settings with mult... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Out-of-Distribution Detection with a Single Unconditional Diffusion Model | Accept (poster) | Summary: This paper proposes an unsupervised anomaly detection method based on diffusion models. The core idea is to leverage the properties of the score function of a pre-trained diffusion model to distinguish samples from different distributions, rather than relying on log-likelihoods or reconstruction error. To this... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the significance of the problem, novelty of our approach, quality of our writing and strong experimental results. Below we provide our response to the reviewer’s concerns and questions.
> method relies on the availability of a reasonably large and diverse d... | Summary: This paper looks at statistics calculated from the path through the diffusion model based on $\int_0^T \| \epsilon_{\phi}(x_t, t) - \epsilon_{\psi}(x_t, t)\|dt$ and use it to discriminate between two distributions $\psi$ and $\phi$. A KDE of the distance of ID scores is used to compute the likelihood of the sc... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the motivation and importance of extending OOD detection to large pretrained generative models, as well as the quality of our writing. Below we provide our response to the reviewer’s questions and concerns.
> “The training distribution matters (as stated by... | Summary: This paper presents a diffusion model trained on a single dataset that can also perform well in OOD detection across diverse tasks. The core concept is Diffusion Paths (DiffPath), which characterizes the properties of the forward diffusion path. Specifically, they measure the rate-of-change and curvature of th... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the novelty, clarity and strong experimental results of our work. Below we provide our response to the questions raised by the reviewer.
> In Figure 3, how extensively does the diffusion model need to be trained on a large, diverse dataset?
To clarify, we ... | Summary: The paper proposes DiffPath, an OOD Detection method with foundation diffusion models (i.e., diffusion models over diverse data) that can be applied to any in-distribution dataset. By measuring properties of the diffusion trajectory mapping images to noise, the paper demonstrates some improvements on OOD detec... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the strong motivation, insightful writing, and interesting experiments in our paper. Below we provide our responses to the questions raised by the reviewer.
>lower performance for near-ood detection tasks
To better study DiffPath's performance on near-OOD ... | Rebuttal 1:
Rebuttal: Thank you to the reviewers for their thoughtful comments and feedback. We are glad that the reviewers found our idea of diffusion paths for OOD detection to be novel, well-motivated and that the paper is well-written.
We find most of the reviewer’s questions revolve around method/experiment clar... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Efficient Recurrent Off-Policy RL Requires a Context-Encoder-Specific Learning Rate | Accept (poster) | Summary: The authors investigate the stability of training deep recurrent policies for POMDP tasks. They hypothesize that the recurrent part of the encoder faces less stable training compared to the fixed-length parts of the encoder (e.g. the MLP input layer) and propose that the former should use a lower learning rate... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and providing us with your valuable feedback.
- **[LSTM]** Our paper discovered a totally different issue from gradient exploding (numerical instability) resolved in LSTM: even if the RNN gradients do not explode and are similar in magnitude to th... | Summary: This paper proposes RESeL which improves recurrent off-policy RL in POMDPs mainly by applying a lower learning rate to the context encoder. This is justified by their theoretical analysis that the recurrence amplifies the output difference in the long run. In practice, they also incorporate several techniques ... | Rebuttal 1:
Rebuttal: We appreciate your time to review and provide positive feedback for our work.
- **[Theoretical understanding]** Thank you very much for pointing out this relevant work. It has been very enlightening for us. RESeL and TTN are closely related, with the MLP value and the small learning rate RNN enco... | Summary: The paper mitigates the training instability issue of the recurrent off-policy RL by using a smaller learning rate for the RNN block.
Strengths: - The paper is well-written and easy to follow.
- The paper proposes a simple solution with analysis.
- The proposed solution is thoroughly verified in different exp... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper, and for your insightful comments.
- **[Dreamer]** It is not a common practice in reinforcement learning to use different learning rates for specific layers in a neural network. Typically, in reinforcement learning, the number of learning rates co... | Summary: The paper contributes to the important and long-standing issue of representing latent state information for successfully finding optimal RL control policies in the context of partially observable Markov decision processes (POMDP). The major contribution is a newly composed RL algorithm (RESeL), which, by desig... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort you have invested in reviewing our paper, as well as your insightful and constructive feedback. Your comments have greatly assisted us in improving the quality of our work.
- **[Skipping CE blocks]** Thank you for pointing out the missing baseline. We i... | Rebuttal 1:
Rebuttal: We appreciate the time and effort all the reviewers have dedicated to our paper and their highly constructive comments. Here, we provide a general response to the common concerns raised.
- **[Novelty/Contribution]**
- We would like to emphasize that our core contribution lies in presenting a un... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD | Reject | Summary: The paper presents a heuristic approach for evaluating the privacy of DP-SGD when only the last model iteration is released. This method contrasts with traditional analyses that consider all intermediate updates, offering a more practical assessment for scenarios where adversaries only access the final model. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and comments.
> 1. I know the linear loss function assumption is common in theoretical analysis but it seems that the proposed method wants to have contributions in the empirical case, so why still make the linear assumption?
Unfortunately, we do not understa... | Summary: This paper proposes a heuristic privacy analysis of releasing only the final model iterate of differentailly private gradient descent (DP-SGD). The analysis is based off of the worst-case differential privacy guarantee of DP-SGD with linear losses, under the assumption that the heuristic can be applied to more... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments. We address their main concerns:
> * I don’t know how useful the heuristic analysis would be in practice.
We believe that such a “sanity check” is useful in practice. In practice, often the standard $\varepsilon$ DP parameter is uncomfortably l... | Summary: The paper proposes a heuristic privacy analysis for DP-SGD that focuses on releasing only the last iterate, as opposed to all intermediate iterates. The authors argue that this approach is more realistic and provides sharper privacy guarantees in practical scenarios. The heuristic is based on a linear structur... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and comments. We address their points below.
> the fact that the privacy adversary has access to all intermediate iterates of the training process makes DP-SGD overly conservative is quite well-known.
The reviewer’s claim that this phenomenon is well-known i... | Summary: The authors provide exact DP guarantees for cases where only the last iterate of DP-SGD is shared with the malicious clients, and linear models with linear loss functions are used. They propose their DP bound to be used as a heuristic that approximates the true DP guarantees for cases where more complex models... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough reading of our paper and thoughtful comments. In particular, we appreciate the reviewer spotting several typos, which we will fix. We respond to the main questions:
> * I am confused by L234. The authors propose to maximize their heuristic over all $t \le ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Token Merging for Training-Free Semantic Binding in Text-to-Image Synthesis | Accept (poster) | Summary: The authors tackle semantic binding, where T2I models often fail to correctly reflect the relations between objects (object binding) or objects and their attributes (attribute binding). To address this, they introduce Token Merging (ToMe), a method that aggregates related tokens to a single composite token, wh... | Rebuttal 1:
Rebuttal: We appreciate your feedback and will incorporate the discussions mentioned below to enhance the quality of our paper. Note that we utilize the numerical references to cite sources within the main paper.
**W1: Related work**
These two methods generally fall under optimization-based approaches. Th... | Summary: The authors propose a method to mitigate semantic binding, a common phenomenon in text-to-image models. While previous methods explicitly control the attention maps so that nouns and attributes attend to the same regions, the authors propose combining the nouns and attributes into a single token. This approach... | Rebuttal 1:
Rebuttal: We appreciate your feedback and will incorporate the discussions mentioned below to enhance the quality of our paper. Note that we utilize the numerical references to cite sources within the main paper.
**W1&Q1&L1: Complex prompts generation**
As demonstrate in General Response 2, our method ToM... | Summary: This paper aims to solve the semantic binding problem in T2I models. The authors introduce a semantic binding method by merging tokens of entities and related attributes. Besides, several other tricks like semantic binding loss and entropy loss are introduced to improve the performance of semantic binding.
St... | Rebuttal 1:
Rebuttal: We appreciate your feedback and will incorporate the discussions mentioned below to enhance the quality of our paper. Note that we utilize the numerical references to cite sources within the main paper.
**W1&Q1: Entropy loss**
In Appendix C.3, we demonstrate that the information coupling of tok... | Summary: This paper focuses on the problem of lack of semantic binding in text-to-image generation models, and specifically on the misalignment between objects and their sub-objects. The paper introduces a training-free T2I method named ToMe after analyzing the properties of CLIP text embeddings and diffusion models. U... | Rebuttal 1:
Rebuttal: We appreciate your feedback and will incorporate the discussions mentioned below to enhance the quality of our paper. Note that we utilize the numerical references to cite sources within the main paper.
**W1&Q2: Token Update**
By training-free, we mean that our method ToMe does not involve train... | Rebuttal 1:
Rebuttal: We appreciate all reviewers (**R1**=**jwvK**, **R2**=**mo93**, **R3**=**85Q2**, **R4**=**8gpY**) for their positive feedbacks. They note that this paper is well-written (**R1,R2**); the idea is simple and straightforward (**R1, R2, R4**); that we present interesting analysis over the token propert... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improved Distribution Matching Distillation for Fast Image Synthesis | Accept (oral) | Summary: The authors propose an improved way of distilling image-generating diffusion models into fast models capable of generating high-quality images with as few as 1-4 steps. Compared to prior work using distribution-matching (DMD), they do away with the regression loss term that tied the teacher path to the student... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer xaYh's constructive feedback. We will fix all typos. Below, we address the remaining concerns.
**How is DMD2 related to ADD and LADD? What is the most significant difference?**
Thank you for the opportunity to discuss how DMD2 relates to other concurrent GAN base... | Summary: This work proposed an improved training method for distribution matching distillation, named DMD2. Notably, compared to DMD, it does need the regression loss which relies on constructing the synthetic noise-data pairs. Instead, DMD2 introduces three new features: 1) a two time-scale update rule for fake score ... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer uheV's constructive feedback. Below, we address the remaining concerns.
**How to select the set of hyperparameters?**
Thank you for your question. Our approach to selecting hyperparameters is straightforward and consistent across all datasets. We utilize the maxi... | Summary: This paper introduces DMD2, a few-step distilled generator to achieve fast sampling while maintaining the decent generation quality of the multi-step diffusion models. DMD2 proposes several new improvements to the training procedure of the original DMD, including (1) replacing the regression loss with the Two-... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer aF5v's constructive feedback. Below, we address the remaining concerns.
**It would be better to include a detailed training algorithm to clearly showcase the modifications over the original DMD training process.**
Thank you for your suggestion. As you accurately ... | Summary: This work addresses identifies reasons for training instability of one of competitive diffusion distillation approaches based on distribution matching, using bi-level optimization and also adopt a GAN based feature space feedback for improved quality. Overall demonstrate very good performance on SDXL, SD check... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer aF5v's constructive feedback. Below, we address the remaining concerns.
**As the authors discuss on not using real-data within current formulation and setup, there could be a tendency for model to have model collapse?**
There may be some misunderstanding regardi... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their constructive feedback. We are grateful for the positive reception of our work, which has been recognized for its well-founded innovations and outstanding quality. Our DMD2 model facilitates the training of a few-step generator that delivers superior image... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces an upgraded version of Distribution Matching Distillation (DMD), i.e., DMD2, which addresses the limitation and inefficiency of previous DMD and improves the performance of efficient and high-quality image synthesis using diffusion models. Specifically, the authors identify the limitations... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer Yut7's constructive feedback. Below, we address the remaining questions regarding numerical instability and human-related metrics.
**Training with GAN often entails numerical instability. Does DMD2 have such concerns? If it is true, could the author provide some ... | null | null | null | null | null | null |
Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss | Accept (spotlight) | Summary: This paper presents a generic framework for end-to-end supervised graph prediction. The proposed framework can take different types of data as input and learn to output graphs. The core of the framework is a novel loss function (PMFGW) that enables generalizability in different scenarios. In the end, the paper... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper! All your questions are very interesting and answering them helped us to significantly improve the paper.
**Weaknesses:**
> W1: The computational performance results could be more comprehensive. While the asymptotic time complexity is provided, it would be valua... | Summary: The authors propose a flexible framework for end-to-end Supervised Graph Prediction (SGP), called Any2Graph, capable of handling various types of input data. This framework leverages a novel, fully differentiable, and node permutation-invariant optimal transport-based loss called the Partially Masked Fused Gro... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and positive feedback.
> I think to improve the readability of of the paper I suggest to extend the description of the graph matching (section 2, paragraph 'Comparing graphs of the same size')
We will follow your suggestion to provide a more detailled introdu... | Summary: This paper presents Any2Graph, a generic framework for end-to-end supervised graph prediction (SGP) with an optimal transport loss. The framework handles various input modalities and output graphs of arbitrary size and node ordering. The novel Partially-Masked Fused Gromov-Wasserstein loss is differentiable an... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We adress your many interesting questions below:
> 1. Can an analysis of computational complexity be provided? Can a computationally feasible solution be proposed for graph generation problems with a large number of nodes (e.g., graphs with more ... | Summary: This work proposes Any2Graph, an end-to-end deep learning framework for Supervised Graph Prediction (SGP) leveraging a novel OT loss called PMFGW. The model consistently achieves state-of-the-art performances across multiple graph prediction tasks and input modalities.
Strengths: - The author combines Partial... | Rebuttal 1:
Rebuttal: We are greatful for the in-depth review of our paper. We also thank you for having pointed out typos. They will be corrected in the final version of the paper. We answer your questions below:
> The author uses PMFGW as the evaluation metric at the graph level, which is also the training objective... | Rebuttal 1:
Rebuttal: First we would like to thank the reviewers for their mostly positive reviews with constructive questions.
The majority of the comments are positive with many reveiwers finding the paper well written (**CHfS**,**bW5k**,**ct8H**), well positioned in the literature (**CHfS**) and with a nice potenti... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This work aims to design an end-to-end pipeline for structured graph prediction (SGP). The proposed loss, PMFGW, is a extension of Fused Gromov Wasserstein to generate graphs with bounded arbitrary sizes, along with a standard pipeline carefully modified from Relationformer. It is empirically verified on publi... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper! Note that we moved the following discussion in the global response as it might interest the other reviewers:
> One may argue that a clearly successful paper could have more novelty, than proposing a loss with an additional term, the significance of which I am no... | null | null | null | null | null | null |
Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing | Accept (poster) | Summary: This paper introduces ALPHALLM, which is an imagination-searching-criticizing framework designed for self-improvement. Inspired by AlphaGo, authors integrate MCTS and LLMs to establish the self-improvement loop. Additionally, authors proposed eta-MCTS, which is a decoding method used to reduce the search space... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer’s insightful feedback. Your recognition of our novelty, clear identification of challenges, and the effectiveness of our results is is highly encouraging to us.
---
> **[W1]** It could be more clear in some parts and including some examples would be helpful. Fo... | Summary: This paper proposes an imagination-searching-criticizing approach called ALPHALLM to enhance the capabilities of large language models (LLMs). ALPHALLM integrates Monte Carlo Tree Search (MCTS) with LLMs to establish a self-improving framework.
Strengths: - This paper introduces a novel approach to LLM self-i... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback and insightful questions. We are encouraged by your approval of the novelty and effectiveness of ALPHALLM, as well as the clarity of this approach.
---
> **[W1]** Given only final math answer annotations, ALPHALLM essentially performs the final-label (rewar... | Summary: The paper proposes AlphaLLM, a tree-search enhanced framework with a few improvements over Data Synthesizing, option-level MCTS, Importance-Based Adaptive Branching, state merging, fast rollout, critic function and policy improvement process. Experimental results verify the framework's effectiveness on GSM8k a... | Rebuttal 1:
Rebuttal: > **[W1]** It is not that clear how the option-level is implemented to separate sentences
Thank you for your feedback regarding the clarity of our writing. To clarify, the termination function for options operates differently depending on the dataset:
- For the GSM8K dataset, the termination con... | Summary: The paper introduces a method for self-imporvement of LLMs called AlphaLLM. The method consists of three components:
Generation of expert trajectories
Effective Monte-Carlo-Tree-Search over the LLM outputs (nablda-MCTS)
A series of critics for evaluating reliable reward signal (value function, Process Reward ... | Rebuttal 1:
Rebuttal: Your suggestions have helped us a lot to improve the work, though we do think there might exist misunderstandings about our method. We hope the following clarifications and additional evidences would be possible for a re-evaluation.
---
> **[W1]** Lots of experimental details are missing. There ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
An Image is Worth 32 Tokens for Reconstruction and Generation | Accept (poster) | Summary: This paper proposes a novel way to tokenize images to benefit image reconstruction and generation. This paper argues that the convention of compressing images into 2D latent spaces in the VQVAE/VQGAN setting limits the VQ model’s ability to fully exploit the redundancies present in images.
During encoding, a ... | Rebuttal 1:
Rebuttal: > **W1: More details on two-stage training?**
Please see ***"General Questions and Concerns - Details of two-stage training."*** At the warm-up stage, the model's input is still RGB images, and output is the proxy codes, with cross-entropy loss for supervision.
> **W2: Ablation test of 2D varian... | Summary: This paper introduces TiTok (Transformer-based 1-Dimensional Tokenizer) that can tokenize images as compact 1D sequences instead of 2D latent grids. Accompanied with bidirectional non-autoregressive image generator, TiTok achieves SOTA performance on imagenet 256x256 benchmark with much faster generation proce... | Rebuttal 1:
Rebuttal: > **W1&2: two-stage training method, Improved public available tokenizer training recipe?**
Please see ***"General Questions and Concerns - Reviewer Mn4Y W1&2: Two-stage training and better single-stage recipe from Open-MAGVIT2?"***
> **Q1: Comparison to SEED?**
We thank the reviewer for the su... | Summary: This paper introduces a transformer-based image tokenizer (ViT) designed to convert 2D images into a 1D discrete sequence, named TiTok. The authors demonstrate that an image of size 256x256 can be discretized into a compact space of only 32 discrete tokens. This new tokenizer encodes semantic information, in c... | Rebuttal 1:
Rebuttal: > **W1: Why proxy code (two-stage training) instead of VQGAN loss (recon + LPIPS + GAN)?**
It is noteworthy that the two-stage training is ***not*** necessary, as is demonstrated in ***"General Questions and Concerns-Single-stage training for TiTok"***. We also show that TiTok can work well with ... | Summary: Background: VQ-GAN with 2D grid of latent tokens and fixed downsampling factors.
This paper proposes to use 1D tokens instead of 2D tokens.
Key ideas:
* Redundancies: adjacent regions are similar. 2D grid explicitly couples the latents and the pixels in the same relative coordinates.
* 1D tokens are enough fo... | Rebuttal 1:
Rebuttal: > **W1.1: Grounding experiment and analysis of 1D tokens, advantages of 1D tokens against 2D tokens. What if we mask a subset?**
Main advantages of 1D tokens are "semantic-meaningful" and "more compact". The "semantic-meaningful" is grounded by experiments in Fig. 4c, FID score in Tab. 1, 2, and ... | Rebuttal 1:
Rebuttal: # General Questions and Concerns
We thank all reviewers for the initial positive scores and acknowledgements. We address the shared concerns below and upload additional visualization in the PDF attachment.
> **Details of two-stage training:**
To begin with, we describe the two-stage training in... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper introduces TiTok, a 1D image tokenization method that can represent images using significantly fewer tokens compared to existing 2D approaches. The key contributions are:
1. A 1D tokenization scheme that breaks the fixed grid constraints of 2D tokenizers, allowing more flexible and compact image rep... | Rebuttal 1:
Rebuttal: > **W1.1: 2D v.s. 1D in image generation**
We appreciate the valuable insights and comments. We note that the "2D advantages" of "higher resolution images and better effects" mainly stem from more tokens being used. While the 1D formulation in this paper mainly aims at a compact tokenization, we ... | null | null | null | null | null | null |
Microstructures and Accuracy of Graph Recall by Large Language Models | Accept (poster) | Summary: This paper conducts a comprehensive evaluation of LLMs' capability to recall graph (sub)structures when using natural languages as the interface. Through extensive experiments on diverse graphs from different domains, it points out interesting phenomena like LLMs' underperformance in graph recall tasks. It she... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review!
**Question 1**
We believe the conclusions will be relatively close to those of our results obtained when using domain-free narrative style to describe the graph (i.e. naming nodes as "node 1", "node 2", etc.). Our work has used this narrative style to test ... | Summary: The article discusses the issue of graph recall in large language models (LLMs) and conducts experiments on this topic. It tests the ability of LLMs to recall graphs, identifies factors influencing this ability, and examines the impact of this ability on subsequent tasks.
Strengths: 1.This article is the firs... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review!
