paper_id stringlengths 19 21 | paper_title stringlengths 8 170 | paper_abstract stringlengths 8 5.01k | paper_acceptance stringclasses 18
values | meta_review stringlengths 29 10k | label stringclasses 3
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nips_2022_Bqk9c0wBNrZ | Semi-Parametric Neural Image Synthesis | Novel architectures have recently improved generative image synthesis leading to excellent visual quality in various tasks. Much of this success is due to the scalability of these architectures and hence caused by a dramatic increase in model complexity and in the computational resources invested in training these mode... | Accept | This paper tackles the general image synthesis problem (unconditional, conditional, text-guided) using a semi-parametric manner. It first retrieves relevant samples from external dataset, and use them as additional conditions for image generation. It is verified with different image synthesis frameworks, e.g. Diffusion... | train | [
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" Thank you for raising the score, we are pleased to see that the reviewer is satisfied with our answers .\nHere are two further clarifications:\n\n**Size of databas... | [
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nips_2022_Yc4MjP2Mnob | Recommender Forest for Efficient Retrieval | Recommender systems (RS) have to select the top-N items from a massive item set. For the sake of efficient recommendation, RS usually represents user and item as latent embeddings, and relies on approximate nearest neighbour search (ANNs) to retrieve the recommendation result. Despite the reduction of running time, the... | Accept | The paper introduces a method for top-n item recommendation based on approximate nearest neighbor search (ANN). The authors formulate ANN as a sequence to sequence problem, the input being the user profile and activity, and the output being the top-n recommendations. The focus of the paper is on the computational effic... | train | [
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" Thanks for clarifying the experimental settings. I would like to raise my evaluation score and vote for acceptance on this work.",
" Thanks for your approval of our work and insightful suggestions.\n\n... | [
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nips_2022_lkrnoLxX1Do | Self-Supervised Image Restoration with Blurry and Noisy Pairs | When taking photos under an environment with insufficient light, the exposure time and the sensor gain usually require to be carefully chosen to obtain images with satisfying visual quality. For example, the images with high ISO usually have inescapable noise, while the long-exposure ones may be blurry due to camera sh... | Accept | All three reviewers voted to accept the paper, and the detailed rebuttals from the authors helped to clarify reviewers' original concerns. One remaining concern from one of the reviewers is whether this method should be referred to as "self-supervised". However, authors clarified that it is reasonable to consider this... | test | [
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nips_2022_dO11Niyc225 | A Non-asymptotic Analysis of Non-parametric Temporal-Difference Learning | Temporal-difference learning is a popular algorithm for policy evaluation. In this paper, we study the convergence of the regularized non-parametric TD(0) algorithm, in both the independent and Markovian observation settings. In particular, when TD is performed in a universal reproducing kernel Hilbert space (RKHS), we... | Accept | The paper studies the convergence of non-parametric temporal-difference learning in the non-asymptotic regime. All referees agree that the paper is technical sound and the result is important to further our theoretical understanding of reinforcement learning. The paper merits acceptance to the conference. | train | [
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nips_2022__zPG0ShaZTc | The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes | Convolutional neural networks were the standard for solving many computer vision tasks until recently, when Transformers of MLP-based architectures have started to show competitive performance. These architectures typically have a vast number of weights and need to be trained on massive datasets; hence, they are not su... | Accept | The paper shows that using final fully-connected layers helps the generalization of convolutional neural networks in low-data regimes. The addition of these layers significantly improves model quality resulting in a network with the same number of parameters and better generalization performance.
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nips_2022_rUc8peDIM45 | The alignment property of SGD noise and how it helps select flat minima: A stability analysis | The phenomenon that stochastic gradient descent (SGD) favors flat minima has played a critical role in understanding the implicit regularization of SGD. In this paper, we provide an explanation of this striking phenomenon by relating the particular noise structure of SGD to its \emph{linear stability} (Wu et al., 2... | Accept | The paper investigates an important topic of why SGD converges to flat minima.
Overall the reviewers felt that this is a nicely written paper with a nice contribution to the state of the art.
