title string | paper_decision string | review_1 string | rebuttals_1 string | review_2 string | rebuttals_2 string | review_3 string | rebuttals_3 string | review_4 string | rebuttals_4 string | global_rebuttals string | dataset_source string | conference_year int64 | review_5 string | rebuttals_5 string | review_6 string | rebuttals_6 string | review_7 string | rebuttals_7 string | review_8 string | rebuttals_8 string |
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Debiasing Scores and Prompts of 2D Diffusion for View-consistent Text-to-3D Generation | Accept (poster) | Summary: The paper proposes two simple methods to solve the widely known Janus problem in zero-shot text-to-3D generation, which is an essential issue. The proposed methods are intuitive and simple. They are more like optimization tricks instead of sysmetic formulations. From the qualitative results, the improvement of... | Rebuttal 1:
Rebuttal: **Qualitative results.** The reviewer mentioned that the qualitative comparisons in Figs. 6 and 7 do not show a clear improvement of the proposed method. However, we would like to note that in the supplementary material, we have provided more examples (Figs. 1 and 3) and images from 360-degree ang... | Summary: This paper proposes two approaches to debias the score-distillation frameworks for view-consistent text-to-3D generation. The first approach is called score debiasing, involves cutting off the score estimated by 2D diffusion models and gradually increasing the truncation value throughout the optimization proce... | Rebuttal 1:
Rebuttal: **Success rate.** The reviewer raised a valid suggestion regarding the evaluation, especially concerning the success rate of generation. We concur that quantifying the number of generated results from the 70 prompts that do not exhibit the Janus problem would indeed provide compelling evidence. In... | Summary: This paper addresses the Janus problem appearing in Score-Distillation-based 3D generation methods, where the most canonical view of an object appears in other views. In particular, the author propose two components for debiasing the score distillation and the prompt used for the generation. First, the authors... | Rebuttal 1:
Rebuttal: **Bias of diffusion models.** This relates to the limitations of our proposed method including other approaches, specifically regarding the bias of diffusion models. As we mentioned in our paper, if the 2D prior generates a severely deformed 3D field in the baseline, our method might not be able t... | Summary: This paper proposes debiased score sampling (D-SDS) for improving 2D diffusion-based 3D generation, targeting at the Janus problem. The method is composed of two parts: (i) score debiasing that cuts off scores from 2D diffusion and (ii) prompt debiasing that fixes the discrepancy between view prompts and objec... | Rebuttal 1:
Rebuttal: **Applicability.** Our method is widely applicable to a variety of concurrent methods that use score distillation for text-to-3D models, including the very recent work on variational score distillation (VSD) in ProlificDreamer [Wang et al., 2023]. In general response, we have provided a PDF file i... | Rebuttal 1:
Rebuttal: **General response.** We deeply appreciate the insightful feedback provided on our manuscript and have thoroughly examined all comments. The reviewers recognized the critical nature of the Janus problem in text-to-3D generation, underscoring the importance of our contribution to the field. The rev... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
From ViT Features to Training-free Video Object Segmentation via Streaming-data Mixture Models | Accept (poster) | Summary: This paper proposes a novel method for semi-supervised video object segmentation. The method combines pre-trained deep features from still images with streaming-data clustering techniques to model the object and the background as dynamic ensembles of von Mises-Fisher mixtures. The method does not require any a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful, useful, and positive review.
- Our claim is for SOTA results only on the VOS task, and we make no claims to solve other tasks. However, the methods we cited and compared to are the current SOTA (which we beat…) in VOS, so we had to compare with them even... | Summary: This paper tackles the semi-supervised video object segmentation problem. It presents a method that relies on clustering features from a pre-trained ViT model. The presented method has a low memory footprint and does not need any additional training. It shows SOTA results on DAVIS-2017 and YouTube-VOS 2018 val... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful, useful, and positive review.
**Adopting Deeper Networks:**
It is important to note that simply adopting deeper networks doesn't necessarily guarantee superior results. As observed in previous self-supervised learning approaches like VFS [1], performance ... | Summary: This paper proposes a training free framework for semi-supervised VOS. Here, the features of objects from a pretrained ViT are represented as vMF, where the objects across frames are associated to perform propagation from initial masks. The results are better than previous self-supervised methods that trained ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful, useful, and overall-positive review.
- **Large memory footprint**. MobileVOS and AOT are supervised methods that indeed accomplish admirable memory management and computational efficiency. However, these achievements are grounded in the utilization of tr... | Summary: The method tries to combine classical technique for SVOS task namely vMF and CRFs with advance Deep Learning based ViT representations. By doing so, the proposed method eliminates the training requirements on video data. The authors perform comprehensive experimentation to evaluate their approach.
Strengths: ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful, useful, and overall-positive review.
- **_"The performance of the model decreases when it encounters unseen examples"_**.
The decrease in performance on unseen examples, as noted in Table 2, does not stem from a need for training, but rather the complex... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful reviews and constructive criticism. We are glad that, overall, the paper was positively received.
Here, in the general response, we touch upon several points common to more than one reviewer. Below, we reply to each reviewer separately.
### **Memory ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper essentially shows, that a combination of classic techniques such as stream-data clustering, using an EM-algorithm and dynamic updates, in combination with strong pre-trained features, allows to achieve state-of-the-art performance on two standard video segmentation datasets (Davis 2017 and YouTube-VO... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and useful review.
We also thank the reviewer for recognizing the strong performance of our method on both the DAVIS and YouTube datasets, as well as appreciating the integration of classic techniques.
We would like to underscore the unique contribution ... | null | null | null | null | null | null |
MKOR: Momentum-Enabled Kronecker-Factor-Based Optimizer Using Rank-1 Updates | Accept (poster) | Summary: This work introduces a new optimization algorithm for deep neural networks. Building upon the baseline Kronecker-factored curvature (K-FAC) algorithm, this new approach approximates each activation and gradient covariance matrix within K-FAC as a rank-one matrix. This approximation enables the efficient calcul... | Rebuttal 1:
Rebuttal: In the below and the attached PDF figure-file we show that the covariance matrices in training neural networks can be approximated by rank-1 matrices both empirically and theoretically. We will include these discussions in our paper to address the reviewers’ concerns.
**Experimental Results:** A... | Summary: MKOR introduces Kronecker rank-1 representation of covariance for second-order optimizers. Authors try to solve the complexity of large language models in second-order optimizers while the Fisher information approximation is computed using rank-1 Kronecker matrix factorization. They also propose a hybrid metho... | Rebuttal 1:
Rebuttal: **Higher Rank Approximations:**
As shown in figure 2 in the attached PDF figure-file, our experiments show that the covariance matrices can be approximated with rank-1 matrices with low error and higher rank approximations are unnecessary in practice. Figure 2 (in the attached PDF figure-file) sh... | Summary: The work introduces a second-order optimizer that uses rank 1 covariance activation and gradient statistics, and efficient inversion algorithms to accelerate an approximated estimate of 2nd order information. Another addition is the rescaling of gradients and norm-based stabilization, which help to stabilize t... | Rebuttal 1:
Rebuttal: MKOR is currently the most efficient second-order optimizer that can be used on LLMs and CNNs. As the reviewer has pointed out, similar to most state-of-the-art optimizers, MKOR has hyperparameters that users have to tune to use it properly. We have tried to automate as many of these tunings as po... | Summary: The paper proposes a second-order optimizer named MKOR based on K-FAC. Compared with K-FAC, MKOR uses a rank-1 update to construct Kronecker factors enabling the inverse of Kronecker factors to be efficiently computed via the Sherman-Morrison-Woodbury (SMW) identity. In addition, the rank-1 update of factors i... | Rebuttal 1:
Rebuttal: **Comparison to Eva:**
What the reviewer has pointed out as the strengths of Eva (using KVs to not store the factor matrices), is at the same time a weakness for Eva, leading Eva to being less accurate and less efficient vs. MKOR. Using KVs instead of factor matrices disables the proper use of mo... | Rebuttal 1:
Rebuttal: We thank all reviewers for their very informative feedback. We have provided a separate answer to each reviewer and have also attached a PDF file with figures and tables that add to our rebuttal (which we refer to as the attached PDF figure-file in our per-reviewer responses).
Pdf: /pdf/6654d82a2... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Interaction Measures, Partition Lattices and Kernel Tests for High-Order Interactions | Accept (poster) | Summary: The authors introduce a hierarchy of $d$-order interaction measures by introducing a family of tests based on factorizations of the joint probability distribution that generalize to any order $d$ and define non-parametric tests to establish the statistical significance of these d-order interactions. They link ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and useful suggestions. We appreciate that one must consider both the overall complexity and the practical implementation of our tests. Therefore, we have provided a more in-depth analysis of both.
First, in Table 2 in the main response we provide the ove... | Summary: The paper describes a kernel test for estimating d-th order interaction, and computing its significance using permutation test. The authors propose two tests based on Lancaster interaction and Sreitberg interaction. The authors demonstrate the effectiveness of these measures on simulated data with higher order... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and positive response to our paper.
Regarding the suggestion to compare against non-kernel implementations of Lancaster and Streitberg tests, we are not aware of any such alternative approaches. However, we would be open to exploring alternatives for comp... | Summary: The authors conduct a very systematic study of tests that measure couplings between variables where these couplings include higher-order interactions. Authors uncover connections with lattice theory that then leverage them to formulate these tests more efficiently and in a more interpretable way. The authors d... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and comments. The theoretical results in our paper leveraging lattice theory enable us to define and implement kernel-based $d$-order Lancaster and Streitberg interaction tests, whose computational complexity was previously prohibitive, thus opening up their ... | Summary: The paper is a nice introduction to measuring high order interactions between groups of random variables and an experimental demonstration of measuring these interactions for up to 4 variables inclusively. The main focus is on the Lancaster and Streitberg interactions tests via kernel methods such as the Hilbe... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments.
- **Novelty**: Although, as the reviewer points out, the mathematical link between lattice theory and the $d$-order probabilistic Streitberg measure was indicated in [25] and briefly mentioned in [33], in neither reference were interaction tests implemen... | Rebuttal 1:
Rebuttal: We thank the reviewers for their clear reviews and thoughtful questions. In addition to the specific answers in the responses to each reviewer, we would like to briefly address here three overarching themes that have appeared in the reviews: the computational complexity of our method; implications... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Generator Born from Classifier | Accept (poster) | Summary: This paper tackles the problem of reconstructing an image generator, without relying on any data. The authors propose a learning paradigm in which the generator is trained to ensure that the convergence conditions of the network parameters are satisfied over the generated distribution of the samples.
Strength... | Rebuttal 1:
Rebuttal: ### **Why does the generator generate realistic-looking samples even though it could cheat by learning to generate adversarial examples?**
We appreciate the reviewer's question. Indeed, our loss design inherently penalizes the generation of adversarial samples. Since our loss is derived from the K... | Summary: This paper addresses a novel and intriguing issue, namely, training a generator reliant on a pre-trained classifier, rather than extensive training data. The authors propose an innovative solution, fundamentally aimed at enabling the generator's training process to continuously extract and leverage information... | Rebuttal 1:
Rebuttal: ### **How the random samples are generated when calculating Lambda?**
We appreciate the reviewer's question. The definition of a quasi-homogeneous function is independent of the dataset; here, the random samples refer to random noise, not samples selected from the original dataset. For instance, i... | Summary: The paper propose a method to generate image samples from a trained neural classifier. It proposes a loss to train an image generator where the loss is based on recent results from the realm of the implicit bias of gradient descent of quasi-homogenous functions. It shows empirical results on 2D data and on mod... | Rebuttal 1:
Rebuttal: ### **Comparison with [1] Haim et al. 2022 and [2] Buzaglo et al. 2023**
We appreciate the reviewer's comparison of our work with [1] and [2]. We kindly direct the reviewer to the global response, where we systematically compare ours with [1] and [2].
#### **The importance of Lambda**
Lambda's i... | Summary: The work extends the dataset reconstruction (DR) method [1] of reconstructing the dataset from a classifier to the generative scenario. It provides several extensions to it:
- Instead of reconstructing particular data points, it aims to build a generator for the original dataset.
- Extends to the multi-class s... | Rebuttal 1:
Rebuttal: ### **Scalability**
We thank the reviewer for pointing out the concern about the scalability of our method. To address the concern, we scale up the classifier and conduct the experiments. We consider two types of architectures: (1) a 10-layer fully-connected network and (2) a network with 3 convol... | Rebuttal 1:
Rebuttal: Esteemed Senior AC, AC, and Reviewers,
We deeply appreciate the reviewers' and ACs' dedication to reviewing and managing our submission. It is with great pleasure that the reviewers highlighted several strengths of our work, such as the importance and the potential impact of the idea/task/problem... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Design from Policies: Conservative Test-Time Adaptation for Offline Policy Optimization | Accept (poster) | Summary: This paper proposes an iterative bi-level offline RL algorithm that separates an "inner level" and "outer level" optimization for reinforcement learning. A novel variant of model-based optimization (including task decomposition and task embedding) is proposed to enable flexible test-time adaptation. Experiment... | Rebuttal 1:
Rebuttal: Thank you very much for the insightful comments.
**(1) the terms "inner level" and "outer level", and the raised three questions.**
Thank you for your valuable suggestion. We will include it in our revision. Thank you very much!
**(2) Comparison to related works in offline RL with test time ad... | Summary: The paper proposes a non-iterative offline RL algorithm, in which the policy is trained in a bi-level optimisation process. As opposed to commonly known iterative algorithms, the authors propose to split the optimisation into an inner loop training to mitigate any OOD issues, and an outer loop optimisation for... | Rebuttal 1:
Rebuttal:
Thank you very much for the insightful comments!
**(1) description of the three questions and answers.**
Thank you for the suggestion, we will clarify this in our revision of the paper.
**(2) term: "non-iterative, bi-level optimization". I think what you mean is that there is no iteratively ... | Summary: The paper tackles offline reinforcement learning, and considers two classes of algorithms — iterative (at each step, policy evaluation and policy improvement is done sequentially) and non-iterative. The main contribution of the paper is to move the part of policy improvement from offline training to during tes... | Rebuttal 1:
Rebuttal: Thank you very much for the positive comments.
**Q1: which of the three decomposition rules from B.2 is used?**
We use Rank(N, M) in the main paper. See Line 035 in the appendix. We will clarify it in our main paper. Thank you.
**Q2: Rank(N, M) rule: My guess is first M trajectories (with the ... | Summary: This paper proposes a method, namely DROP, to adapt the policy during the inference time. The authors achieve this by dividing the optimization process into two phases. In the first phase, the authors train a contextual behavior policy, a score model, and a deterministic task embedding model. At the second pha... | Rebuttal 1:
Rebuttal: **About Weaknesses:**
**(a): concerns about the ideas.**
+ *the difference between CQL and DROP:* The main difference is that DROP performs non-iterative bi-level offline RL optimization, while CQL performs iterative bi-level offline RL optimization (see Figure 1 in the main paper).
+ *comparison... | Rebuttal 1:
Rebuttal:
**(1) comparison to prior adaptive baselines.**
1) The following table shows a comparison with APE-V [1] in the D4RL suite. The APE-V results are taken from the original paper, which uses SAC-N as the base offline RL implementation. Despite the comprehensive approach we have taken, it is impor... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Moral Responsibility for AI Systems | Accept (poster) | Summary: In this paper the authors put forward a formalised definition of moral responsibility that can be applied to AI systems. To do so, they compare and contrast how existing definitions fare with regard to a causal and an epistemic condition for responsibility. Using these contrasting cases, the authors argue for ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive and encouraging feedback!
The reviewer raises a good point that the epistemic condition in particular poses some challenges once we generalize to a notion of collective responsibility. This issue is not novel though, as research in social epistemology (an... | Summary: This paper formalizes the notion of responsibility of an agent by using
contrastive necessary element of a sufficient set causation (CNESS) for the
causal condition and Halpern and Kleiman-Weiner's notion epistemic
responsibility. The resulting formulation of responsibility benefits from the
strong alignment ... | Rebuttal 1:
Rebuttal: We must say that we were very surprised to read this review. The reviewer recommends a strong rejection, and yet they fail to point out a single flaw in our paper. (For completeness, we should point out that their summary does contain a flaw, as we do not use HK's epistemic condition but rather pr... | Summary: This paper presents a novel formal definition of moral responsibility tailored for AI systems, filling in both causal and epistemic conditions. The work effectively draws comparisons to BvH and HK's works, favoring the Counterfactual NESS definition of causation and a nuanced epistemic condition.
Strengths: ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments, and their positive evaluation of our paper!
Regarding their question about real-world scenarios: we actually completely agree! We would also like to see more discussion of our definition to real-world scenarios, and we very much would like to offer this i... | Summary: The paper aims to link causal and moral responsibility through formal modeling using structural causal models. Engaging with existing literature on causality, the paper argues for the benefits of certain definitions over others in introducing a 3-part _responsibility schema_ based on a control condition (that ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed engagement with our paper, but we fail to understand why they recommend rejection. The core contribution of our paper is an analysis that is an improvement over two important existing analyses. As they do not point to any concrete flaws in our analysis, it ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift | Accept (poster) | Summary: This paper evaluates several BDL algorithm on real-world datasets (and real-world distribution shifts) in terms of generalisation (accuracy/correlation metrics) and uncertainty estimation quality (calibration/signed calibration).
Strengths: 1. I think there is a need for benchmarking BDL techniques, and the ... | Rebuttal 1:
Rebuttal: Thank you for your effort in reviewing our paper. We are happy that you found our results interesting and that you think there is a need for benchmarking BDL techniques. We will address your concerns regarding our evaluation below and refer to our global response, which summarizes our takeaway mes... | Summary: This paper presents an empirical comparison between various Bayesian methods/approaches on out-of-distribution (OOD) data. The authors focus on non-MCMC based methods (such as Bayes By Backprop, SWAG, and Laplace approximation). Methods are compared on challenging OOD benchmarks from the WILDS collection and u... | Rebuttal 1:
Rebuttal: Thank you very much for your overall positive remarks. We are happy that you think "that the paper makes a nice contribution to the community" and that we made "good choices" regarding methods, datasets, and metrics. We address your remaining questions below. Please also see the global response fo... | Summary: This paper presents a systematic comprehensive evaluation of a wide range of scalable Bayesian deep learning (BDL) algorithms in distribution-shited real-world data scenarios. A signed version of calibration error is presented, which allows for the identification of overconfidence and underconfidence rather th... | Rebuttal 1:
Rebuttal: We thank you very much for your helpful and positive feedback. We are thrilled that you find that our paper "makes real progress" towards practically applicable BDL and that it is "thorough and convincing". We answer your remaining questions below. Please also see the global response for a list of... | Summary: The paper conducts a large-scale benchmark of Bayesian deep learning (BDL) methods for distribution-shifted data. The focus is on evaluating the quality of the posterior approximation, generalization ability, and calibration using signed versions of calibration metrics to distinguish between under- and overcon... | Rebuttal 1:
Rebuttal: We thank you a lot for your helpful and positive feedback. We are happy that you find our choice of algorithms to be "relevant", and the failure of ensembles on transformer-based finetuning tasks to be "insightful". We answer your remaining questions below. Please also see the global response for ... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their time and efforts in providing detailed and insightful feedback, which we will incorporate in our revision.
**We are pleased that the reviewers are convinced that our paper "provides a useful benchmark of BDL on more realistic datasets" (Reviewer CBPk... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper compares a variety of Bayesian Deep Learning methods on the WILDS dataset collection comprised of regression and classification tasks with the aim of evaluation generalization performance under distribution shifts. In addition to previously proposed single-mode approximation methods, the authors ext... | Rebuttal 1:
Rebuttal: Thank you very much for your constructive and detailed feedback. We hope the following points help to resolve all your questions regarding our work. If you have any further questions, please let us know so that we can clarify things further. Please also see the global response for a list of takeaw... | null | null | null | null | null | null |
RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths | Accept (poster) | Summary: This paper presents a new large model RAPHEL (short for distinct image regions align with different text phases in attention learning ) for text-to-image generation. Technically, RAPHEL builds upon the LDM pipeline, with VAEs as image encoder-decoder, and then incorporates the MOE layers for spatial and tempor... | Rebuttal 1:
Rebuttal: Dear reviewer fnVX,
Thanks for your comments. We will address your concerns below.
**Q1: RAPHAEL uses internal datasets and many computing resources**
We argue that this is not a weakness for rating specific to the text-to-image diffusion model community. Numerous academic papers accepted by to... | Summary: The paper trains a large-scale latent diffusion model for image synthesis. It trains on a mix of LAION-5B (post-processed + filtered by aesthetic score) and in-house data. It proposes two novel technical contributions: 1) using a "spatial" mixture-of-experts (MoE) where an expert is predicted from each text to... | Rebuttal 1:
Rebuttal: Dear Reviewer NLKN,
Thanks for giving so many constructive suggestions for our paper, I will clarify the settings.
**Q1: The setting of ablation study**
We conducted an ablation study in the following manner:
For Fig.6a, we resume the final model trained for two months, and train each point i... | Summary: The paper proposed RAPHAEL, a text-to-image diffusion model. The model adopt MoE layers, including space-MoE and time-MoE layers. In addition, edge-supervised learning is proposed to enhance performance. RAPHAEL establishes a new state-of-the-art with a zero-shot FID-30k score of 6.61 on the COCO dataset, and... | Rebuttal 1:
Rebuttal: Dear Reviewer 8jJJ,
Thanks for appreciating our work and your advice. We will address your concerns below.
**Q1: The paper mentioned that "The training dataset consists of LAION-5B and a few internal data.". How many internal data is used? Its category distribution and collecting sources?**
The... | Summary: This paper proposes RAPHAEL, a new text-to-image generative model, based on the latent diffusion model framework. The main methodological contribution is the use of space-mixture-of-experts (space-MoE) layers. These are layers that focus on different concepts from the text prompt in different spatial areas of ... | Rebuttal 1:
Rebuttal: Dear Reviewer PszJ,
Thank you for appreciating our approach. We will address your concerns below.
**Q1: Details of VAE**
Yes, we follow the setup of Stable Diffusion and use KL-based VAE. When training the VAE, we add an extra discriminator following the pipeline of LDM. And the downsampling ra... | Rebuttal 1:
Rebuttal: General response:
We express our sincere appreciation to all the reviewers for their valuable time and efforts in reviewing our paper. We are delighted to learn that the reviewers have generally recognized and appreciated the contributions made in our work, which include:
1. State-of-the-art perf... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
SPRING: Studying Papers and Reasoning to play Games | Accept (poster) | Summary: Open-world survival games such as Crafter pose a significant challenge for RL agents. In this paper, the authors propose a novel framework called SPRING that studies Crafter's latex paper source code and uses the knowledge to reason and play the game through large language models (LLMs) such as GPT4. SPRING ha... | Rebuttal 1:
Rebuttal: Thank you for recognizing the solidness of our work. Here are our responses to the questions and concerns:
W1 proprietary models:
Thank you for pointing out this important concern. We do notice that SPRING gets better after switching from GPT-4-0314 to GPT-4-0613, and we are hopeful that we get... | Summary: This paper proposes SPRING to read the game’s original academic paper and use the knowledge learned to reason and play the game throught a large language model (LLM).
Strengths: 1. This paper is well-writen and easy to follow.
2. The idea of reading paper with LLM to interact with environment directly is inte... | Rebuttal 1:
Rebuttal: Thank you for recognizing the novelty of our work. Here are our responses to the questions and concerns:
W1 Online Copra:
No other online corpora exist for crafter since it is an academic benchmark environment. This benchmark also provides us the chance to test our framework on unseen data. Da... | Summary: The authors propose a method, SPRING, to design an agent based on LLM question answering for the Crafter environment. Their method uses the Hafner 2021 paper describing the Crafter environment and its objectives as context to prompt the LLM about the next best steps in a chain-of-thought fashion using a DAG to... | Rebuttal 1:
Rebuttal: Thank you for appreciating our chain-of-thought approach and recognizing the potential of our work. Here are our responses to the questions and concerns:
W1 Game complexity:
Thank you for pointing out an important aspect that the Crafter lacks mechanical difficulty, which is indeed the intended... | Summary: SPRING is a method that reads a game (Crafter)'s original academic paper and reasons to play the game with a large language model (LLM).
1. study the paper: a fixed set of questions are used to summarize gameplay and action space information, with parapraph-level decomposition and then question-wise aggregati... | Rebuttal 1:
Rebuttal: Thank you for recognizing the novelty and importance of the direction of our work. Here are our responses to the questions and concerns:
W1 Rule-based agents:
Please note that the complexity of the game often goes beyond human inductive bias. The game involves a tech-tree that resembles Minecr... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Spuriosity Rankings: Sorting Data to Measure and Mitigate Biases | Accept (spotlight) | Summary: Shorcut learning has received increasing attention from the community recently. The paper proposes measuring and ranking data by "spuriosity," or the degree to which relevant spurious cues are present, as a way to detect and mitigate biases in deep models that arise from their tendency to rely on spurious corr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and comments. Also we appreciate the reviewer for saying the problem we tackle is ‘timely and important’, our ‘results are insightful’, and ‘the paper is well written’. We are very happy to hear you ‘enjoyed reading it’.
**Clarity**
We thank the reviewer for s... | Summary: This paper propsed a framework to measure model biases by ranking images within their class based on the strength of spurious cues and evaluate the gap in accuracy on the highest and lowest ranked images.
The analysis is comprehensive for a very large number of models.
Strengths: This paper propsed a simple... | Rebuttal 1:
Rebuttal: We thank the reviewer for their kind words. We now address each concern.
**On the modality-agnostic potential of Spuriosity Rankings**. While we demonstrate our method on a fundamental task (classification) in a fundamental modality (vision), we believe our framework and the lessons obtained by t... | Summary: The paper proposes a method to rank images in a given dataset in order of their "spuriosity". The proposal uses methodology from previous work [42] which examines most-active neural features of a trained model at per-class level, and hand-labels some sample images w.r.t. whether those features are “core” or “s... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to truly engage with our paper and provide insightful comments. We now address main concerns.
**Validating Rankings**. Given the complexity of sorting thousands of images, evaluating the quality rankings is non-trivial, especially since a single ground-tr... | Summary: - This work proposed Spuriosity, a quantity for determining the spuriosity ranking of data. The framework builds upon [42], which identifies spurious and core neural features by analyzing the neural activation map of an adversarially trained model. Spuriosity is defined based on these spurious neural features.... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for taking the time to read our paper and provide insightful comments. We address them below.
**Comparison to existing baselines**
We thank the reviewer for noting that our method tackles the “challenging problem [of] figuring out spuriosity on large scale datasets... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their time and insightful comments. A couple reviewers wonder if the involvement of a human in our framework is a limitation. We provide extensive discussion below, where we argue that having a human in the loop is a strength, though it is not necessary, as aut... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
When are ensembles really effective? | Accept (poster) | Summary: The authors offer a novel theoretical analysis of the majority vote classifier. In the process they define the "ensemble improvement rate" and give lower and upper bounds on it in terms of another entity they call the "disagreement-error ratio", under a novel assumption (competence) that they show empirically ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review and very insightful questions. We completely agree on the notational points, and will improve this in an updated draft of the paper.
As we mention in our response to Reviewer 3, the analysis is actually very simple in the regression case. In this c... | Summary: The paper "When are ensembles really effective?" discusses the question asked in the title: When are ensembles really effective? To do so, the authors introduce novel theoretical measures called the ensemble improvement rate and the disagreement-error ratio. The ensemble improvement rate measures the relative ... | Rebuttal 1:
Rebuttal: Thank you for your strong review of our work, and very helpful feedback.
Thank you for noticing the K/C typo -- we will correct this. We also agree that a better discussion of the relation of our work to weak learnability would be helpful; we will do our best to incorporate this in an updated dr... | Summary: The paper asks the following question: when is ensembling beneficial? The benefit is measured by the ensemble improvement rate (EIR), defined as the difference of average and majority voting risk, divided by the average risk. It is shown that 'competent ensembles never hurt', in the sense that under mild assum... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review and helpful feedback. The classification/0-1 loss setting represents the most significant challenge from the theoretical perspective, hence the focus on this case. For example, in regression with MSE loss, one has (via a simple bias-variance decompos... | Summary: Analysis the ensemble improvement rate vs. the disagreement-error ratio
Strengths: A solid analytical analysi.
Weaknesses: It is not clear for me what is the usefulness of obtained results.
The scope of the experiments is limited.
Technical Quality: 2 fair
Clarity: 3 good
Questions for Authors: What is th... | Rebuttal 1:
Rebuttal: Thank you for providing a review of our paper. As the title and results suggest, the usefulness of this study is in describing when we can expect ensembling methods to be effective. In particular, our empirical result suggests that ensemble improvement is much less pronounced for interpolating ver... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This study explores when ensembling leads to significant performance enhancements in classification tasks. Theoretical findings reveal that ensembling improves performance considerably when the disagreement rate among classifiers is large compared to the average error rate. The study also establishes improved ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review of our work, and useful feedback. While the aim of this work is not necessarily to provide guidance on how to build better ensembles, we hypothesize that some of our insights might lead to the development of better methods in future work. In particul... | null | null | null | null | null | null |
Learning Better with Less: Effective Augmentation for Sample-Efficient Visual Reinforcement Learning | Accept (poster) | Summary: This paper conducts a detailed study on what attributes of data augmention in Visual Reinforcement Learning are playing essetianal roles. With extensive experiments on DMC tasks, four main findings are given regarding the strength and diversity. Based on these findings, authors introduce two new data augmentat... | Rebuttal 1:
Rebuttal: We appreciate your thorough review and insightful feedback. We will address each of your comments and concerns below and also in our revised manuscript.
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**W1**: *Lack of novelty.*
**A**: We appreciate you raising the concern regarding novelty. While I understand your perspective that the pr... | Summary: This paper explores the crucial attributes of domain adaptation (DA) in achieving sample-efficient visual reinforcement learning (RL) and emphasizes the specific requirements of DA for visual RL. Extensive experiments are conducted to investigate these attributes.
The paper introduces two practical guidelines... | Rebuttal 1:
Rebuttal: We appreciate your thorough review and insightful feedback. We will address each of your comments and concerns below and also in our revised manuscript.
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**Q1**: *"What is different in below (i), (iii), (iv) on the page 2. [...]"*
**A**: These three key findings correspond to the three sets ... | Summary: This paper explores the fundamental aspects of data augmentation (DA) in the context of visual reinforcement learning (RL) and introduces two methods, Random PadResize (Rand PR) and Cycling Augmentation (CycAug), to enhance its efficacy. Extensive experiments on the DeepMind Control suite and CARLA are conduct... | Rebuttal 1:
Rebuttal: We appreciate your thorough review and insightful feedback. We will address each of your comments and concerns below and also in our revised manuscript.
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**W1**: *"[...] observe and evaluate the performance of the proposed methods in relation to these techniques (such as SRM and PlayVirtual) ... | Summary: This paper explores the conditions for achieving sample-efficient visual RL with data augmentation. Based on the findings, two guidelines are proposed: one emphasizes sufficient spatial diversity with minimal hardness, leading to the introduction of Rand PadResize. Additionally, the data-sensitive nature of RL... | Rebuttal 1:
Rebuttal: We appreciate your thorough review and insightful feedback. We will address each of your comments and concerns below and also in our revised manuscript.
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**W1**: *"Does this imply that training the strategy using the original, unmodified data precedes the selection of suitable augmentation te... | Rebuttal 1:
Rebuttal: # Global Response
---
Dear reviewers,
We are sincerely appreciative of the time and effort you dedicated to reviewing our manuscript. Your comprehensive feedback has offered us valuable insights for enhancing clarity and quality. We have individually responded to each reviewer's queries and sug... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper presents a thorough empirical analysis of visual data augmentations and their effects on RL training. They benchmark various spatial augmentation on two axes of variation, spatial diversity, and hardness, measured by the amount of distortion created in the image. The authors perform a series of exper... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and thorough review. We added the suggested experiments to the `Response PDF`. In the following, we seek to address each of your concerns.