**Weakness 1**
We not only study the recall's microstructures, but also study:
- the recall's accuracy (as the title suggests, and throughout the paper),
- its correlation with other prediction tasks such as link prediction (Section 5) and node classificat... | Summary: This paper studies how well LLM models can recall graph structured information they have been provided with. While the core of it is a fairly straightforward evaluation of LLM graph recall, it has an interesting experimental design inspired by psychology that adds substantially to the paper. Some of the grap... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review!
**Weakness 1**
Please see Question 1.
**Weakness 2**
We agree with the reviewer and will remove this mini-study or move it to the appendix. As Section 4.3 explains, this mini-study was inspired by the previous human experiment “Sex and network recall a... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers,
We deeply appreciate your reviews. New experimental results have been provided in the attached pdf to this post. Our response to each reviewer has been posted separately.
We very much look forward to further interacting with you in the discussion period.
Warm regards,
Authors ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
TinyLUT: Tiny Look-Up Table for Efficient Image Restoration at the Edge | Accept (poster) | Summary: To address the storage explosion challenge of LUT, this paper proposes a separable mapping strategy (SMS) and a dynamic discretization mechanism (DDM) to decompose the kernel and activation to reduce the storage consumption. Specifically, the SMS decomposes the convolution into independent sub-operations to re... | Rebuttal 1:
Rebuttal: **Response to Weakness 1** We thank the reviewers for their comprehensive review. It must be clarified that the proposed SMS strategy will not result in information loss or performance degradation due to neglecting the relationships between local indexes. As for the original depthwise convolution... | Summary: This paper introduced a separable mapping strategy that solves the storage issue of LUT-based methods. In addition, a dynamic discretization mechanism is designed to decompose the activation and compression quantization scales. Experimental results show the potential of this work for image restoration tasks.
... | Rebuttal 1:
Rebuttal: **Response to W1** The reviewer's reminder is very important and constructive. To address the reviewer's concern, we conduct a comparative study with the abovementioned two methods[1,2]. The comparison indicates that our TinyLUT is more effective than the latest storage saving LUT methods. Literat... | Summary: The paper presents TinyLUT, a method that significantly reduces LUT-based image restoration storage requirements for edge devices through separable mapping strategy and dynamic discretization mechanism, achieving competitive accuracy with over 5 times faster inference.
Strengths: 1. The proposed separable map... | Rebuttal 1:
Rebuttal: **Response to Weakness 1** The reviewer's comment is very insightful. The proposed SMS and DDM methods are applicable to other LUT-based models such as SRLUT[1], SPLUT[2] and MULUT[3].
Our proposed SMS enables decomposition of the input from RF and channels of these models for fewer LUT storage o... | Summary: This paper proposes to reduce the size of LUT for image restoration and to make it applicable on edge devices. The main idea is using depthwise separable convolution to replace the vanilla convolution. When transfer to LUT, the storage is significantly reduced. Experiments show the effectiveness and efficiency... | Rebuttal 1:
Rebuttal: **Response to Weakness 1** We appreciate the reviewer's meticulous work. Indeed, our proposed TinyLUT as well as existing LUT-based methods are mainly designed for CNN-based architectures that are widely deployed on resource-constrained edge devices. Primarily because the convolution operation is... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their valuable and professional comments. We are thankful that most of the reviewers shared positive feedback for our paper. Meanwhile, we are glad to read that our work was efficient (JnS6, G5Ti, MFFN), effective (G5Ti, MFFN), valuable (2kHn), potential (... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SCube: Instant Large-Scale Scene Reconstruction using VoxSplats | Accept (poster) | Summary: This paper presents a method to reconstruct large-scale 3D scenes from a sparse set of posed images. The method has two stages: the first stage is for voxel grid reconstruction, which is based on the XCube [38], a 3D generative model, and the authors introduce the image condition to the model. The second stag... | Rebuttal 1:
Rebuttal: Thank you so much for your detailed review. We are glad to understand that you have found the results promising and the paper well-written. We hereby quote and answer the raised questions as below:
> **Naming of VoxSplat.** The authors may consider using a more accurate term to describe the repre... | Summary: - The paper proposes a new method for sparse-view 3D reconstruction using 3DGS.
- The framework uses two stages:
1) it learns a latent voxel grid (based on XCube) to represent the geometry.
2) it trains an appearance model to decode the latent voxel grid into a set of Gaussians
- The method further use... | Rebuttal 1:
Rebuttal: Thank you for your encouraging feedback for SCube, we are glad to understand that our method is easy to follow and you enjoy the idea and the results of our paper. In the following text, we will try to address your concerns.
> **Details about the diffusion loss.** Section "Training and Inference"... | Summary: The paper introduces a pipeline for street scene reconstruction given a sparse set of images as input. The reconstruction process follows a feed-forward manner. The method builds upon the existing XCube work. First, it generates sparse voxels of the scene to represent the geometries, then each voxel feature is... | Rebuttal 1:
Rebuttal: Thank you very much for the constructive feedback and your positive comments on the results and technical contribution. We appreciate your effort in this process. In what follows we will quote your questions and try to resolve them.
> **Necessity of a Diffusion Model.** I am curious if we really ... | Summary: The paper proposes a new method to reconstruct 3D outdoor scenes from a sparse set of posed images. The key idea is to utilize a new hybrid 3D representation, which assigns 3D Gaussians to each sparse voxel. Given input images, the paper first adapts XCube to condition on sparse view images. Along with the gen... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback, which helps us improve SCube. We are happy that you agree that the problem we solve is challenging and important, and the techniques are sound and well-written. In the following, we will quote your original comments and try to resolve them.
> **Concerns a... | Rebuttal 1:
Rebuttal: We appreciate the insightful comments provided by the reviewers who all agree that SCube is technically sound and easy to understand, the results are impressive, and the problem solved is challenging. We post responses to reviewers individually in the corresponding section, while referenced figure... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Consistency Purification: Effective and Efficient Diffusion Purification towards Certified Robustness | Accept (poster) | Summary: This paper presents an innovative approach to image purification using diffusion models, known as Consistency Purification. Traditional methods like the Denoising Diffusion Probabilistic Model (DDPM) and the Stochastic Diffusion Model face challenges in balancing efficiency and effectiveness. While DDPM provid... | Rebuttal 1:
Rebuttal: > Question A: Comparison with multistep DDPM methods.
We conducted an experiment with multistep DDPM using 25 sampling steps, and the results are shown in the table below. We found that multistep DDPM has lower certified accuracy than onestep DDPM. This aligns with Carlini et al.'s [1] finding th... | Summary: The paper introduces the Consistency Model for one-step purification, ensuring the data manifold of the purified samples while maintaining the efficiency of one-step purification. At the same time, the paper proposes Consistency Finetuning to fine tune the Consistency Model to ensure semantic consistency of th... | Rebuttal 1:
Rebuttal: > Concerns about the limited innovation in our paper
We summarize our contributions and novelty in three points:
1. Using Consistency Models(CM) are efficient and effective:
We provide theoretical (section 3, lines 143-174) and empirical support (Table 2) showing that CM enables us to achieve a... | Summary: This paper introduces Consistency Purification, a novel framework that integrates consistency models with diffusion purification to enhance the certified robustness of deep neural networks, achieving efficient and effective image purification. The framework is further refined through Consistency Fine-tuning wi... | Rebuttal 1:
Rebuttal: > Question A: Comparative analysis with non-diffusion-based pripor studies.
To compare our consistency purification method with various non-diffusion-based approaches, we conducted additional experiments to compute the certified accuracy under three non-diffusion-based methods [1,2,3]. Cohen et a... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SILENCE: Protecting privacy in offloaded speech understanding on resource-constrained devices | Accept (poster) | Summary: This paper investigates the perturbation of audio signals to impair the accuracy of automatic speech recognition (ASR) models, thus enhancing privacy protection, while ensuring that spoken language understanding (SLU) models maintain high accuracy for interpreting user intentions. The key insight is that ASR m... | Rebuttal 1:
Rebuttal: Thanks for comments and questions. Here are the answers to them:
> *W1. Concern about limitation to passive adversaries.*
We clarify it and add evaluation of defending against reconstruction adversaries in **Q1**.
> *W2. This approach may not apply to all scenarios*
We admit that *detecting k... | Summary: The paper presents SILENCE, a method designed to address privacy concerns in cloud-based speech services by selectively obscuring short-term dependencies in speech signals. This technique preserves speech understanding functionality while protecting sensitive information. Implemented on the STM32H7 microcontro... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and questions. Here are the answers to the questions:
> *Q1. How does SILENCE perform against adaptive adversaries using advanced reverse-engineering techniques?*
Our system can still preserve content privacy under advanced reconstruction attacks, achieving... | Summary: This paper presents a lightweight speech intent understanding paradigm for wimpy devices, with heavy concern on the privacy. It targets the recently-popular disentanglement-based approaches for speech processing. It reached a decent balance between efficiency and privacy preservation, and make the SLU system w... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments, and we greatly appreciate your high praise regarding the novelty of our work.
We hope that our rebuttal response will further enhance the soundness of our research.
Below are the answers to your concern and questions:
> *W1. The paper does not have an int... | Summary: This paper proposed a private speech processing system that selectively obscures short-term details to reduce privacy leakage in cloud-based Spoken Language Understanding (SLU). A differential mask generator is learned to automatically mask out portions of audio signals along with online cloud inference with t... | Rebuttal 1:
Rebuttal: Thank you for your comments and questions. Here are the answers to the points:
> *W1. Could provide more background info for attack scenarios.*
Attacks may involve passive and active adversaries attempting to automatically recognize private entities in uploaded command audio files.
Passive adver... | Rebuttal 1:
Rebuttal: Dear Reviewers,
**Please see the attached PDF for a one-page summary with an illustrative description of different attack scenarios, visualizations of generated masks and reconstructed waveforms, and additional experiment results against advanced active reconstruction attacks.**
We would like to... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
What Matters in Graph Class Incremental Learning? An Information Preservation Perspective | Accept (poster) | Summary: This paper studies graph class incremental learning (GCIL), which requires the model to classify emerging nodes of new classes while remembering old classes. The paper provides a theoretical analysis of GCIL and finds that preserving old graph information that corresponds to local-global low-frequency and high... | Rebuttal 1:
Rebuttal: Thanks for your positive comments!
>**Q1: Why only separate local and global components for low-frequency and not separate local and global components for high-frequency?**
A1: The rationale for maintaining low-frequency global information (LG) without high-frequency global information (HG) is t... | Summary: This paper studies the graph class incremental learning problem, and specially focuses on theoretically investigating what matters in preserving the information from the old classes.
The authors theoretically demonstrate that maintaining the graph information can preserve information of the old model, such t... | Rebuttal 1:
Rebuttal: Thanks for your positive comments!
>**Q1: The writing requires improvements. The abstract and introduction do not provide a clear overview of the work, since there are multiple unclear terms like graph information and local-global parts. I would recommend the authors to revise this part. At least... | Summary: This paper proposes an innovative framework named graph spatial information preservation (GSIP), which alleviates catastrophic forgetting in graph class incremental learning (GCIL), by preserving low-frequency local-global information and high-frequency information in both the feature space and the topological... | Rebuttal 1:
Rebuttal: Thanks for your valuable suggestions!
>**Q1: From line 50 to line 52, how to quantitatively understand “a larger distortion”? According to Figure 1, does “a larger distortion” indicate that the black dotted ellipse in Figure 1 (a) has a longer major axis than that in Figure 1 (c)? If so, since th... | Summary: The paper focuses on the challenge of graph class incremental learning (GCIL), where a model must classify new nodes of emerging classes while retaining knowledge of previously learned classes. The primary issue of GCIL is identified as catastrophic forgetting, where new learning overwrites old knowledge. To a... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments!
>**Q1: The design of the method, which only capture low and high frequency information is not comprehensive. Why does the method only consider the low-/high- frequency information in the graph’s spatial domain?**
A1: Considering the preservation of low-/high... | Rebuttal 1:
Rebuttal: We extend our gratitude for the valuable feedback and insightful suggestions provided by all the reviewers. We have diligently addressed the questions and suggestions raised during the official review process and have provided comprehensive response to the reviewers in the corresponding rebuttals.... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection | Accept (poster) | Summary: This paper provides a novel method to tackle the issue of precision loss in more complex domains. They introduce an anomaly personalization method with a diffusion model to utilize the diffusions to obtain the normal sample distribution and exchange the anomaly image into normal ones. Finally, a triplet contra... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reviews and constructive suggestions. We answer the questions as follows.
***
>W#1: Will the utilize of the diffusion model bring a long inference time, which may not be effective for real-time application
**A#1:** Thank you for the constructive question. 1)... | Summary: This paper proposes a new few-shot anomaly detection method based on one-to-normal personalization of query images using a diffusion model and a triplet contrastive inference process. It leverages a diffusion-based generation model to transform a query image into a personalized image towards the distribution o... | Rebuttal 1:
Rebuttal: ***
>W#1: Details about the experimental settings are not fully provided, which limits reproducibility. The hyperparameters such as alpha and beta for $A_score$ and Details about the memory bank M.
**A#1:** Thank you for your constructive comments. 1) We set the parameters $\alpha$ and $\beta\$... | Summary: This paper addresses the issue of few-shot anomaly detection, which introduces an anomaly personalization method by using an anomaly-free customized generation model and performing a triplet contrastive anomaly inference strategy. Experiment evaluations across eleven datasets in three domains demonstrate its s... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reviews and constructive suggestions. We answer the questions as follows.
***
>W#1: There is a lack of discussion of the computation cost and inference speed of the proposed method since there are many generation steps. And the explicit description and exact ... | Summary: The paper focuses on a practical yet challenging anomaly detection in a few-shot-normal-image setting. Instead of directly matching features between the query image and a few normal reference images, the core insight is to replace the reference image with a personalized normal image generated by an anomaly-fre... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reviews and constructive suggestions. We answer the questions as follows.
***
>W#1: It is essentially a memory-augmented reconstruction-based anomaly detection (AD) method [1], which attempts to reconstruct the query image to its most similar anomaly-free coun... | Rebuttal 1:
Rebuttal: We appreciate all reviewers for their careful reviews and constructive suggestions. In this rebuttal, Individual concerns have been carefully addressed in the response to each reviewer, with an uploaded PDF for more results suggested by reviewers. In the final version, we will revise the paper fol... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DG-SLAM: Robust Dynamic Gaussian Splatting SLAM with Hybrid Pose Optimization | Accept (poster) | Summary: This paper proposed a dynamic RGB-D SLAM system based on 3D Gaussian Splatting (3D-GS) and DROID-SLAM. Semantic segmentation masks and depth warping residuals are used to generate motion masks to remove the dynamic part of the scene. Experiments on three real-world datasets show the proposed approach achieves ... | Rebuttal 1:
Rebuttal: ### Q1: Technical novelty.
A1: Thank you for your advice. We aim to clarify your concerns from the following perspectives: (1) As dynamic 3D Gaussian-based SLAM remains in its nascent stages of research and diverges markedly from traditional approaches, this paper presents preliminary explorations... | Summary: The paper introduces the first robust dynamic visual SLAM system based on 3D Gaussian Splatting. It provides precise camera pose estimation and high-fidelity reconstructions. Strategies such as motion mask generation, adaptive Gaussian point management, and hybrid camera tracking are proposed to improve the ac... | Rebuttal 1:
Rebuttal: ### W1: More experements results.
A1: Thank you for your suggestion. We have added experiments comparing ORB-SLAM3[2] and Refusion[1]. As shown in Table 4 (one-page global PDF), our method presents a more robust tracking capability.
### W2: Comparing with MonoGS.
A2: Thank you for your suggesti... | Summary: The paper present DG_SLAM to address the inconsistent observation of geometry and photometry in dynamic environments among the current slam-related field. Specifically, the authors develop several technologies such as the motion mask generation, adaptive gaussian point management, and a hybrid camera tracking... | Rebuttal 1:
Rebuttal: ### Q1: The details of the motion mask generation strategy.
A1: During our experiments, we observed that inaccuracies in pose estimation, coupled with noise in the captured ground-truth depth values, can result in unstable motion segmentation outcomes when relying solely on a single warp mask. How... | Summary: The paper combines existing methods for deep SLAM (DROID-SLAM) and 3D Gaussian splatting for 3D mapping into a system and adds an approach for dynamics filtering by depth warping to make tracking more robust in dynamic scenes. The approach is evaluated on several dynamic SLAM benchmarks and compared with recen... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's meticulous and detailed review of our work, which helped us improve the quality of this paper. It should be noted that some of the issues you raised are typically treated as foundational definitions in the 3D Gaussian Splatting paper and traditional SLAM lite... | Rebuttal 1:
Rebuttal: We are immensely grateful for the time and effort expended by all reviewers in reviewing our manuscript. The technical evaluations and detailed comments provided have been invaluable and have substantially enhanced the quality of our work. In this response, we have meticulously addressed each ques... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Verified Safe Reinforcement Learning for Neural Network Dynamic Models | Accept (poster) | Summary: The paper proposes methods to learn formally verified neural network control policies for (continuous-space, discrete-time) non-linear dynamical systems, whose dynamics are also represented by a neural network. It builds on existing methods for NN verification, and their application to a k-step composition of ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful comments and feedback. Our responses are below.
> **Question 1:** What is the experimental setup in terms of timeout (if any) used to compare tools.
**Response 1:**
We did not use any timeouts in our experiments, as all algorithms completed within reasonab... | Summary: The authors primarily propose a novel method to learn verified safe control policies for nonlinear neural dynamical systems, aiming to achieve safety in the sense of bounded reachability. By leveraging memorization, forward reachability analysis, and differentiable reachability over-approximation, the authors ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments and suggestions. Our responses are below.
> **Question 1:** Can the author explain the soundness of the approach proposed in the manuscript?
**Response 1:**
Soundness is a direct consequence of our use of a sound verification tool $(\alpha,\beta)... | Summary: This paper tackles the problem of synthesizing verified control policies for dynamical systems with the use of neural networks. The authors focus on nonlinear discrete-time system dynamics for which the use of neural networks is motivated by the challenge of reaching the goal without colliding with obstacles. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments and feedback. Our responses are below.
> **Question 1:** A motivating example to guide the reader through the algorithm would be helpful.
**Response 1:**
We appreciate the suggestion and will use vehicle avoidance as such an example in the revision... | Summary: This paper studies safe reinforcement/control learning by optimization of long-horizon safety verification and learning multiple initial-state-dependent controllers. The authors propose several novel ideas, including a curriculum learning to increase the verification horizon, incremental verification, and spli... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments and suggestions. Our responses are below.
> **Question 1:** The authors may have to discuss their incremental verification vs. NNCS verification tools; POLAR-Express[1], CORA[2], etc.
**Response 1:** Indeed, incremental verification is a well-e... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Graph Neural Flows for Unveiling Systemic Interactions Among Irregularly Sampled Time Series | Accept (poster) | Summary: Authors propose a novel idea to learn the interactions between time series which is crucial to make reliable forecasts in interacting systems. Paper proposes a new algorithm GNeuralFlow; a new graph-based continuous time model to learn systematic interaction. A GNN is used to learn the interactions between tim... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful comments, we reply to your questions below.
**RE: computational cost**
The computational cost can be cubic for the number of nodes because the evaluation of the DAG constraint and the gradient involves the computation of the matrix exponential. One possible mitigatio... | Summary: This paper proposes a novel graph-based continuous-time model GNeuralFlow for learning systemic interactions and interactions are modeled as a Bayesian network. Experimental results for regression problems show that the proposed GNF achieves state-of-the-art performances on time series classification and forec... | Rebuttal 1:
Rebuttal: **RE: theoretical analysis**
Our work mainly focuses on modeling. We offered certain analyses to justify some modeling choices, including guaranteeing contractive mappings required by the neural flows (Theorem 1, Theorem 2, and other inline text within section 4.3).
**RE: sota GNN**
Thank you f... | Summary: The paper focuses on a graph-based model to capture dependencies in irregularly sampled time series data. The framework employs a causal prior—a directed acyclic graph—where nodes are conditionally independent of non-descendants given their parents, specifying component dynamics dependencies. The proposed mode... | Rebuttal 1:
Rebuttal: **RE: additional experiments (Q1)**
Thanks for your comment.
Here we provide additional baselines. Specifically, we provide the comparison with GRU-D (Che et al 2016), NRI (Kipf et al., 2018), and dNRI (Graber and Schwing 2020) on the synthetic datasets.