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nips_2022_DSoFfnmUSjS | Recommender Transformers with Behavior Pathways | Sequential recommendation requires the recommender to capture the evolving behavior characteristics from logged user behavior data for accurate recommendations. However, user behavior sequences are viewed as a script with multiple ongoing threads intertwined. We find that only a small set of pivotal behaviors can be ev... | Reject | This paper presents Recommender Transformer (RETR) with a pathway attention mechanism that can dynamically zeroing-out the interactions (e.g., the trivial/noisy ones) in transformer-based sequential recommender systems. Extensive experimental results demonstrate the effectiveness of the proposed architecture.
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nips_2022_VYYf6S67pQc | Mildly Conservative Q-Learning for Offline Reinforcement Learning | Offline reinforcement learning (RL) defines the task of learning from a static logged dataset without continually interacting with the environment. The distribution shift between the learned policy and the behavior policy makes it necessary for the value function to stay conservative such that out-of-distribution (OOD)... | Accept | All reviewers are generally positive or borderline about this paper. Reviewer's note that the method is theoretically sound and practical to implement. Even though all of the components have been explored previously, the authors combine them in a novel approach that convincingly improves over prior works.
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nips_2022_kOIaB1hzaLe | Contrastive Neural Ratio Estimation | Likelihood-to-evidence ratio estimation is usually cast as either a binary (NRE-A) or a multiclass (NRE-B) classification task. In contrast to the binary classification framework, the current formulation of the multiclass version has an intrinsic and unknown bias term, making otherwise informative diagnostics unreliabl... | Accept | The three reviewers agreed that the work is a valuable contribution to its field, and presents extensive experiments.
For the readers' benefit, I kindly ask the authors to take into account reviewers comments while preparing the camera-ready version.
In particular, the revised version should include:
- the updated ... | train | [
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nips_2022_QotmVXC-8T | Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging | Decentralized optimization is increasingly popular in machine learning for its scalability and efficiency. Intuitively, it should also provide better privacy guarantees, as nodes only observe the messages sent by their neighbors in the network graph. But formalizing and quantifying this gain is challenging: existing re... | Accept | The paper eventually received a perfectly consistent evaluation from all the reviewers (4 times "accept"), so I can only recommend the acceptance. | test | [
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nips_2022_IPcgkUgw3t1 | UniGAN: Reducing Mode Collapse in GANs using a Uniform Generator | Despite the significant progress that has been made in the training of Generative Adversarial Networks (GANs), the mode collapse problem remains a major challenge in training GANs, which refers to a lack of diversity in generative samples. In this paper, we propose a new type of generative diversity named uniform diver... | Accept | This paper proposes UniGAN to alleviate mode collapse in GANs. They encourage the uniform distribution by arguing that samples on the manifold are equally accepted as real samples for training GANs.
The paper is comprehensive in both theory and experimental results. It receives average rating score 6, leading to an `... | train | [
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nips_2022_1WZyphXPLwC | Split-kl and PAC-Bayes-split-kl Inequalities for Ternary Random Variables | We present a new concentration of measure inequality for sums of independent bounded random variables, which we name a split-kl inequality. The inequality combines the combinatorial power of the kl inequality with ability to exploit low variance. While for Bernoulli random variables the kl inequality is tighter than th... | Accept | This meta review is based on the reviews, the authors rebuttal and the discussion with the reviewers, and ultimately my own judgement on the paper. There was a consensus that the paper contributes an interesting new concentration of measure inequality and derive a useful PAC-Bayes inequality. I feel this work deserves ... | train | [
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nips_2022_CTqkruS5Bb | Unsupervised Object Detection Pretraining with Joint Object Priors Generation and Detector Learning | Unsupervised pretraining methods for object detection aim to learn object discrimination and localization ability from large amounts of images. Typically, recent works design pretext tasks that supervise the detector to predict the defined object priors. They normally leverage heuristic methods to produce object priors... | Accept | The paper received mixed reviews. Three reviewers rated borderline accept and one reviewer rated borderline reject. The authors provided detailed responses to the raised concerns/questions and supported their responses with additional ablation study, experimental result on new dataset (e.g., VOC).