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**W1:** *"The combination of Rand PR and CycAug shows improved performance over previous methods. I failed to find... | null | null | null | null | null | null |
Is Learning in Games Good for the Learners? | Accept (spotlight) | Summary: The paper studies several trade-offs that existed in learning in games. The first is the tradeoff between regret and rewards, for which a generalized notion of equilibria is introduced. It is then investigated whether running a no-swap regret learning algorithm is efficient. It is shown that this depends on th... | Rebuttal 1:
Rebuttal: Thank you for your comments! We will certainly take another pass on clarifying the narrative in the introduction, and can move some of the discussion of results to later in the paper.
In terms of mean-based algorithms, all of these algorithms resemble approximate/smoothed versions of Follow the ... | Summary: This submission studies questions surrounding playing against a no-regret learner in a repeated game setting. These questions are motivated by previous observations that while it is known that when all players play no-regret strategies the empirical frequency of play approaches an equilibria, a player can some... | Rebuttal 1:
Rebuttal: Thank you for your feedback! Indeed, we view our results as indicating that the answer to the question of “what should you do when playing against a no-regret learner?” is very much “it depends”, and we aim to explore several branches of this decision tree (is their algorithm known? is the game kn... | Summary: The paper explores tradeoffs between reward and regret in repeated gameplay between two agents. It introduces a concept of generalized equilibrium that allows for different regret constraints, resulting in feasible values for each agent. The paper shows that such equilibria can be reached by algorithms maintai... | Rebuttal 1:
Rebuttal: Thanks for your comments! While our focus for the paper is intended to be primarily theoretical, in the appendix we give examples of games where we show explicit separations between feasible equilibrium values, and we are happy to explore simulating algorithms on these games. | Summary: This paper addresses several interesting questions regarding the tradeoff between reward and regret in repeated gameplay between two agents. Three problems are sequentially investigated. 1. The paper provides a characterization of the setting when running a no-swap-regret learning algorithm is preferred over p... | Rebuttal 1:
Rebuttal: Thanks for the review! Indeed there are several remaining open questions that we think would be interesting to explore in future work. | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper considers equilibria between agents that have arbitrary regret benchmarks (corresponding to different equilibrium concepts), and the relation between those equilibria and the interactions of no-regret learning agents. It is known in the literature that an agent who knows that the other is playing a... | Rebuttal 1:
Rebuttal: Thank you for your comments!
You are correct that Theorem 1 applies to exogenously specified equilibria, and does not require “learning” the target equilibrium on the fly. However, our focus is on two-player games, for which it is also possible to efficiently optimize over each of the equilibriu... | null | null | null | null | null | null |
IEBins: Iterative Elastic Bins for Monocular Depth Estimation | Accept (poster) | Summary: The paper introduces an Iterative Elastic Bins (IEBins) approach for monocular depth estimation. Many conventional monocular depth estimation approach uses a soft-argmax representation (Eq. (4)) that sums the product between depth probability and pre-defined depth bins. However, the large number of pre-defined... | Rebuttal 1:
Rebuttal: ### __We thank our reviewer for the constructive feedback and comments.__
### _W1: Question on the number of stages (Fig. 3)_
A1: During both training and inference phases, we find that when the number of stages exceeds 6, the performance changes very little. As we know, more stages require longe... | Summary: The paper introduces a method for monocular depth. The method uses a recurrent network based on RAFT to predict a probability distribution over a set of bins, which enables the depth at each iteration to be computed as the expectation over all bins and the margins of each bin to be adjusted using the computed ... | Rebuttal 1:
Rebuttal: ### __We thank our reviewer very much for the highly positive feedback.__ | Summary: This paper introduces a classification-regression-based monocular depth estimation pipeline.
Previous classification or classification-regression-based MDE approaches often suffer from huge complexity and loss of generalizability issue, due to the nature of requiring more depth hypothesis for better performanc... | Rebuttal 1:
Rebuttal: ### __We thank our reviewer for the constructive feedback and comments.__
### _W1: What happens when iteration is less or more than 6? How much does the number of iterations affect the final performance?_
A1: The results of IEBins at different stages are shown below:
|Stage Abs Rel &nb... | Summary: The paper tackles the task of monocular depth estimation which is of fundamental importance in computer vision and has many downstream applications. Several recent works use bin-based approaches and follow the adaptive binning framework where the distribution of bin centers on the depth interval (that are trea... | Rebuttal 1:
Rebuttal: ### __We thank our reviewer for the constructive feedback and comments.__
### _W1&Q4: Incomplete literature review. Comparison with LocalBins._
A1: We will add these two interesting and relevant works to our revised version.
Similarities with LocalBins: Both IEBins and LocalBins use a multi-st... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes iterative elastic bins (IEBins), a multi-stage coarse-to-fine method for monocular depth estimation (MDE). It progressively searches the target depth bin on top of the previous step. To reduce the error accumulation in the iterations, an elastic bin is proposed whose width is adjusted based... | Rebuttal 1:
Rebuttal: ### __We thank our reviewer for the constructive feedback and comments.__
### _W1: The contributions are not enough to my knowledge._
A1: Apologies for not stating the contributions clearly. In this work, we introduce a novel iterative elastic bins (IEBins) strategy for monocular depth estimatio... | Summary: The paper proposes an iterative elastic bins (IEBins) strategy for monocular depth estimation. The IEBins use a small number of bins adaptively at each iteration. It use a GRU to predict the depth distribution at each stage. The authors conduct experiments on 3 commonly used datasets and the proposed method sh... | Rebuttal 1:
Rebuttal: ### __We thank our reviewer for the constructive feedback and comments.__
### _W1: The motivation of iterative updates is not very clear. I don't see where the new information comes from in MDE._
A1: The IEBins embodies the idea of iterative division of bins. Each stage first divides the elastic ... | null | null | null | null |
Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning | Reject | Summary: The paper studies offline RL with non-linear function approximation. The paper is mainly motivated as existing sample complexity guarantees on offline RL algorithms with general function approximation yield suboptimal dependency on the function class complexity, e.g. when the bounds are translated to the linea... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We address your concerns and questions point-by-point.
**Q1**: Non-linear bonus oracle is a strong requirement. It removes the difficulties related to pessimism in offline RL
**A1**:We would like to clarify that our method does not transfer the primary compu... | Summary: This paper considers variance-weighted least-squared regression for offline RL with general function approximation. Under a uniform data coverage assumption, they show that the proposed algorithm obtains a sub-optimality bound that scales with the $D^2$-divergence of the offline data set, the positive lower-bo... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We address your concerns and questions point-by-point.
**Q1**: Uniform data coverage assumption is very strong, in such a case, why would we even need pessimism?
**A1**: We agree that pessimism does not require the uniform data coverage assumption. However, ... | Summary: This paper proposes a pessimistic nonlinear least-squares value iteration algorithm to tackle the offline reinforcement learning problem. The main motivation of the paper is to propose an algorithm that are both computationally efficient and minimax optimal w.r.t. the complexity of nonlinear function class. Th... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We address your concerns and questions point-by-point.
**Q1**: The presentation needs some work.
**A1**: Thanks for your feedback. We will address these problems one by one.
- The consistency of the $D^2$ definition:
Our current definition in Section 3.2 pe... | null | null | Rebuttal 1:
Rebuttal: Dear reviewers,
Based on the questions of reviewer gXdR and reviewer 17B8 on technical challenges, we would like to emphasize the difficulty of this problem being studied and our novel techniques to tackle it. Firstly, the variance information in the context of Least-Squares Value Iteration (LSVI... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Fast Partitioned Learned Bloom Filter | Accept (poster) | Summary: The authors propose two methods to reduce the construction time of the partitioned learned Bloom filter (PLBF):
1. Fast PLBF, which can construct the same data structure as PLBF but with a smaller time complexity of O(N^2k) and 2. Fast PLBF++, which can construct the data structure with a time complexity of O... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's positive assessment of our paper. We would like to address their concerns.
> The authors may provide further ablation studies on the sensitivity of hyper-parameters in practice
In response to this comment, we performed an ablation study to verify the sensitivity of t... | Summary: The authors propose faster dynamic programming variants of the learned partitioned Bloom filter data structure of Vaidya et al. that requires $O(N^3 k)$ time.
Two solutions are proposed, the first constructs the same data structure in time $O(N^2 k) $ time, and another, that constructs a potentially different ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful comments. We are happy to answer the questions raised.
> How do you set N?
Finding the optimal N and k is difficult, but setting N to about 1000 and k to about 100 may be a good choice. The additional ablation study using two real-world datasets shows the ... | Summary: The paper presents two improvements of the algorithm PLBF (Partitioned Learned Bloom Filter), called fast PLBF and fast PLBF++. PLBF learns the distribution structure and uses it to minimize the memory allocation. An ML model (LightGBM is used in this paper) is trained to predict a score between [0,1] indicati... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful comments. We are happy to provide additional clarification on the unclear points raised.
> It is not clearly explained what computations are redundant in PLBF and how they are avoided by fast PLBF.
> Can you explain in more detail, or even intuitively, wh... | Summary: This paper improves upon the Partitioned Learned Bloom Filter (PLBF), which is an essentially optimal learned Bloom filter introduced in ICML 2021. Construction time of PLBF is $O(N^3k)$, where $N > k$ are hyper-parameters. The authors show how to construct the same filter much more quickly; their Fast Partiti... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive evaluation of our paper. Let us answer the questions.
> Could you please find the (N,k) combination that approximately minimizes the false positive rate and then measure and show the construction times for that?
In Appendix E, we performed an ablation stu... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
CluB: Cluster Meets BEV for LiDAR-Based 3D Object Detection | Accept (poster) | Summary: This paper combines two streams of LiDAR-based 3D object detectors into a single model: conventional BEV-based detectors and emerging cluster-based detectors. The main contribution is that It is the first one to combine the two streams of methods, leveraging the high-recall of BEV representation and fine-grain... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive and detailed comments. We respond to your comments below.
> *detail errors and format correct*
Thanks for pointing out these typos. We have addressed all the errors you mentioned, and we will proofread them very carefully. Since the manuscript can not be... | Summary: This work combines the BEV-based and cluster-based representations into a unified framework named CluB. At the feature level, the Cluster Feature Diffusion module and the imitation loss are proposed to fuse the features obtained from the BEV branch and cluster branch. At the query level, the Cluster Query Gene... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive and detailed comments.
> *The argumentation of the core hypothesis is insufficient. I think that the max pooling in BEV-based detectors is able to reduce the feature shape and aggregate the feature of the object to the center, just like the cluster-based ... | Summary: The authors explore and analyze the existing LiDAR-based 3D object detection framework, and propose to adopt BEV-based and cluster-based methods to aggregate those features from LiDAR input. The experimental results on Waymo and nuScenes are better compared to the existing methods.
Strengths: 1. The task of 3... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review. We respond to your comments below
> *Computation/memory footprint comparison.*
Thank you for the suggestion. We have computed the statistics for computation and memory usage of our CluB model and the widely used BEV-based baseline detector Transfusion-L [1] o... | Summary: The paper introduces a new 3D object detection framework called CluB that combines the strengths of BEV-based and cluster-based detectors. CluB effectively integrates the context-aware BEV representation and object-centric cluster representation at both the feature level and the query level.
The proposed metho... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive and detailed comments. We respond to your comments below.
> *According to the expression in the paper, Diffused Vote BEV features and Dense BEV features represent the center point semantic information and edge information respectively. But in the design o... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their detailed and insightful comments, as well as the favorable recommendations. We also thank the area chair for your time and efforts in handling our paper. We appreciate the positive comments, e.g., "well-written" and "novel motivation" from Reviewer Z2j1, ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a CluB framework to improve the accuracy of 3D object detection by taking advantage of both BEV-based and cluster-based paradigms. The motivation is that the cluster features in voting-based cluster method can largely preserve the 3D structure details of each object, thus supplements the we... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review. We respond to your comments below.
> *The demonstration of the class-aware BEV diffusion is not clear. How to leverage the classification results to control the expansion magnitude? Figure 3 is also confusing, how to get Class-aware Vote BEV?*
Thanks for your... | null | null | null | null | null | null |
Gradient Flossing: Improving Gradient Descent through Dynamic Control of Jacobians | Accept (poster) | Summary: The authors propose a novel approach called "gradient flossing" to tackle the instability of gradients in the training of recurrent neural networks (RNNs). The process of pushing the Lyapunov exponents toward zero is referred to as "flossing" the gradients. This stabilizes the gradients and improves network tr... | Rebuttal 1:
Rebuttal:
Thank you for your thoughtful review and feedback on our manuscript. We appreciate the acknowledgment and recognition of our novel approach involving the use of Lyapunov exponents towards zero to address the challenges of vanishing and exploding gradients in RNNs.
> While toy tasks are more tra... | Summary: The authors propose a new technique for handling gradient instability (vanishing/exploding) in neural network models, especially sequence models such as RNNs. Specifically, by leveraging results from recent works that establish a connection between Lyapunov growth exponents (from dynamical systems) and the sin... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their constructive feedback which has greatly contributed to improving the quality of our manuscript.
> Eq. (2) does not necessarily need to refer to the ‘long-term Jacobian’ unless $\Delta t \equiv t-\tau\rightarrow \infty$. That should be clarified.
We have ... | Summary: Authors propose gradient flossing for RNNs, which adds an additional regularization term that keeps the sum of Lyapunov exponents close to zero. This essentially encourages the singular values of the long-term Jacobian to be close to 1, hence addressing the vanishing/exploding gradient problem in RNNs. The aut... | Rebuttal 1:
Rebuttal: We deeply appreciate reviewer ZKKk for their thorough and insightful review of our manuscript. Your constructive feedback is invaluable, and we are committed to addressing each concern raised to enhance the quality and clarity of our work.
> I’m still unclear how QR decomposition is able to avoid... | Summary: The presented paper proposes a new method for tackling numerical instabilities during training for recurrent neural networks.
The proposed method exploits a theoretical link between Lyapunov exponents and the singular values of the long-term Jacobian.
The set of experiments is well-chosen to showcase the exten... | Rebuttal 1:
Rebuttal: We deeply appreciate the insightful feedback provided by the reviewer. Your comments and concerns have not only broadened our perspective but also greatly aided in refining our manuscript to a higher standard. Specifically, we acknowledge the importance of evaluating gradient flossing on real-worl... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and valuable feedback on our manuscript, "Gradient Flossing: Improving Gradient Descent through Dynamic Control of Jacobians". We have carefully addressed each of the reviewers' comments in the subsequent sections. Here, we provide a concise summary to address... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation | Accept (oral) | Summary: This paper approaches the task of predicting optical flow from a pair of images and depth from a single image. It proposes to do so using diffusion models, providing a training pipeline which includes contributions to deal with noisy training data. The proposed method is competitive with SOTA in depth predicti... | Rebuttal 1:
Rebuttal: Thank you for your review, and for your thoughtful comments and questions. They will certainly help improve the revised paper. Please see our response below.
> Finetuning performance on real data yields weaker performance on Sintel vs. other methods (minor weakness, but addressing could further s... | Summary: This paper proposes to use diffusion models to solve monocular depth and optical flow estimation tasks. Unlike previous task-specific models for depth and flow, this paper uses a generic diffusion model. This paper studies the effect of training data (synthetic and real) and processing of sparse depth and flow... | Rebuttal 1:
Rebuttal: Thank you for your review, and for your thoughtful comments and questions. They will certainly help improve the revised paper. Please see our response below.
> Due to the uncertainty in diffusion models, a same model might produce different results when running twice. How large is the fluctuation... | Summary: The authors study the use of diffusion models for the tasks of single-image depth estimation and optical flow estimation. Self-supervised pre-training, supervised fine-tuning with synthetic and real data, combined with a couple of tricks to leverage imperfect GT, lead to competitive results with nearly no task... | Rebuttal 1:
Rebuttal: Thank you for your review, and for your thoughtful comments and questions. They will certainly help improve the revised paper. Please see our response below.
> Do the claims hold on higher-level tasks such as semantic segmentation
The extent to which a generic diffusion model is effective on oth... | Summary: The paper demonstrates that diffusion models are effective general-purpose solutions for dense optical flow and monocular depth regression tasks. The paper shows that the same architecture and loss functions lead to at-par or better performance on these tasks, compared to existing methods that use domain knowl... | Rebuttal 1:
Rebuttal: Thank you for your review, and for your thoughtful comments and questions. They will certainly help improve the revised paper. Please see our response below.
> limited technical novelty
One of the main motivations for this paper is to understand how well vanilla diffusion models perform on class... | Rebuttal 1:
Rebuttal: References
[1] RAFT: Recurrent All-Pairs Field Transforms for Optical Flow, Teed and Deng, 2020
[2] FlowFormer: A Transformer Architecture for Optical Flow, Huang et al, 2022
[3] Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer, Ranftl et al, 2020
... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning Repeatable Speech Embeddings Using An Intra-class Correlation Regularizer | Accept (poster) | Summary: Starting with the observation that supervised embeddings should vary from one class to another but not be sensitive to variations within a given class, this paper proposes to add an intra-class correlation (ICC) regularization to the contrastive loss in representation learning. This new regularizer is derived ... | Rebuttal 1:
Rebuttal: We thank reviewer hWT5 for the comments.
**Response to "Is ICC competitive with other techniques? Or could it be complementary?":** In Lines 30-31, we identify two primary groups of approaches for managing the intra-class variance: (1) enhancing the diversity of the training data, and (2) utilizi... | Summary: The paper introduces the ICC regularizer, a novel regularization technique that complements the contrastive loss to enhance the repeatability of embeddings. The authors illustrate the reason why the ICC regularizer better minimizes the intra-class variance than the contrastive loss, offering a fresh perspectiv... | Rebuttal 1:
Rebuttal: We thank reviewer 89AX for the comments.
**Response to Question 1:** We evaluate two methods objectively by using a speaker similarity score and word error rate (WER). We use an opensource speaker encoder (huggingface.co/speechbrain/spkrec-ecapa-voxceleb) to calculate the speaker similarity score... | Summary: The paper presents a simple but seemingly effective idea to regularize contrastive embedding extractors. The regularizer aims at the intra-class correlation to ensure the repeatability of the embedding.
The effectiveness is confirmed experimentally for the task of speaker verification, voice style conversion, ... | Rebuttal 1:
Rebuttal: We thank reviewer Yotw for the comments.
It is true that the ICC relies only on second-order statistics and it’s possible that it may not capture complex, nonlinear relationships within or between groups. That said, it does have several benefits over other more complex measures:
1. This simplici... | Summary: In this paper, authors propose to use an intra-class correlation coefficient (ICC) regularizer as a complimentary component for contrastive loss to increase the repeatability of the supervised speech embedding. Authors conduct simulation for ICC regularizer and contrastive loss (GE2E loss) to explain their sim... | Rebuttal 1:
Rebuttal: We thank reviewer efaw for the comments.
**Response to Weakness 1:** In this study, our focus is not on demonstrating that the proposed method can reach SOTA performance across various tasks. Rather, we aim to illustrate that the proposed ICC regularizer can improve the repeatability of learned e... | Rebuttal 1:
Rebuttal: We thank all reviewers for the comments.
**Response to "discussion on potential negative societal impact":**
Methods for learning new feature representations that focus on separability between classes can amplify biases that exist in the data. This is a well-known problem and it can occur when t... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this paper, the concept of repeatability from measurement theory was introduced for representation learning. Intraclass Correlation (ICC) was proposed as an evaluation metric and the ICC regularizer was used as an additional element to contrastive loss for training. This aims to enhance the repeatability of... | Rebuttal 1:
Rebuttal: We thank reviewer wtbk for the comments.
**Response to "more controlled experiments may be needed":** We follow the reviewer's suggestion and run more testing on audio samples with noise added. We evaluate three SNR levels, 30, 35 and 40 dB. For each input audio, we randomly generated Gaussian... | Summary: This paper proposes to use a traditional intra-class correlation coefficient (ICC) to assess the repeatability of speech embeddings learnt by neural network. The proposed ICC regularizer has characteristics similar to well-known contrastive loss which aims to minimize the intra-class variance and maximize the ... | Rebuttal 1:
Rebuttal: We thank reviewer Qvpp for the comments.
**Response to "ICC is a traditional metric and widely used for various classification tasks":** The ICC is primarily a statistical measure that describes how strongly units in the same group resemble each other. It's typically used in various research fiel... | null | null | null | null |
Hierarchical Adaptive Value Estimation for Multi-modal Visual Reinforcement Learning | Accept (poster) | Summary: This paper presents a new vision-based reinforcement learning algorithm for autonomous driving scenarios. In previous feature fusion methods, a single critic could amplify dominant features and obscure other features. To address this issue, the authors propose a local critic for each feature and combine them i... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude for your thoughtful review of our paper. Your positive remarks on our writing and analysis are truly appreciated, and we feel encouraged by your feedback. Regarding your concerns about the novelty of our work, we sincerely apologize if our presentatio... | Summary: The authors propose a Local modality-customized Value Estimation (LVE) paradigm, which dynamically estimates and adjusts the importance weight of each modality from the perspective of value-level to compensate for the multi-modal vision-based RL methods that usually use fused modality features for learning Glo... | Rebuttal 1:
Rebuttal: We are truly grateful for your time and effort invested in reviewing our work. Your positive feedback has been both encouraging and instrumental for the further improvement of our paper. Herein, we address the points raised in your comments:
>1. The experiments for this paper are still not adequ... | Summary: This paper proposes a fusion scheme for multi-modal RL, with a particular focus on fusing the modalities in estimating the value function. The authors propose a combination of global value estimation, local value estimation and another conditioned fusion of the global and local value estimation. The approach i... | Rebuttal 1:
Rebuttal: We are truly thankful for the thoughtful remarks and the experimental recommendations you have provided. These suggestions shed light on what we can improve, and we believe they will be instrumental in refining our work further. We address your main concerns as follows:
> ...fusing the q-values i... | Summary: The paper proposes a Hierarchical adaptive value estimation (HAVE) framework for multi-modal (RGB, event, depth) visual reinforcement learning. Firstly, a modality-specific value function learning process is proposed, and an assignment module is proposed to weight different modality. Second, a re-fusion is pro... | Rebuttal 1:
Rebuttal: First and foremost, we would like to express our gratitude for your positive comments. We also deeply appreciate the practical experimental suggestions given in your constructive feedback, which offers valuable perspectives that will strengthen our work. Below are our responses to your concerns:
... | Rebuttal 1:
Rebuttal: **To All Reviewers (Additional Experiments on New Environment)**
We thank all the reviewers for their valuable time and insightful feedback. In this general response, we would like to address the concerns about the effectiveness of our HAVE in environments other than autonomous driving. Specifica... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Jailbroken: How Does LLM Safety Training Fail? | Accept (oral) | Summary: This paper offers an insightful examination of adversarial misuse, or "jailbreak" attacks, against large language models (LLMs) such as OpenAI's GPT-4 and Anthropic’s Claude v1.3. By analyzing two proposed failure modes of safety training—competing objectives and mismatched generalization—the authors provide a... | Rebuttal 1:
Rebuttal: Thank you for your review of our paper, and for pointing out areas of concern. Here's our response to the issues you raised:
1. **Definition of jailbreak attacks.** We would like to highlight that we present a formal threat model for jailbreak attacks in Section 2.1 in terms of restricted behavio... | Summary: The paper studies jailbreak attacks. More specifically authors hypothesize two failure modes of LLMs 1. competing objective and 2. mismatched generalization and base their discussion on why jailbreak attacks succeed based on these hypothesis made. They then quantitatively perform experiments on different jailb... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful and detailed review! Responding to each of the points you’ve raised:
1. **On defenses and small-scale experiments.** Towards better defenses, in Section 5 we argue that successful defenses may need to move beyond the existing pretrain-then-finetune paradigm. In this ... | Summary: This manuscript provides an initial exploration of the robustness of LLM systems against adversarial misuses, specifically focusing on "jailbreak" attacks. The authors successfully summarize existing threats, propose plausible hypotheses, and conduct empirical evaluations on three LLMs: GPT-4, Claude v1.3, and... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and constructive feedback! To respond to the points you’ve raised:
1. **On background on LLMs and safety training.** Thanks for the suggestion—we will provide a more comprehensive review of background in the final version to make the paper more accessible for a b... | Summary: The paper evaluates GPT models and Claude models against Jailbreaks and comes up with two possible explanations for why jailbreaks are successful- 1. competing objectives between pretraining + IF finetuning and safety finetuning; and 2. pretraining+finetuning generalizing better than safety tuning. It also arg... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and feedback! We're glad you found the writing and hypotheses compelling. To respond to the points raised:
1. **On the number of prompts evaluated.** We acknowledge the desire for a more extensive set of prompts. However, our choice of 317 prompts for evaluatio... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper analyzes why safety training of LLMs fail and what could be the root causes for them. They offer two explanations: competing objectives and inadequate coverage of the model capabilities during safety training. They categorize existing attacks and propose some new ones into these two buckets, and eva... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and feedback! To respond to the points you raise:
1. **On studying open-source models.** We highlighted white-box studies involving open-source models as a future direction in the Conclusion because no safety-trained open-source large language models existed a... | Summary: The paper studies jailbreaking of large language models. The authors identify two categories of causes of jailbreaks, competing objectives and mismatched generalization, and use this insight to analyze existing jailbreaks and construct new ones. They empirically study the effectiveness of these jailbreaks on s... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review of our paper! Responding to the points you raise:
1. **On which models were queried.** Attacks were developed by querying GPT-4 and Claude on a subset of the 32 curated prompts, in line with the threat model described in Section 2.1. In addition, attacks from j... | Summary: The paper investigates two reasons why jailbreak attacks against SOTA LLMs (GPT-4, GPT-3.5 Turbo, Claude v1.3) succeed despite extensive safety training: a) competing training objectives (safety objectives vs. pretraining/instruction tuning) and failure of safety training to generalize to conditions covered by... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful and comprehensive review! To respond to the weaknesses and questions that you bring up:
1. **On the focus on two failure modes.** We view simplicity here as a strength rather than a weakness, as we identify two core issues that underlie almost all of the diverse set o... | null | null |
On Masked Pre-training and the Marginal Likelihood | Accept (poster) | Summary: The authors set out to show that the good generalization of Masked Pre-Training can be explained as the equivalence with model's high marginal likelihood. The exchangeability assumption in sequential inputs is handled via a combinatorial choice over the subset of masking features, such that the masked pre-trai... | Rebuttal 1:
Rebuttal: Thanks for the constructive feedback and the time taken to review our paper. We have addressed all your comments individually below. If any concerns remain, we would be also happy to clarify.
**Point 1** & **Point 2**
> *I am not very convinced by the arguments in the paper that claim that maximi... | Summary: This paper derives the equivalence between log marginal likelihood and negative masked pre-training loss which is often used for training text models. The paper argues that as marginal likelihood is the Bayesian way to do model selection, doing training using the masked pre-training loss inherits good generali... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive consideration of our work, and the useful feedback including the technical questions on the derivations. We are interested in addressing all the questions brought in the review for a full understanding of the reviewer. If there is still something unclear, we ... | Summary: This paper shows that MLM is effectively maximizing the model's marginal likelihood, perhaps explaining why MLM has been successful. Beyond providing a proof, they run several empirical experiments suggesting their results hold in practice.
Strengths: 1. Clear introduction; Clear highlighting of paper strengt... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive consideration and the useful feedback on our work. We also appreciate the highlighted strengths and contributions. We addressed the main comments and concerns included in your review. If any other concern remains, we would be also happy to clarify during the ... | Summary: They show empirically that randomly masking a fixed number of M tokens and predicting with the remaining tokens produces a biased estimate of the log marginal likelihood $\log p(x)$ (LML).
Furthermore, they prove that repeating the masking for M from 1 to D (the total number of tokens), and summing up the esti... | Rebuttal 1:
Rebuttal: We thank the reviewer for the acknowledgement of the main contributions of our work, the useful comments and the relevant feedback provided on the technical side. We have addressed _all_ your comments individually in the lines below. If there is still something unclear, we would be also happy to ... | Rebuttal 1:
Rebuttal: **General comment to all reviewers**
We thank all reviewers for their useful comments, positive consideration and relevant feedback on our paper. It seems that the reviews are positive in general and acknowledges our main contributions and soundness of our work. We have addressed each comment and... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes viewing masked pretraining methods (like BERT or MAE) as optimizing a biased form of the log marginal likelihood $\log p_\theta(x)$. Using a parallel to exhaustive leave-M-out cross validation, the authors derive the exact relationship between the LML and a weighted sum of masked pretrainin... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive consideration and the useful feedback on our work. We are particularly glad for the scores marked as *excellent* and for hearing that the connection between LML and the weighted sum of masked pretraining losses is considered *original* and *enlightening*. Ove... | null | null | null | null | null | null |
Learning from Both Structural and Textual Knowledge for Inductive Knowledge Graph Completion | Accept (poster) | Summary: This paper proposes an inductive knowledge graph completion method based on both textual and structural information. It introduces a new task of how to leverage information from both modalities when there are both structured and textual data available. This paper proposes a rule mining method called LSTK-TELM,... | Rebuttal 1:
Rebuttal: Thanks for your helpful comments. The following addresses concerns and questions.
> [R1] There is a lack of discussion on related works in the field of inductive link prediction.
[A] Due to space limitations, we have only discussed the related work on learning rule-based systems for inductive l... | Summary: This paper proposes a two-stage framework that imposes both structural and textual knowledge to conduct knowledge graph completion. The first stage aims to extract some soft triples with confidence scores. And then the second stage designs some tailored rules for entity link prediction, including text-enhanced... | Rebuttal 1:
Rebuttal: Thanks for your helpful comments. The following addresses concerns and questions.
> [R1] The text-enhanced entity representation models have been widely studied, such as [1][2]. Related work should be discussed
[A] Due to space limitations, we have not discussed the text-enhanced entity represen... | Summary: In this paper, the authors try to solve Knowledge Graph Completion (KGC) by proposing a two-stage framework Learning from Structural and Textual Knowledge (LSTK), that imposes both structural and textual knowledge to learn rule-based systems. In the first stage, the authors compute a set of triples with confid... | Rebuttal 1:
Rebuttal: Thanks for your helpful comments. The following addresses concerns and questions.
> [R1] The paper does not refer to the recent KGC model that can conduct KGC with unseen entities [1].
[A] Due to space limitations, we have only discussed the related work about learning rule-based systems for ind... | Summary: This paper proposes a rule-based inductive KGC method with two stages. In the first stage, it extracts soft triples from a text corpus using distant supervision. In the second stage, the obtained soft triples are mixed with the original hard triples and used to learn rule-based model for KGC.
Strengths: (1) I... | Rebuttal 1:
Rebuttal: Thanks for your helpful comments. The following addresses concerns and questions.
> [R1] This paper uses both structural and textual knowledge for inductive KGC, but there is no discussion about other related works which also use structural and textual knowledge, such as KEPLER[1] and BERTRL[2].
... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper addresses the problem of knowledge graph completion by proposing a two-stage framework — learning from structural and textual knowledge (LSTK). This novel framework leverages both structural and textual knowledge to learn rule-based systems, which provides a unique approach in the realm of KGC resea... | Rebuttal 1:
Rebuttal: Thanks for your helpful comments. The following addresses concerns and questions.
> [R1] The authors utilize a single method to estimate the confidence scores of the soft triples, resulting in a potential lack of generalizability.
[A] We have conducted an ablation study on replacing the proposed... | null | null | null | null | null | null |
DynPoint: Dynamic Neural Point For View Synthesis | Accept (poster) | Summary: DynPoint is an algorithm proposed to enhance the synthesis of novel views in monocular videos using neural radiance fields. It focuses on predicting explicit correspondence between neighboring frames, enabling information aggregation for view synthesis. The demonstrated experimental results show a significant ... | Rebuttal 1:
Rebuttal: # Section 4 - Reviewer BEKz
We thank the reviewer for the constructive assessment of our work. In the following, we address the concerns point by point. Please feel free to use the discussion period if you have any additional questions.
## 4.1 Weakness
**4.1.1 Deformable sprites**
Unlike our i... | Summary: The paper proposes a novel method for performing novel view synthesis from monocular captured videos. This is done by learning scene flow and depth parameters which enable the accurate aggregation of appearance information from nearby frames. The paper proposes a novel method of acquiring both consistent depth... | Rebuttal 1:
Rebuttal: # Section 3 - Reviewer Rm9i
We thank the reviewer for the constructive assessment of our work. In the following, we address the concerns point by point. Please feel free to use the discussion period if you have any additional questions.