We observe that our GNF-gru can improve ... | Summary: This paper addresses the problem of multivariate time series prediction with feature interactions. It proposes learning a Directed Acyclic Graph (DAG) to model interactions, encoded by a Graph Neural Network (GNN), and using a neural flow to model the dynamics. The experiments demonstrate improvements over gra... | Rebuttal 1:
Rebuttal: **RE: flow matching (W1)**
Our effort consists in extending Neural Flows (Bilos et al 2021), to integrate additional interdependency information in the form of a learned graph. Note that (Bilos et al 2021) was proposed in the context of time series, differently from Flow Matching. We are not pro... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes a continuous-time model to discover the causal structure from irregular multivariate time series data. The idea is to introduce the DAG (directed acyclic graph) to model the interaction of different time series at the same time step in the vector field that generates the multivariate time se... | Rebuttal 1:
Rebuttal: **RE: ..I suggest changing the description to "the vector fields..**
Thanks for the feedback. We will update the text.
**RE: .. How do these DAG learning algorithms impact the model performance**
Thanks for pointing out this part. We address it as follows.
A limitation of the proposed model i... | null | null | null | null | null | null |
Identity Decoupling for Multi-Subject Personalization of Text-to-Image Models | Accept (poster) | Summary: MuDI is a novel framework designed for multi-subject personalization in text-to-image diffusion models. It effectively decouples identities of multiple subjects, using segmented subjects from a foundation model for data augmentation and generation initialization. A new metric is introduced to evaluate multi-su... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and effort in reviewing our paper. We appreciate your positive comments that
- Well written paper
- Comprehensive experiments
- Success in multi subject personalization
- Good proposed metric
We initially address your concerns below.
---
**C1. Test-time fin... | Summary: This work introduces a training and inference pipeline and an evaluation benchmark for multi-concept customization for text-to-image diffusion models. Specifically, the pipeline comprises a Seg-Mix training stage, which can be viewed as a data augmentation trick to prevent the fine-tuned model from learning mi... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and effort in reviewing our paper. We appreciate your positive comments that
- Well written paper
- Effective methods
- Convincing evaluation benchmark
- Interesting applications
We initially address your concerns below.
---
**C1. Novelty of using descripti... | Summary: The paper introduces MuDI, a novel framework designed to improve multi-subject personalization in text-to-image diffusion models. Unlike current methods that often mix identities and attributes from different subjects when generating multiple subjects simultaneously, MuDI effectively decouples these identities... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and effort in reviewing our paper. We appreciate your positive comments that
- Interesting topic and novel
- Good presentation
We initially address your concerns below.
---
**C1. Small and monolithic style dataset**
To address your concerns regarding style... | Summary: The paper proposes MuDI, a novel method for generating images with multiple personalized subjects. By leveraging segmented subjects from reference images for both training and inference, MuDI effectively addresses the challenge of identity mixing in multi-subject image generation. Key contributions include a n... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and effort in reviewing our paper. We appreciate your positive comments that
- Novel approach
- Clear paper with solid experiments
- Easy to follow
- Contribution to the field
We initially address your concerns below.
---
**C1. Comparison with FastComposer*... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We sincerely thank you for reviewing our paper and for the insightful comments and valuable feedback. We appreciate the positive comments that emphasize the novelty of our work and the advantages of our proposed method:
- Novel approach (JDvE, amUB)
- Well-written and easy to fol... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference | Accept (poster) | Summary: The authors extend the work of Wenger et al. (2022) on "computational uncertainty" in Gaussian process regression models, which introduced the IterGP class of approximations. These approximations treat the limited computation in approximate GP methods as a source of uncertainty, resulting in uncertainty predic... | Rebuttal 1:
Rebuttal: ### Summary
Thank you for your feedback on our paper! We appreciate your detailed recommendations for improvement. Based on these we've added a new set of experiments where we trained SGPR and an exact (Cholesky)GP with LBFGS in double precision (see the rebuttal PDF).
We believe we have addresse... | Summary: The authors cast the IterGP method of Wenger et al, which is guaranteed to give conservative estimates of uncertainty relative to the posterior, as a variational procedure. This allows for hyperparameter selection and selection of parameters controlling the quality of the approximation ($\mathbf{S}$) through m... | Rebuttal 1:
Rebuttal: ### Summary
Thank you for your positive review of our paper and helpful input!
We responded to your questions below. In particular, we've reformulated the motivation behind choosing actions according to an information-theoretic objective and we've added a statement proving the monotonic decrease... | Summary: This paper presents a substantial extension to an existing class of models called IterGP. By introducing a novel training loss which combines both the hyperparameters and a sparse action matrix, the IterGP-Opt model offers linear-time scalability. Experiments over a range of UCI datasets demonstrate that IterG... | Rebuttal 1:
Rebuttal: ### Summary
Thank you for your time and effort in reviewing our paper and in particular for suggesting improvements to our benchmark experiments.
We have addressed your main concerns with a set of new experiments where we train SGPR using LBFGS in double precision. These changes close the gap bet... | null | null | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their time and effort in reviewing our work!
We feel that your helpful suggestions enabled us to improve our paper, in particular, we've added the following additional experiments (see the attached PDF and our individual rebuttals below):
- **Generalizatio... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SEL-BALD: Deep Bayesian Active Learning with Selective Labels | Accept (poster) | Summary: This paper explores the setting where a learner has a label budget that can be used to acquire labeled data from a user. However, differently from traditional active learning approaches, the paper assumes that the user may not want/be able to label some examples. That is, for specific (limited) examples, the u... | Rebuttal 1:
Rebuttal: We're encouraged by the reviewer's positive comments on the novelty of the problem and our method contribution. Below, we address the concerns raised in your review.
- **Datasets:** Thanks for the suggestion. We followed your recommendation and added additional experimental results on Fashion MNI... | Summary: This paper presents a direct extension of BALD sampling criterion to address the need for active annotation rejection. The method assumes a rejection distribution over the candidate data samples in pool-based active learning and proposes an active sampling strategy that jointly considers the BALD informativene... | Rebuttal 1:
Rebuttal: We are delighted to learn your acknowledgment of our Bayesian approach to a practical problem in active learning. We address your points of concern in detail below.
- **W1 (Estimating $e(x)$):** (i) We now add more implementation details of the Bayesian network $e_\phi(x)$ in the appendix. In ... | Summary: The paper introduces the Active Learning with Instance Rejection (ALIR) problem, addressing the issue of selective labeling where human discretion impacts the labeling process. In particular, humans might not always provide a label for a point returned by active learning; this abstention needs to be modeled ex... | Rebuttal 1:
Rebuttal: Thank you very much for your positive comments on our problem, method, and writing. Nevertheless, we understand there are concerns impacting the evaluation.
To address these:
- **Computational Complexity:** In this paper, we mainly focus on improving the BALD method, therefore the baseline model... | Summary: This paper considers an active learning scenario where the human annotation is done under the restriction that the annotator is biased in deciding the labeling. The Bayesian framework makes the e-BALD and various versions based on a posterior sample of labeling probabilities. The authors consider the three cas... | Rebuttal 1:
Rebuttal: We appreciate your recognition of the novelty of the problem and the clarity of our approach.
**Other AL Metrics**: Thanks for the suggestion! While we mainly focus on the BALD objective in this paper, our methods can be generalized by replacing the mutual information with other metrics in the ... | Rebuttal 1:
Rebuttal: # Response to All:
We thank the reviewers for your positive and constructive feedback. Your comments significantly help us improve the paper.
## Datasets
In addition to the synthetic data, MNIST, and the loan fraud detection data in the paper, we further evaluate the proposed and the baseline me... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Physics-Informed Variational State-Space Gaussian Processes | Accept (poster) | Summary: The paper introduces a method for training spatio-temporal Gaussian processes which incorporate physics constraints including satisfying the governing equations at a number of collocation points and satisfying curl / divergence free constraints. Their approach scales both linearly in time and space by leveragi... | Rebuttal 1:
Rebuttal: Thank you for your review. We address the points you raised below.
**W1: Problem statement.**
Our work contributes to the growing field of data-driven physics-informed models. These are hybrid models that aim to exploit physical inductive biases and data observations. Recently there have been p... | Summary: The paper introduces a formulation for state-space GP which aims to capture the behaviour of PDE-based systems. The paper suggests multiple techniques to speed up the GP inference, including through decomposed kernel, variational methods on natural parameterisation of distribution, use of inducing points
Stre... | Rebuttal 1:
Rebuttal: Thank you for your review and positive view of our work. We address the points you raised below.
**W1: Clarity.**
To aid understanding we will provide an additional notation section and nomenclature table in the appendix as well as the proposed additions mentioned in the reply to reviewer 4ngK.... | Summary: The paper introduces a novel approach for solving partial differential equations (PDEs) using a Gaussian process prior. It leverages established methods for approximate inference and provides a cohesive framework that unifies existing results in probabilistic differential equation solving.
Strengths: The pape... | Rebuttal 1:
Rebuttal: We thank you for your review and for raising questions that have helped improve the presentation of the paper.
We understand your concern related to the complicated notation. To aid understanding we will use the additional page in the camera-ready stage to include a notation section that will cl... | Summary: In this paper, the authors present a physics-informed Gaussian process based approach to learn the solution of ODE and PDE systems. In particular, they address the challenge of the cubic computational complexity with respect to the number of spatial observations. It is shown that multiple state-of-the-art appr... | Rebuttal 1:
Rebuttal: Thank you for your review and positive view of our work. We address the points you raised below.
**W1: Typos.**
Thank you for highlighting these typos, we have addressed them all. However, line 167 is *not a typo*; the computational complexity is $O(N (N_s \, d_s \, d)^3)$ because the expected ... | Rebuttal 1:
Rebuttal: We thank all five reviewers for their time and constructive reviews. This work *introduces a novel approach for solving partial differential equations (PDEs) using a Gaussian process prior* (**4ngK**) that *fits into recent literature on dynamical systems, Gaussian processes, and physics-informed ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The submission discusses conditioning spatiotemporal Gaussian processes using differential equation constraints and observational data.
Specifically, the temporal component is handled by a Markovian prior (to achieve linear complexity), and the spatial component is dealt with by variational methods.
As such, t... | Rebuttal 1:
Rebuttal: Thank you for your positive review. We address your two comments below.
**W1: Novelty of the proposed approach.**
Whilst we agree that some special cases of our framework are known, the 'beauty and originality of the proposed framework is its unifying property' (reviewer **z1FQ**). Not only do ... | null | null | null | null | null | null |
Gorilla: Large Language Model Connected with Massive APIs | Accept (poster) | Summary: This paper proposes Gorilla, a fine-tuned LLaMA model, outperforms GPT-4 in crafting API calls and adapts well to document changes with a document retriever, reducing hallucination issues. It also provides APIBench, a new dataset for evaluation, includes APIs from HuggingFace, TorchHub, and TensorHub. Gorilla'... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and thoughtful comments. We are encouraged you enjoyed the exposition of our pipeline and find the APIBench, and the AST subtree-matching metric to contribute to mastering techniques to evaluate APIs.
**1. Highlighting the contributions and potential impact o... | Summary: The authors present a new fine-tuned language model that is trained to map from user instructions to code snippets that invoke the appropriate APIs. The authors also introduce a new dataset which includes the information for roughly 1600 APIs from a variety of online sources, which is used to train the model. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and their thoughtful comments. We are encouraged that you found our choice of model and dataset useful to the larger community. We will clarify some of the questions below:
**1. Fine-tuned version of another model and comparison**
We agree that GPT and Claude... | Summary: This paper introduces Gorilla, a fine-tuned LLaMA model designed to improve large language models' ability to use APIs accurately. The authors created APIBench, a comprehensive dataset of ML APIs from HuggingFace, TorchHub, and TensorFlow Hub, and used self-instruct to generate instruction-API pairs for traini... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and thoughtful comments. We are motivated that you found Gorilla can significantly reduce API argument hallucination errors compared to other models, the retriever-aware training to be a novel contribution that allows Gorilla to adapt to test-time changes in AP... | Summary: This paper addresses a pipeline to call adequate APIs among massive pools to accomplish users’ instructions. For that, the authors construct and release the APIBench dataset that contains more than 1645 APIs, and propose the Gorilla model, which is a retrieval-aware finetuned Llama-7B model for API calls.
Str... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and thoughtful comments. We are encouraged that you find our contributions of APIBench, and AST tree matching evaluation metric, along with open-sourcing of the LLM models to be valuable contribution to the community.
We clarify the questions below:
**1. Det... | Rebuttal 1:
Rebuttal: We are encouraged by the insightful reviews and appreciate the recognition of the key strengths of our work. The reviewers highlighted:
1. The timely relevance of our problem statement in the realm of Large Language Models (LLMs) and API ecosystems, emphasizing our novel approach in automatizing A... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Optimal Algorithms for Augmented Testing of Discrete Distributions | Accept (poster) | Summary: This paper consider the problem of hypothesis testing for discrete distributions, including identity testing, closeness testing and uniformity testing. The authors investigated these testing problems under the setting where a predicted data distribution is available. Utilizing this additional information, the ... | Rebuttal 1:
Rebuttal: **presentation**
Thank you for your feedback. The algorithm is given on page 8 of the main body. We give technical overview in Section 2 before the algorithm to give a high level picture about how our upper bounds and lower bounds (in the appendix) fit together to give a complete characterizatio... | Summary: The paper studies the problem of property testing (specifically uniformity, identity, and closeness testing) in the context of the learning-augmented algorithms framework. They show how a prediction about the underlying distribution could be harnessed to provably reduce the number of samples required when the ... | Rebuttal 1:
Rebuttal: **Comparisons to the previous work**
[29] is an example of studying distribution testing when both samples and a prediction distribution $\hat{p} = p$ are available. However, their algorithm does not receive $p$ directly and can only query $p$. Since they do not see the whole $p$, they would req... | Summary: The paper looks at the problem of hypothesis testing for discrete distributions where a predicted data distribution is available. The paper gives algorithms (Algorithm 1; closeness testing, Algorithm 3; identity and uniform testing) which either reduces the number of samples required for testing, or do no wors... | Rebuttal 1:
Rebuttal: **Suitability for the conference**
We would like to point out that many related works on distribution testing have appeared in recent ML conferences such as NeruIPS/ICML/ICLR. These works are relevant to the learning-theory, privacy, algorithmic statistics, and sublinear algorithms community with... | Summary: This paper studies the task of identity/closeness testing when the tester is augmented with predictions of the unknown distribution involved apriori. In particular, in identity testing, in addition to sample access to the unknown distribution $p$, the algorithm is also given some $\hat p$ that may or may not s... | Rebuttal 1:
Rebuttal: **Clarification on evolving distributions**
For our results, the algorithm requires access to a prediction distribution $\hat{p}$, which is intended to be close to the unknown distribution $p$ (in addition to samples from $p$). Our algorithms are applicable in settings where the extra informatio... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their feedback. We will integrate all your editorial comments regarding the presentation of our paper in the future version. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Interpolating Item and User Fairness in Multi-Sided Recommendations | Accept (poster) | Summary: The paper addresses the challenge of balancing multiple stakeholder interests in online recommendation systems, which include platforms, items (sellers), and users (customers). To tackle this challenge, the authors introduce a novel fair recommendation framework, FAIR, formulated as a constrained optimization ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback! We'd like to address your questions below.
**Regarding choosing the right fairness notion,**
- Thank you for raising this insightful point! Indeed, choosing the right fairness notion is not trivial and very much depends on the context, such as the type of o... | Summary: This paper focuses on an important and interesting research direction: achieving multi-sided fairness for recommendation. Specifically, the authors aim to answer two research questions: (1) What constitutes a fair recommendation within a multi-sided platform? and (2) How would a platform implement a fair recom... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback! We'd like to address your questions below.
**Regarding related works,** thank you for pointing us to these works! We'll add them and the following discussion to our related works section.
- **A short summary of [1,2,3]**. [1] focuses on achieving producer a... | Summary: This paper presents a novel fairness recommendation framework called FAIR, and an online algorithm called FORM for solving multi-stakeholder fairness problems. The main contributions include 1. The FAIR framework is proposed to balance the platform revenue and the fairness of multi-stakeholders (items and user... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback! We’d like to address your questions below.
**W1. Regarding complexity and scalability,**
- Please see (1) in our global response.
**W2. Regarding choosing fairness parameters,**
- Please see (2) in our global response for a detailed answer to this question... | Summary: The paper introduces a fair recommendation framework, FAIR, for balancing the interests of multiple stakeholders in recommendation systems, namely the platform, sellers, and users. The framework is formulated as a constrained optimization problem that addresses fairness concerns for both items and users in a d... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback! We’d like to address each of your questions below.
**W1. Regarding computational complexity and scalability,**
- Please see (1) in our global response.
**Q1. Regarding our sublinear regret upper bound**, we’d like to highlight the following:
- **Challenges ... | Rebuttal 1:
Rebuttal: We’d like to express our sincere gratitude to all reviewers for your insightful feedback! Below we’ve addressed several common questions from the reviewers. We’ll incorporate all discussions in our response into the revised version of the paper.
(1) **Regarding computational complexity and scalab... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SELMA: Learning and Merging Skill-Specific Text-to-Image Experts with Auto-Generated Data | Accept (poster) | Summary: The paper proposes a workflow in which synthetic Text To Image data is generated in order to improve faithfulness of T2I models. Specifically, they generate prompts with LLMs, then Images with T2I models and finally fine-tune pre-trained T2I models with LORA fine-tuning. They fine-tune multiple LORA experts an... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback. Below we address your questions with further clarifications and experiments.
> **W1. The strength mentioned above is also a weakness of the paper. The paper is motivated by faithfulness. However, the proposed method tackles faithfulness at best as a side effect.... | Summary: This article goes through four stages: (1) collecting skill-specific prompts using in-context learning of LLMs, (2) self-generating image-text samples for diverse skills without the need for human annotation or feedback from reward models, (3) fine-tuning the expert T2I models on these datasets separately, and... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback. Below we address your questions with further clarifications and experiments.
> **W1-1. In the comparison between multi-LoRA and single LoRA, are the parameter counts of multi-LoRA and single LoRA the same, or is each LoRA within multi-LoRA equivalent in paramete... | Summary: The paper analyzes the merging of skill-specific LoRA-finetuned models, trained on generated data and compares this approach to other training approaches (i.e. finetuning, PPO). Moreover, the paper compares the use of GT images and prompts to the use of generated data. The results suggest that this approach pe... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback. Below we address your questions with further clarifications and experiments.
> **W1. Weak-to-Strong Generalization**
Regarding experiments with weaker LM, we would like to bring your attention to Table 9, where we experiment with a LLaMA 3 (8B), which is a publ... | Summary: The goal of this paper is to improve the faithfulness of text-to-image generation models. New datasets and fine-tuning frameworks are introduced to address this limitation. Specifically, this paper first adopts LLMs to generate multiple datasets of text prompts that can teach different skills and then generate... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback. Below we address your questions with further clarifications.
> **W1: The technical novelty is a bit weak. LoRA fine-tuning and merging experts are not original. The prompt generation seems to be straightforward too.**
We would like to first clarify that our mai... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback. We also appreciate that they acknowledge SELMA's strengths:
- Clear advantages over other methods for text-image alignment (Reviewer dhqF, xcmQ, WQ94)
- Our proposed automatic data generation pipeline and LoRA merging approach are well studied an... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Stochastic contextual bandits with graph feedback: from independence number to MAS number | Accept (poster) | Summary: The authors consider the problem of contextual bandits with finitely many contexts, stochastic rewards and a directed feedback graph (assumed to contain all self loops) across actions. They study the setting of "complete cross-learning" where the reward feedback of the chosen action is observed across all cont... | Rebuttal 1:
Rebuttal: We thank the reviewer for highlighting the difference between [1] and our work. Indeed [1] primarily studies the RL setting so their upper bound is more general than ours; however, our lower bound instance could be embedded into the tabular RL setting of [1]. Consider an episodic tabular RL with $... | Summary: In this paper, the authors consider the problem of contextual bandits with a feedback graph, for finite context space. In the presented setting, taking an action reveals the rewards for all neighboring actions in the feedback graph for all contexts. The authors propose $\beta_M(G)$, a theoretical quantity in ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review and the insightful questions.
1.(a). In general, the oracle we compare in contextual bandits to is able to take different optimal actions under different contexts, so it is a stronger benchmark. Also, our work assumes adversarial context, so intuitively, a lar... | Summary: This paper studies contextual online learning when the feedback received by the learner is regulated by a feedback graph. The setting is as follows: the actions constitute the nodes of a directed graph $G$, and playing action $a$ at time $t$ when the context is $x_t$ reveals not only the loss incurred by that ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review and the insightful questions.
1. Our work assumes adversarial context. When the reward is also adversarial, as shown in (Balseiro et al. 2019), cross-learning is not helpful and the optimal minimax regret is proved to be $\sqrt{M\alpha T}$: this essentially cor... | Summary: In this work, the authors consider the problem of contextual bandits with feedback graphs and aim to achieve a tighter dependency on graph-dependent quantities.