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nips_2022_2EufPS5ABlJ | Spherical Sliced-Wasserstein | Many variants of the Wasserstein distance have been introduced to reduce its original computational burden. In particular the Sliced-Wasserstein distance (SW), which leverages one-dimensional projections for which a closed-form solution of the Wasserstein distance is available, has received a lot of interest. Yet, it i... | Reject | This paper has generated a long discussion and although it has strong theoretical merits, we all concord that the paper lacks of empirical motivations as well as a strong empirical evaluations with respect to distance distributions not exploiting manifold sructure and thosed define on a manifold. Hence, we believe tha... | test | [
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nips_2022_xvLWypz8p8 | On Margins and Generalisation for Voting Classifiers | We study the generalisation properties of majority voting on finite ensembles of classifiers, proving margin-based generalisation bounds via the PAC-Bayes theory. These provide state-of-the-art guarantees on a number of classification tasks. Our central results leverage the Dirichlet posteriors studied recently by Zant... | Accept | All reviewers uniformly agree on the paper being interesting and worth publishing -- a very fine read. While the authors have already uploaded an updated version of their paper with minor revisions, I encourage them to use the camera-ready version to carry further improvements taking into accounts all reviews.
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nips_2022_LC1jyMUalIA | Transferring Textual Knowledge for Visual Recognition | Transferring knowledge from task-agnostic pre-trained deep models for downstream tasks is an important topic in computer vision research. Along with the growth of computational capacity, we now have open-source Vision-Language pre-trained models in large scales of the model architecture and amount of data. In this stud... | Reject | The paper aims to study the idea of transferring textual knowledge from vision-language pertained models to visual recognition or specifically the adaption of CLIP for downstream visual recognition tasks. The authors proposed to revise the role of the linear classifier and replace the classifier with the embedded langu... | train | [
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nips_2022_NL05_JGVg99 | Open-Ended Reinforcement Learning with Neural Reward Functions | Inspired by the great success of unsupervised learning in Computer Vision and Natural Language Processing, the Reinforcement Learning community has recently started to focus more on unsupervised discovery of skills. Most current approaches, like DIAYN or DADS, optimize some form of mutual information objective. We prop... | Accept | After a strong rebuttal from the authors and an extensive discussion among the reviewers, I believe the paper's pros outweigh its cons and this paper will be a valuable contribution to NeurIPS. I recommend it for acceptance and encourage the authors to address the reviewers comments for the camera-ready version of the ... | train | [
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nips_2022_AdK9_GTEvG | LeRaC: Learning Rate Curriculum | Most curriculum learning methods require an approach to sort the data samples by difficulty, which is often cumbersome to perform. In this work, we propose a novel curriculum learning approach termed Learning Rate Curriculum (LeRaC), which leverages the use of a different learning rate for each layer of a neural networ... | Reject | The paper proposes a model-level curriculum learning strategy, which assigns higher initial learning rates to shallow layers than deep ones and continues increasing all learning rates until they reach the same value during the training process. It is a model- and task-agnostic approach.