## 3.1 Weaknesses
**3.1.1 Comparison with DynIBaR**
This ... | Summary: This paper proposes an algorithm for novel-view synthesis in dynamic scenes. According to the abstract and introduction section, this paper takes a monocular video as an input (L.98) and cope with uncontrolled or length scenarios (L.3). Using geometric priors, such as monocular depth and optical flow from off-... | Rebuttal 1:
Rebuttal: # Section 2 - Reviewer nc3b
We thank the reviewer for the constructive assessment of our work. In the following, we address the concerns point by point. Please feel free to use the discussion period if you have any additional questions.
## 2.1 Weakness
**2.1.1 Wrong experimental setup**
Weakn... | Summary: The paper proposes a method for dynamic scene synthesis from monocular video. The authors aim to speed up training and deal with long-duration videos. To do this, they propose to estimate scene flows of surface points supervised by the signals which are generated by the proposed consistent depth algorithm. The... | Rebuttal 1:
Rebuttal: # Section 1 - Reviewer J3AP
We thank the reviewer for the constructive assessment of our work. In the following, we address the concerns point by point. Please feel free to use the discussion period if you have any additional questions.
## 1.1 Weakness
**1.1.1 Equation 3**
Thank you for point... | Rebuttal 1:
Rebuttal: # Section 0 - Author rebuttal to ACs
Dear Reviewers, we truly appreciate your thoughtful review, which has been immensely valuable in refining our paper. Your insights have contributed significantly to the enhancement of our work.
## 0.1 Experiments on HyperNeRF
In order to comprehensively evalu... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Scaling Data-Constrained Language Models | Accept (oral) | Summary: This paper studies an important empirical question: what is the optimal tradeoff between LLM size and the amount of data available for training it. The authors follow an existing line of work (e.g., Chinchilla), and additionally consider the role of data repetition (via multiple epochs), e.g., what is the effe... | Rebuttal 1:
Rebuttal: Thank you for your thorough review.
Weaknesses:
> There is something unclear about using the validation or the test loss. The authors say (#42) that they are reporting test loss, but many of the tables and graphs say that they are showing validation loss. Which one is it? Reporting test loss is... | Summary: This paper demonstrates the scaling law both mathematically and experimentally in data-constrained situations. They analyze three experimental protocols: allocation (fixed unique data), return (fixed FLOPs), and parametric fit, and obtain new conclusions that were not found in previous research. For example, t... | Rebuttal 1:
Rebuttal: Thank you for your review. We agree that future work confirming these findings at >10B would be very interesting. The Galactica 120B model from prior work does provide preliminary evidence that you can repeat at these larger scales, but more experiments are needed.
Questions:
> Data-Constrained... | Summary: This paper investigates scaling properties of languages, in the presence of *repeated data*: plenty of work has studied the scaling properties of LLMs (and ML architectures in general), both with respect to the number of parameters and amount of data, but they generally assume each new datapoint is unique. How... | Rebuttal 1:
Rebuttal: Thank you for your thorough summary of the work and careful review.
> by repeating data, they are able to train a comparable model (in terms of performance and compute budget) with 27% less parameters.
We have updated the main plot (right) to larger scales comparing a 8.7 billion parameter mod... | Summary: This paper studies the scaling behavior of language models by repeating the training data to multiple epochs. The authors extend the recent successful Chinchilla scaling law to a data-constrained regime by adding exponential decay of data and model terms then fit the empirical observations. The key takeaway is... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and thought-provoking points.
Weaknesses:
Choice of the parametric form:
- Decay: We agree that these experiments do not rule out other parameteric forms, such as polynomial. We think exponential was an intuitive starting point out of possible decay formulatio... | Rebuttal 1:
Rebuttal: We thank all reviewers for their thorough reviews. We have made the following updates to the paper:
Increased the scale of the models in the first plot (right) showing even more significant results in support of scaling data faster than parameters when repeating (also in terms of downstream perfo... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
What You See is What You Read? Improving Text-Image Alignment Evaluation | Accept (poster) | Summary: This paper aims to develop a method for evaluating the level of semantic alignment between text and image. In order to achieve this, the authors construct datasets of image and text pairs, and collect human judgments to determine if the text and image are semantically aligned. The paper proposes two methods fo... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the value of our human image-text judgments, that will aid in the development and validation of alignment evaluation methods.
### Variability in $VQ^2$ Sampling [weakness 1]
Our strategy for question-answer pair generation is based on established works li... | Summary: This paper addresses the problem of text-image alignment evaluation by proposing a benchmark named SeeTRUE and two alignment metrics named VQ^2 and end-to-end VNLI.
SeeTRUE covers a variety of real and synthetic images and captions. The synthetic captions, which are generated by large language models (LLMs),... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging that our methods can facilitate the evaluation and development of image captioning and text-to-image generation models.
### Evaluation Scenarios with Different Negative Captions [weakness 1]
SeeTRUE's diverse architecture encompasses seven distinct test s... | Summary: The authors propose a benchmark and two methods for evaluating fine-grained and complex image-text alignment. Their benchmark involves multiple distracter captions and images involving both real and synthetic images and captions. They propose two methods - one evaluates image-text alignment by asking multiple ... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our approaches and the SeeTRUE benchmark as valuable to the community for evaluating vision-language models. We will address your comments and questions.
### Clarity on $VQ^2$ Design Choices [weakness 1 and question 2]
We've elaborated on the motivations und... | Summary: This paper introduces SeeTRUE, a benchmark for evaluating image-text alignment, encompassing a diverse range of both real and synthetic images and text. The authors proposes two innovative approaches to evaluating alignment: VQ2, which relies on question generation and visual question resolution, and VNLI, whi... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the contribution of the SeeTRUE benchmark to allow a more thorough evaluation of text-image alignment methods, and for recognizing our approaches as innovative. We will now address your comments and questions.
### Dependency on LLM/VLM Quality + LLM Limitat... | Rebuttal 1:
Rebuttal: We appreciate the reviewers highlighting the comprehensiveness of our SeeTRUE benchmark and its potential contribution to the field (j32y). The noted effectiveness of our visual entailment evaluation and advancements in negative captions generation, particularly in addressing prior benchmark limit... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Resolving the Tug-of-War: A Separation of Communication and Learning in Federated Learning | Accept (poster) | Summary: This paper proposes a two-layer federated learning (FL) framework (FedSep) by separating the communication and learning parameters. By a bilevel optimization formulation, FedSep enjoys a convergence guarantee. In addition, two settings, communication-efficient FL and model-heterogeneous FL, are solved in FedSe... | Rebuttal 1:
Rebuttal: Thanks for your positive comment. Below are responses to your questions:
**Q1: is it possible to show speedup in terms of the clients' number and local steps**
**A**: In Corollary 3.8, we focus on achieving the sub-linear rate. Indeed, it is possible to achieve the linear speedup in terms of the... | Summary: The authors proposed a two-layer federated learning framework called FedSep, with one layer for communication and another layer for learning. The two layers are connected through decode/encode operations. Furthermore, the authors proposed an efficient algorithm to solve FedSep by treating it as a bilevel optim... | Rebuttal 1:
Rebuttal: Thanks for your positive comment. Below are responses to your concerns and questions:
**W: The framework rely on the analyticity and strong convexity of the second level problem.**
**A**: In the bilevel optimization literature, the Non-convex-strongly-convex assumption (including the smoothness ... | Summary: This paper asserts that the tasks of communication and learning are at odds in federated learning. As such, a new approach is suggested that separates these tasks. The approach comprises an encode-decode operation, where decoding is cast as an optimization problem. The overall learning task is formulated as a ... | Rebuttal 1:
Rebuttal: Thanks for spending time reviewing our paper. Below are our responses to your questions:
**A(W1: Formal discussion of communication/learning conflict)**: Please refer to the answer for **Q1 in the overall response.**
**A(W2: Comparison with existing compression FL methods)**: Please refer to the... | Summary: The paper uses bilevel optimization in a federated learning setting. A unique decomposition of communication and learning has been identified, which has applications for reducing communication overhead and supporting heterogeneous models. The theoretical convergence guarantee has been presented and experiments... | Rebuttal 1:
Rebuttal: Thanks for spending time reviewing our paper. Below are our responses to your questions:
Response to Concerns:
**A(W1: Additional computational cost)**: Compared to FedAvg, FedSep needs to perform extra decode and encode operations. For the decode operation, we need to perform $I_{dec}$ of gradi... | Rebuttal 1:
Rebuttal: Thanks all the reviewers for their time and effort. Below we provide responses to questions related to the problem formulation, motivation and interpretation of the convergence theorem:
**Q1: Can you formalize the conflict between communication and learning?**
**A**: Formally, the conflicts betw... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Active Vision Reinforcement Learning under Limited Visual Observability | Accept (poster) | Summary: This paper studies an interesting setting where an RL agent needs to simultaneously decide how to act in the motor space and how to act to change the 2D observation space. Under this setting, the authors propose a new framework named SUGARL, mainly consisting of three technical contributions:
- a two-branch j... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our contributions. Please find our response below.
**1\. Experiments on robotics tasks**
Following the suggestion, we conducted new experiments on Robosuite [Zhu et al., 2020] and found that SUGARL performs best, especially on harder manipulator tasks. Pleas... | Summary: The paper introduces an RL setting where an agent take actions to simultaneously perform a task and control its vision. The authors design an algorithm for learning two policies for task performing and vision controlling. They test the algorithm on two domains: Atari and DMC. Results show that the proposed alg... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our contributions. We will address the concerns below.
**1\. The necessity of active perception**
We thank the reviewer for the constructive comments. In order to highlight the necessity of active perception and effectiveness of our approach.
- We ran new... | Summary: The authors develop an RL framework for problems where the agent must control its perception in order to solve the task. Their framework, SUGARL, decouples the task into a sensory and motory policy, and propose a heuristic reward for training the sensory policy stably. They evaluate in Active Vision versions o... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the effectiveness of our approach and providing thoughtful comments. Following the suggestions, we conducted addtional experiments on more realistic robot environments.
**1\. Active perception settings**
Following the suggestion, we tested our algorithm on ... | Summary: This paper presents a method for what the authors call "active RL", a flavor of RL where the agent not only needs to pick a (motor) action, but also needs to select the (sensory) action where to look. The authors do so by training a DQN or SAC agent with separate heads for the motor and sensory policies respec... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our technical contribution and our analysis. Please find our response below.
**1\. Sensory action space**
We follow the suggestion to test SUGARL on continuous camera control on Robosuite, a simulated robotics environment. The agent uses a 5-DoF relative (x,... | Rebuttal 1:
Rebuttal: **General response:**
We thank all reviewers for inspiring comments and questions. In this general response, we address the common concerns of more experiments on (1) robot manipulator tasks, (2) more baselines, and (3) more designs of proposed Persistence-of-Vision Memory (PVM).
1. **New exper... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work focuses on reinforcement learning with a controllable perceptive field where the agent not only learns a motor policy but learns a sensor policy at the same time. The sensor policy controls the information to be obtained. This work proposes to coordinate two policies by introducing an intrinsic rewar... | Rebuttal 1:
Rebuttal: Thanks for the comments and questions. Please see below for our response that addresses the concerns.
**1\. Novelty of this research**
We would like to highlight our contributions as follows:
- The decomposition of policies governing sensory and motor actions. It’s a different approach compared... | null | null | null | null | null | null |
Orthogonal Gradient Boosting for Interpretable Additive Rule Ensembles | Reject | Summary: In this paper, the authors propose a gradient-boosting algorithmic framework for rule ensemble learning, emphasizing the interpretability of produced rule set. Various gradient-boosting algorithms are reviewed in the rule-learning context, and the authors argue that a specific boosting algorithm, called fully ... | Rebuttal 1:
Rebuttal: We thank you for your thoughtful comments and generally positive evaluation. Below we first address your questions and then provide some clarifications regarding your other concerns.
**Test performance**
The results in the original submission unfortunately strongly undersold the proposed method ... | Summary: This paper introduces a novel approach to gradient boosting of decision rules for interpretable machine learning models. By incorporating a
weight correction step and orthogonal projections, the method maximizes predictive gain per rule.
Their experimental evaluation on various classification, regression, and ... | Rebuttal 1:
Rebuttal: We thank you for the positive evaluation of our work as well as for pointing out recent developments in the related literature.
Based on a first assessment the work of Calzavara et al. is a related but clearly distinct problem: the extraction of a rule set from a single tree that is causally fair... | Summary: The paper presents a new algorithm for learning rule ensembles and claims that these are interpretable, but does not present any support.
Strengths: The proposed method is reasonable, but, in the context of other work in this area, not ground-shaking. The experimental evaluation is done well, but does not tou... | Rebuttal 1:
Rebuttal: As stated in the global rebuttal, we take all the concerns regarding the nomenclature around interpretability and the term cognitive complexity serious, and we are happy to modify the language to reflect that gains in ensemble simplicity do not necessarily imply an interpretable model in absolute ... | Summary: This paper introduces Fully-Corrective Orthogonal Gradient Boosting (FCOGB), a novel algorithm aimed at facilitating interpretable rule learning. The study contends that existing rule learning algorithms often yield complex models that pose challenges for interpretation. FCOGB addresses this concern by generat... | Rebuttal 1:
Rebuttal: We thank you for your thoughtful comments and hope you will consider upgrading your evaluation in the light of the following clarifications.
**Generalization performance / test risk**
You identified an unsatisfactory performance in terms of the test risk as the main weakness of the paper and as... | Rebuttal 1:
Rebuttal: We thank all reviewers for their thoughtful comments and their mostly positive assessment. In addition to the individual rebuttals, we would like to clarify here two central points:
1. The test performance of the proposed algorithm and the fact that it can be easily improved by regularization (se... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a framework of fully corrective orthogonal boosting. The main algorithmic difference here is the objective for each next weak model. It is the cosine between the gradient (which is orthogonal to previous weak learners by construction) and the part of the new model that is orthogonal to prev... | Rebuttal 1:
Rebuttal: We thank you for the constructive feedback and overall positive assessment. Please find below point-by-point responses of your concerns and questions.
**Usage of the terms interpretability and cognitive complexity**
We understand your comments regarding the interpretability of rule ensembles and... | null | null | null | null | null | null |
Versatile Energy-Based Probabilistic Models for High Energy Physics | Accept (poster) | Summary: In this paper, the authors describe an energy-based generative framework for modeling the behavior of elementary particles. This framework can then be used to generate High Energy Physics events, similar to those at LHC, as well as be used for anomaly detection, specifically QCD jets.
Strengths: The strength ... | Rebuttal 1:
Rebuttal: Dear Reviewer bfxx,
Thank you for taking the time to review our manuscript and providing thoughtful feedback. We have made revisions according to the comments. The following is the correspondence to the specific questions/comments.
-----
***Weaknesses:***
*The main weakness of the paper is that... | Summary: This paper proposes to use EBMs to model the fundamental interactions between elementary particles. The paper first introduces a set of techniques to enable effective learning of energy functions across elementary particles, including the use of a classifier to model the conditional distributions. The paper th... | Rebuttal 1:
Rebuttal: Dear Reviewer Xiwj,
Thank you for taking to time to review our manuscript and providing thoughtful feedback. The following is our correspondence to the weaknesses and questions.
-----
***Weaknesses:***
>*It might be interesting to see the extent to which the energy function learned by EBM corre... | Summary: This work applies recent EBM techniques to model jet streams from the LHC. The work adopts a transformer architecture for the EBM to allow a permutation invariant representation that captures high order relations between particles. EBM models of LHC particles are learned using techniques derived from recent im... | Rebuttal 1:
Rebuttal: Dear Reviewer PfZ9,
Thank you for taking to time to review our manuscript and providing thoughtful feedback.
-----
***Weaknesses:***
> *The paper lacks theoretical and methodological novelty. The work primarily focuses on applications of EBMs to a new domain.*
> *It appears that the primary pra... | Summary: This paper explores the use of energy based models to modeling the distribution of jets in high energy particle physics. Here, the data consists of a vector of events, each of which is described by a momentum transverse to the beam and 2D spatial coordinates. The goal is to learn the true distribution of jets ... | Rebuttal 1:
Rebuttal: Dear Reviewer npbJ,
Thank you for taking the time to review our manuscript and providing thoughtful feedback. We have made revisions to clarify the technical details. The following is the correspondence to the specific questions/comments.
-----
***Weaknesses:***
> 1. *The ideas in this paper w... | Rebuttal 1:
Rebuttal: Dear reviewers and AC,
We thank all the reviewers for your time and helpful feedback to make this article a better version.
In the present study, we endeavor to synthesize various prior initiatives, with the goal of progressing toward a robust, multi-purpose modeling framework tailored for High... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation | Accept (poster) | Summary: This paper studies the distributed mean estimation problem with communication constraints and central DP. Communication constraints are met by subsampling a Kashin representation of the target vector, and \eps-DP is achieved by adding Gaussian noise. Privacy in the absence of a trusted server can be achieved u... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's acknowledgment of the correctness and technical soundness of our results. However, we kindly disagree with the statement that *"Unlike the LDP constraints, there is no tension between the communication cost and privacy in this case."* Indeed, we believe the s... | Summary: This paper studied the optimal rates of the tradeoff among privacy (central DP), utility ($\ell_2$ estimation error), and communication cost (in bits) for the distributed mean and frequency estimation settings. The authors used Kashin's representation and Coordinate Subsampled Gaussian Mechanism to achieve the... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's careful reading, valuable questions, and constructive feedback.
**Response to Weaknesses.**
- We apologize for the inconsistency in notation; the rates in Lemma G.1 and Proposition G.2 should be $O(C^2 d ...)$ instead of $O(c^2 d ...)$, and our main results (Theorem ... | Summary: The paper proposes a new randomization scheme for differentially private mean estimation. It is based on each user choosing randomly the coordinates to be sent in their messages. Similar scheme has been proposed in reference paper [55] (Hu et al., 2020), however Hu et al. do not sparsify the individual data-el... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's comprehensive summary of our work. We would like to clarify the differences between our techniques and those presented in [Hu et al.]. One of the main distinctions is that Hu et al. consider a mini-batch setting, where sparsification, a form of dimensionality reduction... | Summary: This paper studies the federated learning problem within the central differential privacy model, where a trusted central server gathers information from local clients. The primary focus is on minimizing communication costs while ensuring privacy and maintaining accuracy guarantees. To address this challenge, t... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's recognition of the novelty and significance of our techniques in private SGD and federated learning. As the reviwer notes, our schemes do not amplify local DP since the randomness used for amplification (i.e., random seeds for compression/subsampling) must be known by ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable questions, insightful suggestions, and constructive feedback. We will fix all typos/minor issues in the revision and have provided responses to concerns raised by each reviewer below. We would greatly appreciate if the reviewers could consider updating the... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Near-Optimal Algorithms for Gaussians with Huber Contamination: Mean Estimation and Linear Regression | Accept (poster) | Summary: In this paper, the authors give an improved algorithm for estimating the mean of a multivarite Gaussian random variable under the Huber contamination model. Diakonikolas et al. Have given the first algorithm in this model that achieves the information theoretical optimal $\ell_2$ loss of $\Theta(\varepsilon)$ ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments and feedback.
**(Constants in the breakdown point)** We would like to point out that constants could indeed be optimized, but that work falls outside of the primary aim of our paper. The filtering techniques that we use and their variants have be... | Summary: The authors study here a classical problem in statistics: the estimation of Gaussian means and linear regression with Gaussian covariates in the presence of Huber contamination.
This paper proposes a novel polylog time algorithm to compute the mean of a Gaussian in an $\epsilon$-corrupted, high-dimensional se... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and feedback. We respond to the individual points below:
* **(Simulations)** We refer to the response given to reviewer rxyU regarding the same matter.
* **(``While it is clear that the distribution is Gaussian, are there some constraints on the other distribu... | Summary: Gaussian estimation and linear regression are fundamental statistical tasks, and the Huber contamination model is one of the most well-studied models for robust statistics. This paper works to design algorithms for these tasks with near-optimal sample complexity, error, and almost linear running time. The main... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive evaluation. We respond to their question about general covariance matrices below.
First, we can restrict to the case that the covariance matrix is unknown to the algorithm; otherwise, using an appropriate whitening of the data can reduce to the identity covar... | Summary: This paper presents an algorithm to estimate the mean of a Huber contaminated d-dimensional Gaussian with sample complexity of n=O(d/\epsilon^2) and a run time that is near-linear. They also provide an algorithm for robust linear regression with sample complexity of n=O(d/\epsilon^2) and a run time that is ag... | Rebuttal 1:
Rebuttal: We appreciate the positive feedback on the contributions and the writing.
However, it appears that the reviewer touched upon and subtly intertwined two distinct aspects of the algorithm:
(i) combining the algorithmic techniques used in [DKPP22, Dia+18] that is developed in Section 3, and
(ii) t... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and effort in providing feedback. We are encouraged by the positive comments, and that all the reviewers appreciated the paper for the following: (i) **novelty** (rxyU, BEmV, qzWP), **importance** (rxyU,bgYs,BEmV, 7D1F) (iii) **good** and **friendly** writing ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper solves an important problem in robust mean estimation and robust linear regression, that is, can we achieve optimal error rate with nearly linear sample size and runtime when the inliers are standard Gaussian. All the previous works that have linear runtime suffer from an additional $\sqrt{\log(1/\e... | Rebuttal 1:
Rebuttal: We thank the reviewer for their effort and feedback.
Regarding the summary of the paper, we would like to highlight that the algorithm for linear regression is in fact the *first polynomial algorithm* obtaining error $O(\sigma \epsilon)$, i.e., not only attains nearly-linear runtime and nearly-o... | null | null | null | null | null | null |
GPEX, A Framework For Interpreting Artificial Neural Networks | Accept (poster) | Summary: This paper uses Gaussian Processes trained to match Neural Networks to subsequently "explain" the NN outputs by finding the nearest neighbors to any given test point in the training samples. An evidence lower-bound is derived that encourages the GP’s posterior to match the NN’s output. Scalability is obviously... | Rebuttal 1:
Rebuttal: Please see the attached global pdf.
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Rebuttal Comment 1.1:
Comment: Thank you for your technical comments.
=== "it's not obvious (to me) how one would systematically study a NN in this way, since a human can only visually inspect a small number of examples as is done in the paper. Any commen... | Summary: This article proposes a new training approach for Gaussian Processes (GPs) which encourages their posterior to match a given Artificial Neural Network (ANN) output. Their approach adopt a scheme that permits a scalable training, which is an issue when it usually comes to GPs, and that is motivated by the deriv... | Rebuttal 1:
Rebuttal: Please see the attached global pdf.
\textcolor{blue}{
The article is too dense.
}
\\The paper is dense, because it contains two main ideas. 1. Scalability and 2. Knowledge distillation. Without scalability, successful knowledge distillation wouldn't have happened (as underlined in the analysis o... | Summary: This paper derive an evidence lower-bound that encourages GP's posterior to match ANN's output without any requirement on ANN. And the uses the GPs' kernel functions to explain the ANNs' decisions.
Strengths: 1) this paper provides a theoretical way for the algorithm
2) implementation is publically availabl... | Rebuttal 1:
Rebuttal: Please see the attached global pdf.
\textcolor{blue}{
The main point of this paper is to explain ANN. However, Figure 2 is the only experiment results that are about explanation, which only contains some sample explanations. There is no comparison to existing explanation methods and there is not... | Summary: The paper derives an ELBO to end-to-end distill a deep neural
network into a set of Gaussian processes (one per output) with deep neural
network kernels and the full training set used as inducing points.
By using a low output-dimensionality for the feature map neural network,
the computational cost of the inve... | Rebuttal 1:
Rebuttal: Please see the attached global pdf.
\textcolor{blue}{
The paper reports on GPs, but is not very verbose in the fact that DNN-kernels are used, ....
}
\\In Sec. 2.2. we added the following sentence
\\\textit{
.Note that the kernel functions $\lbrace f_\ell(.)\rbrace_{\ell=1}^L$ are implemented a... | Rebuttal 1:
Rebuttal: the global response
Pdf: /pdf/6df352f572cf66a034ab39a3f47c1240b9d3a015.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Non-adversarial training of Neural SDEs with signature kernel scores | Accept (poster) | Summary: This paper proposes a signature kernel-based Neural SDE by using signature kernel scores. The proposed method eliminates existing state-of-the-art methods' mode collapse and instability using adversarial training.
Strengths: 1. In all experiments, the proposed method outperforms existing methods.
2. The expla... | Rebuttal 1:
Rebuttal: **Contribution**
Please see the response to Reviewer xQxq under the header **Significance**, where we explain that signature kernels allow us to introduce a class of scoring rules for infinite-dimensional spaces of paths, adaptable to spatiotemporal signals, and with strict properness and consist... | Summary: In this work, the authors proposed to generate time-series using neural stochastic differential equations using the scoring rules-based training objective which is on signature paths computed from signature kernels. Combining the idea of generative adversarial network, this work also employ the generator-discr... | Rebuttal 1:
Rebuttal: **Baselines**
Please see the answer to Reviewer xQxq under the **Significance** and **Empirical evaluation** headers.
**Complexity**
As explained in Section 3.3, our signature kernel scores are evaluated by solving scalar-valued PDEs. Therefore, our approach circumvents the computational chall... | Summary: This paper introduces a novel approach for training Neural SDEs (Stochastic Differential Equations) as generative models for sequential data without using adversarial techniques. The authors propose a novel class of scoring rules based on signature kernels, which offer stability and avoid issues like mode coll... | Rebuttal 1:
Rebuttal: **Evaluation Metric**
The two-sample Kolmogorov-Smirnov (KS) test is a nonparametric statistical test used to determine whether two sets of samples come from the same continuous distribution on $\mathbb{R}$. The KS test statistic is the maximum absolute difference between two empirical cumulative... | Summary: This paper considered the training of neural SDEs, where are continuous-time generative models for sequential data. State-of-the-art approaches train neural SDEs in an adversarial manner and suffer from instability. In this work, a non-adversarial training approach is proposed for stable and effective training... | Rebuttal 1:
Rebuttal: **Significance**
We believe the innovation of our work is twofold:
1) the introduction of a new class of scoring rules for infinite-dimensional spaces of paths using signature kernels, adaptable to spatiotemporal signals, and with strict properness and consistency guarantees;
2) the deployme... | Rebuttal 1:
Rebuttal: We thank all the reviewers the useful feedback and insightful questions. We hope that our responses will address all raised concerns. | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces a novel approach to training Neural SDEs using non-adversarial methods based on signature kernel scores. The authors demonstrate that the signature kernel score is strictly proper and provide consistent estimators for such scores. The effectiveness of their approach is demonstrated in var... | Rebuttal 1:
Rebuttal: **Comparisons**
The comparison between SDE-GANs and latent SDEs (and the limitations of the latter) has been discussed in the PhD thesis [1, Section 4.3.3] and further analysis as well as quantitative results can be found in [2]. Therefore we decided to only include the most expressive model amon... | null | null | null | null | null | null |
Open Visual Knowledge Extraction via Relation-Oriented Multimodality Model Prompting | Accept (poster) | Summary: The authors claim that they propose OpenVik which consists of an open relational region detector to detect regions potentially containing relational knowledge and a visual knowledge generator that generates format-free knowledge by prompting the large multimodality model with the detected region of interest. H... | Rebuttal 1:
Rebuttal: ***Technical depth and details***
**A**: We would like to stress several distinct technical contributions of OpenVik compared with existing models:
- **Open relational region detector**: Existing detectors often focus on locating objects, while OpenVik is trained to directly detect regions that ... | Summary: Authors present OpenVik, a method for open visual knowledge extraction. It consists of an open relational region detector to detect regions potentially containing relational knowledge and a visual knowledge generator that generates format-free knowledge by prompting the large multimodality model with the detec... | Rebuttal 1:
Rebuttal: >***The importance of the pre-training for the Open Relational Region Detector is mentioned but not evaluated***
**A**: Thanks for the observation. Please refer to our added results under ***Q2*** in the global response.
>***Ablation of OpenVik vs. BLIP***
**A**: While BLIP primarily focuses o... | Summary: This paper introduces a novel approach to visual knowledge extraction named OpenVik. It comprises three main components: the Open Relational Region Detector, the Format-Free Visual Knowledge Generator, and the Diversity-Driven Data Enhancement module.
The Open Relational Region Detector, built on the object d... | Rebuttal 1:
Rebuttal: ***What unique advantages OpenVik bring compared with existing multimodal models? With the existence of general multimodal models, why do we need OpenVik***
**A**: Thank you for raising the question regarding the need for OpenVik in the context of existing multimodal models. The specific need for... | Summary: This paper that introduces a new paradigm of open visual knowledge extraction called OpenVik. This method generates format-free knowledge by prompting a large multimodality model with detected regions of interest. The proposed framework consists of an open relational region detector and a format-free visual kn... | Rebuttal 1:
Rebuttal: >***The performance of the model on more diverse datasets remains to be studied. Can this method be scaled to larger training dataset, or other backbones***
**A**: In this work, we chose Visual Genome and its relation-enhanced dataset as the benchmark because they are rich in relational region de... | Rebuttal 1:
Rebuttal: >***Q1. Adding contemporary regional captioning baselines***
**A**: We appreciate the helpful suggestions on adding region captioning baselines. Note that although the proposed task in our paper has some similarities to region captioning, we would like to highlight the crucial difference in OpenV... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a new paradigm called open visual knowledge extraction and designs a framework OpenVik to generate format-free knowledge instead of pre-defined format knowledge. The authors also present two data enhancement technologies to ensure the diversity of knowledge. Moreover, the paradigm could als... | Rebuttal 1:
Rebuttal: >***The originality of the work is incremental and made minor modifications to existing models***
**A**: We would like to clarify several distinct differences between OpenVik and existing models:
- **Open relational region detector**: Existing detectors often focus on locating objects, while Open... | null | null | null | null | null | null |
Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models | Accept (poster) | Summary: The authors propose a framework that improves the image classification model's robustness by distilling CLIP models and augmenting adversarial learning with pre-trained generative models. The authors follow classical adversarial learning to generate perturbed examples and then input the examples to VQ-GAN. Las... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments. We will address the concerns about the novelty and justification for our method.
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*The research problem is unclear...they focus more on out-of-distribution robustness in the later parts and the experiments.*
Thank you for pointing this out. To... | Summary: This paper proposes a simple and lightweight framework for improving the robustness of vision models through knowledge distillation. This paper applies pre-trained teacher model to generate adversarial examples and apply VQGAN as a data augmentation method to generate more informative adversarial samples. This... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful suggestions and positive review. We are glad the reviewer found our idea novel and think the experiments and theory prove its effectiveness.
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*The motivation for applying VQGAN is not so clear. The article claims that VQGAN can discretize adversarial examp... | Summary: The paper introduces a new method to improve the robustness of vision models through knowledge distillation by leveraging a large-scale pre-trained teacher model (CLIP) and a VQ-GAN discretizer. The paper does a great job motivating the method and arguing for knowledge distillation when tackling out of distrib... | Rebuttal 1:
Rebuttal: We thank the reviewer for the extensive and positive review and helpful suggestions. We are glad the reviewer found our proposed method novel and relevant.
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*Missing paragraph detailing DAT: since a large proportion of the strongest results seem to be relying on combining DAD with DAT...*
Tha... | Summary: This paper introduces a novel approach called discrete adversarial distillation (DAD) to train small, robust models using large-scale models as teachers. The authors establish the knowledge distillation framework for out-of-distribution robustness and provide a theoretical framework for utilizing large-scale m... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments and positive review. We are glad the reviewer agreed with the core contributions of our setting and the importance of small, robust models.