Figuring out the correct dependency on graph-dependent quantities is a notably challenging problem in the standard MAB framework, as aspects such as w... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review and the insightful questions.
1. While we agree it is possible to improve the logarithmic $\log(MKT)$ term with a more careful concentration argument, it is less obvious how to avoid the $\log^2K$ term when we use a practical algorithm: if we are allowed access... | Rebuttal 1:
Rebuttal: We appreciate the insightful reviews and questions from the reviewers and would like to highlight points that have drawn most attention here.
1. In addition to self-avoiding context (Theorem 1.3), Theorem 1.4 shows that our results are also tight for arbitrary context when the feedback graphs are ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper investigates the problem of stochastic contextual bandits with graph feedback, in which a graph over actions models the feedback structure. The learner selects an action after observing the current context, and then receives the losses of the actions that are neighbors of the selected one in the fee... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review and insightful questions.
1. It is indeed a great question about extending to the class of all strongly observable graphs, and it turns out that the extension is straightforward.
Upper bounds: A key implication of strongly observable graphs is that fo... | null | null | null | null | null | null |
Mitigating Quantization Errors Due to Activation Spikes in GLU-Based LLMs | Reject | Summary: This paper pays attention to extremely large outliers in LLMs and further investigates the reasons behind these "attention spikes." Consequently, the authors propose two methods to enhance the performance of quantized models.
Strengths: 1. The analysis of attention spikes is thorough and comprehensive.
2. Th... | Rebuttal 1:
Rebuttal: We are deeply grateful for your thorough feedback of our work!
We hope our response sufficiently addresses your questions.
**Q1.** The proposed QFeM method is not hardware-friendly, as it maintains some modules at high precision and cannot directly utilize low-bit INT General Matrix Multiply (GEM... | Summary: This paper identifies some of the underlying causes for why activation quantization (PTQ) could lead to low performance and suggests some methods to address these issues.
Strengths: Please see the “Questions” section.
Weaknesses: Please see the “Questions” section.
Technical Quality: 2
Clarity: 3
Question... | Rebuttal 1:
Rebuttal: We sincerely thank you for taking the time to review our work!
We reviewed our paper and fixed some typos, including your suggestions to improve the readability and presentation.
---
**Q1.** The results of Table 2 seem to suggest that SmoothQuant leads to an unacceptably high performance degrada... | Summary: This paper addresses the precision challenges posed by the large language models (LLMs) quantization during inference, specifically focusing on the quantization errors in GLU-based feedforward networks. The authors identify that GLU variants in LLMs cause significant local quantization errors due to excessive ... | Rebuttal 1:
Rebuttal: We deeply appreciate the invaluable feedback provided by the reviewer.
---
**Q1.** My major concern is about the baseline of SmoothQuant reported in Table 4. For example, In Table 7 of SmoothQuant's original paper, they report that W8A8 SQ's PPL of Llama-7B on WikiText-2 dataset is 5.515, while ... | Summary: This paper introduces activation quantization methods for GLU-based LLMs, which often face challenges due to activation spikes. To effectively manage these spikes and enable activation quantization using a PTQ-based approach, the paper proposes a Quantization-free Module (QFeM) and a Quantization-free Prefix (... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful comments and the references provided.
---
**Q1.** The perplexity/accuracy results of the baseline methods deviate from the results reported in previous papers. Why are the evaluation results for the previous methods so different?
**A1.**
Please refer to Q... | Rebuttal 1:
Rebuttal: # Response to all reviewers
We sincerely appreciate all reviewers' thoughtful feedback and constructive suggestions on our paper!
Thanks to valuable comments, our work achieved some breakthroughs and broad contributions:
- Our methods demonstrate effectiveness beyond the W8A8 setting, showing pr... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SLED: Self Logits Evolution Decoding for Improving Factuality in Large Language Models | Accept (poster) | Summary: This work proposes self-evolution decoding, a method to improve LM's factuality without using external knowledge or fine-tuning data. Specifically, the differences between each layer’s logits and the final layer’s logits are utilized to approximate the gradient, which are further used to estimate the inner kno... | Rebuttal 1:
Rebuttal: Dear Reviewer
Thank you so much for taking the time to provide your feedback. Your comments and suggestions are invaluable to us. We appreciate the opportunity to address your concerns regarding the approximation used in our approach, and we are also including additional results to support our me... | Summary: In this work, the authors present a decoding method called Self-Evolution Decoding (SED) to enhance factuality. During the decoding process, SED first estimates the “inner knowledge distribution,” representing the knowledge the model “knows,” by analyzing the difference between the top-layer logits and interme... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you so much for taking the time to provide your feedback. Your comments and suggestions are invaluable to us, especially regarding our methodologies and presentations. We appreciate this opportunity to address your concerns.
> **"However, this does not imply that their diffe... | Summary: This paper introduces Self-Evolution Decoding (SED), a novel decoding strategy aimed at enhancing the factual accuracy of large language models (LLMs) without the need for external knowledge bases or additional fine-tuning. SED optimizes the outputs of LLMs by refining the logits from the final layer through t... | Rebuttal 1:
Title: Rebuttal by Authors
Comment: **Dear Reviewer,**
Thank you very much for your time and supportive comments. We appreciate your suggestions and are committed to improving our paper to meet your expectations.
> **"A better understanding of the underlying mechanics and theoretical justification for the... | Summary: It introduces a novel decoding strategy named Self-Evolution Decoding (SED) aimed at enhancing the reliability and truthfulness of Large Language Models (LLMs). Unlike methods that depend on external knowledge bases or additional fine-tuning, SED is an intrinsic optimization technique that capitalizes on the s... | Rebuttal 1:
Rebuttal: **Dear Reviewer,**
Thank you so much for taking the time to provide your feedback. Your comments and suggestions are invaluable to us, especially regarding our methodologies. We appreciate this opportunity to address your concerns.
> **"SED may introduce additional computational overhead during ... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We express our gratitude towards all reviewers for their time reviewing our submission and providing constructive feedbacks. Along with our rebuttal, we are including a PDF file containing figures that further support our analysis of why the SED method is effective. This additiona... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Approximation-Aware Bayesian Optimization | Accept (spotlight) | Summary: The authors present an extension of the sparse Variational Gaussian Process framework (SVGP) that is better suited for Bayesian optimization (BO) tasks. This is achieved by a new optimization criterion (EULBO) which aims to optimize the parameters of SVGPs s.t. the training data is fit well while achieving a b... | Rebuttal 1:
Rebuttal: > Figure 1: which points were used as inducing points?
The inducing points are initialized to a set of points selected uniformly at random in the search space and then they are learned via gradient descent by minimizing the loss (ELBO or EULBO) when the GP model is trained on the data.
> Figure... | Summary: The paper proposes a new method for training sparse Gaussian processes (GPs) for large-budget Bayesian Optimization (BO), designed to facilitate the sequential decision tasks inherent in BO. This is achieved by introducing the “expected utility lower bound (EULBO),” which formulates sparse GP training as a joi... | Rebuttal 1:
Rebuttal: > (line 110): Could you define \lambda, m and S?
$m$ and $S$ are defined as the (learned) mean and covariance of the variational distribution $q(u)$ in our SVGP model. $\lambda$ should have been defined as $\lambda = (m, S)$, a shorthand for “all of the variational parameters,” which we omitted. ... | Summary: This paper proposes a modification to sparse variational Gaussian processes (SVGPs) used in Bayesian optimization (BO) to better align the SVGP posterior approximation with the goal of optimizing an acquisition function. The key idea is to jointly optimize the SVGP and the acquisition function using a unified ... | Rebuttal 1:
Rebuttal: > The paper focuses specifically on SVGPs, but it would be interesting to explore whether the proposed approach can be extended to other sparse GP approximations, even those without a tractable ELBO. This would broaden the applicability of the method and strengthen the contribution.
> Can the pr... | Summary: The paper proposes a new approach for scaling Bayesian Optimisation to large datasets. Contrary to previous approaches, which fitted a sparse GP and optimised acquisition function independently, the paper proposes a method, which jointly optimises the variational parameters of sparse GP and searches for the ne... | Rebuttal 1:
Rebuttal: > While authors cite previous work, which proved that the selected action satisfies convergence guarantee, it does not directly prove anything about the performance of optimisation process choosing action in such a way (e.g. regret bound or convergence to optimum). The paper could be made much str... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
UQE: A Query Engine for Unstructured Databases | Accept (poster) | Summary: This paper proposed a Universal Query Engine to directly draw insights from unstructured data. For aggregation queries, this paper designed an unbiased sampling algorithm to solve the problem that it’s hard to apply index on virtual columns. Inspired by the workflow of the C++ compiler, this paper also propose... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments, and we do value your opinion regarding the latency issues. Below we try to justify from the use cases and existing baselines, and also provide more experimental results.
### Q1 **"...latency...far distance from practice..."**
The latency is per... | Summary: This paper propose a new Universal Query Engine (UQE) that directly interrogates and draws insights from unstructured data collections.
Further, a Universal Query Language (UQL) is proposed as a dialect of SQL that provides full natural language flexibility in specifying conditions and operators.
UQE leverages... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments. Below we try to address the potential misunderstandings, and provide more experimental results for justifications.
### Q1 **"...machine-specific code...clarify the details..."**
The analogy is to show how to convert the query (in UQL) to the mach... | Summary: This paper proposes an ambitious new framework for analytics on unstructured databases, the Universal Query Engine (UQE). The authors first present a list of semantics and clauses for querying unstructured databases and propose methods for implementing these functionalities, including indexing and compiling. E... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing insightful and constructive feedback. We provide our response below, as well as extra experimental results in the global response. We look forward to learning your further thoughts.
### **"... difference between BINDER..."**
Yes as the reviewer pointed out, ou... | Summary: This paper proposes an unstructured query language based on a small segment of SQL. Its key feature is that it further supports unstructured texts and images because the authors assume that LLMs work on intra-row texts. During the query, the authors propose using online learning on LLM outputs over batches to ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments. Below we try to address the potential misunderstandings, and provide more experimental results for justifications.
### **"...technical report...the strong assumption that LLMs work on intra-row semantic understanding"**
While generating the virtu... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful feedback, and below we provide experimental updates w.r.t baselines to address the reviewers’ comments.
### **Comparison with baselines like BINDER [1] (review 6SvA and E5QU)**
While [1] and UQE use similar SQL-like language, the focus is very diff... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theory | Accept (poster) | Summary: This paper theoretically analyzes the training dynamics of GAIL, a widely discussed issue in GANs, pointing out that the original GAIL cannot converge to the desired equilibrium. From a control theory perspective, the paper propose C-GAIL, which can achieve asymptotic stability. The paper demonstrate that C-GA... | Rebuttal 1:
Rebuttal: Thank you for your review. We hope our response might provide sufficient evidence that our method is general enough to consider upgrading your score. In particular please note the additional experiments on a new environment, and the three additional variants (two already included in Appendix F, on... | Summary: This paper proposes a stabilized version of the Generative Adversarial Imitation Learning (GAIL) through control theory, addressing limitations related to achieving equilibrium and slow convergence. The authors conduct a theoretical analysis using control theory to investigate the properties that influence equ... | Rebuttal 1:
Rebuttal: Thank you for your review. We're pleased you have seen value in our new control-theoretic approach to understanding and stabilizing GAIL. We appreciate several nuanced observations you raised, and respond to these below.
**Q1. One-step vs full GAIL**
A1. Theoretically, our controllers work under... | Summary: The paper formulated training process of GAIL as a dynamic system. From control theory’s view, authors pointed out GAIL would not converge to the desired state where the generator perfectly matches with the expert policy and the discriminator cannot distinguish generated from expert trajectories. Hence, author... | Rebuttal 1:
Rebuttal: Thank you for your review, we are pleased to have communicated the value of our control-theoretic approach. We hope our clarification around the biased-ness of GAIL, and addition of further experiments, might encourage an increase your evaluation of the paper.
**Q1. The entropy term and bias**
A... | Summary: The paper titled "C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theory" addresses the challenge of training instability in Generative Adversarial Imitation Learning (GAIL), a method used to train a generative policy to imitate a demonstrator's behavior. The authors analyze GAIL's o... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We are pleased we were able to communicate the strengths of the work effectively. We value your feedback on the gap between theory and practice. We have included a general response to this point in the global rebuttal, and here offer more targeted... | Rebuttal 1:
Rebuttal: # Global Rebuttal for common questions
Thanks to all reviewers for their constructive feedback. We were pleased our work received a favorable assessment. Whilst we address each reviewer's questions individually, this global rebuttal summarizes our response to common points highlighted by several ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper addresses the problem of unstable training of Generative Adversarial Imitation Learning (GAIL). To this end, the paper studies the convergence of GAIL from a control-theoretic perspective and proposes to employ a regularization term for the discriminator loss function, which can stabilize the traini... | Rebuttal 1:
Rebuttal: Thank you for your positive review. We are delighted that we were able to communicate the value of our work. We address your questions below, and have provided several new results in the global rebuttal.
**Q1. Figure 2 standard deviation**
A1. The results in Figure 2 are indeed aggregated from ... | null | null | null | null | null | null |
Chat-Scene: Bridging 3D Scene and Large Language Models with Object Identifiers | Accept (poster) | Summary: This paper proposes a 3D Multi-Label Language Model (MLLM) designed to perceive and represent 3D scenes at the object level. To interpret individual object instances, the authors develop object identifiers to convert the 3D scene into a series of distinguishable object tokens and present object-centric represe... | Rebuttal 1:
Rebuttal: ## W1: Difference with Set-of-Mark.
- **Different ways to introduce object identifiers.** Set-of-Mark attaches object Identifiers directly onto the image, relying entirely on the multimodal LLM’s OCR capability to perceive the identifiers from the image. This method is indirect and can introduce a... | Summary: The paper proposes a new representation for 3D multimodal LLMs, a family of foundation models that repurpose LLMs to receive multimodal (visual and linguistic) input. Specifically, the paper advocates for an object-centric representation, where objects are first discovered (detected or segmented) with an off-t... | Rebuttal 1:
Rebuttal: ## W1.1: Concerns about the recent trend of one-stage replacing two-stage methods in 2D.
Thanks for pointing out a promising future direction. However, one-stage models require large-scale training, for example, MDETR used 1.3M image-text pairs for pre-training. Given current limited 3D data (120... | Summary: This paper proposes a 3D MLLM that can understand the 3D environment at the object level. The proposed work designs object identifiers that are projected into language token space and can be understood by LLMs, which unifies several 3D scene understanding tasks into the same format. These object identifiers gi... | Rebuttal 1:
Rebuttal: ## W1: Case study of the reliance on pre-trained detectors.
Please refer to Figure 1 the attached PDF in the “Author Rebuttal”.
We provide several qualitative cases where the detected objects are imperfect (such as incomplete point clouds or an object being separated into two or more parts). Des... | Summary: This paper aims to enhance the efficiency in interpreting individual object instances and improve referencing and grounding capabilities for intricate scene comprehension. The method decomposes the input 3D scene into object identifier tokens. Experimental results on 3D scene-language datasets demonstrate the ... | Rebuttal 1:
Rebuttal: ## W1: Details about multi-modal inputs, detectors, encoders, and LLMs used in other methods.
Please refer to Table 1 in the attached PDF in “Author Rebuttal”.
## W2: Results on ScanQA test set.
| | Test w/ object | | | | Test w/o object | | | |
|---|:---:|:---:|:---:|:---:|:---:|:---:|:... | Rebuttal 1:
Rebuttal: ### **[Our Contributions]**
We are glad to find out that the reviewers generally acknowledge our contributions:
(Contribution)
- The combination of object-centric representations and MLLMs is a nice feature [KdPm], a valuable and innovative research direction [G2or], and it is a natural way for p... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Multi-LLM Debate: Framework, Principals, and Interventions | Accept (poster) | Summary: The paper tackles the issue of echo chambers in multi-agent debate (MAD), a critical area that has not been sufficiently explored theoretically in current MAD research as the authors claimed. The authors propose three interventions – diversity pruning, quality pruning, and refuting misconceptions – to address ... | Rebuttal 1:
Rebuttal: ### A Theoretical Issues:
**[Theoretical Results]** We aim to provide theoretical results that help explain why specific behavior would be observed in the debate process. For example, Theorem 5.2 is intended to help understand why one would observe tyranny of the majority LLM debate. While not su... | Summary: The main questions, that is tackled in this paper is: How can the debate of LLMs be influenced to ensure the best possible outcome. This question is broken down into three parts.
The first part is a theoretical model of debate between LLMs. The model is very intreaging. From the presentation it is not clear, ... | Rebuttal 1:
Rebuttal: **[Applying Debate to Other Settings]** We think that applying debate to settings beyond QA tasks would be very interesting. As of now the majority the works on debate focus on QA tasks (while some investigate translation). We hope that our framework inspires further generalization to other settin... | Summary: The paper presents a framework for multi-LLM debate, gives a detailed overview on drawbacks within multi-LLM debate and proposes several interventions to improve debates. The authors show the effectiveness of their interventions in several experiments on four benchmarks.
Strengths: The task of multi-LLM debat... | Rebuttal 1:
Rebuttal: **[Citations and Specificity]** Thank you for pointing this out. We agree with both points regarding the related work. We will add named citations rather than numbered citations. The specific model versions are provided in Table 2 of the supplement, but we will add this information to line 280 of ... | Summary: In this paper, the authors establish a theoretical framework for multiagent debate. The authors find that LLMs are susceptible to issues such as leaning toward majority opinion (tyranny of the majority) and error expansion during debates (shared misconceptions between models). Thus, the authors propose three t... | Rebuttal 1:
Rebuttal: **[Theoretical Assumptions]** While we agree that we make some stylized assumptions, we believe that these assumptions are reasonable in the context of debate and LLMs. For example, we formulate debate in a Markovian manner (i.e., each agent can only see one round of history). This Markovian prope... | Rebuttal 1:
Rebuttal: # General Rebuttal
Thank you for taking the time to provide feedback on our work.
**[Limitations]** While we discuss our work's limitations within the main body, we realize that this discussion is insufficient and needs to be more overt, focused, and in its own section. We propose adding the fol... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
UniFL: Improve Latent Diffusion Model via Unified Feedback Learning | Accept (poster) | Summary: This paper introduces UniFL, a novel approach for improving diffusion models through unified feedback learning. The objective of UniFL is to enhance visual generation quality, preference aesthetics, and inference acceleration. To achieve this, the paper proposes three key components: perceptual feedback learni... | Rebuttal 1:
Rebuttal: Thanks for your kind words about our good writing, sufficient experiments, and the effectiveness of our method. We would like to answer the proposed questions in the following:
1. **Minor Typos**: Thanks for the suggestion, we will modify these places in our revised version.
2. **Selection of hi... | Summary: Considering that current diffusion models still suffer from several limitations, this paper aims to propose a unified framework, UniFL to address the main existing challenges by applying feedback learning. To respectively solve the issues of visual distortion, poor aesthetic appeal, and inefficient inference, ... | Rebuttal 1:
Rebuttal: Thanks for your kind words about our well-motivated method, sufficient experiments, and good writing. We would like to answer the proposed questions in the following:
1. **Visualization on T2I alignment after acceleration**: We visualize the text-to-image(T2I) alignment performance of SDXL accele... | Summary: The work introduces Unified Feedback Learning (UniFL), a unified framework to enhance diffusion models through feedback learning. It addresses three main challenges in diffusion models: visual quality, aesthetic appeal, and inference efficiency. UniFL comprises perceptual feedback learning, decoupled feedback ... | Rebuttal 1:
Rebuttal: Thanks for your kind words about our writing and contribution to the feedback dataset curation. We would like to answer the proposed questions in the following:
1. **Comparison with ReFL**: There are significant differences between our method and ReFL. Specifically: 1) Given the input prompt, ReF... | Summary: This paper proposes a framework to enhance visual quality, aesthetic appeal, and inference efficiency using various methods, including perceptual feedback learning, decoupled feedback learning, and adversarial feedback learning. Good experimental results are observed.
Strengths: The concept of perceptual feed... | Rebuttal 1:
Rebuttal: Thanks for your kind words about the design of perceptual feedback learning and decoupled feedback learning in our method. We would like to answer the proposed questions in the following:
1. **Clarification on unified design**: We claim our method is a unified design as all the modules are seaml... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their constructive comments. We are delighted that the core contributions of our proposed method are regarded as promising and effective (R-qtQN, R-dHXQ, R-af1S), novel (R-dHXQ), the paper is well-motivated (R-dHXQ) and well-written (R-nwEj, R-dHXQ, R-af1S)... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Retentive Network | Reject | Summary: The paper proposes the Retentive Network (RetNet) as a foundation architecture for large language models. RetNet has a multi-scale retention mechanism with three computation paradigms: parallel, recurrent, and chunkwise recurrent.