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nips_2022_pcgMNVhRslj | Alignment-guided Temporal Attention for Video Action Recognition | Temporal modeling is crucial for various video learning tasks. Most recent approaches employ either factorized (2D+1D) or joint (3D) spatial-temporal operations to extract temporal contexts from the input frames. While the former is more efficient in computation, the latter often obtains better performance. In this pap... | Accept | Paper was reviewed by four reviewers, receiving: 2 x Borderline Rejects and 2 x Weak Accepts. Importantly, post rebuttal, [1mVh] mentioned upgrading the rating from Borderline Reject to Borderline Accept (though this is not reflected in final ratings). The general concerns raised by the reviewers included, limited impr... | train | [
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nips_2022_WyQAmQ8WIU | SlateFree: a Model-Free Decomposition for Reinforcement Learning with Slate Actions | We consider the problem of sequential recommendations, where at each step an agent proposes some slate of $N$ distinct items to a user from a much larger catalog of size $K>>N$. The user has unknown preferences towards the recommendations and the agent takes sequential actions that optimise (in our case minimise) some ... | Reject | This paper considers reinforcement learning with unordered slate recommendations and shows that this problem can be decomposed into one Q-value per available item as compared to one value per possible slate in existing work. The authors derive a Bellman equation for this formulation and propose model-free algorithms ba... | train | [
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nips_2022_zzDrPqn57DL | BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework | Fusing the camera and LiDAR information has become a de-facto standard for 3D object detection tasks. Current methods rely on point clouds from the LiDAR sensor as queries to leverage the feature from the image space. However, people discovered that this underlying assumption makes the current fusion framework infeasib... | Accept | The paper proposes a method to fuse two sources of information for Bird’s Eye View (BEV) detection, namely multi-view images and LIDAR data, in a way that any data defects in one source of information does not affect the other. Most existing camera-lidar fusion works decorate lidar points with image features and then p... | train | [
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" #### `Q6: Can you please provide results on at least one more dataset with high quality Lidar such as the Waymo Open Dataset?`\nA6: Thanks for your suggestion. We train BEVFusion equipped with PointPillars as LiDAR stream on WaymoD5-3classes and it barely improves the baseline. Due to the time constraints of rebu... | [
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nips_2022_-V1ITIKPH6 | Active Learning for Multiple Target Models | We describe and explore a novel setting of active learning (AL), where there are multiple target models to be learned simultaneously. In many real applications, the machine learning system is required to be deployed on diverse devices with varying computational resources (e.g., workstation, mobile phone, edge devices, ... | Accept | This paper studies a novel active learning setting adapted to learning multiple target model. The authors propose a setting that can benefit to all tasks by focusing on regions with high disagreements. This contribution shows in a sense that the active learning procedure can be transferable to multiple tasks. A theoret... | train | [
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nips_2022_bZzS_kkJes | Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence | Existing pipelines of semantic correspondence commonly include extracting high-level semantic features for the invariance against intra-class variations and background clutters. This architecture, however, inevitably results in a low-resolution matching field that additionally requires an ad-hoc interpolation process a... | Accept | The paper concerns itself with computing high resolution matchings. The authors propose to use represent matchings as maxima of neural "matching" fields, which is a novel and interesting theoretical contribution that allows to obtain high resolution matchings with fixed representation size of the neural field. The matc... | train | [
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" Dear reviewers,\n\nSince the rebuttal discussion is about to end soon, if there is any other concern that we did not adequately address or is not resolved, please let us know, and we will come back to you as soon as possible if we can. \n\nThank you and best regards,\n\nThe authors of Paper 2300.\n",
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nips_2022_ReB7CCByD6U | Beyond Mahalanobis Distance for Textual OOD Detection | As the number of AI systems keeps growing, it is fundamental to implement and develop efficient control mechanisms to ensure the safe and proper functioning of machine learning (ML) systems. Reliable out-of-distribution (OOD) detection aims to detect test samples that are statistically far from the training distributio... | Accept | The paper proposes a out-of-distribution detection approach using integrated rank weighted (IRW). Its main novel feature is leveraging the information from all layers of the model for this task. The detector can be applied to new transformer models without any training, as opposed to data-driven methods. The method is ... | train | [
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nips_2022_KWN3I1koJsU | Learning Generalizable Risk-Sensitive Policies to Coordinate in Decentralized Multi-Agent General-Sum Games | While various multi-agent reinforcement learning methods have been proposed in cooperative settings, few works investigate how self-interested learning agents achieve mutual coordination in decentralized general-sum games and generalize pre-trained policies to non-cooperative opponents during execution. In this paper, ... | Reject | The paper presents a novel approach for improving coordination in general-sum games by using risk-sensitive policies based on distributional RL. While the idea is promising, there are significant questions about the paper.