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*The reviewer found it hard to understand the main idea at first glance, the paper could improve its c... | Rebuttal 1:
Rebuttal: We are thankful for the generally positive reviews and useful feedback. We are glad reviewers found our idea novel (Reviewers gGrQ, Y3Hr, qyW3), setting significant (Reviewers gGrQ, Y3HR), and analysis convincing (Reviewers GKv1, qyW3, ktZ5). We provide an updated main figure, updated Table 2, and... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents a knowledge distillation framework for vision model, by leveraging a teacher model (CLIP). In the setup, a discretizer (VQGAN) model is introduced to the adversarial examples from the teacher model. Adversarial training (AT) objective is adapted in the knowledge distill setting. The propose... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments and positive review. We are glad the reviewer found our idea well established, experiments thorough and comprehensive, and writing easy to follow. We address the concerns about the comparisons and results below.
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*One main concern is on comparin... | null | null | null | null | null | null |
Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models | Accept (poster) | Summary: This paper presents PatchDiffusion, a novel framework designed to address the scalability challenges faced by most diffusion models in terms of training and sampling. The proposed framework adopts a patch-wise training approach, where a denoising network is trained on image patches rather than the entire high-... | Rebuttal 1:
Rebuttal: We thank Reviewer NLEo for providing constructive suggestions. Below, we address each concern raised in your comment point by point. Please let us know if you have any further questions or whether this adequately addresses all these issues.
> Q1+Weakness 1: it could be worthwhile to consider an a... | Summary: This paper presents a new training technique that improves the training speed of diffusion models. Instead of training the diffusion model on the entire image, the authors propose training on sampled patches of the image. This approach maintains the theoretical foundation of the diffusion model by keeping the ... | Rebuttal 1:
Rebuttal: We thank Reviewer Vqdw for providing the positive feedback and constructive suggestions. We address your questions and provide more clarifications below.
> Firstly, there is a lack of analysis concerning spatial conditions.
Thanks for pointing this out. We agree proper positional embedding cou... | Summary: The paper introduces a path-wise diffusion algorithm for faster training. The authors propose patch coordinate conditioned diffusion models and present a patch-size conditioning scheduling technique for efficient training. The method has similar motivation with patch based GAN such as COCOGAN, but it is applie... | Rebuttal 1:
Rebuttal: We thank Reviewer ExTh for providing positive feedback. We address your questions and provide more clarifications below.
> Weakness
The patch-size scheduling could be flexibly set as other values. Different scheduling will induce different levels of gain in training efficiency. The setting show... | Summary: The authors propose a new formulation of training diffusion models by sampling different-sized patches from the training data. The models trained with this formulation have comparable FID scores to models trained on full images on many datasets.
Strengths: The authors proposed a way to train faster diffusion ... | Rebuttal 1:
Rebuttal: We thank reviewer qnyP for providing positive feedback and valuable suggestions. Currently, the pretraining scope of our experiment is limited to class conditional experiments due to the limit of computational resources. Thanks for pointing out interesting and promising potentials. We will conside... | Rebuttal 1:
Rebuttal: # Response to All
We'd like to thank all five reviewers for their insightful comments and suggestions. We hereby provide the image extrapolation results that Reviewer ExTh, Vqdw and NELo are interested in, and the state-of-the-art ImageNet-1K(256x256) FID **2.74** for Patch Latent Diffusion with ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes a novel training framework for diffusion models, that significantly reduces the training time, while improving data efficiency. For the first time, the proposed method suggests patch-wise diffusion training, which can be deployed to any UNet-based diffusion models. Experimental results show ... | Rebuttal 1:
Rebuttal: We thank Reviewer nnk4 for your positive and valuable feedback. We appreciate the time and effort you've taken to review our work. We have carefully considered your comments and suggestions, and we would like to respond to each of them.
> Albeit the empirical evidence, the theoretical proof of co... | null | null | null | null | null | null |
Saddle-to-Saddle Dynamics in Diagonal Linear Networks | Accept (spotlight) | Summary: This paper characterizes the trajectory of gradient flow on 2-layer diagonal linear networks for linear regression tasks. Specifically, the paper considers the model parameterized as $x \mapsto \langle u \odot w, x \rangle$ in the linear regression setting. By interpreting the gradient flow on the nonconvex lo... | Rebuttal 1:
Rebuttal: Thank you very much for the feedback.
We answer to your questions below and answer to your comment on the restrictive setting in the *official comment* section since it was made by nearly all the reviewers.
**Finite initialisation**: unfortunately, as we acknowledge line 267, our results do not ... | Summary: This work concerns the behavior of gradient flow of 2-layer diagonal linear neural networks when the initialization scale goes to zero. The authors show that this limiting behavior is governed by a piecewise constant trajectory consisting of jumps from saddle to saddle.
Strengths: The incremental learning and... | Rebuttal 1:
Rebuttal: Thank you very much for the extensive and valuable feedback. We answer to your remarks and questions below.
**Final point $\beta_p$**: the final point $\beta_p$ is indeed the minimum $\ell_1$ norm solution $\beta^*_{\ell_1}$, we tried to make this clear by pointing out that $\beta_p = \beta^*_{\e... | Summary: This paper analyzes a two-layer diagonal linear network, which is a linear regression model where the linear weight is parameterized as the point-wise product of two weight vectors. It is shown that, with vanishing initialization, gradient flow will jump between saddle points of the training loss and eventuall... | Rebuttal 1:
Rebuttal: Thank you very much for your feedback.
We answer to your questions below and answer to your comment on the restrictive setting in the *official comment* section since it was made by nearly all the reviewers.
**Evidence for practical networks**: we would like to re-emphasize that saddle-to-saddl... | Summary: In this paper, the authors studied the training dynamics of gradient flow that minimizes mean-square loss with 2-layer diagonal linear networks and data in general position. Under the small initialization (initialization scale goes to 0), the authors showed that the limiting dynamics follows a saddle-to-saddle... | Rebuttal 1:
Rebuttal: Thank you very much for the valuable feedback.
We answer to your comment on the restrictive setting in the *official comment* section since it was made by nearly all the reviewers.
**Dimension and number of samples**: your comment on the input dimension $d$ having to be larger than the number o... | Rebuttal 1:
Rebuttal: We thank all the reviewers for the time they spent reviewing our paper and for the valuable feedback. An overall comment made by all reviewers (except reviewer P2pV) is that the considered setting (2-layer diagonal linear network) is too restrictive. We naturally agree that our setting is very far... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies the saddle-to-saddle dynamics in Diagonal Linear Networks.
The authors present solid theoretical understanding.
They show that over 2-layer diagonal linear network, gradient flow starting with vanishing initialization visits then jump from saddles jumps from a saddle of the training loss t... | Rebuttal 1:
Rebuttal: Thank you very much for the valuable feedback.
We answer to your questions below and answer to your comment on the restrictive setting in the *official comment* section since it was made by nearly all the reviewers. We answer to both of your questions below.
**Practical initialisations and sadd... | null | null | null | null | null | null |
Improving Compositional Generalization using Iterated Learning and Simplicial Embeddings | Accept (poster) | Summary: This paper proposes an iterated learning method with simplicial embeddings (SEM-IL) for systematic generalization.
The method is inspired by iterated learning for humans, encouraging compressibility and expressivity.
The empirical experiments show the improvement in vision tasks (known latent factors) and real... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for pointing out the potential and shortcomings of our work. Please also refer to the overall response part for some common concerns.
> Q1: As mentioned in the conclusion section …
Our claim that “a theoretical understanding is still missing” might be too conservative. ... | Summary: This work addresses the challenge deep neural networks face with systematic generalization. Systematic generalization refers to the ability to apply learned concepts to new, unobserved combinations. The authors draw on a cognitive science theory known as "iterated learning", a hypothesized process explaining h... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for pointing out the potential of our work. Please also refer to the overall response part.
> Q1: The method section …
Thanks for highlighting this issue; as mentioned in the general response Q1, we had trouble conveying these aspects within the space limitations, but i... | Summary: This paper aims to develop methods to improve systematic generalization. The paper proposes several theoretical criteria related to representation learning to improve systematic generalization. Motivated by these principles, the paper then studies whether two methods, iterative learning (IL) and simplicial emb... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for pointing out the potential of our work. Please also refer to the overall response.
> Q1a: While I liked …
The analysis of this part originates from the information bottleneck principle (we will make this clearer in revision), but there are also some distinctions. T... | Summary: Iterated learning is hypothesized to help human language develop representation ability. The paper proposes to use iterated learning with deep network models containing simplicial embeddings to obtain approximately discrete messages. They show that this combination of changes improves systematic generalization... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s feedback and comments. Please also refer to the overall response for some common concerns, and here is the piece-to-piece response.
> Q1. A more detailed explanation …
Thanks. We will fix it in the revision.
> Q2. The intuition of using …
We can start from the ladder... | Rebuttal 1:
Rebuttal: We appreciate the reviewers’ feedback and comments, which are quite helpful for us in improving the paper. In this overall response, we summarize some common concerns from different reviewers and provide links to the corresponding responses. Some new experimental results are also discussed in this... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Punctuation-level Attack: Single-shot and Single Punctuation Can Fool Text Models | Accept (poster) | Summary: The paper proposes a novel mode of textual attack, punctuation-level attack, which aims to fool text models by conducting punctuation-level perturbations, including insertion, displacement, deletion, and replacement. This paper also introduces Text Position Punctuation Embedding (TPPE) as an embedding method a... | Rebuttal 1:
Rebuttal: Q1: Whether LLMs can also be fooled
A1: Due to time constraints, we focused our efforts on the summarization task using ChatGPT, limiting our use to a mere 20 samples per task. Our attack method involved the insertion of individual punctuation. Following the application of this attack strategy, ... | Summary: By proposing a new type of adversarial attacks, i.e., the punctuation-level attacks, This paper can fool text models with less impact on the semantic information by human beings understanding. Its effectiveness is verified by experimental results on various datasets/tasks and victim models. What’s more, the at... | Rebuttal 1:
Rebuttal: Q1: It would be better if more methods are selected for evaluation.
A1: Tables B, C, and D present the results of our endeavor to extend punctuation-level attacks, TPPE, and TPPEP algorithms to encompass three distinct tasks: summarization, semantic similarity scoring, and text-to-image generatio... | Summary: This paper introduces a new approach to textual attacks called the punctuation-level attack. The method aims to fool text models while minimizing its impact on human perception and understanding. The paper discusses the effectiveness of this attack strategy, presents a search method to optimize its deployment,... | Rebuttal 1:
Rebuttal: Q1:The adversarial attacks discussed in this paper can be categorized as non-pure white-box attacks.
A1:We greatly appreciate your suggestions, and we will incorporate the necessary revisions in the revised version of the paper. This aspect will be emphasized in the revised version as well.
Q2... | Summary: This paper introduces an adversarial attack against NLP models based on punctuation perturbations. The authors introduce an attack called Text Position Punctuation Embedding (TPPE) that comprises an insertion, displacement, deletion, and replacement attack based on textual punctuation (e.g., commas or periods)... | Rebuttal 1:
Rebuttal: Q1:Compare punctuation-level attack to the existing works focusing on punctuation attacks
A1:First and foremost, it is imperative to clarify that the existing research concerning punctuation attacks does not pertain to the punctuation-level attack. The works referenced as [18, 16] are essentially... | Rebuttal 1:
Rebuttal: Upon receiving the reviewers' feedback, several sections have been incorporated to refine the original paper.
# **1 Defense Methord**
We have introduced an in-depth discourse concerning defense strategies aimed at countering
punctuation-level attacks. In the realm of real-world systems, we me... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Adaptive recurrent vision performs zero-shot computation scaling to unseen difficulty levels | Accept (poster) | Summary: The paper introduces a combination of Convolutional Recurrent Neural Networks (ConvRNNs) with a learnable termination mechanism from Adaptive Recurrent Neural Networks (AdRNNs), with the purpose of solving complex, variable-difficulty vision tasks. The paper adapts to the purpose a ConvGRU architecture, or alt... | Rebuttal 1:
Rebuttal: We take the PDEs proposed in (Li, 1998), which model the connections between V1 neurons in continuous time. We take these PDEs, apply Euler integration to them in order to convert them into discrete-time difference equations and implement them using convolutional Recurrent Neural Networks. The fin... | Summary: Authors combine Adaptive computation time (ACT) with convolutional recurrent neural networks to solve two tasks with which their generalization properties can be studied. These adaptive-timestep RNNs were found to halt quicker for easier problems while taking more steps for harder ones. This also leads to gene... | Rebuttal 1:
Rebuttal: "I am curious to hear why the authors say RNNs used in Bansal et al., (2022) "are not adaptive, human intervention is required to specify the number of recurrent computational steps by brute force during the testing phase" since the RNN can be run till response stops changing"
1. As this is an im... | Summary: The manuscript presents adaptive recurrent networks for processing of static images. The proposed approach augments convolutional recurrent neural networks with the adaptive computation time mechanism, in which the RNN at each step computes an additional halting unit, the value of which is used to determine wh... | Rebuttal 1:
Rebuttal: 1. Our work is for the most part similar to the original ACT work, however, a key difference between our work and ACT is that our visual reasoning task involves static inputs whereas Graves (2016) can deal with variable-length sequences. Owing to this difference, our halting mechanism is the same ... | Summary: The primary subject of this paper is fostering computational efficiency in RNNs solving vision tasks by flexibly adapting the number of computing steps depending on the difficulty of the input. This is achieved through the addition of ACT (Adaptive Computation Time introduced by Graves, 2016) to recurrent visi... | Rebuttal 1:
Rebuttal: "It is good to see the prediction confirmed. Still, the finding that ACT will flexibly halt computation early for easier inputs does not tremendously advance the field, considering it’s what Graves (2016) already reported. With an ACT-like method (“DACT”) Eyzaguirre and Soto (2020) have already fo... | Rebuttal 1:
Rebuttal: Thank you all for taking the time to carefully review our paper. Here, we address common points based on overlapping comments in the reviews. We provide our responses to the constructive comments common to all reviewers in the following sections of our response:
### Differences with respect to Li... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: Authors show that using recurrent networks for adaptively processing static inputs for a variable number of iterations allows for zero-shot generalization to more difficult problems by simply unrolling the model predictions for more time steps at inference. Authors propose LocRNN and show model performance on ... | Rebuttal 1:
Rebuttal: 1. Our experiments show that RNNs which scale computation are the only networks which are able to generalize to more challenging test samples by using more recurrent iterations at inference. We hypothesize that the vast levels of compute employed by SOTA networks today is thus driven by the long t... | null | null | null | null | null | null |
Generalized Belief Transport | Accept (poster) | Summary: This paper describes a unifying view at various learning settings in machine learning, such as Bayesian inference, optimal communication, supervised classification and frequentist inference. All three learning settings are shown as special cases of an objective function, which can be interpreted from the lens ... | Rebuttal 1:
Rebuttal: Thank you for your generous comments and suggestions.
We are grateful that you find our proposed framework generalises learning concepts in a general form.
* Regarding your comment on 'what new insight can be gained from this': thank you for the guiding questions on how we shall emphasize implica... | Summary: This work aims to unify learning approaches by specifying a 3D space where the dimensions represent modalities of learning that can be combined to specify various learning approaches, e.g., Bayesian and Frequentist approaches. The 3D space is defined by three learning constraints within Unbalanced Optimal tran... | Rebuttal 1:
Rebuttal: Thank you for your generous comments and suggestions.
* We are surprised, given the other reviewers' assessments, and disappointed that you find our paper hard to read.
It would be helpful if you provided specifics regarding what details were glossed over. Without that information it is not possi... | Summary: Standard models of machine learning treat different internal constraints (e.g., prior knowledge) and external constraints (e.g., time availability, environmental non-stationarity) as separate problems, and thus hinder the development of unified learning agents. This paper proposes a framework called Generalize... | Rebuttal 1:
Rebuttal: Thank you for your constructive suggestions and insightful comments.
We are excited that you find our proposed 'framework allows flexible combinations of and helps the development of unified
learning agent'. Please see our answers to your questions below.
* Empirical significance and limitations:... | Summary: The authors introduce the concept of Generalized Belief Transport to unify and parameterize 3 different axes of learning from within the formalism of Unbalanced Optimal Transport. Corresponding limits points on the "cube" of these 3 axes recapitulate many common learning paradigms from the literature (e.g., b... | Rebuttal 1:
Rebuttal: Thank you for your generous comments and suggestions.
We are grateful that you find our utilization of UOT is novel and our presentation is clear and straightforward.
Most importantly, your request for a *convincing* example hit home with us. The proposed addition to the introduction is, we belie... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their generous comments and suggestions.
Here we clarify the practical implications of proposed framework.
Specific comments are addressed for each reviewer separately.
We are encouraged that all reviewers agree that Generalized Belief Transport (GBT) est... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a framework called Generalized Belief Transport that unifies different types of machine learning models. The authors have proven a number of properties about their proposed framework.
Strengths: * The problem proposed seems relevant given the different number of approaches to machine learn... | Rebuttal 1:
Rebuttal: Thank you for your comments, please see further clarification on the practical implications in the general response. We are hopeful that you will appreciate the importance of this work! | null | null | null | null | null | null |
History Filtering in Imperfect Information Games: Algorithms and Complexity | Accept (poster) | Summary: The submission considers the problem of approximating public belief states. The submission's main contributions are:
- Showing that certain fundamental computational problems related to public belief states are generally FNP-complete.
- Defining a subclass of games and showing that enumerating public belief st... | Rebuttal 1:
Rebuttal: The reviewer's suggestions concerning related work and motivation have provided us with the means to significantly improve the presentation of the paper. This will be accomplished by modifying the abstract and introduction, replacing Sections 2.2 and 2.3 with text that motivates public belief stat... | Summary: This paper studies the algorithmic complexity of history enumeration and generation in imperfect information games. The authors define these problems with a formal model of factored observation stochastic games (FOSGs). They proved computational complexity results, and empirically demonstrated the effectivenes... | Rebuttal 1:
Rebuttal: The non-approximability results mentioned by the reviewer are of interest to our area of work in general, but concern the hardness of computing or approximating solutions to POMDPs (in the form of optimal policies) rather than finding the sequence of unobservable states corresponding to some input... | Summary: The paper presents theoretical analysis on the hardness of history generation in public belief states, a concept used in imperfect information games (IIGs). Then for a game where enumeration is prohibitively expensive (trick-taking card game Oh hell), they devise a specialized Gibbs sampler to generate histori... | Rebuttal 1:
Rebuttal: Our response to Reviewer 8l8p describes how we will address potential misrepresentations of prior work.
It's true that for any fixed, finite game, we can choose some polynomial with arbitrarily high degree to bound the size of PBS support. However, complexity arguments, such as sparsity, only app... | Summary: The paper concerns history-filtering method to estimate values of imperfect-information subgames. Such a sub-task proved to be a crucial component of previous depth-limited solving algorithm developed in the related literature.
The contribution of the paper is twofold:
- on one hand, the paper analyses the co... | Rebuttal 1:
Rebuttal: Valid criticisms of the bipartite nature of the paper has led to clarity issues regarding our contributions. First, we seek to formalize the problem of history filtering and show that it is hard in general (Theorem 1). Theorem 2 then provides a condition called sparsity which describes the domains... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
AIMS: All-Inclusive Multi-Level Segmentation for Anything | Accept (spotlight) | Summary: This paper proposes a new task that segments visual regions into three levels: part, entity, and their relation. The authors build a unified AIMS model through multi-dataset multi-task training to address the two major challenges of annotation inconsistency and task correlation. Extensive experiments show the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and insightful comments.
### Q1: Clarity issue on the decoder.
We adhere to the transformer decoder design in the Mask2Former method, encompassing a set of learnable queries and nine transformer blocks. Each block is composed of a cross-attention l... | Summary: This paper introduces a new task and a model for All-Inclusive Multi-Level Segmentation (AIMS), which segments images into three levels: part, entity, and relation. AIMS can also segment images based on mask prompts, which specify the region of interest. The paper proposes a unified AIMS model that uses multi-... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments to improve our paper further.
### Q1: Why it would make sense to segment visual regions in relation level?
We introduce relation-level segmentation results in entity pairs for two key reasons.
First, entity pairs represent the minimal relationa... | Summary: This work presents AIMS, a multi-level image segmentation model with levels representing parts, instance, and relation. Further, a curated dataset is created from several existing segmentation datasets. AIMS outperforms the baselines on the curated dataset.
Strengths: 1. The proposed architecture reasonably b... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and insightful comments.
### Q1: Unclear about the prompt mask used in inference.
For a fair comparison, we conduct all three levels of segmentation using the full-image mask prompts throughout our experiments, without unfairly introducing any spec... | Summary: This paper introduces a novel task called All-Inclusive Multi-Level Segmentation (AIMS) and proposes a unified AIMS model to address the challenges of annotation inconsistency and task correlation. The model consists of a shared image encoder and three independent decoders for part, entity, and relation predic... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and insightful comments.
### Q1: Missing performance analysis on images with a large number of obejcts.
We present the recall@100 comparison of the entity-level results of between SAM and our model on selected LVIS validation data that includes mor... | Rebuttal 1:
Rebuttal:
We thank the reviewers for the insightful comments regarding our work. We have carefully addressed each of your concerns, and our responses can be found in the respective rebuttal sections for each reviewer.
In addition, we have included a PDF containing three figures, as requested by some of ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work proposes a unified multi-level segmentation (AIMS) approach. For better generalisation, the model is concurrently trained on multiple dataset consisting varied hierarchical level annotations across parts, entities and relations. In order to utilise signals from multiple hierarchical level as well as ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and insightful comments.
### Q1: AIMS is class-agnostic model and cannot label the output segments.
Thanks for the suggestion. Although mask labeling is related to our work, the main goal of our work is to develop an all-inclusive segmentation mode... | null | null | null | null | null | null |
Semi-Supervised Contrastive Learning for Deep Regression with Ordinal Rankings from Spectral Seriation | Accept (poster) | Summary: This paper tried to deal with semi-supervised deep regression problems via contrastive learning. To fully use the unlabeled data, they let the feature similarity between unlabeled samples be in agreement with the ranks. Therefore, the accurate ordinal relationship can be recovered through spectral seriation al... | Rebuttal 1:
Rebuttal: Thank you for the positive comments! We address remaining concerns below.
\
### W1) Optimizing Eq. 7
We make use of the differentiable combinatorial solver proposed in [1] to optimize Eq. 7. The loss function $\ell$ enforces predictions for discrete combinatorial values, such as rankings... | Summary: This paper extends contrastive regression methods to a semi-supervised setting using unlabeled data. They leverage the feature similarity matrix between these samples to infer ordinal relationships, a process guaranteed robust if the error is within defined bounds.
Strengths: This paper successfully extends ... | Rebuttal 1:
Rebuttal: Thank you for the encouraging feedback! We address questions below.
\
### Q1) Labels for contrastive learning
To clarify, it is possible to perform contrastive learning using unlabeled data **for classification** (e.g. SimCLR, MOCO).
It is **NOT possible** for existing contrastive lea... | Summary: In this paper, the authors propose a sophisticated method for deep semi-supervised learning using contrastive loss function. The main idea is to estimate the ordinal relations among the unlabeled data samples by using the similarity matrix of the unlabeled data samples and then use these relations to improve t... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments! We are glad you liked our idea! We address remaining concerns below:
\
### W1) Improvements to Related Work section
Thank you for your helpful feedback. These will be considered in our revision.
\
### W2) Use of cosine similarity
It is true... | Summary: The paper presents a novel approach towards extending contrastive learning methods for deep regression in a semi-supervised setting. The authors address the challenge of using unlabeled data for contrastive learning in deep regression tasks by applying spectral seriation algorithms to infer the ordinal relatio... | Rebuttal 1:
Rebuttal: Thank you for your positive comments! We are glad you found our idea novel and well supported theoretically. We address remaining questions below:
\
### W1) Application beyond medical settings
See response to Q2
\
### Q1) Computational complexity
Our proposed method, CLSS, does... | Rebuttal 1:
Rebuttal: We thank the reviewers for taking the time to provide their thoughtful comments and feedback. We are glad that in general, the reviewers found our method novel and well supported theoretically. We individually address remaining questions and concerns below. These will also be included in our final... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Frequency-domain MLPs are More Effective Learners in Time Series Forecasting | Accept (poster) | Summary: This paper presents FreTS, a framework that addresses both channel-wise and time-wise dependency learning in the frequency domain for time series prediction. FreTS introduces a specially designed frequency-domain MLP structure that processes the real and imaginary parts of the frequency components interactivel... | Rebuttal 1:
Rebuttal: We appreciate your review. Hope our response can address the misunderstandings or concerns
w1.
1. The concepts of "point-wise" and "information bottleneck" are widely recognized in the literature. In fact, we provided an explanation of these terms within the context of time series forecasting in ... | Summary: This paper investigates time series forecasting in the frequency domain. By utilizing MLPs in the frequency domain, the proposed FreTS effectively captures the patterns of time series with a global view and energy compaction. Frequency learning is applied to both inter-series and intra-series scales, allowing ... | Rebuttal 1:
Rebuttal: We appreciate your review and the positive comments regarding our paper. Below, we address your comments.
**W1. ...still retains all the frequency components..., thereby not fully leveraging this advantageous characteristic.**
We preserve the entire frequency components and feed them into the fr... | Summary: In this paper, the authors investigate the problem of time-series forecasting. Since the frequency domain can preserve the information from a global view and enjoy the advantage of energy compaction, the authors propose the FreTS model, which is composed of the Frequency Channel Learner and the Frequency Tempo... | Rebuttal 1:
Rebuttal: We appreciate your review and the positive comments on our work. We address each of them as follows.
**W1. To capture the dependencies among channels, we propose the frequency channel learner, which applies the FreMLP on the variables from each timestamp, e.g., $\mathbf{H}_t^{:}$. However, since ... | Summary: The authors argue that MLP-based forecasting methods suffer from point-wise mappings and information bottlenecks and explore an interesting direction of applying MLPs in the frequency domain for time series forecasting. They further analyze the inherent characteristics of frequency-domain MLPs and propose the ... | Rebuttal 1:
Rebuttal: Many thanks for your constructive comments and suggestions. We provide a point-by-point response to your comments below.
**W1. The experimental results are convincing, but only one MLP-based baseline is compared in the experiments. Additional MLP-based baselines help to verify the advantages of F... | Rebuttal 1:
Rebuttal: Dear Reviewers, ACs and the SAC:
We thank you all for the review and valuable comments. We'll clarify them in the final version to address all relevant questions and constructive suggestions.
To address the common concerns regarding our frequency channel learner (Reviewer u93d, Reviewer BMVc, ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper studies time series forecasting problem under the deep learning paradigm. Authors propose a new network architecture with MLPs in the frequency domain to capture both inter-series and intra-series correlations.
Strengths: 1. The paper is well-written and easy to follow.
2. Learning the spatio-tempo... | Rebuttal 1:
Rebuttal: We appreciate your review and the positive comments regarding our paper. We would like to respond to your comments as follows.
**W1. As mentioned in the related work part, there are already several works in the frequency domain for time series forecasting. It should be clearly discussed what’s th... | null | null | null | null | null | null |
GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels | Accept (poster) | Summary: This paper addresses the problem of evaluating the performance of well-trained Graph Neural Networks (GNNs) on unseen test graphs without ground-truth labels. Traditional evaluation methods that rely on annotated datasets are not applicable in real-world scenarios where test graphs are unlabeled. The paper pro... | Rebuttal 1:
Rebuttal: **Response to Reviewer ZAqu**
We sincerely appreciate your valuable suggestions and comments on our work, and we are pleased to learn that the practical value of our proposed GNNEvaluator is positively identified by the reviewer. The following are our detailed responses to the reviewer’s thoughtf... | Summary: The paper presents a novel problem called GNN model evaluation, aiming to assess the performance of a Graph Neural Network (GNN) on unseen graphs without labels. The authors propose a two-stage GNN model evaluation framework, which includes DiscGraph set construction and GNNEvaluator training and inference. Th... | Rebuttal 1:
Rebuttal: **Response to Reviewer jBkz**
Thanks for your insightful and constructive review of our work. We especially appreciate your interest in more exploration experiments of our proposed GNNEvaluator, and we are encouraged to know that our efforts on "the meta-graph set construction and the three chara... | Summary: This paper studies the problem of evaluating a GNN model, to estimate the accuracy on unseen unlabeled data for a well trained model. The goal of this paper is not to improve the generalization error of a GNN model, rather estimate the well-trained model error. This is a novel track for model generalization er... | Rebuttal 1:
Rebuttal: **Response to Reviewer 3iA3**
We sincerely appreciate your thoughtful review of our paper. We are so encouraged by your recognition of the “new domain of research for GNNs” on GNN model evaluation, and this means a lot to advancing the GNN model inference and deployment in real-world applications... | Summary: This work proposes a new research problem named GNN model evaluation, to evaluate well-trained GNNs on observed training graphs for testing on real-world unobserved test graphs without labels. To achieve this goal, this work (1)constructs a DiscGraph set to model the distribution differences of graph datasets,... | Rebuttal 1:
Rebuttal: **Response to Reviewer nFGD**
We sincerely appreciate your thoughtful review of our paper. We are glad to hear that you recognize the significance of GNN model evaluation problem proposed by our work. We have carefully considered your comments and suggestions, and the following are our detailed r... | Rebuttal 1:
Rebuttal: **Common response to all reviewers**:
We thank all reviewers for their thorough review and valuable suggestions. We are delighted that our contributions have been positively acknowledged, including:
**(1) Novel research question for new domain exploration of GNN model evaluation problem ( @All R... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Maximum Independent Set: Self-Training through Dynamic Programming | Accept (poster) | Summary: Maximum Independent Set:
A Dynamic programming approach (DP) to GNNs. The idea is to solve a Combinatorial Optimization problem, in particular the Maximum Independent Set (MIS): max set of nodes so that no two nodes are neighbors (set of nodes not linked by edges). The problem is NP-Hard but a GNN can generate... | Rebuttal 1:
Rebuttal: We thank the reviewer v2kw for their feedback. We address their concerns below:
> Q: “How do the parameters of the $CMP_{\theta}$ evolve? How far/close are they returning consistent/inconsistent responses? In other words, use the analysis of these parameters to diagnose how hard the problem is fo... | Summary: This paper studies the combination optimization problem, maximum independent set, via self-training. By training a consistent graph comparator function to determine the larger MIS of two different graphs, the MIS of original graph can be obtained recursively efficiently.
Strengths: 1. The notations and presen... | Rebuttal 1:
Rebuttal: We thank the reviewer T1ds for their feedback. We address their concerns below:
> Q: Include a more detailed explanation on why the learned graph comparator will be consistent.
Notice that the optimization problem of Eq. 2 effectively asks for more and more consistent comparators.
Nevertheless... | Summary: This work uses a dynamic programming framework for approximately solving the maximum independent set (MIS) and minimum vertex cover (MVC) problems. A graph neural network (GNN) is used to replace a heuristic step in the randomized version of the DP algorithm. Essentially, MIS (and MVC) can be broken down into ... | Rebuttal 1:
Rebuttal: We thank the reviewer b8RC for their feedback. We address their concerns below:
> W1: Unclear impact of the paper and applicability to other CO problems
We agree with the reviewer that extending to other CO problems is not trivial, but we respectfully disagree on the significance of our work. Th... | Summary: This paper proposes a method to solve the maximum independent set (MIS) problem using self-trained dynamic programming and carefully-designed GNNs. The MIS problem is decomposed into dynamic programs and solved by comparing two reduced graphs.
Strengths:
It is interesting and innovative to address the MIS pr... | Rebuttal 1:
Rebuttal: We thank the reviewer YVwa for their feedback. We address their concerns below:
> Q1: Baseline comparisons
In Line 74, we present the various DNN approaches for various CO settings and adapting those to MIS might not be trivial. In our experimental evaluations we compare with the existing appro... | Rebuttal 1:
Rebuttal: Dear reviewers,
We are thankful for your time and effort to handle the paper. Please find enclosed the single-page pdf. We respond to each question of the reviewers below.
Pdf: /pdf/a220a545a16b0db3ad66aefb9b9d166da521c6e4.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes a GNN based method to approximate the largest independent set for a graph. The algorithm proposes to train a GNN layer to decide at each iteration whether a randomly selected node should be a part of the independent set, the resulting graph (either losing the neighbours of the chosen node or... | Rebuttal 1:
Rebuttal: We thank the reviewer qbTV for their feedback. We address their concerns below:
> Q1: MIS cannot be classified as NP-hard or anywhere in that complexity hierarchy
We respectfully disagree with the reviewer. Maximum independent Set (MIS) is an NP-hard problem (e.g. see [D] published in ICML’20).