The retention mechanism starts with a recurrent modeling formulation and derive... | Rebuttal 1:
Rebuttal: Thanks for your positive comments.
>Q1: Can you provide more insights into why RetNet starts to outperform Transformer when the model size is larger than 2B?
A1: Because of the recurrence nature of the proposed method, the dimension of "hidden states" is critical for model performance, which is... | Summary: The authors propose a linear attention model called RetNet for language modeling, which has a linear training complexity and constant inference complexity.
Strengths: 1. RetNet has both linear time complexity and constant inference memory complexity.
2. RetNet has a chunk recurrent form which can be benefici... | Rebuttal 1:
Rebuttal: >Q1: The authors introduce a new term called "Retention," but this is essentially the same as Linear Attention without the denominator, which has already been proposed in `The devil in linear transformer`.
A1: The term is proposed to avoid confusion with the pioneer work "Transformers are RNNs: F... | Summary: This paper presents Retentive Network (RetNet), a family of efficient models that incorporate exponential decay within a linear attention-like structure. RetNet shares similarities with state-space models and linearized attention, enabling both training parallelism and O(1) inference cost. Additionally, RetNet... | Rebuttal 1:
Rebuttal: Thank you for the positive review.
>Q1: The inference results in Figure 5 start with 2048. What's the inference speed for shorter sequences?
A1: The RetNet inference speed remains almost constant across length, and Transformers' speed can be extrapolated according to Fig 5. As shown in Figure 5(... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Off-Policy Selection for Initiating Human-Centric Experimental Design | Accept (poster) | Summary: The paper presents the First-Glance Off-Policy Selection (FPS) framework, aimed at improving policy selection for human-centric systems (HCSs) like education and healthcare, by addressing participant heterogeneity. FPS groups participants with similar traits, augments each sub-group with trajectories generated... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and efforts on evaluating our work. Please find our point-by-point response below.
Q1. The proposed method is applicable only to problems with a finite number of policies, as policy selection is based on evaluating each candidate target policy separately.
R1. Go... | Summary: This paper studies off-policy selection in healthcare settings where users are heterogeneous and in situations where new users can appear in the policy deployment phase. To deal with new participants, this paper proposes a two-stage evaluation procedure: (1) learning a partitioning function of users and (2) ch... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and efforts on evaluating our work. Please find our point-by-point response below.
Q1. How is this sub-group partitioning actually conducted?
R1. We used an off-the-shelf algorithm called Toeplitz inverse covariance-based clustering (TICC) [1] to obtain the init... | Summary: This paper introduces First-Glance Off-Policy Selection (FPS), a novel approach to off-policy selection (OPS) in human-centric systems (HCSs) such as healthcare and education, where the heterogeneity among participants requires personalized interventions.
FPS addresses this by segmenting participants into su... | Rebuttal 1:
Rebuttal: Thank you for your time and efforts on evaluating our work, and your positive comments that the paper is making an important impact. Please find our point-by-point response below.
Q1. Assumption of Independent Initial State Distributions The FPS framework assumes that the initial state distributi... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Expressive Power of Tree-Structured Probabilistic Circuits | Accept (poster) | Summary: The paper studies the how expressive probabilistic circuits (PCs) whose underlying structure is a tree are compared to those whose structure is a DAG. This is motivated by the fact that algorithms for learning PCs usually construct trees, and thus do not potentially take advantage of the potentially more expre... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for acknowledging our contributions, and we deeply appreciate for pointing out the cluttered notation and the limitations of the conditional lower bound. The following is our responses for the questions.
> In Sec. 1.1, you state that "[the] restriction on the graph... | Summary: This paper considers the structure expressive power of Probabilistic Circuits (PCs) from a theoretical perspective. Specifically, this paper explores how PCs with directed acyclic graph (DAG) structures and those with tree-like structures contrast each other in terms of circuit size and expressive power. The c... | Rebuttal 1:
Rebuttal: We are sincerely grateful to the reviewer for acknowledging the novelty of our work and its presentation, and we deeply appreciate the insightful questions.
> Could the authors provide some new insights on how this work can help further research in PC structure learning?
We agree with the review... | Summary: This paper studies the expressive power (i.e., expressive efficiency) of tree-structured probabilistic circuits (PCs). Specifically, this paper shows that:
- Any decomposable PC over n random variables can be transformed into a tree-structured PC of depth O(log n) with n^{O(log n)} nodes.
- A super-polynomial ... | Rebuttal 1:
Rebuttal: We are sincerely grateful to the reviewer for appreciating our contributions. | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning-Augmented Dynamic Submodular Maximization | Accept (poster) | Summary: Authors consider submodular maximization with cardinality constraint
in dynamic setting: Algorithm sees a series of $n$ insertions and deletions
of elements and has to maintain a subset of active elements
maximizing given submodular function.
Best known algorithms for this problem by Lattanzi et al., Monemizad... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments!
- *"where does dependence on epsilon appear in your bounds in Theorem?*"
Our approximation is 1/2 - epsilon. In addition, the query complexity per update during the streaming phase and the query complexity during the precomputation phase both have a poly... | Summary: The authors studied monotone submodular maximization under a cardinality constraint in a dynamic model where predictions of insertions and deletions are given. In submodular maximization, a ground set of elements and a function assign a value to any subset of these elements. A function is submodular if adding ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments!
- *"Line 37: cited [30] twice"*
- *"Some sentences are too long and make it hard to read. For example, look at the sentences from lines 60 to 63, and the next sentence."*
Thank you, we have fixed the double citation and shortened these sentences to “A dy... | Summary: The paper studies the monotone dynamic submodular maximization problem under a cardinality constraint $k$ in the framework of algorithms with predictions. The authors consider a prediction model where the insert and delete times of the elements are predicted at time 0, and for any window size $w$, the predicti... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments!
- *“In the case where k=o(logn) and the prediction error η=Ω(n), the update time of the algorithm is worse than the update time O(k⋅polylog(k)) achieved in reference [7]. Thus, in some cases, the algorithm performs worse than a worst-case algorithm with... | Summary: This paper studied the dynamic submodular maximization problem with predictions. The goal of the problem is to maintain a high-quality solution in the presence of insertions and deletions. The main contribution is leveraging predictions, in the form of the pattern of insertions and deletions, to accelerate the... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments!
- *“If there exists one prediction with poor accuracy, it might dramatically hurt the results.”*
We believe that there might have been a misunderstanding about the prediction error. The prediction error is the number of elements whose predicted inserted... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Toward Efficient Inference for Mixture of Experts | Accept (poster) | Summary: The authors propose three techniques to speed up inference of mixtures of experts: 1) dynamic gating: during the all-to-all exchange process, the authors enable sending different number of tokens to each expert, which requires sorting and sending an extra message about the number of tokens; 2) expert buffering... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review and suggestions. The reviewer raised several questions which we address in order:
- **Is this paper suitable for NeurIPS?**
We note that per the [call for papers](https://neurips.cc/Conferences/2024/CallForPapers), NeurIPS infrastructure track calls for submis... | Summary: This paper addresses the challenges of efficient inference for Mixture-of-Experts (MoE) models. The authors identify key inefficiencies in MoE inference, particularly in language modeling and machine translation tasks. They propose three main optimization techniques: dynamic gating, expert balancing, and exper... | Rebuttal 1:
Rebuttal: We thank the reviewer for the very detailed review and suggestions. Here are our responses to each point raised by the reviewer:
- **Including evaluation on representative configurations such as DeepSeek-MoE (2401.06066).**
We appreciate the reviewer’s suggestion to include a discussion on the D... | Summary: This paper dives deep into the Mixture of Experts architecture, trying to identify its weaknesses and inefficiencies while coming up with novel solutions to improve the architecture in terms of token throughput, memory use and load balance. The authors find out that the static gating function that assigns toke... | Rebuttal 1:
Rebuttal: We are encouraged to hear that the reviewer found our work to be thorough and our method effective. Here are our responses to each point raised by the reviewer:
- **On details about how the perplexity or BLEU scores were impacted by adding these optimizations.**
Our optimizations will not negativ... | Summary: This paper analyzed the behavior of standard MoE Transformer workloads and pointed out the bottleneck in inference latency and memory usage. Then it introduces a Dynamic Gating policy instead of static-size computation to improve the efficiency of the gating operation. It also proposes Expert Buffering which o... | Rebuttal 1:
Rebuttal: Thank you for your enthusiastic and encouraging review of our work. Below are our responses towards each point and question raised in the review:
- **The meaning of notation S, C, E and D in Section 4.**
We appreciate the reviewer pointing out that these notations are not clearly defined when th... | Rebuttal 1:
Rebuttal: We would like to thank reviewers for providing us with valuable feedback.
We have taken note of the concerns raised by each reviewer and addressed them in detail. Here, we provide responses to the most shared questions first followed by a detailed response to each reviewer's concern in the rebut... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DI-MaskDINO: A Joint Object Detection and Instance Segmentation Model | Accept (poster) | Summary: This work investigates the detection-segmentation imbalance issue in MaskDINO. It proposes DI-MaskDINO model with the residual double-selection mechanism to alleviate the imbalance. The framework mainly includes De-Imbalance and Balance-Aware Tokens Optimization. Experiments prove the effectiveness.
Strengths... | Rebuttal 1:
Rebuttal: We are greatly encouraged by your positive comments, including "the finding of detection and segmentation imbalance at the beginning of MaskDINO is interesting" and "the whole framework is clear and easy to follow".
**[W1]** Thank you very much for the insightful observation. To clearer clarify t... | Summary: This paper focuses on the detection-segmentation imbalance issue and proposes DI module with the residual double-selection mechanism to alleviate the imbalance; moreover, Balance-Aware Tokens Optimization (BATO) is proposed to guide the optimization of the initial feature tokens. The proposed method termed DI-... | Rebuttal 1:
Rebuttal: We appreciate your positive comments on our work, such as "the proposed method achieves SOTA results" and "the paper is clear and the experimental results are detailed". There are two concerns regarding technological innovation and whether the performance improvement stems primarily from mitigatin... | Summary: This paper initially observes that in the current state-of-the-art model MaskDINO, the performance of object detection lags behind instance segmentation at the initial layer of the transformer decoder, resulting in a performance imbalance phenomenon.
To explore whether this "performance imbalance issue" is a ... | Rebuttal 1:
Rebuttal: **[W1]** We are greatly encouraged by your positive comments "the paper identifies a novel issue, and this work is of significant importance for advancing research in the fields of object detection and instance segmentation, particularly by providing new perspectives and solutions for dealing with... | Summary: The paper starts from an observation regarding imbalance in the intermediate results between detection and instance segmentation, which motivates the authors to propose DI-MaskDINO, which tries to improve the imbalance through the DE-Imbalance module and Balance-Aware Tokens Optimization module. Evaluated on C... | Rebuttal 1:
Rebuttal: We are greatly encouraged by your positive comments, including "paper is generally well-written and technically solid" and "the starting point of det/seg imbalance sounds interesting".
**[W1]** We supplement the experiment under the condition of epoch = 50 when the backbone is Swin-L. The results... | Rebuttal 1:
Rebuttal: We appreciate the reviewers for their constructive comments and suggestions.
We are particularly encouraged that the reviewers unanimously acknowledge **our work is interesting** (aN5C, 5ayP, TyXi, and KbFf). Reviewers commend us for **achieving state-of-the-art results** (aN5C, 5ayP, TyXi, and K... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Verified Code Transpilation with LLMs | Accept (poster) | Summary: This paper proposes an LLM-based approach (LLMLIFT) to building verified lifting tools. LLMLIFT leverages LLMs to generated both code and proof annotations together. For four real-world DSLs, LLMLIFT not only outperforms previous symbolic-based tools in both the number of benchmarks transpiled and transpilatio... | Rebuttal 1:
Rebuttal: **LLMs just seems to generates loop invariants for validation and then combine them with validators. Compared with traditional VL, does the LLMs-based VL proposed in this paper have any other advantages besides being better in finding IR expression sequences?**
To clarify, LLMLift does not direct... | Summary: This work proposes LLMLift that leverages large language models (LLMs) to perform program transpilation. It first uses a prompt to lift the source program into an intermediate representation of the operators in the target language, called a program summary. Then, it prompts the LLM to generate loop invariants ... | Rebuttal 1:
Rebuttal: **How do you produce the target program from the program summary?**
Once we have the verified program summary in the IR, the code generation phase uses syntax-driven rules to map IR operators to the concrete syntax of the target DSL. Below we show a snippet of a code generation function that tran... | Summary: The authors propose an approach named LLMLift, which utilizes large language models (LLMs) to achieve verified code transpilation. LLMLift not only translates a given program into its corresponding equivalent in the target language but also generates proofs for functional equivalence. The paper claims that thi... | Rebuttal 1:
Rebuttal: **Why use GPT-3.5?**
The use of GPT-3.5 in Figure 3 was to illustrate a key challenge in applying LLMs to the task of verified lifting: the difficulty these models face in generating correct code for DSLs, even when the DSL is not particularly new. Our experiments with GPT-3.5 showed that despite... | Summary: The paper aims to solve the problem of conversion of source code from one language to any domain specific language (DSL) leveraging LLMs eliminating manual intervention. The primary contribution lies in the domain of automating the transpilation along with providing functional correctness proof simultaneously.... | Rebuttal 1:
Rebuttal: **Why GPT-4**
With LLMLift, our objective was to demonstrate that LLMs can be effectively used for the task of VL without requiring fine-tuning. We needed a model that was good in two things:
1. Instruction Following: The model needed to generate programs strictly using the defined operators in a... | Rebuttal 1:
Rebuttal: We thank the reviewers for their helpful comments and suggestions. We will incorporate all suggestions and clarify the confusion in our next version. Below, we address some of the common concerns that the reviewers raised.
**How do you verify the equivalence of the source program and the program... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Synthesize, Partition, then Adapt: Eliciting Diverse Samples from Foundation Models | Accept (poster) | Summary: This paper proposes a method to generated diverse responses for the language model. Given a synthetic finetuning dataset and test dataset, they calculate the importance of the finetuning data on each test data using methods such as influence function, and then partition the dataset into several smaller ones. F... | Rebuttal 1:
Rebuttal: Thank you for your insightful review. We'd like to address your main concerns.
> Parallel Generation and Memory Efficiency
Recent advances in serving multiple LoRA adaptations in parallel significantly mitigate the need to store multiple full checkpoints. Notably, the S-LoRA system [1] demonstrat... | Summary: This paper presents a framework for eliciting diverse outputs from language models while maintaining quality/accuracy. The framework consists of: first partitioning a (synthetic) dataset of supervised instruction tuning data, then training parameter efficient model adapters on each partition, and finally at in... | Rebuttal 1:
Rebuttal: Thank you for your insightful review. We'd like to address your main concerns.
> Accuracy on NL tasks
The accuracy of SPA methods (influence-based, lexical-based, and random) all achieve similar accuracy to the single model, reaching the same conclusion as the pass@1 metric in Fig. 4a. We didn't ... | Summary: The paper introduces Synthesize-Partition-Adapt (SPA), a framework designed to generate diverse and high-quality responses from foundation models. SPA uses synthetic data and data attribution methods to partition data into subsets, training multiple model adaptations for these subsets.
Strengths: I believe th... | Rebuttal 1:
Rebuttal: Thank you for your insightful review. We'd like to address your main concerns.
> Motivation of why doing SPA and why doing so leads to diverse samples. Role of synthetic data and the generalizability of it.
We would first like to clarify that LLMs must typically go through synthetic data tuning b... | Summary: This paper aims to improve the diversity of generations of LLMs. Current methods are not great because when they offer more diversity, they lose in quality (for instance worse performance). This is for instance the case of temperature sampling. In this paper, they propose synthetise partition and adapt which c... | Rebuttal 1:
Rebuttal: Thank you for your insightful review. We'd like to address your main concerns.
> In-context v.s. Fine-tuning
We can formulate the difference as sampling from $P_{\phi_1} (y|x)$ and $P_{\phi_1}(y|x')$ for in-context versus $P_{\phi_1}(y|x)$ and $P_{\phi_2}(y|x)$ for our approach ($\phi$ denotes mo... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable feedback. Here, we address the concern raised by reviewer j8SA regarding limited comparisons.
Our initial comparisons focused on ablations as SPA represents a novel multi-model adaptation method addressing diminishing returns of abundant synthetic data. U... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Interpretable Generalized Additive Models for Datasets with Missing Values | Accept (poster) | Summary: The paper discusses challenges posed by missing data in important datasets for machine learning models. Existing methods like imputation or using indicator variables for missingness can compromise model interpretability or introduce complexity and reduced sparsity. The authors propose M-GAM, a sparse, generali... | Rebuttal 1:
Rebuttal: Thank you for your review! We appreciate your recognition that this work is well written, novel, and contains extensive experiments, and we are open to discussing any additional concerns you might have.
> The proposed method is constrained by its reliance on l_0 regularization.
While $\ell_0$ re... | Summary: The paper presents M-GAM, a novel generalized additive model that that incorporates missingness indicators while maintaining a sparse model via l_0 regularization. Results shows that on augmented datasets with missing at randomness M-GAM provides better performance; on real-world (not augmented) datasets, M-GA... | Rebuttal 1:
Rebuttal: We appreciate your review, and your recognition of this work’s novelty, theoretical foundations, and clear writing. We have responded to each of your criticisms below, and look forward to any continued discussion.
> My understanding is that the setting considered in this paper seems to be limited... | Summary: The paper introduces M-GAM, a novel extension of Generalized Additive Models (GAMs) designed to maintain interpretability while handling datasets with missing features. By incorporating missingness indicators and their interaction terms through ℓ0 regularization, M-GAM balances accuracy and sparsity. The model... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review – we appreciate your recognition that sparsity and interpretability are particularly important for practitioners. We hope we have addressed each of your concerns below, and look forward to any ongoing discussion.
> The title should convey that the study is limi... | Summary: This paper introduces M-GAM, which incorporates concept of missingness into Generalized Additive Model(GAM). Since GAM represents arbitrary function with sum of univariate functions which take each input feature as input, M-GAM maintains sparsity and interpretability for inference with missing data.
Strengths... | Rebuttal 1:
Rebuttal: Thank you for your review. We appreciate your recognition of the novelty of this work and its strong experimental backing. We look forward to discussing any ongoing questions or concerns you may have.
> I think the main weakness of M-GAM is its performance which just barely match other baselines'... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their thoughtful reviews. We have responded to each reviewer separately. Attached to this message, please find figures used in responses to individual reviewers.
We look forward to further discussion in the days to come.
Pdf: /pdf/198c67d05e608ff91cad783a0f134904bd... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper "Interpretable Machine Learning for Datasets with Missing Values" proposes generalized additive models incorporating missingness indicators and their interaction terms with sparseness ensured by l0 regularization. Therefore, the authors combine GAMs with the Missing indicator method and l0 regulariza... | Rebuttal 1:
Rebuttal: Thank you for your review. We hope we have addressed your primary concern below, and look forward to any further discussion.
>Proof only works since the unobserved noise for k1 and k2 is chosen as it is... If the assumption is switched or equal signs added the proposition will fall apart. It seem... | null | null | null | null | null | null |
The Iterative Optimal Brain Surgeon: Faster Sparse Recovery by Leveraging Second-Order Information | Accept (poster) | Summary: This paper proposes a theoretically convergent iterative Optimal Brain Surgeon (OBS) algorithm, which generalizes the classic Iterative Hard Thresholding (IHT)-based algorithms by incorporating approximate second-order information in the sparse projection step. The author also provides practical variants of th... | Rebuttal 1:
Rebuttal: Thank you for the valuable questions and positive comments. We address your questions below:
**Q1, Regarding the gap between the practical and theoretical schemes:**
We note that for the theoretical scheme we have $|| \theta\_{t+1} - \theta^* ||\_2 \leq \left(1+ \sqrt{\frac{L}{\mu}\frac{d-k^*}{d... | Summary: This work combines second-order curvature information with sparse recovery algorithms to demonstrate, both theoretically and empirically, that the curvature information leads to improved convergence rates and generalization performance in post-training iterative pruning and sparse recovery
Strengths: * This w... | Rebuttal 1:
Rebuttal: Thank you for the detailed review, as well as your valuable questions and comments. We address your questions and concerns below
**Q1 and W1 Regarding the training time of the pruning results**
For the experiments on ViTs (Table 1 in the paper), it took about 4 hours to run 100 iterations and t... | Summary: The paper presents a new family of algorithms called Iterative Optimal Brain Surgeon (I-OBS), extending the post-training Optimal Brain Surgeon (OBS) framework to an iterative setting commonly used in sparse recovery. I-OBS algorithms utilize second-order information during the sparse projection step, enhancin... | Rebuttal 1:
Rebuttal: Thank you for the valuable questions and comments, we address the questions and comments below:
**W1. The difference between the practical and theoretical version of the algorithm**
We compare the practical and theoretical version of Algorithm 1 below:
(1) The way of choosing the mask is differen... | Summary: Having clarified my concerns in their rebuttal, I have updated my score for acceptance.