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nips_2022_T7114JzrwB | ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference Time | Humans have the remarkable ability to recognize and acquire novel visual concepts in a zero-shot manner. Given a high-level, symbolic description of a novel concept in terms of previously learned visual concepts and their relations, humans can recognize novel concepts without seeing any examples. Moreover, they can acq... | Accept | The focus of this work is on the introduction of a compositional reasoning model that enables zero-shot generalization. While there are a number of limitations (e.g. the small domain, limited concepts) but reviewers were content that the demonstrated results on low-resolution image domains proved the approach can scale... | train | [
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nips_2022_xvZtgp5wyYT | Learning to Accelerate Partial Differential Equations via Latent Global Evolution | Simulating the time evolution of Partial Differential Equations (PDEs) of large-scale systems is crucial in many scientific and engineering domains such as fluid dynamics, weather forecasting and their inverse optimization problems. However, both classical solvers and recent deep learning-based surrogate models are typ... | Accept | The paper presents a new method for accelerating the simulation and inverse optimization of partial differential equations (PDEs) of large-scale systems. The proposed approach learns the evolution of dynamics in a “global” latent space (i.e., with fixed dimensionality). The reviewers agree the proposed approach is nove... | train | [
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nips_2022_dcmp81De77k | Localized Curvature-based Combinatorial Subgraph Sampling for Large-scale Graphs | This paper introduces a subgraph sampling method based on curvature to train large-scale graphs via mini-batch training. Owing to the difficulty in sampling globally optimal subgraphs from large graphs, we sample the subgraphs to minimize the distributional metric with combinatorial sampling. In particular, we define a... | Reject | The majority reviewers consider that this paper should be rejected. Their concerns include clarity of presentation, a comparison to previous work and finally a number of individual points which were not addressed in the rebuttal period.
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nips_2022_JY6fLgR8Yq | Graph Self-supervised Learning with Accurate Discrepancy Learning | Self-supervised learning of graph neural networks (GNNs) aims to learn an accurate representation of the graphs in an unsupervised manner, to obtain transferable representations of them for diverse downstream tasks. Predictive learning and contrastive learning are the two most prevalent approaches for graph self-superv... | Accept | This paper proposes a novel self-supervised learning strategy by considering the quantitative discrepancy of two perturbed graphs, which is measured by graph edit distance. The major concerns come from the motivation of the proposed approach. This has been well addressed in authors’ rebuttal, with additional new experi... | train | [
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nips_2022_EQgPNPwREa | Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints | Distributionally robust optimization (DRO) has been shown to offer a principled way to regularize learning models. In this paper, we find that Tikhonov regularization is distributionally robust in an optimal transport sense (i.e. if an adversary chooses distributions in a suitable optimal transport neighborhood of the ... | Accept | This work focuses on robust stochastic optimization (under a Wasserstein constraint), and shows the efficiency of Tikhonov regularization for this problem.
There has been a lively and constructive discussion between authors and reviewers, and ultimately all agree that this work should be accepted, and so do I. | train | [
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nips_2022_4maAiUt0A4 | Boosting Out-of-distribution Detection with Typical Features | Out-of-distribution (OOD) detection is a critical task for ensuring the reliability and safety of deep neural networks in real-world scenarios. Different from most previous OOD detection methods that focus on designing OOD scores or introducing diverse outlier examples to retrain the model, we delve into the obstacle f... | Accept | This paper received unanimous recommendations of acceptance. Concerns were expressed regarding the similarity between the proposed method and ReAct, but the concerns were addressed by the authors. The AC agrees with the reviewer regarding the contribution of this paper and recommends acceptance. | train | [
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" Thanks to the authors for their thorough answers to my comments and to all other reviewers. I think the responses and the modifications to the manuscript cover my questions appropriately and I found some of the answers ... | [
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nips_2022_W4ZlZZwsQmt | Symplectic Spectrum Gaussian Processes: Learning Hamiltonians from Noisy and Sparse Data | Hamiltonian mechanics is a well-established theory for modeling the time evolution of systems with conserved quantities (called Hamiltonian), such as the total energy of the system. Recent works have parameterized the Hamiltonian by machine learning models (e.g., neural networks), allowing Hamiltonian dynamics to be ob... | Accept | Learning from continuous-time physical systems when input data is noisy & sparse, and without access to time derivatives, is a hard problem. The authors propose a novel algorithm using Gaussian Processes, guided by physical knowledge. Reviewers agreed that the work was original. One reviewer raised concerns about the r... | train | [
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nips_2022_L9YayWPcHA_ | Plan To Predict: Learning an Uncertainty-Foreseeing Model For Model-Based Reinforcement Learning | In Model-based Reinforcement Learning (MBRL), model learning is critical since an inaccurate model can bias policy learning via generating misleading samples. However, learning an accurate model can be difficult since the policy is continually updated and the induced distribution over visited states used for model lea... | Accept | All the reviewers agree that this is a good paper. The idea is original and the paper has good empirical results. There were some confusions, which were resolved during the discussions and the revised paper. I recommend this paper to be accepted, possibly as a spotlight presentation.