... | Summary: This paper proposes a novel graph neural network (GNN) framework for solving the maximum independent set (MIS) problem. The idea is to use a randomized divide-and-conquer algorithm (which is termed as dynamic programming in the paper though) to pick a random node or its neighborhood and divide the problem into... | Rebuttal 1:
Rebuttal: We thank the reviewer rVy4 for their thoughtful feedback. We address their concerns below:
> W1: Is dynamic programming appropriate for this paper?
MIS is a problem with overlapping subproblems - given the maximum independent set of the sub-graphs $G/\{u\}$ and $G/\{N(u)\}$, one can directly fi... | null | null | null | null |
EvoPrompting: Language Models for Code-Level Neural Architecture Search | Accept (poster) | Summary: This paper proposed an interesting idea for neural architecture search. Specifically, the idea is based on evolutionary computation and an LLM, where the LLM plays the role of genetic search, i.e., crossover operation and mutation operation. The experiments are conducted on two benchmarks: MNIST-1D and CLRS, w... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time and energy to read our paper and provide feedback. Below, we provide responses to the main concerns and questions raised in your review.
"I am not sure of the particular meaning of: "any" in this claim."
- Sorry for the confusion – we use "any" to disting... | Summary: The paper presented an evolutional neural architecture search method that utilizing LLM as mutator. During the evolutional search, the LLM is updated according to the evaluation result. Meanwhile the evaluation results are fed back to the LLM during the mutation process in the format of in-context learning.
... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort spent on providing valuable feedback on our work. Below we provide responses to the concerns mentioned.
"One missing part of the paper is why we will be interested in using LLM for NAS"
- Lines 139-150 in our paper are relevant to this question. LLMs ... | Summary: This is an example of LLMs being used in evolutionary algorithms. The authors use an LLM to crossover code snippets that execute to define a graph or neural architecture. It does this by generative means, not syntactic manipulation as typically in EAs. They use code in the prompt for context and they improve t... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and thorough feedback - we appreciate the significant time and effort this took. Your questions also gave us very interesting thoughts and directions to think about. We respond to some of the feedback and questions below:
"...not compared to other NAS techniques...Ca... | Summary: This paper introduces a method that uses LLMs as mutation and crossover operators in an evolutionary search process that generates diverse and high performing neural architectures. They evaluate their method on two datasets, MNIST-1D and CLRS, a benchmark measuring algorithmic reasoning. The evoprompting proce... | Rebuttal 1:
Rebuttal: Thank you for your detailed and insightful comments – we particularly appreciated your references to other related work and how those relate or are different from our work. We detail below our responses to some of the weaknesses and questions.
"Weaknesses: Comparing to other NAS approaches"
- We... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper explores prompt tuning for neural architecture search. While language models can produce code snippets with prompting, it is often quite difficult for language models to succeed at this task. The authors propose EvoPrompting, a prompt tuning method that produces neural architectures. The authors expe... | Rebuttal 1:
Rebuttal: The authors thank the reviewer for taking the time to carefully note both the strengths and weaknesses of our approach – we particularly appreciated the detailed notes about the particulars of applying LLMs and the associated implications for the performance of EvoPrompting.
"the paper uses a lar... | null | null | null | null | null | null |
Interpretable Prototype-based Graph Information Bottleneck | Accept (poster) | Summary: The authors propose a new GNN explanation framework, which combines prototype learning and the information bottleneck. They define the Prototype-based Graph Information Bottleneck framework in detail, and experiments have demonstrated the effectiveness of their proposed framework.
Strengths: 1.The authors int... | Rebuttal 1:
Rebuttal: **W1.**
**# Method Selection Inspired by Motivation and Purpose**
As highlighted in our introduction, ProtGNN utilizes prototypes to explain the model's training process (i.e., reasoning process (RP)). However, we found that ProtGNN overlooks the key substructures in input graphs, leading to l... | Summary: Interpretable graph learning can promote the use of graph-based scientific applications by providing model explanations. This paper focuses on extracting key subgraphs and employing a case-based reasoning process (also known as prototype learning) for model prediction. To make the extracted substructures more ... | Rebuttal 1:
Rebuttal: **W1** Thanks for raising this issue. In fact, we are aware of ProtoPShare, and thus we cited it in Sec 3.5 Line 223 reference “[15]”. While ProtoPShare is the first work that applies prototype merging in image classification, our work is the first work to demonstrate the effectiveness of prototyp... | Summary: The paper introduces a new design of interpretable graph neural networks (GNNs) that combines ideas from information bottleneck approaches and prototype based approaches for by-design interpertation. The system titled PGIB extracts a subgraph from a given input graph and compares the embedding of the extracted... | Rebuttal 1:
Rebuttal: ---
**Weaknesses**
**W1.** Too many moving parts.
**A1.** Thank you for your valuable feedback. We will make adjustments to the placement of the figures and tables to ensure they are easy to follow in our paper.
---
**W2.** Hyperparameter selection / Ablation studies.
**A2.** We sincerely a... | Summary: This paper investigates the usage of prototype learning for GNN explainability, focusing in particular on identifying key subgraphs through the graph information bottleneck principle.
Extensive experiments are conducted, which consider several baselines and different molecular datasets.
Strengths: - The scop... | Rebuttal 1:
Rebuttal: ---
**Weaknesses**
**Q1.** The main weakness in my opinion is that it is hard to conclude that the proposed method consistently outperforms all the baselines across all the metrics, given the reported results. For example, are the results in Table 1 statistically significant, given how large the ... | Rebuttal 1:
Rebuttal: We appreciate the reviewers for their valuable comments on our paper. We have conducted additional qualitative analysis, which is provided in the attached PDF. In this analysis, we compare the effects of different choices for $\alpha_1$, $\alpha_2$, $\alpha_3$ (i.e., loss weights) and the number o... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
DELIFFAS: Deformable Light Fields for Fast Avatar Synthesis | Accept (poster) | Summary: The paper introduces a method for real-time rendering of animatable full-body avatars. The main contribution is a hybrid mesh-lightfield representation: two surfaces are produced with an off-the-shelf motion-to-surface model, and normals of those surfaces are then used as input to two UNet, the output of those... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback to further improve our work. Please see our visual and quantitative ablations in the global response-rebutal.pdf.
* * *
**Details about the mesh**
The method we use to compute the deformed mesh is DDC [1] and it is only conditioned on the motion h... | Summary: This work proposes a method for human avatar reconstruction from multiview video data. This work leverages deformable light fields to model the geometry and texture. Experiments show that this method outperforms the existing methods in terms of novel view and novel pose synthesis.
Strengths: 1. The idea of us... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback to further improve our work. Please see our visual and quantitative ablations in the global response-rebutal.pdf.
* * *
**Difference to UV volumes**
Please note that Chen et al. [1] is a concurrent work presented in CVPR 2023, which was held afte... | Summary: The paper introduces DELIFFAS, an innovative method for generating controllable and photorealistic digital human avatars in real-time. This system utilizes a deformable two-surface representation to parameterize a surface light field, deforming two surfaces according to a deformable mesh model, allowing the li... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback to further improve our work. Please see our visual and quantitative ablations in the global response-rebutal.pdf.
* * *
**Pose variety**
Typically our method is robust to rather challenging poses, however, completely out-of-distribution poses such ... | Summary: This paper aims to learn digital human body avatars from multi-view videos. To achieve both photorealism and fast inference speed, the authors introduce a new representation based on a surface light field. The surface light field is attached to a drivable human mesh model, and is conditioned on skeleton motion... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback to further improve our work. Please see our visual and quantitative ablations in the global response-rebutal.pdf.
* * *
**Bounded to mesh**
We conducted an experiment where we use the SMPL model as the deformable mesh model (see rebuttal.pdf-Fig.4)... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and effort to review our work and to further improve it. We address individual concerns in the reviewer-specific rebuttal dialogue and provide a pdf (rebuttal.pdf) comprising additional visualizations and experiments.
Pdf: /pdf/df0dc0a31372eb80f5345ea5... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper presents a real-time approach for generating controllable and photorealistic digital human avatars. The main contribution of the paper is to learn the texture of the avatar using a light-field representation. In contrast to other continuous representations (e.g., NeRF), the proposed method can predic... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback to further improve our work. Please see our visual and quantitative ablations in the global response-rebutal.pdf.
* * *
**Novelty**
We would like to emphasize that this work is about finding an appearance representation that is, both, efficient and... | null | null | null | null | null | null |
Similarity-based cooperative equilibrium | Accept (poster) | Summary: The paper proposes similarity-based cooperative equilibrium, which extends program equilibrium to a setting of partial transparency. This modification is more practical (as full transparency is much less realistic and is hard to work with) and has useful theoretical properties (e.g., the folk theorem states th... | Rebuttal 1:
Rebuttal: We thank Reviewer ty7R for their efforts in evaluating our manuscript! We are glad that the reviewer found the paper so interesting.
>There is some very recent work about cooperative equilibria + MARL that could be discussed/mentioned in the camera ready. https://dl.acm.org/doi/10.5555/3545946.35... | Summary: The paper presents 2-player “difference meta games,” which augment standard one-shot games with similarity information about the other player. The paper shows that such games have Pareto optimal cooperative equilibria that cause (seemingly naive) “ML algorithms” to cooperate rather than defect. Results are e... | Rebuttal 1:
Rebuttal: We thank Reviewer xnVF for their thoughtful comments!
We address the question about realism and whether the similarity metric can be duped in the overall response, because Reviewer EVhh asks a similar question.
>the CCDR algorithm appears to rely on the fact that there are “cooperative” and “def... | Summary: Training AI agents that will reliably cooperate, both with humans and with other AI, is one of the principal goals of AI alignment research, and the prisoner's dilemma (PD) is simple game that has long been used to study cooperation. PD is interesting because it has only one Nash equilibrium: to defect (i.e. ... | Rebuttal 1:
Rebuttal: We thank EVhh for this insightful review! We address the point about realistic settings and gaming the similarity metric in the general response.
>My main concern is this is mostly a paper on game theory, so I question whether NeurIPS is really the right venue for it.
First, we believe our paper... | Summary: This paper considers the problem of two agents trained by machine learning who interact in a social dilemma. The agents are able to observe a numeric measure of the similarity of their learned policies, and condition on this similarity when choosing their actions (in contrast to the "full transparency" case, ... | Rebuttal 1:
Rebuttal: We thank Reviewer 2ynN for their detailed review!
>My main concern is that the results really only apply to a very specific class of games (player-symmetric additively decomposable games). It makes sense that the equilibrium uniqueness results of theorem 4 should be narrowly targeted (it's hard t... | Rebuttal 1:
Rebuttal: We thank the reviewers for their efforts in evaluating and helping us improve our paper! We are pleased that they found our paper “well written” (EVhh, ty7R), “easy to read and understand” (xnVF) and the results “clearly presented” (2ynN). It’s encouraging that the reviewers find our approach “int... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
PCF-GAN: generating sequential data via the characteristic function of measures on the path space | Accept (poster) | Summary: This paper proposes PCF-GAN, which aims to improve the effectiveness of the discriminator in differentiating the time series distributions by utilizing the path characteristic function (PCF) as a principled representation of the time series distribution within the discriminator. The authors also give the theo... | Rebuttal 1:
Rebuttal: We address all the questions in detail as follows.
>The presentation of this work is kind of confusing, making it difficult to fully comprehend the content. Specifically, in Figure 3, it is unclear what the generator loss represents if it is computed on the embedding vector. Additionally, it is u... | Summary: The paper looks at generative models for times series data. It proposes a new GAN method, based on a novel discriminator. The path characteristic function (PFC) is used as a representation of the time series distribution. Using this, a distance between two distributions is defined (PCFD) as well as a way to ap... | Rebuttal 1:
Rebuttal: We address all the questions in detail as follows.
>The reconstruction functionality is not motivated, nor explained. It can be unclear to the unfamiliar reader why one would want it, given that we can just generate images.
Time series reconstruction potentially has broad applications in privacy... | Summary: The paper proposes an approach to improve time-series modeling in Generative Adversarial Network framework by using path characteristic function as the embedding of the time series sample. It explains the feasibility of PCF distance and how to integrates it as a distance measure between two time series data in... | Rebuttal 1:
Rebuttal: We address all the questions in detail as follows.
>Comparison with existing baselines like COT-GAN on metrics like (a) the sum of the absolute difference of the correlation coefficients between channels avg over time (b) absolute difference between the correlation coefficients of real and genera... | Summary: This paper proposes a path characteristic function GAN, named PCF-GAN, for learning to generate time-series data. More specifically, the authors mainly employ the rough path theory to build the PCF distance, such that the temporal cues can be encoded by unitary features, enabling the PCF to learn sequential da... | Rebuttal 1:
Rebuttal: We address all the questions in detail as follows.
**1. Temporal dependency**
The path characteristic function (PCF) is proven to characterize the law of the stochastic processes, or time series. Hence, PCF completely determines the temporal dependency, *e.g.*, statistics like auto-correlation. ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful comments and constructive suggestions. We are pleased that all the reviewers find our work novel, sound, and theoretically motivated. We also acknowledge the shared questions from the reviewers on related work and numerical evaluation.
**Comparison... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper presents a new metric for the distributions on the path space via PCF and provides theoretical proofs for analytic properties of the proposed loss metric which benefits GAN training. It introduces a novel PCF-GAN to generate & reconstruct time series simultaneously. It compares the proposed method wi... | Rebuttal 1:
Rebuttal: We address all the questions in detail as follows.
**Comparisons with related work**
We first refer to the comparison to the existing literature in *Reply to all reviewers*. Then we further elaborate on the comparison between our method and [A, B].
[A] proposed a GAN framework to generate time... | null | null | null | null | null | null |
Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond | Accept (spotlight) | Summary: The article theoretically bounds the estimation error of NCE depending on the path. The paper first shows that NCE has minimum variance among all estimators using K bridging distributions and then continues by showing that the standard polynomial path can achieve polynomial error in the limit of infinitely man... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and pointing out relevant references. In the following, we will answer the reviewer’s comments point-by-point. We hope we address their concerns and hope the reviewer will consider raising their score.
**“I would suggest to discuss [1] in the art... | Summary: This paper investigates the benefits of using bridge distributions to estimate the unknown normalizing constant $Z_1$ of a given density $p_1 = f_1 / Z_1$, with $f_1$ known. There are many ways for estimating a normalizing constant, e.g. importance sampling, bridge sampling, umbrella sampling and noise contras... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback, and we are glad the reviewer finds the paper “original, interesting and significant”. We next address comments point-by-point and hope the reviewer will consider raising their score.
**The assumptions made in the paper are very strong [...] Thi... | Summary: This paper studies annealing schedules used in noise contrastive estimation (NCE). In particular, it conducts rigorous theoretical analyses of existing annealing schedules used in NCE. The paper's framework is general and yields importance sampling, umbrella sampling, and bridge sampling as special cases. The ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and pointing out the relevance of SMC literature to this problem. As we understand, the reviewer broadly makes two points. First, our analysis assumes “perfect sampling” while SMC acknowledges “imperfect sampling” which is more realistic. Second, r... | Summary: In this work, the authors make a number of contributions to the area of estimating normalization factors using annealing from some "proposal" $p_0$ to a "target" $p_1$.
The authors start off by nicely extending recent works by Chehab et al. (2023) on the relation between importance sampling (IS) and noise-c... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback! We also thank the reviewer for spotting the difference between the $2 \pi^2 / N$ in eq. 17 in our paper and the $\pi^2 / N$ from [1] which is correct. This is due to an unfortunate typo: in the supplementary material of our paper, eq 112 defines $... | Rebuttal 1:
Rebuttal: We thank all four reviewers for their feedback, and for suggesting references which we plan to include in a camera-ready version to give further context to our results. In this general reply, we further clarify the relevance of our results and we address specific concerns in detail in the individu... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
On the Identifiability and Interpretability of Gaussian Process Models | Accept (poster) | Summary: The authors carefully examine additive mixture of Matern kernels for Gaussian processes. For the single-output case, the authors show that a mixture of Matern kernels is equivalent to the least smooth component of the mixture (Theorem 2). Consequently, "there is no advantage in including any other component ap... | Rebuttal 1:
Rebuttal:
We sincerely thank the reviewer for the meticulous and insightful examination of our work. Your comprehensive feedback has been instrumental in enhancing the quality and clarity of our paper. In response, we have addressed each of the concerns raised and revised the manuscript accordingly. While ... | Summary: This paper intoduces a few results on identifiability of Gaussian Processes.
It is shown that a Gaussian process with a kernel that is a mixture of kernels, the smoothness is that
of the least smooth component.
It is shown that for two mixtures of Matern kernels, the induces processes are equivalent if cer... | Rebuttal 1:
Rebuttal: We wish to express our gratitude to the reviewer for your meticulous feedback and constructive comments. Enclosed within the 1-page PDF for additional figures, we address each point as follows.
**Weakness 1: definition of equivalence of measures**
A: Thanks for highlighting this. The symbol $\... | Summary: This paper investigates additive and separable mixture kernels in the context of Gaussian process regression. Concretely, it tests the intuition behind the convex combination of Matern processes and identifies limitations in the interpretability of the resulting mixture kernel that might contradict the intuiti... | Rebuttal 1:
Rebuttal:
We are deeply grateful to the reviewer for your thoughtful and thorough examination of our manuscript. Your observations and suggestions have been invaluable in refining the narrative and content of our paper. While we're unable to present the full revised manuscript at this time, please find our... | Summary: This work looks at idenfiability in the context of mixture kernels in GP regression. They look at additive mixtures of Matern kernels and multivariate separable kernels in the context of multivariate GPs (multiple outputs). They show theoretically and through simulation that ML-II learning cannot discover the ... | Rebuttal 1:
Rebuttal:
Thank you for presenting us with such insightful questions and observations regarding our manuscript. Given the submission constraints, we're unable to provide an updated version of the full manuscript at this juncture. However, to facilitate a more tangible understanding of our clarifications an... | Rebuttal 1:
Rebuttal: We express our sincere gratitude to the reviewers for their meticulous examination of our manuscript and their insightful comments. Your observations and recommendations have significantly enhanced the quality and content of our paper, and we have learned much from your comments. In this global re... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Adversarial Training from Mean Field Perspective | Accept (spotlight) | Summary: The authors proposed a new theoretical framework based on the mean field theory to analyse adversarial training from several perspectives, such as the upper bounds of adversarial loss, the time evolution of weight variance and adversarially trainable conditions. Besides the theoretical analysis, the authors co... | Rebuttal 1:
Rebuttal: We would like to thank your careful reading.
> The verification experiments were only conducted on the easy dataset (MNIST); it may strengthen the findings if additional experiments are conducted on more challenging datasets.
We have only tested our theory on simple datasets. This is because ful... | Summary: The authors propose a mean field based framework for theoretically analyzing the training dynamics of adversarial training for MLP and residual networks with ReLU non-linearities. Based on this framework, the authors provide tight bounds on the adversarial loss (squared loss between clean and adversarial examp... | Rebuttal 1:
Rebuttal: ## Structure and writing
We extend our deepest appreciation to you for the considerable time and effort you have devoted to reviewing our paper. Taking the reviewer’s comments into account, we plan to improve the manuscript as follows.
**Simplified version of Theorem 4.1.**
We plan to present a ... | Summary: The paper provides a mean field analysis on relu networks for adversarial training. The main insight is that networks without residual connections are not most likely inevitably suffer from gradient explosion or vanishing and thus are not adversarially trainable, unlike vanilla network training.
Strengths: An... | Rebuttal 1:
Rebuttal: We would like to appreciate your fruitful suggestion and question.
> The verification of the theorems might need more effort. E.g., Figure 4 is showing accuracy of vanilla network, it would be helpful to also show the curves for residual networks.
The training accuracy of adversarial training in... | Summary: The theoretical understanding of adversarial training is an important and valuable topic. This work proposes a new theoretical framework for this based on mean field theory. With the proposed framework, the authors analyze the properties of adversarial training from multiple aspects, including the upper bounds... | Rebuttal 1:
Rebuttal: We would like to thank your insightful comments.
> It studies several different adversarial training characteristics in the main paper. Is there any correlation between these different characteristics？ Why do we choose these aspects for analysis?
Our theoretical results on adversarial training c... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' critical reading, constructive comments, and overall positive scores. We have carefully taken into account all the comments and questions. Please kindly refer to the response to each reviewer. If our answers require further explanation or clarification, we are more tha... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Trichotomy for Transductive Online Learning | Accept (poster) | Summary: This paper studies realizable transductive online learning on a fixed and *known* sequence $x_1,\dots,x_n$ (meaning the order is given). The authors state a trichotomy of error rates depending on the finiteness of the VC dimension and Littlestone dimension of the given hypothesis space. Along the way they impr... | Rebuttal 1:
Rebuttal: **Comment:
Most of the claimed results are already known or exist implicitly. In particular, all the bounds used in the trichotomy are standard [...]. Even the additional lower bound given by the threshold dimension was essentially covered by an example (called "sigma_worst") in [BKM97] (without u... | Summary: This paper studies transductive online learning, which differs from standard learning in that the adversary must commit to a sequence of instances to be labelled by the learner at the start of the game. The adversary's strategy can thus only be adaptive w.r.t. the labeling of the sequence, not the sequence its... | Rebuttal 1:
Rebuttal: **Comment:**
**Apart from the proof of Theorem 3.1, the proofs seem relatively straightforward, so the originality/novelty of the work is mainly conceptual, not technical. [...] I just think there is some technical weight missing in the paper.**
**Answer:**
We believe our paper makes some neat an... | Summary: The paper considers the realizable case in the transductive online learning setting. It shows that, for a sequence of length $n$, the number of errors is $\Theta(n)$, $\Theta(\log n)$ or $\Theta(1)$ depending on the finiteness of the VC dimension and the Littlestone dimension. In the last case, the paper also ... | Rebuttal 1:
Rebuttal: **Comment:**
**The significance of the main contribution. In Theorem 4.1, the only non-trivial case is the upper bound in Item 2, and, as I understand, it follows from [KK05]. Maybe pointing out the trichotomy is significant in itself, but I'm not in the area, so I can't judge it.**
**Answer:**
... | Summary: This work considers transductive online learning where the pool of examples to be labeled is fixed and known to the learner. Thus, the adversary can
control the order that the points are presented to the learner and the label that the learner receives in each round. The goal of the learner is to predict lab... | Rebuttal 1:
Rebuttal: **Comment:
My main concern about the main result (the trichotomy of Theorem 4.1) of this work follows rather easily from prior works. It is not hard to show that when the VC dimension is infinite the adversary can always make the learner to do
mistakes. Moreover, with bounded VC dimension (less ... | Rebuttal 1:
Rebuttal: **General Comment 1:**
**A recurring comment is that the technical contribution of the paper is not substantial enough (e.g., Reviewer gZ3U: “the main result of this work follows rather easily from prior works”).**
**General Answer 1:**
- First, we believe that our trichotomy (Theorem 4.1) is ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies transductive online learning. In this setup, the adversary fixes the sequence of unlabeled instances and reveals labels sequentially following learner's prediction in each round. The paper shows a trichotomy of rates for binary classification: $n$, $\Theta(\log{n})$ and $\Theta(1)$ based on ... | Rebuttal 1:
Rebuttal: **Comment:
I might be speaking with a hindsight bias here, but I think most of proofs regarding trichotomy (Theorem 4.1) is straightforward except that of Claim 3.4 (also see my question below).**
**Answer:** We believe our paper makes some neat and non-trivial contributions, see our **_General A... | null | null | null | null | null | null |
Low-Rank Learning by Design: the Role of Network Architecture and Activation Linearity in Gradient Rank Collapse | Reject | Summary: The paper provides a comprehensive understanding of gradient rank in deep neural networks (DNNs) and how architectural choices and data structure affect gradient rank bounds. The authors highlight the emergence of low-rank learning as an inherent aspect of certain DNN architectures and propose a theoretical an... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback regarding our work on low-rank learning! We wholeheartedly agree that our approach to analyzing gradient rank can help in designing new modules and activation functions. We believe this positive direction of future work which you insightfully point out further ... | Summary: The authors present an investigative study into the learning dynamics of neural networks, specifically low-rank neural networks. The motivation is that training dynamics of neural networks are not fully understood. Low-rank models have practical advantages (time / memory). This work provides a theoretical and ... | Rebuttal 1:
Rebuttal: Thank you so much for recognizing the novelty and significance of our work! We appreciate your thorough critical feedback regarding the writing in particular. We have taken your feedback to heart and have worked diligently to ameliorate these issues while still working within page limitations. Par... | Summary: This paper studies the gradient rank of DNNs and examine how certain design choice, in particular architectural choices of the model and the structure of the data affect the gradient rank bounds. The paper mainly focuses on theoretical results with some empirical experiments to validate their empirical claims.... | Rebuttal 1:
Rebuttal: We would like to thank you for your positive review of our work! We wholeheartedly agree that one of the strengths of our analysis is the simplicity at its core, which we believe can help lead to better intuition in the growing study of learning dynamics in deep neural networks!
## Response to G... | Summary: This paper explores the collapse of gradient rank in DNNs during training. Based on the simple linear network, the authors theoretically examinate the rough upper bound of the gradient rank for simple MLP, current network and CNN. Furthermore, they analyze the numerical effect of rank on Leaky-ReLU activation.... | Rebuttal 1:
Rebuttal: We would like to sincerely thank you for your review of our work! We are especially encouraged by your recognition of the originality of our work—we believe that this rank-based approach to investigating learning theory in Deep Neural Networks is extremely promising, and the results we share in th... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for taking the time to critically evaluate our submission on Low-Rank Learning in Deep Neural Networks! All four of the reviewers recognized the novelty and importance of the work, and we are encouraged by this positive feedback! Our analysis provides important... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Uncertainty-Aware Alignment Network for Cross-Domain Video-Text Retrieval | Accept (poster) | Summary: This paper focuses on text-video retrieval for the challenging unsupervised domain adaptation setting. For this problem, the model is trained on a source only set of supervised video/text pairs and is adapted to a target domain which consists of videos and text labels with no pairing ground truth. The proposed... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and for the positive feedback, pointing out that our method was created `FROM THE GROUND UP FOR EMBEDDING SPACES'! We will revise the typos in the final version.
**Question1**: values of a and b in Eq. 7 and symbols in Eq. 10-12.
**Response1**: In Eq. 7-8 of D-V... | Summary: The paper tackles the task of unsupervised domain adaptation for text-video retrieval. The authors propose an Uncertainty-aware Alignment Network (UAN), which exploits the semantic information of both modalities in target domain. Specifically, in order to tackle the one-to-many relationships in the target doma... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and for the positive feedback. We will add the missing papers in the final version.
**Question1**: details of training CE method for image-text retrieval task.
**Response1**: For a fair comparison, we use the same settings for UDA image-text retrieval in the fol... | Summary: This paper proposed a method named Uncertainty-aware Alignment Network (UAN) to address the one-to-many issue which means in the real scene, one text usually corresponds to multiple videos and vice versa. Specifically, the proposed method achieves a new state-of-the-art in Cross-Domain Video-Text Retrieval thr... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and for the positive feedback.
**Question1**: add baseline in Fig. 4.
**Response1**: Thanks for your suggestion, we will add baseline performance in Fig. 4 to reflect the robustness of the proposed module in the camera version of the paper.
**Question2**: ablat... | Summary: This paper proposed a unsupervised domain adaptation method tailored for video-text retrieval by exploring the one-to-many correspondences between video and text on the target domains. The proposed method is shown effective and superior to existing approaches which considered only the one-to-one video-text mat... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and for the very constructive feedback. We will revise the typos in the final version.
**Question1**: drawbacks of directly adapting classification-based DA methods in UDAVR task.
**Response1**: Previous approaches are mostly derived from classification based do... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their efforts, detailed reviews and interest for our submission. We will integrate all their remarks in the revised version of the paper. | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper addresses the unsupervised domain adaptation video-text retrieval problem by two proposed components, the D-VLDA (Distribution-based Vision-Language Domain Adaptation) and the UAM (Uncertainty-Aware Alignment). The proposed D-VLDA aims at alleviating the domain discrepancy via moment-based method, w... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and for the positive feedback.
**Question1**: unfair comparison in Tab. 3.
**Response1**: Thank you for pointing out this, and the corrected experimental data is as follows:
|**Method** | | Tf->Mt | | | Mt->Tf | |
| :----: | :----: | :---: | :---: ... | null | null | null | null | null | null |
Agents Explore the Environment Beyond Good Actions to Improve Their Model for Better Decisions | Reject | Summary: Model-based planning agents such as MuZero leverages a learned model of the environment dynamics to learn a policy to follow in the actual environment. By planning using the learned model dynamics, the agent may generate a stronger policy than when limited to only experience from the real environment. The draw... | Rebuttal 1:
Rebuttal: Thank you for your review.
1. Equation (4) is derived from equation (3) after concretising the policy p_normal to be the improved policy of Gumbel MuZero. The improved policy of Gumbel MuZero is described in detail in the Gumbel MuZero paper (see answer to question 4).
2. In line 40 we refer to ... | Summary: The paper presents a method to improve the exploration of the MuZero agent in games. The authors propose an hybrid policy that mixes an exploratory policy and the optimized policy. The exploratory policy is meant to reduce brittleness of optimal policies.
The new method is demonstrated on Tic-Tac-Toe.
Strengt... | Rebuttal 1:
Rebuttal: Thank you for your review. Your feedback is very helpful for us to improve.
Our main task is to create a MuZero implementation – that is set for us. We are aware that in moving from AlphaZero to MuZero, the model needs to additionally learn to move forward in time, allowing the agent to operate i... | Summary: Inspired by the failure of KataGo against an amateur-level agent, the author proposes to use an additional randomization scheme to encourage the agent to explore the less experienced part of the decision tree. Such a randomization scheme allows the agent to randomly deviate from the planned policy, and then sw... | Rebuttal 1:
Rebuttal: Thank you for your review. | Summary: To improve prediction accuracy, this paper divides the training phase's policy into exploration and normal policies. The proposed algorithm is tested in the game of Tic-Tac-Toe, and different noise strategies are introduced for experimentation.
Strengths: The paper introduces a novel method for increasing po... | Rebuttal 1:
Rebuttal: Thank you for your review.
1. We use a proven very strong algorithm with well-founded and tested exploration strategies, namely Gumbel MuZero as the baseline. During the implementation, we were looking for an integration test - and by choosing Tic-Tac-Toe as such a test, we believe that we have ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning To Dive In Branch And Bound | Accept (poster) | Summary: The authors propose L2Dive to learn dataset-specific diving heuristics with graph neural networks. Specifically, they employ generative models to predict variable assignments and leverage the duality of linear programs to make diving decisions based on the model’s predictions. Experiments show improved perform... | Rebuttal 1:
Rebuttal: # Thank you. We made revisions based on your feedback.
**Thank you very much for your thoughtful review. We have made revisions to our paper based on your feedback and address your concerns in more detail below.**
## We performed additional ablations to isolate the effects of dual reasoni... | Summary: This paper introduces a new framework to improve B&B MIP solvers by neural networks. This paper proposes a neural network-based primal heuristic, namely L2Dive. The authors implement L2Dive with SCIP and conduct extensive experiments on several datasets.
---------------------
Post-rebuttal: Thanks a lot for... | Rebuttal 1:
Rebuttal: # Thank you for your review.
**Thank you very much for your feedback. We address your concerns in more detail below.**
## Other primal heuristics are complementary to diving, no heuristic rules them all!
>*“The authors should make more efforts to address the original technical contributio... | Summary: This paper presents a technique for heuristically generating good feasible solutions for mixed-integer programming problems by leveraging known, good feasible solutions for related problem instances. The technique is a "diving" heuristic, which subsequently fixes subsets of the integer decision variables, and ... | Rebuttal 1:
Rebuttal: # Thank you for your positive reception of our work.