----
This paper presents a variant of the classic Optimal Brain Surgeon (OBS) method to iteratively prune multiple weights of a neural network at once, with each step consisting of obtaining a pruning mask for the fraction ... | Rebuttal 1:
Rebuttal: Thank you for your detailed reviews, as well as the insightful comments and questions. We address your comments and questions below
**Q1**. CBS assumes the model gradient is zero for post-training pruning, while I-OBS does not, making I-OBS more general. We retain the gradient term because model... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their detailed feedback. We briefly summarize our responses here for clarity:
1. One general question was regarding the positioning of our work relative to prior work on pruning.
In this context, our work tries to provide the first rigorous analysis of a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Adapting Diffusion Models for Improved Prompt Compliance and Controllable Image Synthesis | Accept (poster) | Summary: This paper propose a new family of diffusion model: FG-DM for better prompt compliance. Unlike traditional diffusion model, FG-DM models the joint distribution of images and conditioning variables like semantic, sketch, depth or normal maps via a factor graph. These extra factors largely boost prompt complianc... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments and useful feedback on the paper.
Q1. How much more time do we need for the conditional generation chains? (for example, how much more time do we need when we use one or two conditions compared to original SD?)
Regarding time, we make several con... | Summary: This paper proposes a method for sequentially generating images using a frozen SD. Starting from a text prompt, the process iteratively generates several visual conditions and images, with each step depending on the previous ones. The model uses the VAE in SD to encode and decode visual conditions and employs ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments and useful feedback on the paper.
Q1. The newly introduced iterative process adds extra loop to each generation step.
Although the proposed FG-DM adds an extra loop in the generation process, in Table 4 we show that using lower resolution and few... | Summary: The paper proposes unified generation framework of simultaneously generate image, segmap. depthmap, etc.
Strengths: The idea of simultaneous generation of images with various conditioning maps is very interesting.
Weaknesses: 1. Although the method shows good performance in image editing for generated images... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments and useful feedback on the paper.
Q1. Does it can be applied to inverted real images? Does the framework can still predict the segmentation map and keypoints of given real images? If it is still applicable to real images, then please show some res... | Summary: This paper introduces a Factor Graph Diffusion Models (FG-DMs) to address limitations in current generative models. FG-DMs model the joint distribution of images and conditioning variables using a factor graph decomposition, offering advantages like efficient sampling for better prompt compliance, fine-grained... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments and useful feedback on the paper.
Q1. The proposed method seems promising, however, it's very important to compare with current SOTA works to justify its effectiveness. The author only put Stable Diffusion in Table 5, which is not fair. Please inc... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and comments. The rebuttal for individual reviews are posted below each review. Here we list the summary of the responses.
- In the rebuttal pdf, we added the qualitative results of ablation study with and without attention distillation loss (Figure 1). ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Impact of Initialization on LoRA Finetuning Dynamics | Accept (poster) | Summary: The paper investigates the impact on training dynamics of two initialization schemes for LoRA. For this purpose the authors investigate the asymptotic behaviour of activations and weights for LoRA adapters. The authors find that init[A] where A is intialized randomly and B is initalized with zeros leads to mor... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's feedback, but we respectfully disagree with several points and believe there are significant misunderstandings in their assessment. We address these below:
1) **Initialization schemes**: The reviewer suggests investigating "different initialization schemes aside from ... | Summary: This paper investigates the impact of initialization schemes on the finetuning dynamics of Low Rank Adaptation (LoRA) in large language models. The authors compare two initialization approaches: Init[A], where A is initialized randomly and B to zero, versus Init[B], where B is initialized randomly and A to zer... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive and constructive comments. We address their main questions below:
1) **Sensitivity to LoRA rank**: Our results hold for different rank values. For LoRA rank $r$, we used two primary values: $r=8$ (for RoBERTa) and $r=64$ (for LLama). We also conducted limi... | Summary: The paper analyzes the impact of different initialization techniques for A and B matrices in LoRA adapters. Typically, either A or B matrix is initialized with zero while the other is initialized form a Gaussian distribution. This is done so that fine-tuning starts with LoRA adapters ($A \times B = 0$) having ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments. We provide some details that explain the scope and contributions of our paper.
1) **Scope and contributions**: As correctly noted, in low-rank adaptation (LoRA), we generally aim to initialize the model such that $BA=0$. This naturally leads to t... | Summary: Finetuning large language models has become a common technique among practitioners, however due to the memory footprint of practically viable LLMs, there is a dire need of techniques that allow memory-lightweight finetuning (PEFT). Among those techniques LoRA has gained immense popularity. In LoRA a dense matr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback. We believe there are some misunderstandings and we take this oppotunity to address them.
1) **Connection to other LoRA variants**: We emphasize that we do not introduce a new method in this paper; rather, we study the finetuning dynamics of ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Online Estimation via Offline Estimation: An Information-Theoretic Framework | Accept (poster) | Summary: This work studies the possibility of converting offline estimation algorithms into online estimation algorithms using an information-theoretic approach. It introduces the Oracle-Efficient Online Estimation (OEOE) framework, where the learner interacts with a data stream indirectly through a sequence of offline... | Rebuttal 1:
Rebuttal: Weakness:
>> While the paper provides a comprehensive theoretical analysis, it acknowledges the computational inefficiency of the proposed Oracle-Efficient Online Estimation (OEOE) framework in general cases. The authors could improve this aspect by exploring potential heuristic approaches or app... | Summary: This paper proposes an algorithm that can convert offline estimation oracles into online estimation algorithms in a black-box fashion. The conversion is built within the OEOE framework, which manipulates the learner, offline oracle, and environment simultaneously. The authors also propose certain upper bounds ... | Rebuttal 1:
Rebuttal: >> I understand that many works assume knowing the intrinsic of the data-generating, but assuming that is known would exclude some cases of statistical estimation, which might reduce the generality of OEOE.
The OEOE is concerned with transforming an offline guarantee into an online one. Even when... | Summary: This paper studies the methods to convert offline estimation algorithms into online estimation algorithms in a black-box fashion. This work introduce a new protocol, Oracle-Efficient Online Estimation, which provides an information-theoretic abstraction of the role of online versus offline estimation.
Strengt... | Rebuttal 1:
Rebuttal: Weakness:
>> The organization of the paper requires significant improvement as it is currently hard to follow. The introduction section occupies almost half of the main text, leaving the main methodology and theoretical results insufficiently discussed and lacking in clear theoretical insights.
... | Summary: The paper considers an online estimation problem, in which the learner does not have a direct access to the past values (labels), but rather instead just an oracle access to an offline estimator based on past covariate-value pairs. The main assumption is that the loss of these offline estimators is bounded. Th... | Rebuttal 1:
Rebuttal: Weakness:
>> The main proposed algorithm is somewhat brute-force and just keeps the hypotheses that agree with the data – in this case it is covariance-estimator pairs.
This is the optimal approach from an information-theoretical perspective, achieving the optimal minimax guarantee for our setup... | Rebuttal 1:
Rebuttal: ## General rebuttal:
### Theoretical insights for Theorem 3.1:
We would like to highlight the technical challenges and our contributions. Online learning has been extensively studied for decades. The most classical algorithm is the exponentially weighted aggregation, which weights all parameter ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SfPUEL: Shape from Polarization under Unknown Environment Light | Accept (poster) | Summary: This paper proposes SfPUEL, which estimates surface normals and material (metallic or dielectric) under unknown environmental light using a single polarization image. The proposed network integrates polarization information with photometric stereo priors using a photometric stereo feature extractor and a polar... | Rebuttal 1:
Rebuttal: Thanks for the constructive reviews and questions. Below, we address the concerns raised by Reviewer bz5V.
> W1: Comparisons with other material segmentation methods.
Material segmentation in our paper is only for boosting the performance of normal estimation, we do not intend to make the accur... | Summary: This paper addresses Shape from Polarization under Unknown Environment Light (SfPUEL) from a single polarimetric image. Existing SfP methods have the ambiguity of surface normal caused by unknown illumination and materials and make some assumptions on reflection type or illumination. This paper introduces a no... | Rebuttal 1:
Rebuttal: Thanks for the insightful reviews and valuable questions. Below, we address the concerns raised by Reviewer 7r7M.
> W1: Photometric stereo methods take as input polarization images
As suggested, we further verify the photometric stereo network taking as input polarization images by qualitative a... | Summary: This paper tries to solve normal estimation tasks using a single image captured from a polarization camera under unknown environmental light. To handle unknown environmental light conditions, this paper adopted a deep learning-based strategy to take advantage of inductive bias from the training dataset. Compar... | Rebuttal 1:
Rebuttal: Thanks for the detailed and constructive suggestions. Below, we address the concerns raised by Reviewer 66Sa.
> Q1.1 Analysis of photometric stereo (PS) and shape-from-polarization (SfP)
Please refer to *Further analysis of photometric stereo taking as input polarization images* in the global re... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful and valuable comments. We are encouraged by reviewers’ positive comments: “This paper proposes a “novel” framework for shape-from-polarization” (Reviewers **7r7M** and **bz5V**); “the quality of the results seems fairly good” (Reviewer **66Sa**) and ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
OneRef: Unified One-tower Expression Grounding and Segmentation with Mask Referring Modeling | Accept (poster) | Summary: The authors propose a unified one-tower referring framework that introduces the MRefM paradigm to capture the referential relationships between vision and text. They demonstrate the effectiveness and generality of MRefM across three settings on REC, PG, and RES tasks, consistently achieving good results.
Stre... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful feedback. Please find below our responses to the questions raised in the review.
Firstly, due to the character limit in the reply box, we have included the figure and experimental tables in the PDF file at the top of this page. Please click on t... | Summary: This paper proposes a Mask Referring Modeling (MRefM) paragram and a unified and extremely concise grounding and referring segmentation framework named UniRef that no longer requires the fusion or interaction of the Transformer structure and the special grounding tokens. A masked referring modeling (MRefM) is ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful feedback. Please find below our responses to the questions raised in the review.
> **Q1. Lack of discussion of related work [a]. I suggest discussing the difference between this paper's referring-aware mask language modeling and masked contrasti... | Summary: This manuscript proposes UniRef, a framework aimed at unifying visual and linguistic feature spaces for referring expression comprehension and segmentation. The key presented is the Masked Referring Modeling (MRefM) paradigm, which includes referring-aware MIM and MLM.
This approach seeks to streamline the ar... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful feedback. Please find below our responses to the questions raised in the review:
Firstly, due to the character limit in the reply box, we have included the figure and experimental tables in the PDF file at the top of this page. Please click on th... | null | null | Rebuttal 1:
Rebuttal: Dear reviewers, area chairs, senior area chairs, and program chairs,
We sincerely thank for the valuable and the thoughtful comments. It is pleasure that this work has been recognized by the three reviewers, including "the MRefM paradigm effectively captures the referential relationship", "a simp... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Association of Objects May Engender Stereotypes: Mitigating Association-Engendered Stereotypes in Text-to-Image Generation | Accept (spotlight) | Summary: The authors observe that some stereotypes only appear when an association of objects is required in T2I and propose to address association-engendered stereotypes for the first time. They use a pre-trained Text-Image-Text CLIP to map a prompt to its potential stereotypes and generate sensitive constraints using... | Rebuttal 1:
Rebuttal: **Weaknesses (the first part)**
- **W1 (TIT CLIP mapping issue):** In fact, the association-engendered stereotype in T2I can only manifest in the generated images. Therefore, we need to use images as a medium to connect the relationship between prompts and stereotypes. In our task, we consider ea... | Summary: This paper presents a novel framework (MAS) to address biases in text-to-image (T2I) models. Traditional methods focus on individual object stereotypes but fail to tackle stereotypes arising from object associations. MAS models the stereotype issue as a probability distribution alignment problem, utilizing a T... | Rebuttal 1:
Rebuttal: **Weaknesses**
- **W1 (the complexity issue):** As shown in Section 4.2 of the paper, we conducted experiments to evaluate the impact of MAS on the computational load of the T2I diffusion model. Table 6 demonstrates that MAS effectively mitigates stereotypes while maintaining image generation eff... | Summary: This paper presented the first step to mitigate association-engendered stereotypes in Text-to-Image (T2I) diffusion models. A probability distribution alignment problem was first formulated, and then a probability distribution model was constructed for non-association-engendered and association-engendered ster... | Rebuttal 1:
Rebuttal: **Weaknesses**
- **W1 (the algorithm issue):** The output of Algorithm 1 is the embedding of prompt, image, and stereotype.
- **W2 (about the suggestions of output examples):** We sincerely appreciate your constructive suggestions. We will add examples of outputs from MAS and other baselines to t... | Summary: The paper proposes a framework to detect and mitigate stereotype association in Text-to-Image models. They conduct extensive experiments to demonstrate the usability of the framework.
Strengths: + The authors aim to assess "association-engendered" stereotypes in T2I models. They model the stereotype mitigatio... | Rebuttal 1:
Rebuttal: **Weaknesses**
- **W1(the acronym issue):** We sincerely appreciate your constructive suggestions. The original acronym, Text-Image-Text CLIP, was chosen from an encoding perspective, focusing on the encoding of Text (prompt, stereotype description) and Image. We agree that this name may not be p... | Rebuttal 1:
Rebuttal: **Dear Chairs and Reviewers,**
We kindly thank all the reviewers for their time and for providing valuable feedback on our work.
In response to the reviewers, we have added the **`supplementary_images.pdf`** file. This file contains annotations and explanations for the data used in our expe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MiniCache: KV Cache Compression in Depth Dimension for Large Language Models | Accept (poster) | Summary: This paper proposes a KV cache compression method by merging keys and values of consecutive layers. Based on the empirical observation that keys and values of consecutive layers after the mid-depth layer have high cosine similarity, the authors propose a merging strategy using angular interpolation. Additional... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for the valuable comments.
**Q1: Ad-hoc design choices (mid-layer merging, only two layers) need improvement.**
**A1:**
It is worth noting that MiniCache is the pioneer work to explore KV cache compression along the depth dimension (Lines 48-50). This insight has been high... | Summary: Authors proposed a KVCache Compression Method, Minicache, is introduced as a method to efficiently compress the Key-Value (KV) cache in large language models (LLMs) by leveraging the high similarity of KV states between adjacent layers in the middle-to-deep portion of LLMs. This compression is achieved by dise... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for the valuable comments.
**Q1 : Concern about computational overhead during reparametrization and restoration stages. Does this compression strategy increase computational overhead?**
**A1:**
Reparametrization-based compression involves computing magnitude and direction ... | Summary: The paper proposes a novel method called MiniCache for compressing the KV cache in large language models (LLMs) by merging cache states across layers. The authors argue that this approach significantly reduces the memory footprint and enhances inference throughput without significant performance loss. The pape... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for the valuable comments.
**Q1: Lack of Baseline Comparisons. In Table 1, the authors only include quantization methods for performance comparison, neglecting other KV cache eviction methods such as those proposed in [14] and [15].**
**A1:** We have included the baselin... | Summary: This paper introduces MiniCache, a novel approach to compressing the Key-Value (KV) cache in large language models (LLMs) to enhance inference efficiency. The KV cache is crucial in storing key-value states of previously generated tokens, significantly reducing redundant computations and lowering latency durin... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for the valuable comments.
**Q1: Distance-Based Threshold for Retention (Line 226)**. **Why did you choose a distance-based threshold instead of the merging ratio of overall tokens as the control for retention? Can you show the effect on accuracy and efficiency as the ratio... | Rebuttal 1:
Rebuttal: ## Response to all reviewers
We sincerely thank all reviewers for their valuable comments.
All reviewers agree that:
**The Novel Approach:**
- "The paper introduces a unique method …. This novel perspective impacts the field in a new, impactful way." (Reviewer TyMG)
- "The idea ... introduces ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Multi-scale Consistency for Robust 3D Registration via Hierarchical Sinkhorn Tree | Accept (poster) | Summary: This paper proposed a hierchical skinhorn tree approach to extract the correspondences that are consistent across multiple feature scales for the point cloud registration task. Besides, an overlap-aware module is proposed to better locate the correspondences around the overlap regions. The proposed methods are... | Rebuttal 1:
Rebuttal: We thank the reviewer for your valuable comments and suggestions.
**Response to Weakness 1**: Thank you for pointing out this issue. We apologize for any misunderstanding caused by the term "inlier correspondence" in Eq. (1). It should be revised as "putative inlier correspondences". As you men... | Summary: This paper presents a method to enhance the performance of GeoTransformer by filtering outlier correspondences at the coarse level using a multi-scale, overlap-guided Sinkhorn algorithm. It introduces an overlap-aware Sinkhorn Distance designed to detect potential overlapping points, thereby enhancing the robu... | Rebuttal 1:
Rebuttal: We first would like to thank the reviewer for giving us valuable comments.
**Response to the comments on our work (first paragraph of Weaknesses)**: Thank you for your insightful comments on our work. We understand your concern that our method builds upon the foundation laid by existing work. How... | Summary: This paper studies the problem of correspondence retrieval for point cloud registration. To this end, this paper proposes the Hierarchical Sinkhorn Tree, which is a pruned tree structure designed to hierarchically measure the local consistency of each coarse correspondences. To validate the proposed methods, t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments and feedback. We hope that our responses can address your concerns.
**Response to Weakness 1**: Thanks you for your valuable comments. First, we would like to emphasize that modeling multi-scale consistency (MSC) is non-trivial, even with the he... | Summary: This paper introduces the Hierarchical Sinkhorn Tree (HST) for reliable correspondence identification in point cloud registration. The core idea is to hierarchically evaluate the local consistency of each correspondence at multiple feature scales using Sinkhorn distance, thereby filtering out the locally dissi... | Rebuttal 1:
Rebuttal: We first would like to thank the reviewer for providing us valuable comments and suggestions.
**Response to Weakness 1**: Thank you for your question regarding Eq. (2) about the k-NN local exploration part. We apologize for the typo in Eq. (2) and for any misunderstandings it may have caused.
You... | Rebuttal 1:
Rebuttal: We first would like to thank all the reviewers for providing insightful comments and we are immensely grateful for your thorough feedback on our manuscript. It is encouraging that the reviews found
* Our paper is well-written
- "The authors have effectively organized their ideas, making the... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Prototypical Hash Encoding for On-the-Fly Fine-Grained Category Discovery | Accept (poster) | Summary: This paper proposes a prototypical deep hashing framework to address the fine-grained on-the-fly category discovery problem. The proposed method includes two main loss functions: first, distance minimization between the encoded hash features to the category-representative hash coding after projection $\mathcal... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer’s recognition of our method’s good performance and its robustness to varying coding lengths (sensitivity issue). We value the reviewer’s insightful comments and will incorporate these into our final revision.
**Q1: Writing**. Thanks for your constructive suggestion... | Summary: This paper introduces a novel framework called Prototypical Hash Encoding (PHE) for On-the-fly Category Discovery (OCD), which aims to discover both known and unknown categories from streaming data using labeled data of known categories. PHE first learns many prototypes for each category and then maps the lear... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer’s recognition of our motivation to address the limitations of previous OCD models. We value the reviewer’s insightful comments and will incorporate these into our final revision.
**Q1: Sharing the same core idea of utilizing hash codes for OCD**. We indeed use hash... | Summary: This paper addresses the On-the-fly Category Discovery (OCD) task, which involves utilizing existing category knowledge to recognize both known and unknown categories in new data streams in real-time. To tackle the high sensitivity and suboptimal feature representation issues of existing methods when dealing w... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer’s recognition of our motivation and experimental results in addressing the hash sensitivity issue. We value the reviewer’s insightful comments and will include these into our final revision.
**Q1: Improving accuracy in recognizing unknown categories.**
1. **Limite... | Summary: This paper focuses on On-the-fly Category Discovery (OCD), which is to determine novel categories during inference. OCD methods first compute the hash code of the image, this code then becomes a "cluster index", if not matching with existing code, then it is a novel category.
However, the problem with the prev... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for acknowledging that our idea is clever and simple, and for the positive comments on the writing and experiments.
**Q1: More baseline with deep hash methods**.
Following the meaningful suggestion, we have conducted additional comparative experiments with vario... | Rebuttal 1:
Rebuttal: We sincerely thank the ACs and reviewers for their considerable efforts in handling our paper.