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nips_2022_zTQdHSQUQWc | FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting | Recent studies have shown that deep learning models such as RNNs and Transformers have brought significant performance gains for long-term forecasting of time series because they effectively utilize historical information. We found, however, that there is still great room for improvement in how to preserve historical i... | Accept | Paper provides a time series modeling technique combining the use of Legendre polynomials for projections and Frequency based low rank approximation / selection.
The reviewers found the paper to be interesting, and the results convincing and possibly usable in other sequence modeling tasks.
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nips_2022_3MZnNARib5 | SAPipe: Staleness-Aware Pipeline for Data Parallel DNN Training | Data parallelism across multiple machines is widely adopted for accelerating distributed deep learning, but it is hard to achieve linear speedup due to the heavy communication. In this paper, we propose SAPipe, a performant system that pushes the training speed of data parallelism to its fullest extent. By introducing ... | Accept | This paper proposes a new algorithm to speed up data-parallel distributed training, focused on mitigating staleness-induced issues that arise when limiting communication between nodes.
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nips_2022_6hzH8pohyPY | Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms | In this paper, we study the combinatorial semi-bandits (CMAB) and focus on reducing the dependency of the batch-size $K$ in the regret bound, where $K$ is the total number of arms that can be pulled or triggered in each round. First, for the setting of CMAB with probabilistically triggered arms (CMAB-T), we discover a ... | Accept | Thank the authors for their submission.
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" Thank you for the response. This will be a good addition to the final version of the paper. ",
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nips_2022_H-6iczs__Ro | A Unified Diversity Measure for Multiagent Reinforcement Learning | Promoting behavioural diversity is of critical importance in multi-agent reinforcement learning, since it helps the agent population maintain robust performance when encountering unfamiliar opponents at test time, or, when the game is highly non-transitive in the strategy space (e.g., Rock-Paper-Scissor). While a myri... | Accept | This paper provides a unifying framework for promoting diverse behaviors in multi-agent RL. The framework---the unified diversity measure--- is general enough to be able to capture several other recently proposed measures as special cases (associated with specific kernel functions). The paper then provides extensions t... | train | [
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nips_2022_wiBEFdAvl8L | GLIPv2: Unifying Localization and Vision-Language Understanding | We present GLIPv2, a grounded VL understanding model, that serves both localization tasks (e.g., object detection, instance segmentation) and Vision-Language (VL) understanding tasks (e.g., VQA, image captioning). GLIPv2 elegantly unifies localization pre-training and Vision-Language Pre-training (VLP) with three pre-t... | Accept | All three reviewers provided positive reviews and scores for this paper. They were happy to see the strong empirical evaluations and improvements over GLIP, impressed by the zero shot results, and found the new combination of pre-training objectives interesting. A few questions and concerns were brought up by reviewers... | train | [
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nips_2022_08Yk-n5l2Al | Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding | We present Imagen, a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding. Imagen builds on the power of large transformer language models in understanding text and hinges on the strength of diffusion models in high-fidelity image generation. Our key disc... | Accept | This paper proposes Imagen that uses large transformer language models and diffusion models for text-to-image generation. The major finding is that using large language models pretrained only on text data as text encoders are effective. Dynamic thresholding and Efficient U-Net architecture are proposed to improve the t... | train | [