**We are happy to integrate the dual LP into the main text. We have fixed the typos you found in the main text. We address your outstanding questions below.**
>*“Please confirm that a different set of 500 instances are used for validation and... | Summary: The paper develops a learning strategy to enhance variable selection in primal diving heuristics. More specifically, such a methodology relies on a generative model based on graph neural networks to predict the likelihood of variables assuming specific values. The predictions of the mean values are then integr... | Rebuttal 1:
Rebuttal: # Thank you. We made revisions based on your feedback.
**Thank you very much for your thoughtful review. We have made revisions to our paper based on your feedback and address your concerns in more detail below.**
## We performed additional ablations to isolate the effects of dual reasoni... | Rebuttal 1:
Rebuttal: ## Thank you for your thoughtful feedback!
**We have incorporated the feedback of the reviewers and made the following revisions to our draft:**
- We present an additional ablation study (see attachment!) on capacitated facility location to study L2Dive’s variable selection (dual reasoning and m... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Parallel Submodular Function Minimization | Accept (spotlight) | Summary: This paper considers the problem of parallel submodular function minimization, where the function is assumed to be integer valued with range $[-M, M]$. The authors propose two algorithms; one which runs in $\tilde{O}(n^{1/3} M^{2/3})$ rounds with $\tilde{O}(n^2 M^2)$ query complexity, and another which runs in... | Rebuttal 1:
Rebuttal: We thank the reviewer for a careful reading of our paper and writing the review.
In the weakness section, the reviewer points out that our results follow from existing work and two observations namely (i) our reduction from constrained to unconstrained for Lipschitz functions and (ii) that we can... | Summary: This paper studies the parallel complexity of submodular function minimization: given a submodular function defined on the $2^n$ subsets of $[n]$ taking values between $-M$ and $M$, find a minimizing subset of $[n]$. It provides two upper bounds: one algorithm with polynomial query complexity in both $M$ and $... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time to carefully read and review our paper.
In the weakness section, the reviewer points to the clarification between “adaptivity” versus “parallelism”; we agree that explaining this clearly is a good idea, and we will include this explanation in the next version ... | Summary: The paper studies the parallel complexity (number of rounds of queries) for polynomial query-complexity submodular function minimization. The main result of the paper is an $\tilde{O}(n^{1/3} M^{2/3})$ bound for this, where $n$ is the size of the ground-set and the function is integer valued with an upper bou... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time to carefully read and review our paper.
In the weakness section, the reviewer mentions how it is well known that SFM can be reduced to $\ell_{\infty}$-Lipschitz optimization. We agree. However, we believe that showing that the $\ell_{\infty}$ Lipschitzness of... | Summary: This paper considers the submodular minimization problem (SFM), where the submodular function $f$ is bounded between $-M$ and $M$. There has been a large number of papers that focus on solving this problem in as few queries as possible, but the proposed algorithms are generally highly sequential (at least $\Om... | Rebuttal 1:
Rebuttal: We thank the reviewer for a careful reading of our paper and writing the review.
In the weakness section, the reviewer points to the lack of experiments. As the reviewer also notes, the main focus of the paper is exploring algorithms for SFM and $\ell_{\infty}$ convex optimization through a theor... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this paper, the author(s) investigate the classic submodular minimization problem within the parallel computing regime. The setting is an integral submodular function $f$ defined on $2^{[n]}$ which is bounded ($|f| \le M$) and normalized ($f(\emptyset) = 0$. There primary contribution is obtaining query-ef... | Rebuttal 1:
Rebuttal: We thank the reviewer for a careful reading of our paper and writing the review.
In the weakness section, the reviewer points out several typographical errors; we agree with them all, and will fix them in the next version. For line 359 specifically, indeed what we should have stated is that the s... | null | null | null | null | null | null |
A Unified Model and Dimension for Interactive Estimation | Accept (poster) | Summary: This paper studies a general interactive learning setting, which is termed "interactive estimation". In this model, we are given a set of alternatives, which contains the target. At each step, the learner can choose an alternative and receive the similarity of this alternative with the target. Based on this in... | Rebuttal 1:
Rebuttal:
We thank the reviewer for their overall feedback and suggestions.
- “Decaying estimation” - First, we note that the analysis of the eluder dimension [24] exhibits a similar dependence: their upper bound relies on both the eluder dimension and the log of the covering number. As the reviewer points... | Summary: In this work the authors introduce a framework for interactive estimation, where the learner interacts with some oracle that provides information in the following way: the learner submits a query $z$ and the oracle returns a stochastic reward whose mean is given by a measure of dissimilarity between the query ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their overall feedback and suggestions.
- “My main concern… not very natural. …” - Note that we are *not* suggesting a new protocol for bandit learning. Instead, we are presenting a reduction from bandit learning to our setting, and using that to obtain a tighter analysi... | Summary: The paper introduces a learning model where an unknown target exists, and in each round, the learner selects a choice and receives a stochastic feedback indicating the similarity between the choice and the target. The objectives examined involve minimizing regret or providing a PAC-style guarantee, ensuring th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their overall feedback and suggestions.
- “Szörényi 2009 - real-valued functions?” - Perhaps we are missing something, but it seems that in the Preliminaries section of [25], Szörényi defines a “concept” to always be a mapping from the domain to binary labels {-1, 1}. The... | Summary: This paper presents a novel unified framework for interactive estimation. The framework involves a learner making repeated queries to the environment and observing rewards that correspond to the similarity between the query and an unknown target. The algorithm then uses these observed signals to estimate the t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their overall feedback and suggestions.
- “Dissimilarity measure” - To clarify, it is not our goal to quantify dissimilarity. Instead, our goal is to quantify learning complexity. As in similar approaches for characterizing learnability (e.g., the classical VC dimension f... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Bicriteria Approximation Algorithms for the Submodular Cover Problem | Accept (poster) | Summary: This work focuses on designing approximation algorithms for the submodular cover problem. In this problem, we are given a submodular function $f$ over subsets of a ground set $U$, and the goal is to find the smallest subset $S$ such that $f(S)$ is greater than or equal to a given threshold. The authors present... | Rebuttal 1:
Rebuttal: Thank you for your time and comments on the paper.
In this paper, we do use a variety of techniques inspired by those found in literature on the cardinality constrained submodular maximization problem (SMP). However, a few examples of components of our algorithms and analyses that are particularl... | Summary: The paper studies bicriteria approximation algorithms for sub-modular cover problem (SCP). In the problem, we are given an oracle to some sub-modular function f, and a threshold tau. Our goal is to find the smallest set X such that f(x) >= tau.
The paper is not well-written at all. There are critical typos i... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their time and comments on the paper. We address each concern and question below.
(1) "The authors stated that they have an algorithm that achieves the best approximation guarantee with O(n ln(n)) queries. I do not see any proof of the result."
We will exp... | Summary: This paper proposes several bicriteria approximation methods for solving the standard monotone submodular cover problem (SCP). Additionally, the authors also propose new variants of the problem: removing the monotone assumption, producing nearly feasible solutions, and adding regularization via a modular cost ... | Rebuttal 1:
Rebuttal: Thank you very much for your time and comments. We address each of your questions below.
- "What are some applications of regularized SCP?"
Many applications of the well-studied regularized submodular maximization problem (SMP) [Harshaw et al., 2019] are also suitable applications for RSCP, just... | Summary: The paper considers the Submodular Cover Problem (SCP), where one
is given a submodular function f through an oracle that returns the
value f(A) for each subset A of the underlying set U (over which
the function f is defined). The goal is to find a minimum cardinality
subset X of a set such that the value f(X)... | Rebuttal 1:
Rebuttal: Thank you for your time and comments about the paper. We will update the manuscript to add your suggested modifications. We address your questions below.
(1) The result we are aware of for SCP is that under the value oracle model of access for $f$, meaning that $f$ is only accessed as a black box... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Joint Prompt Optimization of Stacked LLMs using Variational Inference | Accept (poster) | Summary: The paper introduces a framework called Deep Language Network (DLN) that involves stacking multiple large language models (LLMs) as stochastic layers in a deep network. The prompts at each layer serve as tunable parameters, and the output of one layer is fed as input to the next layer. The LLMs are trained joi... | Rebuttal 1:
Rebuttal: Thanks for your review! We are glad that you appreciated our work.
---
***“comparison to other state-of-the-art language models or architectures”***
We provide additional experimental results as listed in the general response. Please refer to ***Table A/B/C/D*** in the general response for thes... | Summary: This paper suggests stacking multiple large language models (LLMs) together, with tunable parameters represented as prompts at each layer. Given that these prompts are discrete natural language elements, their direct optimization using a gradient-based method is challenging. To address this, the authors propos... | Rebuttal 1:
Rebuttal: Thank you for your review!
***“The paper's title, "Deep Language Networks", is misleading given that it’s only 2 layers”***
Our title expressed the conceptual framework that originated the idea of the variational inference prompt optimization algorithm, which is our main contribution. While we a... | Summary: The authors provide an interpretation of LLMs as shallow language networks. They explained how One-layer language networks can be used for joint prompt training, and then moved to stacked (two) language networks. The authors propose to use variational inference for the training, and figured out a few practical... | Rebuttal 1:
Rebuttal: Thank you for your review and insights.
---
***“An ablation study to study the computational cost and performance effect given different K and N could be interesting.”***
An ablation study would be absolutely interesting and we will add this to the camera ready. Empirically, we find that settin... | Summary: This paper introduces a novel discrete prompt tuning method. The paper is discussed under a scenario that regarding discrete prompt as the only tunable parameters while freezing all other model parameters. In this sense, the author focuses on getting optimized prompt from one LLM and feeding which as the input... | Rebuttal 1:
Rebuttal: Thank you for your review!
Please find hereafter our answers:
---
***“1. The paper is not comparing with correct enough baselines. [...] it means, the only baseline authors are correctly comparing with is APE.”***
In the paper, in addition to APE, we compared to other non 0-shot baselines: ICL ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their comments. In addition to the replies to the specific questions raised by each reviewer, we want to address points that were raised multiple times.
---
***About the discrepancy between the ambition of our proposed framework and its technical instantiation***
Whil... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Simplifying Neural Network Training Under Class Imbalance | Accept (poster) | Summary: The authors show that simply tuning standard hyperparameters provides state-of-the-art performance on a wide variety of class-imbalanced datasets, which may be surprising and give an impact to the community: We have to re-think the experimental settings for performance evaluation on imbalanced datasets.
The ... | Rebuttal 1:
Rebuttal: Dear Reviewer nEkH,
Thank you for your thorough and insightful feedback. We are grateful for your recognition of our work's significance and potential impact. We address each of your points below:
## Hyperparameter Settings and Code Availability:
Regarding hyperparameter tuning, we adopt a stand... | Summary: The paper studies the long-tail recognition problem and the impact of existing components of standard deep learning pipelines on the generalization performance, such as the batch size, data augmentation, architecture size, pre-training, optimizer, and label smoothing. They find that simply tuning those compone... | Rebuttal 1:
Rebuttal: Dear Reviewer nEkH,
Thank you for your thoughtful feedback, and we appreciate the time you took to provide your insight. We address your points below:
## Batch Size Experiment:
The training method used in Figure 1 is ERM. Following your comment, we found that cRT benefits from smaller batch size... | Summary: This paper presents different approaches to enhance the performance of neural network classifiers over imbalanced datasets. Unlike most research focusing on specialized loss functions or resampling techniques, this study promises that state-of-the-art performance is achievable over the neural classifiers by si... | Rebuttal 1:
Rebuttal: Dear Reviewer D6vk,
Thank you for your thorough and insightful review of our submission. We appreciate the time you've dedicated to evaluating our work and providing feedback. We address your points below:
## Exploration of Class Imbalance in Other Data Modalities:
While our study primarily focu... | Summary: In this paper, the authors study the problem of class imbalance. To be specific, they first investigate the effects of different hyper-parameters & design choices in an imbalanced setting. Moreover, the authors use such optimized settings with existing methods to show that significant improvements can be obtai... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We address your points below.
## Contribution:
While the methods we examine have been explored in the context of balanced training, our unique contribution lies in the in-depth analysis of these methods within the challenging environment of class imbalance. O... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thank you for your thoughtful and detailed reviews of our work. We appreciate your time and the constructive feedback you have provided. We have carefully considered your comments and concerns and would like to address them in a unified response:
**1. Diversity of Architectures a... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors suggest tackle the class imbalance issue from a hyperparameter optimization perspective. Throughout an extensive empirical study, they raise questions about the behavior of well-established techniques for balanced data under long-tail data distribution. The synergy of the resulting prescriptions i... | Rebuttal 1:
Rebuttal: Dear Reviewer kUXT,
Thank you for your detailed and thorough evaluation of our submission. We appreciate your comments and address your concerns and questions below.
## Page Limit and Readability:
We acknowledge your concern about the content exceeding the page limit and agree that moving som... | null | null | null | null | null | null |
Stochastic Collapse: How Gradient Noise Attracts SGD Dynamics Towards Simpler Subnetworks | Accept (poster) | Summary: The paper studies the properties of the stochastic gradient descent using its continuous time SDE approximation. The invariance of the stochastic dynamics in the representation space are investigated and rigorous condition where such invariance sets acts as attractors of SDE are derived. Furthermore, it is e... | Rebuttal 1:
Rebuttal: Thank you for your review and the suggestions to make our work better. We will discuss the weaknesses and questions of our paper you pointed out:
**Weakness A**: We appreciate your valuable observation of the confusions that arose from our interchangeable use of noise introduced by minibatch SGD ... | Summary: This paper demonstrate a low-dimensional invariant sets, namely a subset of parameter space, may remain unmodified by SGD. It means that SGD dynamics may lead to simple subnetworks. The theoretical mechanism behind is formally introduced. Moreover, the derived theoretical results revealed that the so-called st... | Rebuttal 1:
Rebuttal: Thank you for your review and the time you spent suggesting ways to make our work better. We discuss the weaknesses of our paper you raised:
**Weakness 1**: We appreciate the referenced works and will incorporate them into our study. We have updated our related work section to cite these works. W... | Summary: This paper studies the implicit bias of SGD, and provides a new perspective by characterizing the invariant set in the parameter space along the SGD dynamics, corresponding to specific model architectures. It is shown that SGD noise induces stochastic attractivity towards such invariant sets, which leads to si... | Rebuttal 1:
Rebuttal: Thank you for your effort on the insightful review and suggestions. We are going to discuss each weaknesses and questions raised:
**Weakness 1**: We value your insight but respectfully disagree. In Section 4 of our paper, Theorem 4.1 yields sufficient conditions for stochastic collapse applicabl... | Summary: The paper explores how stochastic gradient descent (SGD), a common optimization method for deep neural networks, can lead to simpler and more generalizable models. The paper introduces the concept of invariant sets, which are regions of the parameter space that are unaffected by SGD, and shows how SGD can be a... | Rebuttal 1:
Rebuttal: We appreciate your comprehensive review and the time you have taken to suggest improvements for our study. We'd like to respond to the weaknesses and queries you raised regarding our paper:
**Weakness 1**: “_not sufficiently clarifi[ing] the applicability of the attractivity condition to permutat... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their careful and detailed comments. We here attach a single-page pdf with the following three figures to address reviewers’ questions.
1. Figure S1- Empirics for our illustrative example. This shows that the quantitative prediction via Theorem 4.2 agree... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors investigate the implicit bias of the SGD algorithm towards invariant sets -- with a focus on sign and permutation invariance. In particular, they derive a sufficient condition for the stochastic attraction of the SDE describing the first two moments of the SGD process towards these invariant sets, ... | Rebuttal 1:
Rebuttal: Thank you for your insightful review and the time you took to identify areas where our work could be improved. We'd like to address the concerns you raised:
**Weakness A**: We appreciate your observation regarding our use of a continuous formulation of SGD and we agree that this is a limitation o... | null | null | null | null | null | null |
Efficient Bayesian Computational Imaging with a Surrogate Score-Based Prior | Reject | Summary: The paper deals with variational inference (VI) of the posterior distribution when using a diffusion based generative model as the prior distribution. The paper proposes using a lower bound on the log probability of the prior distribution (instead of calculating it through the ODE in standard diffusion based ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. Please refer to the global rebuttal for discussions on computational cost and closeness to the true posterior. Below we address specific questions.
***
Q: Evaluating the distance to the posterior.
A: Thank you for your suggestion to quantitatively assess o... | Summary: This paper focuses on solving inverse problems using diffusion based probabilistic models. The approach considered consists in minimizing the KL divergence between a variational posterior and the true posterior of the diffusion model. Computing this KL involves approximating the log probability of the diffusio... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Please refer to the global rebuttal for discussions on computational cost and closeness to the true posterior. Below we address specific questions.
***
Q: Illustrate on toy examples where the posterior is available and multimodal.
A: Thank you for your suggestion. We... | Summary: Authors propose a non-amortized variational inference approach to solve large-scale Bayesian inference problems where the prior is based on an cheap-to-evaluate approximation to a pretrained diffusion model.
Strengths:
**Originality.** This paper hits the nail on the head when it comes to large-scale Bayesia... | Rebuttal 1:
Rebuttal: Thank you for your encouraging and thoughtful feedback. Below we address specific questions.
***
Q: Diffusion models vs. other generative models as priors for large-scale Bayesian inference?
A: We initially investigated discrete normalizing flows (NFs) and found they performed poorly as image pri... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their encouraging feedback and for recognizing our “significant contribution to the field” (huK6): enabling large-scale Bayesian inference with a diffusion-model prior.
**Key contribution.** Our aim is to make score-based priors computationally feasible for inferen... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
MultiMoDN—Multimodal, Multi-Task, Interpretable Modular Networks | Accept (poster) | Summary: This paper presents MultiModN, a modular network that can deal with multimodal multitask problems and is inherently interpretable. MultiModN architecture consists of one encoder module for each modality, and one decoder module for each task. Each encoder module takes in a previous state and one modality input,... | Rebuttal 1:
Rebuttal: **SUMMARY:**
We kindly thank the reviewer for this careful evaluation of our work and greatly appreciate the positive review. We strongly agree with all points raised.
We have addressed each in a point-by-point response below and feel that the resulting edits have further improved the manuscript... | Summary: This paper describes an architecture for multimodal multitask learning which is robust to missing modalities/tasks both at training and test time. This is composed of:
- encoder specific modules
- (assuming an ordering among modalities) a hidden states which depends on the output of a modality specific encoder... | Rebuttal 1:
Rebuttal: **SUMMARY:**
We kindly thank the reviewer for the careful evaluation of our work and greatly appreciate the review. We address each concern in the point-by-point response below and feel that the resulting edits have further improved the manuscript.
**STRENGTHS:**
We are happy that the reviewe... | Summary: This paper proposes a modular multimodal model that fuses latent representations in a sequence of modality to conduct a combination of predictive tasks. It utilizes a flexible sequence of model and task-agnostic encoders to produce an evolving latent representation for a combination of multi-task, model-agnost... | Rebuttal 1:
Rebuttal: **SUMMARY:**
We kindly thank the reviewer for this careful evaluation of our work and greatly appreciate the positive review of our “excellent” presentation, methodological soundness, and worthwhile contribution.
We strongly agree with all points raised. We have addressed each in a point-by-poi... | null | null | Rebuttal 1:
Rebuttal: We are grateful to the reviewers for their careful evaluation and very happy to receive positive and high-quality reviews. We agree with all points raised and have been able to reflect and respond to each in detail.
We highlight a major concern of the only reviewer (R2) recommending rejection: ou... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Sharp Bounds for Generalized Causal Sensitivity Analysis | Accept (poster) | Summary: The authors generalize a class of causal sensitivity models that includes the traditional MSM, the continuous-treatment CMSM, and the longitudinal (time-varying treatment) LMSM. They show how to compute sharp bounds for the causal estimands by taking inspiration from recent work. Their general framework also a... | Rebuttal 1:
Rebuttal: Thank you for your helpful review!
### Response to “Weaknesses”
* Thank you for your comments. The main motivation behind the GMSM definition (and weighting function) is indeed to unify the three important sensitivity models from the literature.
* We argue that our **main contribution** is t... | Summary: This paper is about sensitivity analysis (SA) of causal queries in SCMs. In practice, given a causal query and a set of models, the goal is to compute a query's lower and upper bounds. The authors first derive a class of models to be used for SA and show how this extends existing models. An algorithm to obtain... | Rebuttal 1:
Rebuttal: ## Response to reviewer H542
Thank you for your review and your helpful comments!
### Response to “Questions”
* Thank you for giving us the opportunity to clarify the difference between two related approaches for bounding causal effects: **Causal sensitivity analysis (CSA)** and **causal partia... | Summary: The authors propose a unified framework for causal sensiitivity analysis under unobserved confounding that generalizes the Marginal Sensitivity Model (MSM). They derive sharp bounds for a diverse set of causal effects such as the CATE, mediation and path analysis effects, and distributional effects. The framew... | Rebuttal 1:
Rebuttal: ## Response to reviewer taFV
Thank you for your positive evaluation of our paper! We took all your comments at heart and improved our paper accordingly.
### Response to “A formal complexity analysis would be nice”
* Thank you for pointing this out. In the following, we assume that all models ar... | Summary: The authors study the problem of bounding a given causal effect. To this end, they propose a generalized marginal sensitivity model (GMSM) that is applicable to multiple discrete, continuous , and time-varying treatments. They also present a new interpretation of the partial identification.
Strengths: The pro... | Rebuttal 1:
Rebuttal: ## Response to reviewer 9HdS
Thank you for your positive review and your helpful comments. We improved our paper in the following ways:
### Response to “Weaknesses”
* Thank you for giving us the opportunity to clarify the assumptions in our paper.
* The assumption that no unobserved confoun... | Rebuttal 1:
Rebuttal: ## Response to all reviewers
Thank you very much for the constructive evaluation of our paper and your helpful comments! We addressed all of them in the comments below and uploaded additional empirical results as a PDF file. Our main improvements are the following:
* We provided **clarificatio... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
CAST: Cross-Attention in Space and Time for Video Action Recognition | Accept (poster) | Summary: This paper proposes a novel two-stream architecture called Cross-Attention in Space and Time (CAST) for video action recognition. The proposed architecture achieves a balanced spatio-temporal understanding of videos using only RGB input.
Strengths: 1. The paper is well-written and well-structured.
2. The imp... | Rebuttal 1:
Rebuttal: We thank the reviewer for the great questions. We address the issues raised by the reviewer below.
* Why does CAST show minor improvement on the SSV2 dataset compared to VideoMAE?
The main reason for the minor improvement is that the SSV2 is an object appearance agnostic action dataset. SSV2... | Summary: This paper presents an approach to action recognition that is based on the fusion of two streams of analysis. This involves a spatial stream and a temporal stream that interact through a novel mechanism to improve classification performance.
Strengths: 1. The paper is generally well written
2. The cross-atte... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive suggestions. We address the issues raised by the reviewer below.
* Straightforward unpacking of B-CAST mechanism
To facilitate a straightforward understanding of the B-CAST architecture, we present two additional figures in the global response PDF a... | Summary: This paper presents two-stream vision transformers, dubbed CAST, for balanced spatiotemporal video representation learning. Given the two experts, CLIP [36] and VideoMAE [46] for spatial and temporal expert, the proposed B-CAST module allows the exchange of complementary information across the separte experts ... | Rebuttal 1:
Rebuttal: **Fair comparison**
For a fair comparison with baselines, we augment Table 1 (a), (b), and (d) of the main paper with total parameters, GFLOPs/View, and throughput.
***Fusion baselines***
For a fair comparison with baseline information exchange methods, we add adapters to add, concat, and latera... | Summary: The manuscript proposes an approach of action recognition. The key idea behind the proposed approach is the usage of a two stream network where one network is specialized to encode the spatial details while the other to encode the temporal details. The approach also presents additional cross connections to eff... | Rebuttal 1:
Rebuttal: **Related work**
CAST is similar to two-stream networks [1,2] as it learns two streams for spatial and temporal context. Unlike these approaches involving optical flow estimation, CAST uses only RGB streams. Similar to CAST, SlowFast networks [3] and bLVNet-TAM [4] also use only RGB streams. Slow... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewers for their valuable and constructive comments. We have taken careful consideration of the points raised by each reviewer. We address these concerns comprehensively. Additionally, we have included a supplementary PDF containing figures that further support o... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a method, namely CAST, for video action recognition based on adapting from large-scale pre-trained models (e.g., CLIP and VideoMAE). The main motivation is to balance and exchange information between spatial and temporal information of two different experts: spatial and temporal. The propos... | Rebuttal 1:
Rebuttal: We thank the reviewer for the great questions. We address the issues raised by the reviewer below.
**Unfair comparison with adapter-methods**
For a fair comparison, we conducted an additional experiment, as shown in Table 4 of the supplementary material. Below, we have included the table for you... | null | null | null | null | null | null |
Geodesic Multi-Modal Mixup for Robust Fine-Tuning | Accept (poster) | Summary: The paper proposes Geodesic Multi-Modal Mixup, which mixes heterogeneous modality embeddings on the hypersphere to generate harder negative samples for the contrastive loss of multi-modal models. Such training scheme improves modality alignment and uniformity. The method was evaluated on various tasks includin... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and suggestions, and they are exceedingly helpful for us to improve our paper. We are grateful that you find strength in our idea, actual method, and experiments!
> **Comment1)** I believe the key hyperparameter of the proposed method is lambda, as this sh... | Summary: The paper proposes three mix-up inspired regularizers for finetuning CLIP as a way to mitigate the visual-text feature gap in the CLIP feature space. The paper proposes some theory to backup why alignment of text feature space and image feature space might be a good idea. Results in cross-modal retrieval and z... | Rebuttal 1:
Rebuttal: > **Q1)** (1) Modality gap is a well-known phenomenon [Liang22] [CyCLIP]. The authors misrepresent their contributions by implying that they discovered this. Additionally, in Tab1 of [Liang22], authors find that regularizing gap can increase or decrease the zero-shot performances and fairness. Thi... | Summary:
This paper studies a data augmentation method for effectively finetuning multi-modal model (i.e., CLIP). To address poor alignment of image and language space, the authors mix the embeddings of image and text while considering the geometry of the hypersphere. The authors give theoretical analysis as well as e... | Rebuttal 1:
Rebuttal: We are grateful for your productive feedback on our paper and your remark on the strength of our methodology and experiments.
> **Q1)** The first contribution regarding poor alignment of the two modal space of CLIP seems to be overclaimed. As far as I know, Liang et al. [24] has clearly disclosed... | Summary: This paper proposes a new method for robust fine-tuning called Geodesic Multi-Modal Mixup. The method improves the uniformity and alignment in multi-modal learning, thereby enhancing the performance of downstream tasks. Previous research has shown promising performance of large-scale pre-trained models on vari... | Rebuttal 1:
Rebuttal: Thank you so much for your valuable feedback and that you find value in our work. We agree with your concerns and have conducted additional experiments based on your feedback.
---
> **Q1)** The paper highlights that the analysis of learned embeddings and transferability for cross-modal tasks has ... | Rebuttal 1:
Rebuttal: # Global Response
We thank the reviewers for taking the time to review our paper and for your valuable feedback. We have carefully considered your comments and reflected in our response. In this global response, 1) we first provide a brief review of our draft and then 2) provide a summary of addit... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper addresses the problem of improving vision-language representation learning using feature-space augmentation. The authors claim that approaches such as CLIP have led to poor alignment between text features and image features, and the space between them lacks uniformity. They propose an approach calle... | Rebuttal 1:
Rebuttal: We appreciate you for your attention and valuable comment. We agree with your concerns and have conducted additional experiments to address them.
---
> **Q)** The improvement was shown in vanilla settings, for example, with basic CLIP and small datasets. It is not clear if such a mix-up scheme ho... | null | null | null | null | null | null |
Cinematic Mindscapes: High-quality Video Reconstruction from Brain Activity | Accept (oral) | Summary: This paper aims to reconstruct high-quality video from brain activity. A novel model called MinD-Video is proposed, which could learn spatiotemporal information from continuous fMRI data.
Strengths: 1. It is quite interesting to reconstruct videos according to human brain regions' actions, Although the task i... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the recognition of our contribution and the invaluable and constructive comments. Our point-by-point response is provided as follows.
> 1. In Figure 4, we can see that Wen (2018) could reconstruct the shape and Kupershmit (2022) could reconstruct the textu... | Summary: This paper focuses on the task of reconstructing human vision from brain activities. The authors propose MinD-Video that learns spatiotemporal information from continuous fMRI data of the cerebral cortex progressively through masked brain modeling, multimodal contrastive learning with spatiotemporal attention,... | Rebuttal 1:
Rebuttal: We thank the reviewer for your time and effort in reviewing our work. We also appreciate your interest in our work and the useful suggestions. Our point-by-point responses are as follows.
> 1. the novelty of this work is limited with respect to diffusion model field.
> 2. The proposed method is ... | Summary: The paper presents a pipeline to decode videos from fMRI brain activity data. The pipeline is divided into several consecutive steps. First, a Transformer-based autoencoder is trained on an unsupervised Masked Brain Modelling task to learn fMRI representations on a large corpus of fMRI data. The fMRI encoder i... | Rebuttal 1:
Rebuttal: We are very grateful for your appreciation of the novelty and potential impact of our work and your important suggestions. Our point-by-point responses are as follows.
> 1. What is the impact of the masked modelling pretraining?
**Response:** We thank the reviewer for raising this important po... | Summary: This research proposes a method called Mind-Video to reconstruct videos from brain activity. By utilizing continuous fMRI data and advanced techniques, Mind-Video can generate high-quality videos with arbitrary frame rates. The model outperforms previous methods in accuracy and structural similarity.
Streng... | Rebuttal 1:
Rebuttal: We thank you for the strong support and the positive comments on our work. Your inspiring questions and comments are valuable for our future work. Our point-by-point responses are as follows.
> 1. The work is very interesting, the resulting video resembles the ground true in terms of activity, b... | Rebuttal 1:
Rebuttal: We are grateful to all five reviewers and AC/SACs for their valuable time, insightful comments, and useful suggestions. We will carefully revise our paper according to the comments. Our point-by-point response to the reviewers’ comments has been added to the individual chat box for each reviewer. ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors have developed an fMRI to video model trained using contrastive learning and stable diffusion. The generted videos are evaluated based on the semantics of their content and pixel level metrics at video and frame level, some of which utilize pretrained classifiers trained on ImageNet and VideoMAE. T... | Rebuttal 1:
Rebuttal: We truly appreciate your recognition of our contributions and novelty. Our point-by-point responses to the comments are as follows.
> 1. The differentiating factors between the current work and [6] is better be highlighted.
**Response:** We thank the reviewer for this important point. Our wor... | null | null | null | null | null | null |
ShiftAddViT: Mixture of Multiplication Primitives Towards Efficient Vision Transformer | Accept (poster) | Summary: This paper proposes an efficient accelerating framework for vision transformer, ShiftAddViT, which reparameterizes pre-trained ViTs with a mixture of complementary multiplication primitives and MoE designs. Specifically, All MatMuls in self-attention modules are reparameterized by additive kernels, and the rem... | Rebuttal 1:
Rebuttal: We greatly appreciate your careful review and constructive suggestions. Below are our detailed responses to your concerns.
**W1: The contributions of multiplication less and MoE are independent? Not very suitable for algorithm conferences like NeurIPS/CVPR/ICML?**
We thank the reviewer for ackno... | Summary: The authors propose re-parameterizing ViTs to speed up inference without a full retraining. To do this, they introduce a new operation ShiftAddViT, which works well when applied to attention. For the MLP in a transformer, the authors use a mixture of experts. This operation reduces latency and energy usage, wh... | Rebuttal 1:
Rebuttal: We greatly appreciate your careful review and constructive suggestions. Below are our detailed responses to your concerns.
**W1: The only qualm is that binary quantization seems too aggressive?**
We thank the reviewer for pointing out this. We examined the sensitivity of different parts in atten... | Summary: This paper proposes a new type of multiplication-reduced model ShiftAddViT, which use the additive kernels to reparameterize the batched GEMM in the attention block and uses the shift kernels to reparameterize other MLPs or linear layers. In this way, it can reduce energy-intensive multiplications. The authors... | Rebuttal 1:
Rebuttal: **W1: Implementation details are not clear enough**
Sorry for not making it clear enough to you. We supplied more settings to Sec. 4 in the Appendix as we focused on the motivation and high-level idea in the main paper. We will clarify more and release code&models upon acceptance.