We have appropriately addressed all concerns raised by the reviewers. These include providing more baselines with deep hashing methods (Reviewer #6xA4, #dwc8) and prototype learning methods (Reviewer #Z... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Aligner-Encoders: Self-Attention Transformers Can Be Self-Transducers | Accept (spotlight) | Summary: This paper proposes a new ASR model that connects a self-aligned encoder and the light text-only recurrence of RNN-T. The proposed model can be trained with label-wise cross entropy loss, which is computationally efficient than RNN-T training. The authors show the limitation of the model to inference on long-f... | Rebuttal 1:
Rebuttal: Thank you for the close review of our work.
Thank you for the suggestion to revise the section on the long-form inference modification. In earlier drafts of the paper, this has been the most difficult part to write clearly. Space permitting, a figure or perhaps a pseudo-algorithm would be helpf... | Summary: The paper introduces Aligner, which is to take the best parts from RNN-Transducer and AED (Attention Encoder-Decoder) models. The idea comes from the intuition that the transformer encoder with self-attention can already learns to align the input and the output -- which is explicitly modeled by previous approa... | Rebuttal 1:
Rebuttal: Thank you for considering our work closely, your summary is accurate to what we intended.
It is a worthwhile question whether pre-training can be combined with our model. As it stands, our model seems to use several of the same layers in the conformer for 1) encoding and 2) alignment, whereas ex... | Summary: This paper proposes a new speech recognition (or, more generally, sequence-to-sequence) architecture. This architecture performs an alignment process between input and output features via self-attention mechanisms in an encoder. The decoder network is a simplified version of the combination of the RNN-T or AED... | Rebuttal 1:
Rebuttal: Thank you for your careful consideration of our work.
Important question about the requirement for U <= T, especially for machine-translation. One possible solution to extend to U>T would be to pad the input with some (fixed) number, P, of learnable input frames, which would provide the model th... | Summary: A new simplified encoder-decoder model is presented without the attention. The decoder is generating the labels auto-regressively as usual until end-of-sentence (EOS). In contrast to attention-based encoder-decoder (AED) models, the attention is replaced by simply taking the same frame in the encoder - i.e. in... | Rebuttal 1:
Rebuttal: Thank you for reviewing our work closely.
The convergence rates are similar among CTC, RNN-T, and our model (good checkpoints are between 100k-150k training steps on LibriSpeech). Interestingly, AED models sometimes did converge faster (as fast as 25k training steps for the best checkpoint). ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DeepLag: Discovering Deep Lagrangian Dynamics for Intuitive Fluid Prediction | Accept (poster) | Summary: Real-world processes exhibit multi-scale spatio-temporal dynamics. Not all of this dynamics is accurately modeled by Eulerian (i.e., field-based) modeling of scientific processes and sometimes the fine-grained patterns are only able to be modeled by Lagrangian paradigms. However explicit Lagrangian-only modeli... | Rebuttal 1:
Rebuttal: Special thanks to Reviewer Kve3 for their detailed review and insightful suggestions, your dedication to evaluating our work despite your busy schedule is greatly appreciated.
> **Weakness1:** About the detailed content selected to represent in the main body of the paper.
Thank you for your valu... | Summary: The authors propose DeepLag as an approach to simulating Eulerian fluid dynamics, which makes use of Eulerian-Lagrangian co-design to improve performance. In particular, the idea is to transfer information back and forth between the Eulerian grid and initially randomly placed Lagrangian particles, which themse... | Rebuttal 1:
Rebuttal: Special thanks to Reviewer PRfM for their detailed review and insightful suggestions.
> **strength2:** On the algorithmic complexity brought by the multi-scale design.
Please recall $\underline{\text{Table 6 in Appendix A.1}}$ that the number of tracking particles at each scale has an exponentia... | Summary: In this paper, the author presents a Lagrangian-Eulerian hybrid paradigm to address the complexities of fluid dynamics. Instead of relying only on Eulerian observations to predict future states, we introduce DeepLag, which uncovers hidden Lagrangian dynamics within the fluid by tracking the movements of adapti... | Rebuttal 1:
Rebuttal: We would like to thank Reviewer PBwE for the detailed review and insightful suggestions.
> **Weakness1:** Explanation of the performance difference between datasets. "Just wondering what is the reason between this discrepancy? Is the proposed method better than in short-term or long-term tasks?"
... | Summary: The authors propose a novel neural network architecture in order to leverage the advantages of the eulerian and lagrangian formalisms for fluid prediction. The so called "EuLag Block" acts on an eulerian grid based representation of the fluid as well as on a lagrangian particle based representation and enables... | Rebuttal 1:
Rebuttal: Sincerely thank Reviewer rCcn for the detailed review and insightful suggestions.
> **Weakness1:** On the difference between DeepLag and FluidNet [Tompson et al, ICML 2017].
Thanks for your recommendation on related work and rigorous questions. We will cite FluidNet in the revised paper. However... | Rebuttal 1:
Rebuttal: ## Global Response and Summary of Revisions
We sincerely thank all the reviewers for their insightful reviews and valuable comments, which are instructive for us to improve our paper further.
This paper proposes a **new deep Eulerian-Lagrangian hybrid paradigm for fluid prediction**, DeepLag, wh... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces a novel approach to predicting fluid dynamics by integrating both Lagrangian and Eulerian paradigms. The model, named ‘DeepLag,’ utilizes transformer blocks to process and integrate information from Eulerian and Lagrangian perspectives. Initially, DeepLag predicts the future state of the E... | Rebuttal 1:
Rebuttal: Many thanks to Reviewer UohQ for the detailed review and suggestions.
> **Weakness1:** The model’s reliance on extensive hyperparameter tuning for particle tracking could pose challenges.
**The only hyperparameter needed for tuning is the total number of the tracking particles in the finest scal... | null | null | null | null | null | null |
DiP-GO: A Diffusion Pruner via Few-step Gradient Optimization | Accept (poster) | Summary: The paper introduces Diffusion Pruning via Few-step Gradient Optimization (DiP-GO), which is a new diffusion model pruning method. The method addresses the high computational cost of diffusion models' multi-step denoising process, which hinders their practical use. Traditional pruning methods require resource-... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude for the detailed and professional attention you have given to our work during the review process.
**Re: Weakness#1**
Yes, the vast search space actually introduces challenges to the learning process for obtaining an optimal SubNet. Therefore, we... | Summary: This paper proposes a novel differentiable pruner for diffusion models. The core of the approach involves transforming the model pruning process into a SubNet search process.
Strengths: 1. The main idea is to transfer the model pruning process into a SubNet search process, eliminating the need to retrain pret... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude for the detailed and professional attention you have given to our work during the review process.
**Re: Weakness #1**
$D$ is the embedding dimension of a learnable query in the pruner network, and the dimension of the prune queries is $T \times ... | Summary: This paper proposes DiP-GO, a novel pruning method to address the computational challenge of Diffusion model during inference. The key innovation lies in the creation of a SuperNet, which includes backup connections based on similar features across adjacent time steps, and a plugin pruner network optimized thr... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort you invested in reviewing our paper. Your acknowledgment of the **significant improvements** in **efficiency and effectiveness** across **different architectures** is highly valued. We are also grateful for your recognition that our method **avoids retra... | Summary: The paper introduces DiP-GO, a novel pruning method for diffusion models. Unlike the majority of existing methods that require extensive retraining with large datasets, DiP-GO employs (1) a SuperNet with backup connections, and (2) a plugin pruner network to identify redundant computations. The authors formula... | Rebuttal 1:
Rebuttal: We are deeply thankful for the thorough and expert review of our work. Your acknowledgment of the **timely and practically-relevant problem** our research tackles is greatly appreciated. Your feedback highlights the **superior performance** and the **extensive experiments** that were conducted.
B... | Rebuttal 1:
Rebuttal: Thank you to all the reviewers. We mainly upload the images needed for the rebuttal here. Detailed rebuttal responses and tables have already been sent to each reviewer separately.
Pdf: /pdf/389831ef85f63173ac12e581fe53c1b5ad68d533.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Beware of Road Markings: A New Adversarial Patch Attack to Monocular Depth Estimation | Accept (poster) | Summary: The paper demonstrates that various MDE models with different architectures, trained for autonomous driving, heavily rely on road regions when predicting depths for different obstacles. Based on this, it provides the Adversarial Road Marking (AdvRM) attack, which camouflages patches as ordinary road markings a... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. Given that the issues you are concerned about, such as (a) the robustness of AdvRM against common image distortions and (b) the practicality of white-box scenarios in real life, are also of concern to other reviewers, we address them in our global response. | Summary: The paper proposes AdvRM, a novel patch attack against MDE models. It proposes introducing adversarial road markings with the attack objective of altering the inferred depth at the output of MDE models corresponding to image regions that contain obstacles in the scene. Unlike prior patch attacks on the MDE tas... | Rebuttal 1:
Rebuttal: Many thanks for providing the detailed and insightful comments! we will carefully consider them to improve our paper.
**[Comment 1] Experiments on baseline and defense.**
Following your suggestions, we conduct additional experiments to compare our AdvRM with the SOTA attack [1], **further confi... | Summary: The paper describes a white box patch attack for monocular depth estimators (MDE) that is independent from an obstacle. The attack is placed on a fixed part of the environment, as an ordinary road mark, and not on the target obstacle itself. It is learned adversarially using a loss that distinguishes pixels of... | Rebuttal 1:
Rebuttal: We appreciate all your constructive comments which can help improve the paper. Below is our point-to-point response.
**[Comment 1] Motivation of putting the attack on the road**
We believe our motivation is reasonable. The intensity of gradient changes is commonly used to explain regression mod... | Summary: This paper introduces a novel adversarial attack called Adversarial Road Marking (AdvRM) on Monocular Depth Estimation (MDE) models, which are crucial for autonomous driving systems. Unlike previous attacks that rely on placing patches on specific obstacles, AdvRM deploys optimized patches on roads, disguised ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback, which will greatly aid in refining the paper. Below is our point-to-point response.
**[Comment 1] Restriction to white-box scenarios**
**We believe the white-box attack scenario is practical in the real world, and it is indeed the main assumption adopted i... | Rebuttal 1:
Rebuttal: We appreciate all reviewers' constructive comments, which can help improve the paper. Due to the 6000-character limit of this response, we will address **two common concerns regarding (a) restrictions to the white-box scenario and (b) experiments on baseline and defense** here. We will provide det... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SOI: Scaling Down Computational Complexity by Estimating Partial States of the Model | Accept (poster) | Summary: This paper presents a method called Scattered Online Inference (SOI) aimed at reducing the computational complexity of ANNs. By applying compression and extrapolation techniques, SOI caches partial states of CNNs, allowing it to skip full model recalculation at each inference. The proposed method is positioned... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper. We appreciate the time you have taken to provide your insights and the opportunity to improve our work. We are pleased that you recognize the importance of the research problem, the practical value of focusing on real-time systems and consumer electronics, and th... | Summary: This paper introduces a novel method called Scattered Online Inference (SOI) aimed at reducing the computational cost of convolutional neural networks (CNNs) by reusing partial states from previous inferences. The method generalizes these states over longer time periods, balancing computational efficiency and ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review of our paper. We appreciate your feedback and the opportunity to clarify and improve our work. We are pleased that you recognize the novelty and significance of the SOI method, as well as the thoroughness of our experimental evaluation and the clarity of our wr... | Summary: The authors propose a novel method that reduces the computational cost of regular ANN models to achieve better online inference efficiency. The core technique named Scattered Online Inference (SOI) is able to reduce computational cost with partial predictions.
Strengths: - The proposed method is designed to a... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback on our paper. We appreciate your constructive comments, which will help us improve the quality and clarity of our work. We are glad to hear that you found the method's design, minimal impact on learning quality, and extensive experiments to be strengths of our ... | Summary: The authors introduce a novel method called Scattered Online Inference (SOI) aimed at reducing the computational cost of convolutional neural networks (CNNs). This method leverages the reuse of network partial states from previous inferences, thereby generalizing these states over extended periods. SOI enables... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review! We appreciate your detailed feedback and the opportunity to address your comments and concerns. We are pleased to see that you recognize the originality, quality, clarity, and significance of our work. Your positive feedback on our approach, extensive experime... | Rebuttal 1:
Rebuttal: In the attached PDF, we provided peak memory footprint and average inference time measurements for a single S-CC layer in our U-Net for the speech separation task. These results were achieved using the Intel Xeon Gold 6246R CPU with a clock speed of 3.40 GHz. The TFLite Model Benchmark Tool with C... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Generalized Eigenvalue Problems with Generative Priors | Accept (poster) | Summary: This paper provides theoretical guarantees for the optimal solution of the generalised eigenvalue problem with generative priors. It also designs an algorithm to approximate the optimal solution within some guaranteed distance.
Strengths: The obtained results look new and rigorously proved.
Weaknesses: + Ass... | Rebuttal 1:
Rebuttal: Thank you for your helpful feedback and comments. Our responses to the main concerns are given as follows.
(**Assumption 2.4 looks too strong and not very convincing. At the first sight, I don't see any pair $(\mathbf{E}, \mathbf{F})$ which satisfies this assumption except the trivial one $\mathb... | Summary: The paper studies generalized eigenvalue problems under generative priors. They show that under suitable conditions on the prior assumptions on the perturbation matrices, the optimal solution vector of the corresponding optimization problem attains the statistically optimal rate. Furthermore, they provide an a... | Rebuttal 1:
Rebuttal: Thank you for your recognition of this paper as well as the beneficial comments and questions. Our responses to the major concerns are as follows.
(**Theoretical analysis taking the approximation into account would be good to see**) Similar to previous works such as [45, 59, 65], we assume exact ... | Summary: This submission proposes a way to solve Generalized Eigenproblems with a constraint on the eigenvectors to be in the range of some generative model. On top of a simple algorithm, the paper proposes bound on the optimal solution and some experiments on some toy data.
Strengths: Generalized eigenproblems are a ... | Rebuttal 1:
Rebuttal: Thank you for your helpful feedback and suggestions. Our responses to the main concerns are given as follows.
(**The presentation of the problem could be improved**) Thank you for the comments. We will undertake revisions on the presentation, encompassing the following aspects: 1) Mention that Eq... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Preferential Normalizing Flows | Accept (poster) | Summary: This paper focuses on the problem of eliciting a complex multivariate probability density from an expert. Existing works mainly use simple distributions. This paper proposes to model the belief density with a flow based model. To apply normalizing flows to this problem, we will need to address a few challenges... | Rebuttal 1:
Rebuttal: > In Proposition 2.1, it is not very clear what is the relationship between W and the limit \\(\lim_{\beta \rightarrow 0} p(\mathcal{D}) = 1\\).
As the “noise level” \\(\beta\\) goes zero, there is no noise in RUM (i.e. \\(W=0\\) with high probability), which implies that the expert always choose... | Summary: The paper proposes a method to learn so-called belief densities based on k-wise rankings or comparisons of alternatives. This belief density is learned by combining function-space Bayesian inference and normalizing flows, which allows for the learning of complex (multivariate) probability densities. The author... | Rebuttal 1:
Rebuttal: > ...At the moment all experiments use k = 5 and it would be interesting to see how the results vary with different values of k. Specifically, k = 2 is a very relevant real-life use case as this type of feedback is given a lot in e.g. chatbots. Similarly, it would be good to get some idea of how t... | Summary: The paper presents an approach for expressing an expert's belief density using a normalizing flow. Interestingly, the flow is trained solely on preferential questions (e.g., comparing and ranking) which follow a random utility model (RUM). The approach avoids several optimization issues that could occur when t... | Rebuttal 1:
Rebuttal: >...Because of the underlying data model for preference data (eq. 3), I suspect that the method here is actually optimizing a lower bound on the log likelihood...
Specifically, their objective may be similar to the Max surjective flow described in Nielsen et. al. [2020, https://arxiv.org/abs/2007.... | null | null | Rebuttal 1:
Rebuttal: We are happy to see the reviewers both understood the paper well and perceived it positively. We thank the reviewers for the detailed constructive comments, and provide responses to specific comments and questions for each reviewer separately.
The rebuttal is accompanied by a pdf that reports res... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
UniGAD: Unifying Multi-level Graph Anomaly Detection | Accept (poster) | Summary: Traditional graph anomaly detection focus on single type of graph object (e.g., node, edge, graph). To address this, this paper introduces the first unified framework (UniGAD) for detecting anomalies at the node, edge, and graph levels jointly. The authors propose two core modules, MRQSampler and GraphStitch, ... | Rebuttal 1:
Rebuttal: We are greatful for your helpful comments! Below are our responses.
### **Q1: How UniGAD avoid Redundant calculation**
Prompt-based methods for handling multiple objects convert all objects into induced graphs during pre-processing. This approach results in the number of induced graphs to be proc... | Summary: The paper introduces UniGAD, a unified framework for multi-level graph anomaly detection, capable of identifying anomalies at the node, edge, and graph levels. Key contributions include the Maximum Rayleigh Quotient Subgraph Sampler (MRQSampler), which optimizes subgraphs to maximize significant anomaly inform... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments to our work! Below are our responses.
### **Q1: Modification of All-in-One and GraphPrompt**
We would like to clarify that we did not alter the core methodologies of GraphPrompt and All-in-One. Our modifications were limited to the data preprocessing componen... | Summary: This article presents an anomaly detection model, UniGAD, designed to be applicable across different levels including nodes, edges, and whole graphs. Leveraging the relationship between the Rayleigh quotient and anomaly degree, as described in Lemma 1, the authors have developed a novel subgraph sampling algor... | Rebuttal 1:
Rebuttal: ### **W1: The discrepancy of scalar node feature in proofs and vector node feature in practice.**
Thank you for highlighting this discrepancy. In theoretical derivations, we followed the established foundations of BWGNN and RQGNN, which consider single-dimensional features in their proofs. This si... | Summary: This paper presents a novel framework for detecting anomalies at node, edge, and graph levels within graph-structured data. The authors introduce the Maximum Rayleigh Quotient Subgraph Sampler (MRQSampler) to transform multi-level tasks into graph-level tasks by sampling subgraphs with high spectral energy, th... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer’s thorough and constructive feedback on our paper.
### **Q1: AUPRC as a complementary metric**
We acknowledge the importance of AUPRC as a complementary metric to AUROC and F1-macro, especially for anomaly detection with imbalanced labels. In light of your sugge... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We are deeply grateful for your constructive and insightful feedback. We sincerely appreciate your recognition of our contributions to the field of graph anomaly detection. The reviewers have highlighted several key strengths of our work, including UniGAD's unique capability to un... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning Human-like Representations to Enable Learning Human Values | Accept (poster) | Summary: The paper explores how representational alignment between humans and AI agents affects the ability of AI systems to learn human values efficiently and safely. The authors propose that AI systems learning human-like representations can generalize human values better and ensure safer exploration during learning.... | Rebuttal 1:
Rebuttal: *> The theoretical analysis relies on strong assumptions, such as specific kernel functions and Gaussian process regression, which may limit the generalizability of the results. Discuss the impact of these assumptions on real-world applicability and consider additional theoretical or empirical val... | Summary: The authors test whether human alignment of LLM representations are related how well LLMs can learn personalised preferences. They collect preference ratings and similarity judgements from humans for various value-related stimuli. The authors use the ratings to construct a reinforcement learning problem for LL... | Rebuttal 1:
Rebuttal: *> The hypothesis that increased representational alignment allows for better learning of human values is not actually tested in the paper. If I understand correctly, all the LLM experiments are done using a kernel derived from representations and some kernel-based function approximator. This is n... | Summary: This paper looks at the importance of having human-like representations for learning human values. It does this for kernel methods specifically, allowing it to operate on the level of the covariance matrix implied by the representation, rather than the representation itself. The paper presents a number of resu... | Rebuttal 1:
Rebuttal: *> The last part of the theoretical analysis in 3.1 looks at a setting with two training examples and one test example. I think this would have been stronger if it had also included the more general setting with N training examples and M test examples.*
Thank you for the great suggestion! We have... | Summary: The paper addresses the challenge of ensuring that machine learning models learn to achieve explicit objectives without causing harm or violating human standards, which is crucial as these models operate in more open environments. They specifically focused on value alignment in LLMs, and note that this is chal... | Rebuttal 1:
Rebuttal: *> The experimental evaluation is not very extensive / the motivation for this is missing and it is hard to interpret the results. The results section must be expanded more thoroughly.*
Thank you for these suggestions. We will expand the results section in the final version of the paper. We have ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Exploring the Precise Dynamics of Single-Layer GAN Models: Leveraging Multi-Feature Discriminators for High-Dimensional Subspace Learning | Accept (poster) | Summary: This work introduces a simplified GAN framework to learn the subspace of a spiked covariance model. The authors are able to derive precise training dynamics given specific assumptions and show they correspond to numerical simulations. They also prove that convergence rate is often faster than previous work tha... | Rebuttal 1:
Rebuttal: We appreciate the valuable feedback provided by the reviewer.