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nips_2022_wKd2XtSRsjl | Mutual Information Divergence: A Unified Metric for Multimodal Generative Models | Text-to-image generation and image captioning are recently emerged as a new experimental paradigm to assess machine intelligence. They predict continuous quantity accompanied by their sampling techniques in the generation, making evaluation complicated and intractable to get marginal distributions. Based on a recent tr... | Accept | The paper studies the evaluation metric for multimodal generation models. The authors propose a method MID based on estimating mutual information of visual and text embeddings at sample and distribution level. From experiments, the MID correlates with human evaluation on multiple tasks (text-to-image and image captioni... | val | [
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nips_2022_8RKJj1YDBJT | Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Camera | We propose Neural-DynamicReconstruction (NDR), a template-free method to recover high-fidelity geometry and motions of a dynamic scene from a monocular RGB-D camera. In NDR, we adopt the neural implicit function for surface representation and rendering such that the captured color and depth can be fully utilized to joi... | Accept | This paper had consistently positive reviews from all reviewers and weaknesses that were expressed were responded to coherently by the authors. I recommend this paper be accepted. | train | [
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" Thank you for addressing my concerns.\nI modified my rating.\n\nbest",
" Thank you for your quick reply and review comments. For BANMo results shown in Fig.5 of the main paper:\n\n- Before submiss... | [
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nips_2022_K2PTuvVTF1L | Variational inference via Wasserstein gradient flows | Along with Markov chain Monte Carlo (MCMC) methods, variational inference (VI) has emerged as a central computational approach to large-scale Bayesian inference. Rather than sampling from the true posterior $\pi$, VI aims at producing a simple but effective approximation $\hat \pi$ to $\pi$ for which summary statistics... | Accept | This paper proposes a novel method for variational inference based on Wasserstein flows. The key contribution is perhaps the rigorous guarantees that are derived from an assumption of log-concavity. While the initial submission was unaware of some existing work on VI that derives guarantees from similar log concavity o... | train | [
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nips_2022_GCNIm4cKoRx | Finite-Time Analysis of Adaptive Temporal Difference Learning with Deep Neural Networks | Temporal difference (TD) learning with function approximations (linear functions or neural networks) has achieved remarkable empirical success, giving impetus to the development of finite-time analysis. As an accelerated version of TD, the adaptive TD has been proposed and proved to enjoy finite-time convergence under ... | Accept | The reviewers agree that the theoretical results presented in the paper are solid and advance our understanding of the behavior of temporal difference (TD) methods, which are at the core of most reinforcement learning algorithms. The contributions of the paper can be summarized in two main results:
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nips_2022_Yul402KcD5d | Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning | Learning medical visual representations directly from paired radiology reports has become an emerging topic in representation learning. However, existing medical image-text joint learning methods are limited by instance or local supervision analysis, ignoring disease-level semantic correspondences. In this paper, we pr... | Accept | A multi-granularity cross-modal alignment framework is proposed, which learns data representations from medical scans paired with the corresponding text reports.
The reviewers find the appraoch novel and the paper well-written with an overall clear structure.