**W2: For DeiT... | Summary: This paper introduces a novel reparameterization method for efficient Vision Transformers (ViT). The method replaces the heavy multiplication operations in ViT with a combination of shift and add operations. By mapping queries and keys to binary codes in Hamming space and reparameterizing multi-layer perceptro... | Rebuttal 1:
Rebuttal: We greatly appreciate your careful review and constructive suggestions. Below are our detailed responses to your concerns.
**W1: Most experiments are small-scale models (largest: 30M parameter and 4G FLOPs), how about the performance of larger-scale models like ViT-Base size or similar?**
Thank ... | Rebuttal 1:
Rebuttal: **Dear ACs and Reviewers,**
First of all, we deeply appreciate the time and effort spent by you in providing the reviews, and truly value your effort, especially considering the substantial scale of a conference like NeurIPS.
We are immensely grateful for the positive feedback our paper has rece... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work proposes ShiftAddViT, which is an efficient ViT reparameterization with a mixture of complementary multiplication primitives, such as bitwise shifts and adds.
The alternative parameterization (quantization) is carefully examined to allocate to different components (MHSA, MLP) in ViT.
In addition, t... | Rebuttal 1:
Rebuttal: We greatly appreciate your positive comments and constructive suggestions. Below are our detailed responses to your concerns.
**W1: I wonder if this work is portable to NLP tasks, especially for LLMs with more tokens?**
We follow your suggestion to test our proposed optimization of MHSA and MLPs... | null | null | null | null | null | null |
Decentralized Matrix Sensing: Statistical Guarantees and Fast Convergence | Accept (poster) | Summary: In this work, the authors proved the convergence of the distributed GD method on the over-parameterized matrix sensing problem. The convergence is based on the proposed in-network RIP condition. Numerical experiments are conducted to verify the theoretical findings.
Strengths: The paper is well-written and ea... | Rebuttal 1:
Rebuttal: We acknowledge the query about the novelty of our convergence analysis relative to [34], providing an opportunity to highlight the unique features of our approach. While both methods share a two-phase structure, our proof contains technical details that substantially diverge from [34], a distincti... | Summary: This paper proposes a decentralized gradient algorithm for the matrix sensing problem via Burer-Monteiro type decomposition. A new concept of RIP termed in-network RIP is introduced for the proposed algorithm, which harnesses the RIP of the measurement operator and intertwines it with the network's connectivit... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their insightful comments and the overall positive assessment of our work. Our response to her/his comments/questions follows, starting from those listed under the `Weaknesses'.
-$\textbf{1.}$ Solving problems in a decentralized fashion is essential in situations in wh... | Summary: This paper studies the problem of decentralized low-rank matrix sensing. The paper presents novel theoretical results on the convergence and generalization of a standard decentralized learning algorithm. In particular, they provide convergence and generalization guarantees for decentralized gradient descent ... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their insightful comments and the overall positive assessment of our work. We are happy the Reviewer liked it. We answer the Reviewer's concerns in order.
-$\textbf{1.}$ We will enlarge the font of the text in the figures and re scale them for visibility. We can change ... | null | null | Rebuttal 1:
Rebuttal: We thank the Reviewers for their careful reading and insightful comments. We are glad that there is a consensus that the paper is well written and contains novel results that are of interest to audiences in optimization and machine learning. Our reply to their specific questions and key comments... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Segment Any Point Cloud Sequences by Distilling Vision Foundation Models | Accept (spotlight) | Summary: This study represents the pioneering effort to utilize 2D vision foundation models for self-supervised representation learning on large-scale 3D point clouds. They introduce Seal, an innovative framework specifically designed to extract informative features from sequences of automotive point clouds. With its e... | Rebuttal 1:
Rebuttal: We thank Reviewer 7qT5 for devoting time to this review and providing valuable comments.
---
> ***Q1:** "The approach in this paper is not considered unsupervised pretraining as it utilizes large models that rely on additional data."*
**A:** Thanks for the comment. We agree with the reviewer tha... | Summary: This paper proposed a novel framework named Seal. The Seal distills VFMs into point clouds, enabling achieves efficient segmentation of various automotive point cloud sequences without requiring extensive annotation during the pre-training stage. It exhibits excellent performance across multiple datasets.
Str... | Rebuttal 1:
Rebuttal: We thank Reviewer 1xK9 for devoting time to this review and providing valuable comments.
---
> ***Q1:** "(i) The results provided by VFMs are at the instance level, while this paper aims to obtain a pretraining model for semantic segmentation tasks. (ii) The negative samples may include instances... | Summary: This paper introduces a novel framework, called *Seal*, that leverages VFMs for self-supervised representation learning on automotive point cloud sequences. The main idea is to leverage the 2D-3D correspondence between LiDAR and camera sensors and construct high-quality contrastive samples for cross-modal repr... | Rebuttal 1:
Rebuttal: We thank Reviewer 3rsk for devoting time to this review and providing valuable comments.
---
> ***Q1:** "Unless the authors conduct more experiments on indoor point cloud datasets, they'd better just state that 'Segment Any Automotive Point Cloud Sequences …' in their title."*
**A:** Thanks for ... | Summary: The manuscript presents Seal, a framework that leverages VFMs (Visual Foundation Models) to segment diverse point cloud sequences in autonomous driving scenarios. The proposed approach employs VFMs to initially segment superpixels in 2D camera images and subsequently projects them to 3D superpoints. Then, it i... | Rebuttal 1:
Rebuttal: We thank Reviewer EPuy for devoting time to this review and providing valuable comments.
---
> ***Q1:** "The use of VFM models for segmentation is widely discussed. The proposed pretraining schemes closely follow common cross-modal contrastive learning approaches. The paper is riding on the popul... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Unified Fast Gradient Clipping Framework for DP-SGD | Accept (poster) | Summary: The paper provides a unification of ad-hoc analysis and interpretations of the ghost clipping algorithm under a single framework. It also shows that for certain operations, such as fully-connected and embedding layer computations, further improvements to the runtime and storage costs of existing decompositions... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our manuscript. Below, you find some responses to some of your questions and concerns.
> The paper focuses only on a specific technique of gradient clipping, i.e., ghost clipping. Whether this ghost clipping lies at the heart of the big field of privacy of ... | Summary: This paper provides a unified framework for efficiently computing the gradient norms of individual examples for a wide range of neural network architectures, which significantly decreases runtime and storage costs for implementing DP-SGD.
Strengths: 1. The considered problem is important: Computing per-exampl... | Rebuttal 1:
Rebuttal: Thank you for your positive evaluation of our work. We hope the below comments will address the issues you brought forth.
> The experimental evaluation does not provide a big-picture idea of the end-to-end savings provided by the proposed framework. Figures 1 and 2 exhibit the runtime/storage cos... | Summary: This paper unifies a framework that enables us to apply ghost clipping algorithms to new layers of neural networks, apart from fully connected, feed-forward layers. In particular, the paper discusses efficient implementations of computing norms for running DP-SGD to train neural networks.
Strengths: The cont... | Rebuttal 1:
Rebuttal: Thank you for your positive impression of our work! Below you will find our comments addressing your main concern.
> Literature on the importance of DP-SGD and clipping operators is lacking in this paper. It is better to include them in Section 3 (Previous work) or in the introduction section to ... | Summary: The paper studies the memory/time complexity of the per-sample gradients norm computation step in the DP-SGD algorithm. The authors present a general theoretical framework that generalizes the idea of ghost norm computation (computing the norm of the persample gradients without having to store the gradients), ... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments towards improving the quality of our manuscript. We hope that the comments and considerations below will help improve your overall opinion of our work.
> Typos: l108 “authors”
This will be fixed in the revision.
> Overall, the experiments are very insuffic... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work unifies the analysis of the ghost clipping algorithm, which was previously analyzed for specific architectures only. The proposed framework extends previous results to a wide class of network architectures including new applications and provides asymptotically faster computation times than the previo... | Rebuttal 1:
Rebuttal: Thank you for taking the time to thoroughly review our manuscript. We hope that our responses below will help improve your impression of our work.
> The main motivation of the work is that when the batch sizes of DP-SGD is large, the runtime and storage can be large. Why this problem cannot be si... | null | null | null | null | null | null |
GloptiNets: Scalable Non-Convex Optimization with Certificates | Accept (spotlight) | Summary: This paper focuses on non-convex global optimization on the hypercube.
The approach is built on top of the framework [7] and relies on non-negativity certificates that are not only restricted to non-negative polynomials since it is applicable to any function with computable Fourier coefficients.
The methodol... | Rebuttal 1:
Rebuttal: ## About the quality of the certificate
We refer to the answer to all reviewers where this matter is discussed extensively with new experiments.
## Comparison with TSSOS
> The efficiency of TSSOS increases when the optimization problem involves sparser polynomials (with low n), which is not t... | Summary: This paper presents GloptiNets, a method to bound the suboptimality of a given candidate solution to the optimization of a smooth (nonconvex) function on the hypercube.
Nonconvex optimization with optimality certificates is difficult. Oftentimes it is relatively easy (fast) to compute a candidate solution, bu... | Rebuttal 1:
Rebuttal:
## Use of GloptiNets in practice
Synthetic datasets offer the advantage of precise parameter control, including the function's norm and the number of coefficients for polynomials. This allows us to determine that the quality of GloptiNets certificates depends on the former and not the latter.
I... | Summary: This paper introduces a method for certain non-convex optimization problems on a hypercube or torus (i.e. with periodic boundary conditions), which also provides a certificate (a measure of how close the fitted function is to the target).
As I understand it, the certificate computation utilizes the Fourier ba... | Rebuttal 1:
Rebuttal: ## Quality of the certificate
We refer the reviewer to the common response on that aspect.
## Implementation details
We would be happy to include implementation details in the main text, as a lot of attention was dedicated to having a structure which can scale to thousands of parameters.
For... | Summary: This paper develops an approach to compute certificates for non-convex optimization on functions that are optimized over the hypercube or torus. It does so by considering the generic certificate recipe from [7] and then relaxing the positive semi-definite constraint to a class of functions that is easier to op... | Rebuttal 1:
Rebuttal: We thank this reviewer for the kind and encouraging feedback!
As you suggest, we will use additional space to provide additional assumption on the derivation of the certificate in Eq. (2). Typically, it relies on the relation
$$
f_\star = \sup_c c ~~ \text{ s.t. } ~~ f - c \geq 0 ~~~~~~~~ \text... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and helpful feedback. Individually, we address each of their questions. Furthermore, we showcase new experiments demonstrating two key points: (1) the ability to obtain tighter certificates than the ones reported in the paper and (2) the versatility of our fra... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: They present a novel approach to non-convex optimization with certificates, which handles smooth functions on the hypercube or on the torus. Unlike traditional methods that rely on algebraic properties, our algorithm exploits the regularity of the target function intrinsic in the decay of its Fourier spectrum.... | Rebuttal 1:
Rebuttal: We direct the reviewer's attention to the new experiments presented in the collective response to all reviewers. Should they have any additional concerns or questions, we would be happy to discuss them further.
---
Rebuttal Comment 1.1:
Title: Thanks for the detailed rebuttal.
Comment: Thanks fo... | null | null | null | null | null | null |
State-space models with layer-wise nonlinearity are universal approximators with exponential decaying memory | Accept (poster) | Summary: The paper provides a constructive proof that state-space models (SSMs) are universal approximators of sequence-to-sequence mappings.
Moreover, it shows that SSMs (and even empirically S4) suffer from exponentially decaying memory just like standard RNNs.
Strengths: Up to my knowledge this is the first (constr... | Rebuttal 1:
Rebuttal: We sincerely appreciate your important questions regarding our proof, as well as your constructive suggestions for enhancing the writing quality. Below is our response to the points raised:
1. "Regrettably, the paper exhibits a significant lack of quality in its writing, containing numerous gramm... | Summary: This paper attempts to show several properties of SSMs: a) SSMs can approximate element-wise functions and temporal convolutions b) Universality of SSMs c) Exponential memory decay. The authors also show experiments of SSM's exponential memory decay and compare them against variants of RNN architectures.
Stre... | Rebuttal 1:
Rebuttal: We sincerely appreciate your helpful comments for us to provide clarifications on the SSM architecture, its relation to RNNs, and therefore the significance of our findings. Below is our response to the points raised:
1. "My main concern is most of the main results seems trivial: When taking away... | Summary: This paper analyzes properties of state space models (SSMs) and verifies the analytic results with numerical simulations.
The primary result is that SSMs have the same basic properties as classic RNNs. They perform temporal convolution on their inputs, with exponentially decaying memory and are universal fun... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive review and insightful questions regarding the reasons behind the outstanding performance of SSMs. Below is our response to the points raised:
### **Weaknesses:**
1. "It's worth thinking more about presentation of the figures. Figure 4 was pretty helpful but ... | Summary: The authors set out to analyze state space models, which have been gaining popularity as alternatives to transformer based systems that can better model long range dependencies and are more computationally efficient. Since such models do not utilize a non-linear activation function along the temporal access, i... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive feedback on providing more explanation and intuition behind the mathematical proof. Below is our response to the points raised:
1. "The writing of the draft can be substantially improved. It would be good to include some explanations into the implication... | Rebuttal 1:
Rebuttal: ## Response to all Reviewers
We thank all the reviewers for their insightful reviews. The reviewers thought the work tackles **"quite a relevant topic on a family of models that are becoming widely popular"**, provides result that **"is an important contribution to our understanding of SSMs"**, wi... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper presents universal approximation results for state-space models (SSM) with layer-wise nonlinearity. In particular, two-layer SSMs with layer-wise nonlinearity can approximate any continuous function over a compact set. Moreover, SSMs can approximate elementwise function and convolution. The paper als... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive comments for us to clarify details in our work. Below is our response to the points raised:
### **Weaknesses:**
1. "Lacking connection to linear RNNs. SSMs are a special case of linear RNNs, and universal approximation results of linear RNNs are available.... | null | null | null | null | null | null |
Learning to Influence Human Behavior with Offline Reinforcement Learning | Accept (poster) | Summary: This paper presents an investigation for learning to influence suboptimal human opponents in agent-human interactions. It claims that by performing offline RL on human-human interaction data, an agent can learn to influence its opponent's actions and latent strategies, even if there is no explicit information ... | Rebuttal 1:
Rebuttal: Thank you for your review. The main concerns in your review seem to center around the evidence to support our conclusions. We hope to remedy this by providing additional evaluations that further support our hypothesis that the human does improve their behavior during evaluation (implying successfu... | Summary: This paper presents an investigation into the application of offline RL on a dataset of human-human interactions to develop a policy capable of influencing human behaviors towards desired outcomes. The authors demonstrate that the learned policy not only affects immediate behavior but also influences the long-... | Rebuttal 1:
Rebuttal: Thank you for your review. You raised some interesting additional evaluations to strengthen our paper that we have added and discussed below. Please let us know if our response fully addresses the issues in your review.
**Compare with approaches that leverage both offline data and online interac... | Summary: This work aims to provide some evidence that offline reinforcement learning techniques can be used in the field of human-agent collaboration to influence or improve the behavior and underlying strategies of humans. The authors first verified that agents trained by CQL can influence the behavior of human player... | Rebuttal 1:
Rebuttal: Thank you for your review. The main concern raised in the review seems to center around the evidence that supports our conclusion. We hope to remedy this by providing additional evaluations that further reinforce our hypothesis that the human does improve their behavior during evaluation trials (i... | Summary: This paper proposes a novel framework to empower an intelligent agent with the capability to effectively influence suboptimal human behavior during interactions. The primary objectives revolve around tackling two key challenges: (1) deducing a new strategy to influence human action and (2) learning to influenc... | Rebuttal 1:
Rebuttal: Thank you for your review. We agree with you regarding expanding the scale and complexity of our evaluation. We view our work as a first-step in showing that influence can be done purely offline (without any environment or human simulators), and aim to show more compelling examples of this in the ... | Rebuttal 1:
Rebuttal: Based on reviewer feedback, we have performed multiple additional evaluations. We reference each of them in our individual responses to each reviewer, but also provide an overview of the new results below.
In Figure 1, we compute the reward improvements of our proposed method across all the expe... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
CLeAR: Continual Learning on Algorithmic Reasoning for Human-like Intelligence | Accept (poster) | Summary: This work tackles the problem of continual learning for rule-based tasks. The method first maps algorithmic inputs to a discrete latent space. This mapping is one-to-many, which allows the method to learn multiple features for the inputs of each task. A model then consumes results from this discrete latent spa... | Rebuttal 1:
Rebuttal: *We thank you for the positive comments and suggestions. We have addressed each of your questions below.*
***
## Weaknesses
**W1** We appreciate your insightful analysis on catastrophic forgetting, especially on recent LLMs. Following your suggestion, we have incorporated the references you mentio... | Summary: This paper introduces CLeAR, a novel algorithmic reasoning (AR) methodology for continual learning (CL) of abstract logical concepts. The main component of CLeAR is a one-to-many mapping strategy that aligns various tasks of different dimensions in a shared mapping space, allowing for the gradual learning of a... | Rebuttal 1:
Rebuttal: *We thank you for the positive comments and suggestions. We have addressed each of your questions below.*
***
## Weaknesses
**W1** (Aligned with Question 1) We appreciate your insightful comment. Frameworks such as **DeepProbLog (2018), HOUDINI (2018), NeuralTerpret (2017)**, and others have been... | Summary: The authors propose a training framework for continual learning in which task inputs are mapped to regions of or distributions over an embedding space, before the resulting embedding is passed to a single model that learns continually to solve the tasks. They apply this continual learning framework to tasks b... | Rebuttal 1:
Rebuttal: *We thank you for the positive comments and suggestions. We have addressed each of your questions below.*
***
## Weakness
**W1** Thank you for the insightful comments. We apologize for the insufficient explanation of the experimental results. As you suggested, we will address these points in the f... | Summary: In this paper, the authors introduce CLeAR, a new method of Continual Learning (CL) for Algorithmic Reasoning (AR). In doing so, several relaxations to standard CL are discussed: (1) scenarios where the same data are used for different tasks, (2) the variation of the input, being not of fixed size, and (3) ana... | Rebuttal 1:
Rebuttal: *We thank you for the insightful comments and suggestions. We have addressed each of your questions below.*
***
## Weaknesses
**W1** Thank you for your sharp comments. And we apologize for the insufficient explanations in certain parts. As you pointed out, we will make sure to incorporate the men... | Rebuttal 1:
Rebuttal: We thank all the reviewers very much for their valuable comments and constructive suggestions to strengthen our work. Also for the positive comments and encouraging remarks: The paper addresses for the first time the continual learning (CL) adaptation of algorithmic reasoning(AR) tasks (iTug, iPYe... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper looks at the continual learning (CL) setting involving tasks which require more abstract reasoning (reusable across input domains), s.a. addition and multiplication. The authors imagine that the inputs of all encountered tasks can be mapped into a common space, which, in turn, can be similarly transf... | Rebuttal 1:
Rebuttal: *We thank you for the insightful comments and suggestions. We have addressed each of your questions below.*
## Weaknesses
**W1**
Thank you for introducing us to these inspiring works. We will add this research to the related work section (we reported a summary in global comment). While all of thes... | null | null | null | null | null | null |
CWCL: Cross-Modal Transfer with Continuously Weighted Contrastive Loss | Accept (poster) | Summary: The paper proposes a novel continuously weighted contrastive loss function for multi-modal models. The proposed method takes consideration of 1) similarity of samples in the same batch 2) the non-binary nature of similarity between input samples, and therefore utilizes a continuous weight matrix in learning a... | Rebuttal 1:
Rebuttal: **W1: Results are incomplete...**
Thank you for raising this point. In Table 4, the row corresponding to CL is indeed the LiT model. We named it CL to indicate that this model was tuned using standard contrastive loss as done in the LiT paper. In fact, the model referred to in this row follow... | Summary: The authors propose a composite loss in contrastive learning in order to preserve structure between two embedding spaces for different modalities. In the setting in which they study, two unimodal encoders are aligned by freezing one of them and computing a "continuous" contrastive weight between samples $(i, j... | Rebuttal 1:
Rebuttal: **W1: There is a...**
Please note the problem setup is only done in Section 2.1. We request the reviewer to elaborate further on what they mean by "lot of setup".
Further, our contributions are multi-fold and are not confined to Section 2.3. The novel CWCL loss function is introduced in Sec... | Summary: In this work, the authors consider the problem of cross-modal zero-shot transfer and propose a new loss function called continuously weighted contrastive learning (CWCL) that extracts better supervision from pretrained models in a single modality and leads to better alignment between two modalities. They run e... | Rebuttal 1:
Rebuttal: **W1 : The main...**
Both references are very interesting and relevant and we will cite them. We provide a detailed comparison below.
**Comparison with "Not all Negatives are Equal:.."**
The ideas explored in this paper are similar in spirit to those considered in our paper. However, ... | Summary: They propose a simple but effective method to align the representation space of two self-supervised models using pairs of examples from two modalities. They propose a CWCL loss where they reweight the contrastive loss of example pairs based on the similarity measured in one modality (equation 2). Specifically,... | Rebuttal 1:
Rebuttal: **W1 The ablation study...**
We provide the comparison between standard contrastive loss and CWCL for the image-text modality in Table. 1. Please note that we have chosen to name the standard contrastive loss-based training as LiT, since we follow their training protocol by first initializing wi... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their time and effort in reviewing our paper. We appreciate all of the comments and they have helped us improve our paper. Here, we first provide a summary of our paper. Then, we outline the major concerns expressed by the reviewers and explain how we h... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A polar prediction model for learning to represent visual transformations | Accept (poster) | Summary: The authors create self-supervised video prediction model that relies on principles of the Fourier shift theorem. The approach is inspired by work suggesting that humans rely on perceptual straightened representations to support prediction. The authors first validate their method on toy cases of simple transla... | Rebuttal 1:
Rebuttal: Thank you for your review and comments.
* **Architectures, metrics and datasets**: we have added a Unet architecture to our comparisons, see global response. We could not find a reference PyTorch implementation of the PredNet architecture, but to ease comparison in the future, we intend to releas... | Summary: Motivated by Fourier shift theorem and its group-theoretic generalization, this paper proposed a new video prediction model.
Strengths: 1. Biological modeling: computational elements can be used to describe primate V1 responses.
2. Fourier shift theorem and its group-theoretic generalization were incorporate... | Rebuttal 1:
Rebuttal: Thank you for your review and comments.
* **Evaluation**: we computed SSIM, the results come out similar and do not change our initial interpretation, see global response. A qualitative example and its interpretation is included in Figure 7 of the supplementary material in the original submission... | Summary: This research develops a self-supervised representation-learning framework that uses the regularities of natural videos to make accurate predictions. The architecture is inspired by the Fourier shift theorem and trained for next-frame prediction. The approach can discover irreducible representations of smooth ... | Rebuttal 1:
Rebuttal: Thank you for your review and comments.
* **Compression**: The prediction method presented in this paper does not constitute a full video compression engine. Indeed the error are not quantized and we have not considered a full rate-distortion tradeoff. But we envision a possible application of th... | Summary: The authors in this work propose a new self-supervised learning technique that is aimed to perform predictive processing of natural videos (although the approach seems broad enough to be applicable to other sequential signals with similar inductive priors to vision). The authors develop two parameter-efficient... | Rebuttal 1:
Rebuttal: Thank you for your review and comments.
* **Costs**: see global response and Table 2 of rebuttal pdf.
* **Error bars**: see global response and Table 1 of rebuttal pdf.
* **Applications**: see global response.
* **Limitations**: see global response. | Rebuttal 1:
Rebuttal: We thank the reviewers for their comments and questions.
The points that were raised by multiple reviewers are addressed in this global response
and the other questions are addressed in individual responses.
**Additions/extensions:**
* **Error bars**: single run prediction errors were reported in... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors present a self-supervised representation-learning framework inspired from the idea of continuous deformations of objects in videos. The framework allows for next-frame prediction given previous ones, and borrows ideas from the Fourier shift theorem. The framework is composed of three stages - an an... | Rebuttal 1:
Rebuttal: Thank you for your review and comments.
* **Implementation details**: the learned transformations and their rationale are introduced in the main text; but, due to space limitation, their full description (architectures, datasets and optimization) is relegated to the supplementary material. We imp... | null | null | null | null | null | null |
Focus on Query: Adversarial Mining Transformer for Few-Shot Segmentation | Accept (poster) | Summary: The paper argues that mining information from the support set does not effectively improve Few-shot Segmentation (FSS) results. Instead, the paper proposes an query-centric FSS model called AMFormer, which uses adversarial learning to generate segmentation results with only rough support guidance.
Strengths: ... | Rebuttal 1:
Rebuttal: ### Response to Reviewer 7rkU
Thank you for acknowledging the strength of our paper. We have carefully considered your insightful and constructive comments and here are the answers to your concerns.
**Q1: Performance of our baseline:**
**A1:** Our baseline can attain a high performance mainl... | Summary: This work proposes a new query-centric FSS model Adversarial Mining Transformer for few-shot segmentation.
However, the idea is exactly same as SSP (ECCV2022).
Strengths: This work proposes a new query-centric FSS model Adversarial Mining Transformer for few-shot segmentation.
Weaknesses: The idea is exact... | Rebuttal 1:
Rebuttal: ### Response to Reviewer 5HhF
Thanks for your comments.
---
**Q1: The idea of mask expansion is similar to the SSP.**
**A1:** There may be some misunderstandings about the contributions of our method. There are some previous methods, such as IPMT[1], that try to exploit the information of the... | Summary: This paper proposes a query-centric FSS method, which first performs rough segmentation based on the support features, then performs mask propagation based on the intra-semantic similarities, which is supervised by an adversarial learning process.
Strengths: 1. The proposed method is technically sound.
2. T... | Rebuttal 1:
Rebuttal: ### Response to Reviewer 5HhF
Thanks for your valuable comments. We will explain your concerns point by point.
---
**Q1: The idea of mask expansion is similar to the SSP.**
**A1:** There are some previous methods, such as IPMT[1], that try to exploit the information of the query branch to min... | Summary: This paper studies few-shot segmentation (FSS). It proposes a new query-centric FSS model Adversarial Mining Transformer (AMFormer), which achieves accurate query image segmentation with only rough support guidance or even weak support labels. The core idea is to have a object mining transformer (G) that can a... | Rebuttal 1:
Rebuttal: ### Response to Reviewer gxBQ
Thanks for your positive comments on the performance of AMFormer.
---
**Q1: About the experiment setting.**
**A1:** Yes, all the methods in Table 2 and Table 3 use the ground truth masks of both support and query images during training, and our method does not ... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We thank all of you for your valuable insightful comments. We have carefully responded to your questions accordingly with the necessary additional experiments and analyses. We hope our responses could address all your concerns. Please let us know if you have any further advice, ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper solves the problem of few shot segmentation. It differs from the existing view of solving this problem which leverages heavily on the exploration of the support samples. The core idea is to shift the framework from the support-centric to the query-centric. The problem method contains an object minin... | Rebuttal 1:
Rebuttal: ### Response to Reviewer ugVV
Thanks for your valuable comments. We will explain your concerns point by point.
---
**Q1:Experimental details about Table 1.**
Previous methods aimed to comprehensively exploit the support features by adopting precise support masks to generate prototypes or to... | Summary: In this paper, the authors introduce a concept for solving the few-shot segmentation problem using weakly supervised learning concept. The method relies on weak support labels like scribbles or bounding boxes that highlight the object regions in the query. The segmentation is achieved through two blocks - the ... | Rebuttal 1:
Rebuttal: ### Response to Reviewer WjSB
Thanks for your valuable comments. We will explain your concerns point by point.
---
**Q1. About the model complexity.**
**A1:** In Table 4 of the paper we compare the number of learnable parameters of our model with several state-of-the-art FSS models. Follow... | null | null | null | null |
Analyzing Vision Transformers for Image Classification in Class Embedding Space | Accept (poster) | Summary: The authors propose a method that explores properties of vision transformer (ViT) features. In particular, tokens of patches at various levels of the sequence of transformer blocks are projected to class-space in the case of models pre-trained for image classification. Building of this, authors provide various... | Rebuttal 1:
Rebuttal: Thank you for your feedback which has helped 1) clarify in our manuscript the rationale, relevance, and novelty of our work, and 2) improve the generalizability and clarity of our findings. We address in detail each concern stated in the revision below.
_On the rationale behind our method_
We ar... | Summary: The authors propose to reverse-engineer pre-trained ViTs for image classification task in order to investigate how the internal representations at different levels are projected onto the class embedding space and reveal how the models construct representations for predictions. It provides insights into the dis... | Rebuttal 1:
Rebuttal: Thank you for the careful reading and useful feedback. We address the comments and questions below.
_Weakness 1_
Thank you for the suggestion. We have now demonstrated the generalizability of our method to all cases mentioned by the reviewer (see general response) and expanded our analyses to ot... | Summary: This work analyzes how Vision Transformers work by analyzing the representations of individual tokens (image patch representations) and how they evolve while passing through the layers of the network. The authors also show how to use their methods to devlop an interpretability method.
Strengths: Originality: ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and suggestions for improving the discussion of our findings. Below we address each of these in detail.
_Weakness 1_
Thank you for pointing out relevant previous work of [1]. We have added a reference in section 5.1 of our manuscript, stating in ... | Summary: Inspired by recent advancements in NLP, this paper introduces a novel framework designed to reverse engineer vision transformers for the purpose of image classification tasks. The framework focuses on analyzing the internal dynamics of Vision Transformers (ViTs) within the class-embedding space, revealing the ... | Rebuttal 1:
Rebuttal: Thank you for the positive remarks and suggestions for improving the clarity and generalizability of our work. Below we address these comments in detail.
_Weakness 1_
Thank you for the concrete suggestion on how to improve the description of our method. In our revised version of the paper, we ha... | Rebuttal 1:
Rebuttal: We thank the reviewers for feedback that helped improve our manuscript. In the general response, we address two common concerns about our work: we (1) report new experiments that provide evidence of the generalizability of our method, and (2) conceptually discuss and empirically demonstrate the ad... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper utilizes a pre-trained embedding matrix to elucidate the mechanism of Vision Transformers. By using the embedding matrix, inner representations at any layer can be investigated in class spaces. Specifically, it offers a visualization of how self-attention and MLP process class categorical informatio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback, which has helped clarify and better demonstrate the novelty, generalizability, and usefulness of our framework and findings.
_Weakness 1_:
Thank you for sharing relevant previous work which we now cite and discuss in the revised manuscript. We ... | null | null | null | null | null | null |
NPCL: Neural Processes for Uncertainty-Aware Continual Learning | Accept (poster) | Summary: This paper proposes a new method for Continual Learning (CL) through Neural Processes (NPs) framework. Especially, they are inspired by MTP [1] which utilizes the global latent and task-specific latent for inter-task and intra-task knowledge representation. In this paper, the authors additionally regularize on... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and remarks on our work. In regard to the questions raised, we provide our explanations below:
- **Motivation**: Our motivation to utilize NPs for CL is two-fold. First, based on lines 20-26, we aim to design parameter isolation methods for CL that ... | Summary: This paper suggests tackling continual learning (CL) with a neural process model (NP). The authors describe a hierarchical latent variable model, with one global latent, and t per-task latents. As is common in NPs, the latent posteriors are a function of a context of datapoints with their corresponding label... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and remarks on our work. In regard to the questions raised, we provide our explanations below:
- **Validity of NPs for CL**: We believe that the remark against the validity of our setting stems from a misunderstanding between the standard meta-learn... | Summary: This paper presents an uncertainty-aware continual learning framework that utilizes Neural Processes (NPs). The NP model employs a hierarchical latent variable model in conjunction with an experience replay buffer, where a global latent variable captures inter-task correlations and task-specific latents encode... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and remarks on our work. In regard to the questions raised, we provide our explanations below:
- **Choice of regularization metrics**: Thank you for your suggestion. We indeed consider KL-divergence as a possible alternative for our regularization m... | Summary: This paper introduces an uncertainty-aware continual learning framework based on neural processes. The proposed method casts CL into a hierarchical latent model from the global variables to the task-specific variables, and the corresponding regularization terms for each to ensure minimum forgetting along the c... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and remarks on our work. In regard to the questions raised, we provide our explanations below:
- **Writing, figures and referencing formats**: Thank you for pointing these out. We will be incorporating these into our paper.