- *The introduction doesn't clearly ...*
We define single-feature to be a discriminator which can only learn one dimension of the subspace at a time. This is quite a slow process, especially when the number of features is high. We see... | Summary: This paper proposes to learn the subspace from the observations by the GAN model. Taking the one-layer GAN model as the starting point, this paper provides a theoretical analysis from the perspective of training dynamics. Specifically, from the technical side, the proposed method trains both the generator and ... | Rebuttal 1:
Rebuttal: While we appreciate the reviewer’s comments, we respectfully disagree with his/her assessment of the level of novelty. We refer the reviewer to our joint statement to all reviewers to clarify the novelty of our paper.
- *This paper provides a theoretical analysis from the perspective of training ... | Summary: This paper focuses on the training dynamics of the gradient-based learning algorithms, and converted into a continuous-time stochastic process characterized by an Ordinary Differential Equation (ODE). Empirical evidence demonstrates the correctness of the proposed method.
Strengths: S1: This paper focuses on ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort. We would like to provide a more detailed summary of our paper, to clarify any confusions you may have about our work.
We focus on representing the training dynamics of a simplified (linear) GAN model using a system of ODEs, which represent the key ... | Summary: This work explores the training dynamics of a single-layer GAN model, especially for high-dimensional subspace learning, presented as a novel approach. By connecting the GAN models with analysis to subspace learning, this work compares the effectiveness of GAN-based methods with former approaches e.g., Oja’s m... | Rebuttal 1:
Rebuttal: We appreciate the valuable feedback provided by the reviewer.
- *Because the subspaces have to be orthonormal, we need to ensure that Eq. 7 maintains this property when performing gradient descents. How can this property be achieved? Or is this not necessary using the approach in this work? Comm... | Rebuttal 1:
Rebuttal: Dear Reviewers and Area Chairs,
We appreciate the valuable feedback provided by the reviewers.
We are encouraged that the reviewers found our paper provides a novel perspective on exploring the precise dynamics of the single-layer GAN model on subspace learning problems. We are also glad to see ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Boosting the Transferability of Adversarial Attack on Vision Transformer with Adaptive Token Tuning | Accept (poster) | Summary: This paper introduces Adaptive Token Tuning (ATT) to improve the transferability of adversarial examples generated from ViTs. ATT is an improvement over traditional gradient-based algorithms, consisting of three independent methods. The first method reduces gradient variance by rescaling the token gradients co... | Rebuttal 1:
Rebuttal: 1. **Answer to Weakness 1 -- query-based attacks:** We appreciate your comment on the broader definition of blackbox attacks, including query-based scenarios where the target model has limited accessibility. We will expand our discussion to encompass these types of attacks, providing a more compre... | Summary: The paper proposes an Adaptive Token Tuning (ATT) method to enhance the transferability of adversarial attacks on Vision Transformers (ViTs). The method introduces three optimization strategies: adaptive gradient re-scaling to reduce token gradient variance, a self-paced patch out strategy to enhance input div... | Rebuttal 1:
Rebuttal: 1. **Answer to Weakness 1 -- Hyperparameters Interaction:**
The hyperparameters in our Adaptive Token Tuning (ATT) method fall into two categories: those related to gradient adjustment and those pertaining to input enhancement.
To mitigate the interaction between hyperparameters,
these categorie... | Summary: n this paper, the authors investigate three strategies to boot the transferability of adversarial attacks on Vision Transformers, including an adaptive gradient re-scaling strategy, a self-paced patch out strategy, a hybrid token gradient truncation.
Strengths: 1 The experiments are solid.
2 The soundness of... | Rebuttal 1:
Rebuttal: 1. **Answer to Weakness 1 -- Adaptive Token Tuning for our manuscript title:** We appreciate the reviewer's emphasis on the traditional definition of token tuning. In our work, token tuning extends beyond mere feature value processing; it encompasses the modification of feature values, attention w... | Summary: This paper investigates the enhancement of adversarial attack transferability on Vision Transformers (ViTs) through innovative adaptive token tuning techniques. It addresses the vulnerability of ViTs to adversarial attacks by introducing three main optimization strategies: an adaptive gradient re-scaling metho... | Rebuttal 1:
Rebuttal: 1. **Answer to Weakness 1 -- ablation experiments for these three strategies:** We apologize for any ambiguity in our manuscript regarding the experimental setups described in Tables 1-3, where we did an ablation study of the input enhancement strategy including both Path Out (PO) and Self-Paced P... | Rebuttal 1:
Rebuttal: 1. In **Table 1** of our rebuttal pdf, three attack methods (including PNA, TGR and ours) are tested under different settings of input enhancement.
**“w / o”** denoted that no input enhancement was added, which is the same as the results shown in Tables 1-2 of our manuscript.
**“PO”** indicated t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Semi-supervised Knowledge Transfer Across Multi-omic Single-cell Data | Accept (poster) | Summary: This paper introduces a novel method to address the knowledge transfer challenge in multi-omic single-cell data. Specifically, it focuses on scenarios where annotations are available partially in one modality, namely scRNA-seq data, and aims to infer annotations in another modality, the scATAC-seq data, withou... | Rebuttal 1:
Rebuttal: We are truly grateful for the time you have taken to review our paper and your insightful review. Here we address your concerns in the following.
> Q1. I'm not entirely convinced about the prevalence of the problem setting described by the author, where only a small subset of the scRNA-seq data i... | Summary: This paper proposes a label transfer method from scRNA-seq to scATAC-seq data. Based on the heterogeneity of single-cell data, this work partitions data into several groups and designs effective strategies to tackle them respectively. Experiments demonstrate the effectiveness of the proposed method.
Strengths... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time you've taken to review our paper and for your insightful comments. Your positive feedback is highly encouraging for us! We'd like to address your concerns in the following response.
> Q1. In the Introduction, the authors claim that "a small fraction of scRNA-seq d... | Summary: This paper introduces a semi-supervised knowledge transfer framework called DANCE, designed to effectively transfer cell type annotations from scRNA-seq data to unannotated scATAC-seq data under conditions of label scarcity. It is similar to the unsupervised domain adaptation task in computer vision. DANCE add... | Rebuttal 1:
Rebuttal: We greatly appreciate your time in reviewing our paper and your insightful comments. Your positive feedback is incredibly encouraging for us! We'd like to address your concerns in the following response.
> Q1. The compared methods, especially the DA method, are relatively old. Comparison with new... | Summary: This paper addresses the challenge of knowledge transfer across multi-omic single-cell data under label scarcity. The proposed semi-supervised framework, DANCE, uses optimal transport to generate pseudo-labels and a divide-and-conquer strategy for handling scATAC-seq data. The framework demonstrates superior p... | Rebuttal 1:
Rebuttal: We are truly grateful for the time you have taken to review our paper, and your insightful comments and support. Your positive feedback is incredibly encouraging for us! In the following response, we would like to address your concerns and provide additional clarification.
> Q1. The exclusion of ... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We thank you for your careful reviews and constructive suggestions. We acknowledge the positive comments such as "effective and well-handled method" (Reviewer W1Pi), "impressive performance" (Reviewer W1Pi), “well-written and organized” (Reviewer W1Pi, Reviewer puzU), "well-motiva... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies | Accept (poster) | Summary: The paper presents empirical scaling laws for the size of the vocabulary for LLMs. The findings in the paper are:
- Empirically, the vocabulary size minimizing the loss increases when FLOPs are increased (Fig 2, right, Fig 3)
- Through mathematical derivations from scaling laws, the optimal vocabulary size de... | Rebuttal 1:
Rebuttal: ### W1: The results are probably mostly applicable to a small number of well-funded labs.
Thanks for pointing this out! We want to clarify that we are not a well-funded lab either. Due to our limited computing resources, we can only afford to train models with up to 3B parameters in our experimen... | Summary: This study primarily explores the role of vocabulary size in scaling large language models (LLMs). Traditional research has focused on model parameters and training data size, often overlooking the impact of vocabulary size. While intuitively larger vocabularies can enable more efficient tokenization by repres... | Rebuttal 1:
Rebuttal: ### W1: Lacks performance on large-scale models, such as whether increasing the vocabulary size to a greater extent performs better than existing models in the market. Table 2's experiments look a little bit less.
Thank you for raising this concern. We share the intention to compete with existin... | Summary: This paper investigates the impact of vocabulary size on the efficiency of large language models (LLMs). Using models with 33 million to 3 billion parameters, it finds that optimal vocabulary size is limited by computational resources. The study introduces two methods to determine the best vocabulary size, sho... | Rebuttal 1:
Rebuttal: ### W1: This paper conducted experiments on language models of various parameter sizes, but the largest model tested was only 3 billion parameters.
We acknowledge the importance of evaluating our approach on larger models to establish its scalability. Increasing the model size necessitates pre-tra... | null | null | Rebuttal 1:
Rebuttal: ### General Response
We are grateful for the reviewers' efforts and the recognition of our contributions:
- **Novel Research Topic:** The paper explores the unique impact of vocabulary size on language model performance, an aspect often overlooked in LLM research [EsaU,Y8zo,zt86].
- **Analyses:**... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CultureLLM: Incorporating Cultural Differences into Large Language Models | Accept (poster) | Summary: This paper introduces a pipeline that can enhance LLM's ability to culture-aware tasks (such as hate speech detection, and bias detection. Their proposed CultureLLM included three stages: sampling, semantic data augmentation, and fine-tuning. They investigate the effectiveness of CultureLLM in nine languages a... | Rebuttal 1:
Rebuttal: **W1: Rely on human-annotated dataset (i.e., WVS). The author used WVS dataset as seed data to augment their fine-tuning dataset. This limits the applicability of the proposed method.**
Using human-annotated data to help research is a popular choice for most of the LLM papers, as shown in [1-7], ... | Summary: This paper introduces CultureLLM, a novel and cost-effective approach to address the cultural biases in Large Language Models (LLMs) that arise from the dominance of English training data. Traditional solutions like prompt engineering and culture-specific pre-training are either expensive or computationally in... | Rebuttal 1:
Rebuttal: **W1: Assumption of Language as Culture. The paper equates languages with cultures, which is an oversimplification. Cultures are multi-faceted and cannot be fully encapsulated by language alone. There are significant cultural differences within the same language-speaking regions that may not be ad... | Summary: The paper presents CultureLLM, a fine-tuned LLM based on GPT 3.5 and fine-tuned on a cultural survey (in English) on 50 survey questions that are increased through semantically aware augmentation. 9 different languages are chosen with geographic choices about which survey to use to represent the languages. Th... | Rebuttal 1:
Rebuttal: **W1: It is important to heavily note that language is not equal to culture and this tends to cause confusion in this paper. Culture is way more complex than language and it might have been easier to call the language splits as culture for writing but this will introduce misunderstandings that wil... | Summary: This research addresses cultural bias in large language models (LLMs) caused by training on mostly English data. Existing solutions can be expensive or require a lot of computing power. Here, they propose CultureLLM, a method that uses existing cultural surveys to create more training data and fine-tune LLMs. ... | Rebuttal 1:
Rebuttal: **Q1: I'm wondering how the performance (or errors) are associated with the coverage of the 50 questions used for fine-tuning.**
We analyze the performance for each task and report the WinRate in the table below.
| | offensive detect | hate detect | stance detect | toxicity detect | ... | Rebuttal 1:
Rebuttal: Dear Reviewers and AC,
We want to thank all reviewers for pointing out our strengths, including:
- problem significance: "addresses a timely and important issue with the current LLM situation", "an interesting problem that faces LLMs and how they are relevant to different locales"
- novel method:... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
HairFastGAN: Realistic and Robust Hair Transfer with a Fast Encoder-Based Approach | Accept (poster) | Summary: This paper proposes a novel framework for hairstyle transfer from single images. As previous works have either suffered from long optimization times or low generation quality, this work introduces a new encoder-based solution that balances both efficiency and quality. The solution decomposes the pipeline into ... | Rebuttal 1:
Rebuttal: We thank Reviewer RjWU for their favorable review of our paper. We appreciate the reviewer's recognition of the value in our research and their contribution to the peer review process. The reviewer's support is significant in advancing our field of study. | Summary: The paper introduces HairFast, a model designed to tackle the task of transferring hairstyles from reference images to input photos for virtual hair try-on. This task is notably challenging due to the diverse poses in photos, hairstyle intricacies, and the absence of standardized metrics for evaluation. Existi... | Rebuttal 1:
Rebuttal: We thank Reviewer fS7X for their valuable input and the time they have invested in reviewing our work.
**Can your method edit hair length, hairstyle type (e.g., wavy, curly), and facial pose post-hairstyle transfer, while preserving the editability of other facial features without overfitting?**
... | Summary: This paper introduces HairFast, a model that addresses the challenge of transferring hairstyles from a reference image to an input photo in near real-time with high resolution and superior reconstruction. Existing methods either suffer from slow optimization processes or low quality due to operating in low-dim... | Rebuttal 1:
Rebuttal: We thank Reviewer JffK for their thoughtful comments and questions. Their insightful feedback has provided us with valuable perspectives to improve our paper. We appreciate the time and effort the reviewer has dedicated to this review.
**1. Since "Fast" is a key highlight of this work, why is the... | null | null | Rebuttal 1:
Rebuttal: This rebuttal document contains tables and figures addressing Reviewers' comments. It includes:
1. A table with performance metrics (execution time, efficiency, parameters, memory usage)
2. A table showing identity preservation metrics
3. Visual results demonstrating the method's robustness on cr... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
GSGAN: Adversarial Learning for Hierarchical Generation of 3D Gaussian Splats | Accept (poster) | Summary: The paper presents a 3D-aware GAN (3DGAN) framework using a 3D Gaussian Splatting model.
Given a collection of single view 2D images (e.g., FFHQ, AFHQ), the proposed method can train 3D aware images of comparable quality to SOTA NeRF-based method, but with a much faster rendering speed (up to 100x) during infe... | Rebuttal 1:
Rebuttal: Thank you for your constructive review.
---
### “Short length of related work and lack of a lot of relevant citations”
We apologize for the lack of relevant citations, and will carefully revise the related works section including the papers below:
Gaussian Shell Maps (CVPR 2024)
They focus on... | Summary: This paper proposes a new 3DGAN based on the 3DGS representation. To maintain stable training and good generation quality, this paper introduces a hierarchical Gaussian to generate the results in a coarse-to-fine manner. Meanwhile, a reasonable transformer-based architecture is proposed to implement the hierar... | Rebuttal 1:
Rebuttal: Thank you for your constructive reviews.
---
### “Effect of $L_\text{pose}$ and details about the pose encoder”
$L_{pose}$ is used for guiding the pose information to the generator. In detail, for real data, the discriminator learns to estimate the pose embedding corresponding to the given image... | Summary: The paper proposes a hierarchical Gaussian Splatting for 3D GAN. The authors claim that such structure lead to stable training and fast rendering.
Strengths: * The hierarchical GS structure is interesting.
Weaknesses: * The motivation to propose such a hierarchical GS structure is not clear. As stated in lin... | Rebuttal 1:
Rebuttal: Thank you for your constructive reviews.
---
### “The motivation of hierarchical GS is not clear and Fig. 6 is confusing to understand”
GANs with 3D Gaussian solve more complex problems compared to previous GANs for 2D image synthesis, as they need to predict various parameters such as 3D positi... | Summary: The paper proposes a method to train 3D GANs using 3D Gaussians. A limitation to training 3D Gaussians in an adversarial training setup is the instability of the scale optimization, which leads to the scale explosion. To address this, the paper proposes a method of hierarchical generation of Gaussians using th... | Rebuttal 1:
Rebuttal: Thank you for your constructive review.
---
### “Reason for not using original 1024 resolution / Can this method be extended to high resolution such as 1024x1024?”
First of all, please let us clarify that we just follow the most general benchmarks used in EG3D, which uses a maximum resolution of... | Rebuttal 1:
Rebuttal: Thanks to all reviewers for their constructive reviews.
We made a rebuttal for each reviewer, so please refer to them.
Additionally, we provide a single PDF containing the below contents.
1) Examples from FFHQ-1024.
2) Predicted meshes from the generated 3D Gaussians.
3) COLMAP dense reconst... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Almost-Linear RNNs Yield Highly Interpretable Symbolic Codes in Dynamical Systems Reconstruction | Accept (poster) | Summary: This paper introduces a new recurrent neural network (RNN) architecture called Almost-Linear (AL)-RNN for reconstructing nonlinear dynamical systems from time-series data. The key innovation of AL-RNN is training parsimonious piecewise linear (PWL) representations of dynamical systems. By combining linear unit... | Rebuttal 1:
Rebuttal: We appreciate the referee’s overall positive assessment and the valuable feedback provided!
**Weaknesses**
**W1 (usefulness of symbolic dynamics):** First, please note that the symbolic encoding itself is not probabilistic, i.e. the symbolic seq. (as shown in Figs. 13, 14 or 17) are as determini... | Summary: The paper proposes to limit the number of non-linear units in a RNN to facilitate the analysis and hence understanding of inferred dynamical systems. The authors show that even with a limited number of non-linear units, the model is able to explain a large portion of the data for the Rössler and Lorentz system... | Rebuttal 1:
Rebuttal: We thank the referee for the supportive and positive feedback, we are happy to hear the referee liked our work!
**Weaknesses**
In a sense this is the first study of its kind. We are not aware of any other work in the DSR field (and beyond) making this link to symbolic dynamics, and attempting to... | Summary: This paper addresses the broad problem of learning interpretable dynamical systems from data; it builds upon existing approaches that use piecewise linear RNNs (PLRNNs), with one interesting twist: constraining the number of linear subregions to be much smaller than the "usual" $2^N$. This is achieved by alloc... | Rebuttal 1:
Rebuttal: We thank the referee for the enthusiastic support and appreciation of our work!
**Weaknesses**
**W1 (consistency of fits/ model recovery)**: One crucial advantage of AL-RNNs is that they indeed consistently deliver the same model over many training repetitions. The errors in Figs. 5d-f are in fa... | null | null | Rebuttal 1:
Rebuttal: **General reply**
We thank all three referees for their thorough reading and the constructive and helpful feedback on our manuscript. We are happy to see that all referees provided a generally supportive and positive assessment of our work. We hope we could address the remaining concerns in the d... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Generalizablity of Memorization Neural Network | Accept (poster) | Summary: The paper studies the generalization capabilities of memorization networks, specifically networks that achieve optimal memorization capacity (i.e. O(sqrt(N)) parameters for memorizing N samples). The authors present a memorization algorithm that is based on the construction of Vardi et al. Next, they show that... | Rebuttal 1:
Rebuttal: The authors thank the reviewer for the valuable and insightful questions and hope that we have answered these questions satisfactorily.
Question 1. Proposition 3.8 - where is the proof? Also, it is not clear to me why the constructions in [54,48] are probabilistic.
We deduce proposition 3.8 from... | Summary: This work studies the generalization properties of neural network memorization algorithms. Several results are proved: (1) Construction of a memorization network for a sampled dataset, with an optimal number of parameters. (2) There exists a constant such that for all datasets sampled from a distribution, ther... | Rebuttal 1:
Rebuttal: For Reviewer QwyD:
The authors thank the reviewer for the valuable and insightful questions and hope that we have answered these questions satisfactorily. Also thank you for pointing out the typos in our writing, we will correct them in future versions.
Question 1. No estimates of $N_D$ and $S_D... | Summary: This paper provides a first, thorough theoretical understanding of the generalization ability of the memorized neural network, including the existence of the memorized neural network, the matching upper bound and lower bound on the sample complexity, the computational infeasibility of attaining optimal sample ... | Rebuttal 1:
Rebuttal: The authors thank the reviewer for the valuable and insightful questions and hope that we have answered these questions satisfactorily.
Question 1. Why consider the distribution D(n,c) defined in Definition 3.1?
D(n,c) is indeed a distribution with relatively good properties, as we explain this... | Summary: - The authors study the memorization capacity of ReLU networks and its generalization theory for i.i.d. datasets over the compact domain, under a binary classification setting.
- They propose two different memorization capacity upper bound of ReLU networks. One bound depends on the size $N$ of the training dat... | Rebuttal 1:
Rebuttal: The authors thank the reviewer for the valuable and insightful questions and hope that we have answered these questions satisfactorily. Also thank you for pointing out the typos in our writing, we will correct them in future versions.
1. Question (W1): Compactness of the input domain.
The curren... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.