- **CL Baselines**: Thank... | Rebuttal 1:
Rebuttal: We thank the reviewers for their careful comments and suggestions. Here we provide a single-page PDF that contains the figures and tables that we want the reviewers to see while considering our responses. We look forward to a helpful discussion period.
- **Effect of Monte Carlo samples on perform... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
$p$-value Adjustment for Monotonous, Unbiased, and Fast Clustering Comparison | Accept (poster) | Summary: p-value Adjustment. The p-value adjusted Rand Index is unbiased. The authors claim that its approximations outperform STD Mutual Information.
First, generalized MI relies on the TSALLIS entropy (same family of RENYI). AMIq comes from subtracting the expectation under random permutations. PMIq is derived and i... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and constructive feedback. We have taken all comments into consideration and summarize our response as follows:
1. **Experiments with segmented images or spectral clustering**
We conducted two additional experiments using spectral clustering, on an image ... | Summary: The article introduces a new measurement called $\text{PMI}_q$ for comparing clustering methods. The name "p-value adjustment" comes from $\text{PMI}_1$, which represents the p-value of the mutual information's variation. This new metric has desirable properties, including type II unbiasedness and monotonicity... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and constructive feedback. We have taken all comments into consideration and summarize our response as follows:
1. **Is $\\operatorname{PMI}\_q$ type I unbiased?**
Yes. Type I unbiased means that when you compare a fixed clustering $A$ to all permut... | Summary: The paper presents a performance measurement method for cluster analysis. The method can avoid Type II bias that exists in previous approaches. A tractable approximation is given. The proposed method demonstrates advantages in both synthetic and real-world data sets.
Strengths: The work consists of solid theo... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and constructive feedback. We have taken all comments into consideration and summarize our response as follows:
1. **Further explanation of Type II unbiasedness**
Type I bias means that certain cluster sizes receive higher metric values. Type II bia... | Summary: The paper introduces a new method called the p-value adjusted Rand Index (PMI2) for comparing clustering and community detection algorithms. The paper highlights the limitations of existing metrics, such as the Rand Index and Adjusted Rand Index, which suffer from bias and non-monotonicity issues. The PMI2 met... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and constructive feedback. We have taken all comments into consideration and summarize our response as follows:
1. **Further comparison to other methods**
We added a table comparing a total of 19 clustering comparison metrics (See attached PDF), mostly ad... | Rebuttal 1:
Rebuttal: We sincerely appreciate all reviewers’ time and efforts in reviewing our paper. We thank you all for the insightful and constructive suggestions, which helped further polish our paper. We attached a PDF with three improvements to our paper that were stimulated by the reviewers' comments:
- We add... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes an improved clustering comparison metric. While many current metrics fix a "type I" bias (being biased towards certain cluster size distributions), many still suffer from a "type II" bias (a bias towards certain clusterings when they are compared to a ground truth clustering). Building on to... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and constructive feedback. We have taken all comments into consideration and summarize our response as follows:
1. **Concern about practical significance**
- While technically the $\\operatorname{SMI}\_2$ was introduced in [1], the authors only provide th... | null | null | null | null | null | null |
AMDP: An Adaptive Detection Procedure for False Discovery Rate Control in High-Dimensional Mediation Analysis | Accept (spotlight) | Summary: This paper proposes a high-dimensional mediation analysis detection procedure with false discovery control. Classic FDR control for multiple testing does not use information across tests making it overly conservative. The authors propose a local FDR-based procedure for identifying mediators and a data-driven a... | Rebuttal 1:
Rebuttal: Thanks for your valuable suggestions. We will add more introduction and motivation in the revised version. Now we respond the comments raised by you point-by-point.
**Question 1:**
* What is local FDR?
**Response:** In the context of multiple hypothesis testing, the false discovery rate (FDR)... | Summary: This paper describes a method to control for false discoveries in high-dimensional mediation analysis. This is the problem of inferring whether any of a large set of possible mediator variables act as a mediator between an exposure variable and an outcome variable. This work is particularly interested in the a... | Rebuttal 1:
Rebuttal: **Question 1:**
* The presentation felt a bit compressed with many relevant details deferred to the appendix, such as the examples described in Remark 2 and many of the plots relating to the prostate cancer dataset.
**Response:** Thanks for you carefully to review our paper. We will make adjust... | Summary: The paper proposes and analyses a new method for FDR control.
Strengths: The paper is clear on the method, the assumptions and the proofs. The empirical section is promising and adequate.
Weaknesses: Some of the assumptions and the writing are not clear, or seem too strong to me. For example the assumption... | Rebuttal 1:
Rebuttal: We thank reviewer 9J5U for your comments and for citing your concerns. 9J5U’s main concern is that some assumptions in our paper seem too strong. We can provide some explanations.
Major questions:
**Question 1:**
* The reviewer concern about the assumption about "no confounding".
**Response:... | Summary: UPDATE
In light of the revisions the authors made I have raised my score and now support acceptance of this work. Thank you very much.
-----------------------------------------
The authors propose a procedure to increase statistical power to identify mediators while controlling the FDR in high-dimensional d... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful comments.
**Weaknesses 1：** Thanks for your reminder. Our method is designed to high-dimensional mediation analysis, but its performance may fail in the low-dimensional designs. We will rewrite our discussion section and incorporate the limitation in revised version to ... | Rebuttal 1:
Rebuttal: Thanks again for the comments. We present additional experimental results under sparse alternatives scenario and dense alternatives scenario with the FDR level of 0.05 in the PDF format.
Pdf: /pdf/c6d4f16413f2303c93719562e49f5b06b774cc9d.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
DensEMANN: How to Automatically Generate an Efficient while Compact DenseNet | Reject | Summary: In this paper, the authors propose an enhanced version of DensEMANN, which efficiently grows and trains small DenseNet architectures. They employ a macro-algorithm to expand new layers and utilize a micro-algorithm to construct new convolution operations. Through iterative layer growth, this method generates n... | Rebuttal 1:
Rebuttal: > The motivation behind this research requires additional clarification.
Our main motivation was to improve upon DensEMANN in order to make it reach its "full potential". More specifically, as stated in the introduction, we designed "a new version of this algorithm with the aim of approaching [i.... | Summary: The authors study an algorithm for neural architecture search (NAS) called DensEMANN, which uses a progressive adaptation of a DenseNet architecture during training to find an efficient neural network for the target task.
Strengths: The paper is very well written and the authors do a good job of explaining ho... | Rebuttal 1:
Rebuttal: > The primary contribution of the paper is comparison of DensEMANN with other NAS methods on CIFAR-10 in section 4.3. The results are certainly interesting, but I think the authors should focus on quality per unit time rather than quality and parameter counts plotted in Figure 2. Plotting quality ... | Summary: The paper presents a new version of DensEMANN, an algorithm for generating small and competitive DenseNet architectures with optimal weight values. The authors aim to approach state-of-the-art performance for well-known benchmarks, or at least the state-of-the-art Pareto front between performance and model si... | Rebuttal 1:
Rebuttal: > The paper does not provide a clear explanation of how the parameter limit of 500k was chosen for the experiments. Does going above these number of parameters make DensEMANN very computationally intensive or is unable to grow the network sensibly especially considering that 500k parameters in mod... | Summary: This paper proposes a new version of the existing DensEMANN, which grows small DenseNet architectures and trains them on target data. It claims that this version can quickly and efficiently search for small and competitive DenseNet architectures. The proposed approach has been evaluated on a number of benchmar... | Rebuttal 1:
Rebuttal: > How this approach performs on Imagenet?
We are currently testing DensEMANN on ImageNet1k. See the global rebuttal.
> How the FLOPs/latency of the discovered models comparing with state of the art?
In the below table we report the latency (in MFLOPs) of our discovered models for different data... | Rebuttal 1:
Rebuttal: ### Concerning larger datasets (in particular ImageNet1k):
* We have run extra tests on CIFAR-100.
The dataset split was identical to that of CIFAR-10: a training set of 45,000 random "training" images, a validation set of 5,000 random "training" images not already in the training set, a test s... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposed a new and improved algorithm to grow small DenseNet architecture from scratch while simultaneously training them from target data.
Strengths: 1. The paper is very clear and readable.
2. The evaluation is comprehensive and detailed, demonstrating the effectiveness.
Weaknesses: 1. The nove... | Rebuttal 1:
Rebuttal: > Please analyze what improvement can be brought by the differences in section 3.2.
1. Changes to the macro-algorithm:
* (a) The macro-algorithm's last layer addition can always be removed because it is in fact always useless–at least from an accuracy improvement point of view. Indeed, the ma... | null | null | null | null | null | null |
UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models | Accept (poster) | Summary: This paper propose a predictor-corrector method to accelerate the diffusion sampling process, where a corrector is proposed to correct the initial estimation of x_t using previous and current points. The experiments are conducted on imagenet and cifar10 both of which outperform existing efficient sampler at ve... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the positive comments on our work, especially the appreciation of our newly proposed unified framework UniPC, our adequate theoretical and empirical analysis, and our superior performance. We hope our work can open a new avenue for improving the sampling quality... | Summary: This paper develops a unified corrector (UniC) that can be applied after any existing DPM sampler to increase the order of accuracy without extra model evaluations, and derive a unified predictor (UniP) that supports arbitrary order as a byproduct. Combining UniP and UniC, a unified predictor-corrector framew... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the positive comments on our work! We address the questions and clarify the issues accordingly as described below.
**Q1: About model-agnostic**
**[Reply]** Sorry for the confusion. By “model-agnostic” we mean that (1): our UniC can be applied after any existin... | Summary: This paper presents a universal predictor-corrector method for faster sampling of diffusion models, with model retraining. The key idea is to further include the current point along with previous $p$ points while estimating the data point by adding a correction step. It shows that this model can achieve an ord... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments. We address the questions and clarify the issues accordingly as described below.
**Q1: About the novelty and design**
**[Reply]**
- As discussed in Section 3.1 and proved in Appendix E.3, our UniC can indeed increase the order of accuracy of the sampling p... | Summary: In this paper, the authors present a novel sampling solver called UniPC for diffusion models. UniPC consists of two parts: UniC and UniP, where UniC corrects the estimation with prediction of current timestep and it can be applied other sampling solvers and UniP is a special case of UniC and share the similar ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the positive comments on our work! We address the questions and clarify the issues accordingly as described below.
**Q1: About experiments on larger resolution images**
**[Reply]** Thanks for your advice. In our original paper, we have already conducted experi... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for the positive feedback and valuable comments on our work. As suggested by Reviewer vcSP, we further compare the sampling quality of our method UniPC and the baseline DPM-Solver++ using _**Stable-Diffusion-XL**_, a newly released model which can generate $1024\ti... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes an ODE sampler for diffusion probabilistic models
(DPM), exploiting the structure of exponential integrators. The paper
claims the proposed sampler can use any existing DPM and achieve high
sampling quality with very few (<10) number of function evaluations
(NFE), and also improve upon rel... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the positive comments on our work! We address the questions and clarify the issues accordingly as described below.
**Q1: About the statistical significance**
**[Reply]** Thanks for the advice. We consider the following hypothesis testing problem:
$$
H_0: M_{\r... | null | null | null | null | null | null |
Boosting Verification of Deep Reinforcement Learning via Piece-Wise Linear Decision Neural Networks | Accept (poster) | Summary: This paper proposes an inverse transform-then-train approach for verifying deep reinforcement learning systems. It encodes a DNN into efficiently verifiable linear control policies and optimizes them via reinforcement learning. The approach is compatible with existing DRL training algorithms and shows that PLD... | Rebuttal 1:
Rebuttal: **Question for the importance of contribution:** (i) It is more like a
synthesis technique, rather call it verification. (ii) Algorithm
compatibility of PLDNN.
**Response:** (i) Yes, it can be understood as a synthesis method for
developing verification-friendly models. We show that the powerful
... | Summary: The paper presents an approach for designing neural network policies, in the context of deep reinforcement learning (DRL) with continuous state and action spaces, that are more amenable to verification compared to standard networks. A standard approach to verifying continuous DRL systems is to abstract the sys... | Rebuttal 1:
Rebuttal: **Weaknesses 1:** (i) It is hard to make an assessment about PLDNNs in
settings where inputs are images. (ii) How the proposed approach would
be used when the action space is discrete?
**Response:** (i) Yes, applying PLDNNs when inputs are images is
difficult due to the high dimensionality. At pr... | Summary: The paper proposed a new neural network architecture, PLDNN, for better verification of DRL trained / neural-network controlled closed-loop controlled systems. PLDNN differs by abstracting the input space with intervals and applying linear mappings in each abstract states. The controller represented by PLDNN c... | Rebuttal 1:
Rebuttal: **Weaknesses 1:** My main concern of the paper is lack of analysis on
the policy of reducing the partitions. Though the experimental results
show strong results of PLDNN, it is not clear how partitioning plays a
role in the performance improvement. Some analysis on how number of
partitions evolves... | Summary: This paper presents an approach towards more easily verifiable DRL agent. Instead of training a neural network and then applying verification tools to it, the paper proposes to partition the input state, train linear policies in each of the partition, and verify the resulting piecewise-linear policy as a hybri... | Rebuttal 1:
Rebuttal: **Weaknesses 1:**\
(i) The observation that a relatively small set of linear functions is
sufficient to achieve comparable performance deserves a closer study.\
(ii) For the same input region where the PLDNN is linear, is the
behavior of the neural network also relatively linear?\
(iii) Are there ... | Rebuttal 1:
Rebuttal: ## Discussion on Limitations
We thank all the reviewers for the valuable feedback. We first briefly
discuss the main limitations of our method, as raised by all the
reviewers.
One limitation concerns a potential rapid increase in the number of
partitions when a preset reward threshold can never b... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Macro Placement by Wire-Mask-Guided Black-Box Optimization | Accept (poster) | Summary: This paper proposes a new black-box optimization framework, called WireMask-BBO, for macro placement, which is an important problem in the electronic design automation (EDA) community. By using different black-box optimization algorithms, The experiments show it can achieve improvements (shorter half-perimeter... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and suggestions. Below please find our response.
### Q1 Runtime comparison; scalability of WireMask-BBO; BO and EA not suitable for large-scale problems?
In our experiments, both the packing-based method SP-SA and our proposed WireMask-EA run for 1000 min... | Summary: This paper presents a framework using BBO for macro placement in VLSI designs. Any placement solution for a set of macros can be optimized using the wire masks (presented in a prior work using RL) where the optimization goal is to minimize the HPWL of the output. In addition to random inputs, the framework can... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. Below please find our response.
### Q1 Does not develop any new BBO algorithm.
We want to emphasize that our main contribution is introducing the general framework WireMask-BBO for macro placement, while not developing new BBO algorithms. WireMask-BBO can be... | Summary: The authors propose a new placement method that is based on the black-box framework. The framework leverages the wire mask-guided information and can achieve significant placement results compared with the state-of-the-art methods.
Strengths: 1. The novel method is based on black-box optimization, which has n... | Rebuttal 1:
Rebuttal: Thank you for your valuable and positive comments. Below please find our response.
### Q1 The full placement cannot surpass DREAMPlace.
In Table 6 of Appendix B.2, though DREAMPlace achieves the best HPWL in 3 out of 7 tested chips, our proposed WireMask-EA has significant improvements in four c... | Summary: This paper proposes a novel black-box optimization (BBO) framework, namely WireMask-BBO, for macro placement in chip design. Specifically, it devises a post-processing technique that legalizes any searched placement solution while optimizing the half-perimeter wirelength (HPWL). The post-processing technique a... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments. Below please find our response.
### Q1 The proposed method is heuristic; insufficient intuitive explanations.
We want to clarify our motivation of designing the proposed framework. To efficiently improve the HPWL of a solution and guarantee non-overlappi... | Rebuttal 1:
Rebuttal: ## General response
We are very grateful to the reviewers for carefully reviewing our paper and providing constructive comments and suggestions. We have revised the paper carefully according to the comments and suggestions, but we cannot upload the paper due to the NeurIPS' rule this year. Our re... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Computing Approximate $\ell_p$ Sensitivities | Accept (poster) | Summary: The authors propose randomized algorithms to approximate $\ell_p$ sensitivity functions for $p \in [1,\infty)$, which extend leverage scores beyond the $\ell_2$ norm. The functions they consider are: 1. Estimating all sensitivities, 2. Estimating total sensitivity, and 3. Estimating maximum sensitivity. They p... | Rebuttal 1:
Rebuttal: We are very grateful to the reviewer for their time, effort, and feedback. We are encouraged that they found the problem we study important and our algorithm and techniques novel. We are also very grateful for all the weaknesses and typos pointed out and questions raised, and we will clarify all t... | Summary: The paper gives algorithms to compute, approximately, the individual sensitivity scores, total sensitivity and maximum sensitivity, for $\ell_p $ norms. The approximation for the individual sensitivity scores is additive while a relative error approximation is obtained for both total and maximum sensitivity. C... | Rebuttal 1:
Rebuttal: We are extremely thankful to the reviewer for raising a very thorough set of questions and comments and will incorporate the clarifications for each of them in our manuscript. We are encouraged by the reviewer’s assessment of our work as important and interesting for the community. We address belo... | Summary: This paper proposes a randomized algorithm for efficiently approximating the $\ell_p$ sensitivities, with a constant approximation parameter guaranteed.
Strengths: This paper presents several novel randomized algorithms for approximating $\ell_p$ sensitivities and related statistics, based on two key ideas:... | Rebuttal 1:
Rebuttal: We are very grateful to the reviewer for their time spent reviewing our submission and are encouraged by their positive assessment of the motivation of our work, our theory, and our experiments.
---------------
In response to the reviewer’s question: Yes, thank you for pointing out the typo in ... | Summary: Given a matrix $A \in \mathbb{R}^{n \times d}$ and $p \in (0, \infty)$, the $l_p$ sensitivity of a vector $a \in \mathbb{R}^d$ with respect to $A$ is defined as $\sigma_p(a) := \max_x |a^\top x|^p / |Ax|_p^p$. It is known that by sampling each row $a_i$ of $A$ with probability proportional to (an upper bound o... | Rebuttal 1:
Rebuttal: We are deeply grateful to the reviewer for their careful reading of our manuscript and for raising very thoughtful questions, which we respond to below.
-----------------
### ”Usefulness of Theorem 1.2”: Runtime ###
**We would like to clarify that our actual runtime is significantly better than... | Rebuttal 1:
Rebuttal: # Top-Level Response #
We thank all the reviewers for their time, effort, and suggestions. Here, we restate some of our key contributions and answer common questions. We also reply to each reviewer individually.
---
## Motivating the problem ##
### Why sensitivities? ###
A common preprocessing... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Global-correlated 3D-decoupling Transformer for Clothed Avatar Reconstruction | Accept (poster) | Summary: This paper aims at reconstructing 3D clothed human models from single images. Current methods rely heavily on 2D image features extracted from the input image, while ignoring information lying in the planes orthogonal to the input image plane. To address this limitation, this paper proposes a new method that u... | Rebuttal 1:
Rebuttal: We deeply appreciate your recognition of the valuable insight behind our method and its potential to inspire future research.
We will address your inquiries and concerns point by point in the following responses.
- **Answer to weakness 1**: Thank you for your keen observation. Because similar... | Summary: This paper proposes a new method for the task of single-view 3D human reconstruction. The main idea is to extract 3D tri-plane features from the input image using a transformer-based architecture, instead of using CNN to extract 2D pixel-aligned features as done in previous works. The experiments demonstrate t... | Rebuttal 1:
Rebuttal: We are grateful for your recognition of the technical soundness of our method and the effectiveness of our design choices. Your acknowledgment of our convincing results and well-structured presentation is highly appreciated.
We will address your inquiries and concerns point by point in the follo... | Summary: This paper presents a single image human reconstruction method. Different from previous pixel-aligned 2D features, authors propose to extend the 2D features to 3D features using triplane. Moreover, SMPL prior is introduced to enhance the extracted features. Qualitative and quantitative experiments are thorough... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive feedback on the clarity of our paper and the effectiveness of our 3D-decoupling decoder. Your recognition of our thorough and compelling experimental results is highly valued. We are grateful for your comments.
We will address your inquiries and concerns po... | Summary: The authors propose to reconstruct a clothed 3D human avatar from a single 2D image by introducing a global-correlated 3D-decoupling transformer to disentangle tri-plane features. A hybrid prior fusion strategy in the feature query phase is introduced to combine the spatial query’s localization capabilities wi... | Rebuttal 1:
Rebuttal: We are truly grateful for your recognition of the novelty in our approach of introducing a global-correlated 3D-decoupling transformer. Your acknowledgment of our comprehensive and sound experimental comparisons is highly appreciated.
We will address your inquiries and concerns point by point... | Rebuttal 1:
Rebuttal: - **Comparison of inference time:**
1. **Table 1(b) in the attached PDF displays comparable time efficiency of our implicit function-based model with PIFu, Pamir, and ICON.** In contrast, ECON, relying on explicit methods, demands more time due to d-BiNI and Poisson inefficiencies. CAPE-NFP da... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The proposed system extracts global features with a 3D transformer backbone for tri-plane feature-based 3D human reconstruction. Unlike existing methods that mainly rely on 2D CNN pixel-aligned features, this paper is the first one to use Vision Transformers for decoupling the 3D tri-plane features for refined... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive remarks on our paper's presentation and the recognition of our method's effectiveness in addressing existing limitations. Thank you very much for your valuable comments!
We will address your inquiries and concerns point by point in the following responses.
-... | null | null | null | null | null | null |
Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training | Accept (poster) | Summary: The paper conducts an extensive empirical study including several popular pruning criteria and analyzes their impact on the DST framework on diverse models. They found that within a stable DST hyperparameter setup, the majority of the studied criteria
perform similarly, regardless of the model architecture and... | Rebuttal 1:
Rebuttal: **Response Reviewer 3Q28**
We are grateful to the Reviewer for the thoughtful comments and feedback. We are pleased to hear the reviewer considered our research questions well-examined by the extensive experiments we performed. We are also thrilled to see that the Reviewer found our experiments o... | Summary: This paper performs a systematic study of pruning strategies for dynamic sparse training (DST) methods, comparing their performance and structural decisions across backbone architectures, datasets, structural change frequencies, connection densities, and batch sizes.
Strengths: Originality: This work is the f... | Rebuttal 1:
Rebuttal: **Response to Reviewer mfmv**
We thank the Reviewer for the review. We are very happy to see that the Reviewer appreciates the originality and significance of our work, recognizing it as the first systematic study on pruning criteria in DST methods. Furthermore, we are glad to hear our experiment... | Summary: This paper does a large scale study of dynamic sparse training, primarily on vision datasets. For a variety of models and hyperparameter settings, pruning using magnitude is found to be as or more effective when compared to pruning using more complex criteria proposed in the literature. The performance finding... | Rebuttal 1:
Rebuttal: **Response to Reviewer xwEQ**
We appreciate the feedback from the Reviewer and the positive comments about both the originality and the value of our work. We are happy to see that the Reviewer finds our experiments well-designed and supportive of our main claims. We were also glad to learn that t... | Summary: This empirical paper looks into the setting of adapting neural network architecture during training (dynamic sparse training), repeatedly pruning and growing the network. Authors try to draw conclusions about the performance and topology of various pruning criteria. They show that in high density regimes, most... | Rebuttal 1:
Rebuttal: **Response to Reviewer aJDG**
We are grateful for the Reviewer’s feedback. We are happy to hear that the Reviewer finds that the questions we ask in our study have not been investigated before. We are also pleased to learn that our study has been recognized as rigorous and well-executed by the Re... | Rebuttal 1:
Rebuttal: **JOINT RESPONSE**
We thank all the Reviewers for the time and effort taken to provide valuable insights and comments on our work. We are very glad that our research has been recognized as useful and beneficial to the community (Reviewers Q7vN, xwEQ, mfmv). Moreover, Reviewers xwEQ and mfmv appre... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper provides a comparison of several dynamic sparse training methods and conclude that they are perform similar unless in the ultra-sparse regime, in which case the magnitude-based pruning performs best.
Strengths: The comparison is through and useful for new researchers in this field.
Weaknesses: Thi... | Rebuttal 1:
Rebuttal: **Response to Reviewer Q7vN**
We thank the Reviewer for the feedback. We are happy to hear that the Reviewer finds our experiments thorough and sees the usefulness of our research. If we understand correctly, the Reviewer’s only concern is in the novelty of the paper.
We kindly request the Revie... | null | null | null | null | null | null |
Iteratively Learn Diverse Strategies with State Distance Information | Accept (poster) | Summary: Reinforcement Learning (RL) algorithms commonly learn a distinct policy that is responsible for a distinct behavior. Learning different, diverse behaviors generally is a difficult task in RL. This paper proposes a new algorithm State-based Intrinsic-reward Policy Optimization (SIPO) that can learn diverse, hum... | Rebuttal 1:
Rebuttal: + there are several other works that should be mentioned as they are using different objectives and hence differently measure the similarity/dissimilarity
We appreciate the reviewer's insightful suggestion and ensure that we will incorporate these relevant works into our paper. The reviewer’s ad... | Summary: This paper addresses the problem of learning diverse policies for a given RL task. First, it studies pros and cons of various formulations in two different dimensions: (i) How to measure diversity, for which it considers measures based on action or state distribution and state distances, (ii) how to compute th... | Rebuttal 1:
Rebuttal: + motivation
Our primary aim is to achieve diversity itself. Agents with similar reward functions can manifest significantly diverse behaviors (e.g., high-reward unexpected behavior[1]). This property is different from standard DL where different local optima (almost) suggest the same results (i.... | Summary: The paper discusses the challenge of optimizing rewards while discovering diverse strategies in complex reinforcement learning problems. This paper examines two design choices for tackling this challenge: diversity measure and computation framework. By incorporating state-space distance information into the di... | Rebuttal 1:
Rebuttal: + Some SOTA diversity enhancement methods in RL and MARL are not analyzed or summarized.
We sincerely thank the reviewer for providing additional relevant literature, but we believe that our paper does well in contrasting against previous methods for developing diverse policies in RL.
[1] and [2... | Summary: This work proposes a solution to the problem of finding diverse policies for complex (multi-agent) reinforcement learning (RL) environments. The paper is presented as a joint study on diversity metrics and learning frameworks. For the former, the authors show the limitations of common diversity metrics like ac... | Rebuttal 1:
Rebuttal: + I would also like to see at least some classic total-reward metrics / comparisons
Appendix B.5 elaborates on the detailed returns accomplished by SIPO. We present the diversity score and average rewards achieved by all algorithms below. These numerical values are averaged across the entire popu... | Rebuttal 1:
Rebuttal: We express our sincere gratitude to the reviewer for their meticulous examination and thoughtful feedback on our manuscript. We response to each reviewer's question in the corresponding channels. Please feel free to drop a message if you have additional concerns.
We acknowledge a typo in the figu... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Test-Time Amendment with a Coarse Classifier for Fine-Grained Classification | Accept (poster) | Summary: The paper considers the problem of fine-grained classification and proposes to use label hierarchy information at test time to improve the performance of the fine-grained classifier. The overarching goal is to improve the top-1 accuracy while at the same time reducing the severity of the mistakes (e.g., miscla... | Rebuttal 1:
Rebuttal: Thank you for your constructive and thoughtful comments. They were indeed helpful to improve the paper. We take this opportunity to address your concerns:
* **Q1: Clarify how you formally derive the intuition for the decision rule defined in Eqs. (2) and (3)?** We agree with your comments and we ... | Summary: This method introduces a novel approach to achieve state-of-the-art fine-grained image classification. It addresses the issue of mistake severity by developing the Hierarchical Ensemble (HiE) loss, which effectively penalizes incorrect predictions of both course and fine-grained labels. The HiE loss combines t... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their positive feedback.
**Evaluation Metrics:** Thank you for the suggestion. We will update the paper to discuss the evaluation metrics in more detail and make it self-contained. | Summary: This paper proposes a novel approach called Hierarchical Ensembles (HiE) to improve the performance of fine-grained classification by utilizing a label hierarchy and coarse-grained predictions at test-time. The method significantly reduces mistake severity while improving top-1 accuracy on benchmark datasets, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback and would like to address their concerns below.
**Typo on lines 242-244:** Thank you for pointing out this inconsistency. We will update the paper to fix this. It should be semi-supervised on lines 242 and 244.
**Overall Innovation of the wor... | Summary: This paper focuses on label hierarchy problems and proposes using Hierarchical Ensembles (HiE) of independently trained networks over coarse and fine-grained levels. The reported experimental results show that the proposed method can achieve comparable performance to a fully supervised baseline, even using mer... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback. We address the reviewer's concerns and questions below:
**Motivation of the paper:** The primary motivation behind the paper is that training independent coarse and fine-grained help to learn complimentary features. We provide the thought process... | Rebuttal 1:
Rebuttal: We describe the steps clarifying the derivation of Eqn 1 in the paper. We use slightly different notations for the sake of improved clarity.
> (X)
> / \
> / \
> / \
> (C) (F)
> Graphical model for separate classifiers trained on coarse and ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning Topology-Agnostic EEG Representations with Geometry-Aware Modeling | Accept (poster) | Summary: The work proposes a cross-dataset cross-electrode montage deep-learning pretraining method. As a model, they use a transformer that uses the electrode coordinates as positional encodings. The transformer model then processes differential entropy features per electrode as tokens with attention across all electr... | Rebuttal 1:
Rebuttal: Thanks for your comments and suggestions, which are valuable for enhancing our paper. The following are responses to individual concerns:
1. **Random seeds (W1)**: We follow the previous work [1,2] to perform subject-dependent classification with five random seeds to reduce the effects of random n... | Summary: This paper aims to provide new architecture that perform pre-training for scalp EEG to utilize the large-scale unlabelled data. However, one of the challenge of performing such pre-training is the different sampling channels selection & inherent structural and spatial information across different EEG datasets.... | Rebuttal 1:
Rebuttal: Thanks for your comments and suggestions, which are valuable for enhancing our paper. We feel excited that our paper provided a pleasant reading experience and that our contributions are well recognized.
Responses to your concerns and questions are hereby presented:
1. **Discussion on more transf... | Summary: The paper proposed an innovative approach to the pre-training of models for the EEG data. Large-scale pre-training which demonstrated great potential in CV and NLP requires a substantial amount of data. While EEG data is relatively easy to collect, their interpretation and labelling often require substantial e... | Rebuttal 1:
Rebuttal: We appreciate your comments and suggestions that truly enhanced the quality of our paper. We are cheerful that we have provided you with a pleasant reading experience, and our contribution is well recognized.
Responses to your concerns are presented as follows:
1. **Text size of Figure 1. (W1)**:... | Summary: This work seeks to improve classification tasks over EEG brain data using pre-training. There exist many EEG datasets, but not all datasets use the same montage format. To leverage all the datasets together, this work presents an approach for learning generic representations of EEG data. This approach proceeds... | Rebuttal 1:
Rebuttal: Thanks for your valuable suggestions, which help us enhance the clarity of this paper and make it more understandable for the wider community. We reviewed your suggestions, and our manuscript to ensure all typos, vague descriptions, and unclear settings we can find (W1, W5, Q4, Q6, Q8) are properl... | Rebuttal 1:
Rebuttal: Global response:
We're cheerful that the reviewers found our method novel (Reviewer rN6e, JXz6, JKwr), well motivated and reasonable (Reviewer tt92, JXz6, JKwr, v1Fw), and important (all reviewers). We're also delighted that reviewers feel reading our manuscript is pleasant (JXz6), joyful (JKwr), ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper explores the application of large-scale pre-training techniques to scalp electroencephalogram (EEG) data. They leverage the abundance of unlabeled EEG data and address challenges related to sampling channel selection, structural information, and spatial information. To enable cross-dataset EEG pre-t... | Rebuttal 1:
Rebuttal: We appreciate your comments and suggestions that truly enhanced the quality of our paper.
1. **Cross-task challenge (W1, Q1)**: We have handled the cross-montage problem. But it is still non-trivial to do broader cross-task transitions because of challenges such as inconsistent time scope, task-s... | null | null | null | null | null | null |
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