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nips_2022_-3Pg7QNIF1S | An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning | Semi-supervised few-shot learning consists in training a classifier to adapt to new tasks with limited labeled data and a fixed quantity of unlabeled data. Many sophisticated methods have been developed to address the challenges this problem comprises. In this paper, we propose a simple but quite effective approach to ... | Accept | This paper aims to improve semi-supervised few shot learning by utilizing negative pseudo-labels. The authors report significant improvement over the previous methods in this setting. The reviewers originally had concerns about the significance of the results, but after the discussion period they all supported acceptan... | train | [
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nips_2022_FhWQzNY2UYR | Geo-SIC: Learning Deformable Geometric Shapes in Deep Image Classifiers | Deformable shapes provide important and complex geometric features of objects presented in images. However, such information is oftentimes missing or underutilized as implicit knowledge in many image analysis tasks. This paper presents Geo-SIC, the first deep learning model to learn deformable shapes in a deformation s... | Accept | Although there were a couple of initial questions/concerns about certain aspects of the paper, all reviewers appreciated the approach, the quality of presentation and the empirical results. After reading all responses by the authors, my impression is that all questions have been answered satisfactorily during the rebut... | train | [
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nips_2022_zGvRdBW06F5 | On-Device Training Under 256KB Memory | On-device training enables the model to adapt to new data collected from the sensors by fine-tuning a pre-trained model. Users can benefit from customized AI models without having to transfer the data to the cloud, protecting the privacy. However, the training memory consumption is prohibitive for IoT devices that have... | Accept | In this work the authors propose a framework for training CV models on tiny IoT devices with very limited memory. The reviewers agreed that the paper is well written and represents a valuable contribution to the area of efficient / on-device ML. Questions raised by reviewers were sufficiently addressed in the response.... | test | [
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" Dear Authors,\nThank you for your response!\nBased on your replies and your promise of opensourcing the code. I am raising my score to 6.\nGood luck!\n\n",
" Dear Reviewer Dz6f,\n\nThanks again for your insightful suggestions and comments. We have not heard from you and the rebuttal window is going to close. We... | [
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nips_2022_StzAAh8RuD | Independence Testing for Bounded Degree Bayesian Networks | We study the following independence testing problem: given access to samples from a distribution $P$ over $\{0,1\}^n$, decide whether $P$ is a product distribution or whether it is $\varepsilon$-far in total variation distance from any product distribution. For arbitrary distributions, this problem requires $\exp(n)$ s... | Accept | The manuscript studies the independence testing problem, given samples from a distribution over several binary random variables. While the sample complexity is exponential (in the number of variables), this paper shows that when the distribution is a Bayesian network with small in-degree, the sample complexity is linea... | train | [
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nips_2022_yewD_qbYifc | PCRL: Priority Convention Reinforcement Learning for Microscopically Sequencable Multi-agent Problems | Reinforcement learning (RL) has played an important role in tackling the decision problems emerging from agent fields. However, RL still has challenges in tackling multi-agent large-discrete-action-space (LDAS) problems, possibly resulting from large agent numbers. At each decision step, a multi-agent LDAS problem is o... | Reject | While the ideas in this paper are promising, there are issues with the paper's presentation and experimental results. The paper needs to be (further) updated to clarify the proposed method and discuss additional related work. More extensive experimental results are also needed to show the benefits of the proposed appro... | val | [
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nips_2022_9GXoMs__ckJ | On the Effect of Pre-training for Transformer in Different Modality on Offline Reinforcement Learning | We empirically investigate how pre-training on data of different modalities, such as language and vision, affects fine-tuning of Transformer-based models to Mujoco offline reinforcement learning tasks. Analysis of the internal representation reveals that the pre-trained Transformers acquire largely different representa... | Accept | The paper unanimously receives positive rates thanks to strong motivations and interesting results. As the reviews show satisfaction on the authors’ feedback, the final draft needs to respect it accordingly, for example, about the limitations of this research. | train | [
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nips_2022_BK0O0xLntFM | Estimating and Explaining Model Performance When Both Covariates and Labels Shift | Deployed machine learning (ML) models often encounter new user data that differs from their training data. Therefore, estimating how well a given model might perform on the new data is an important step toward reliable ML applications. This is very challenging, however, as the data distribution can change in flexible w... | Accept | The authors study the important problem of distribution shift under a new SJS model. Identifiability results are proved and empirical experiments illustrate the value of the proposed model. During discussion, some concerns on the experiments were addressed. Overall, there was a weak consensus to accept this paper, whic... | train | [
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nips_2022_GWcdXz0M6a | PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits | In sparse linear bandits, a learning agent sequentially selects an action from a fixed action set and receives reward feedback, and the reward function depends linearly on a few coordinates of the covariates of the actions. This has applications in many real-world sequential decision making problems. In this paper, we ... | Accept | The paper is motivated by the design of low-regret algorithms for high-dimensional sparse linear bandit problems. The challenge is to obtain regret guarantees even in the data-poor regime where the number of samples the learner can gather may be smaller than the dimension.
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nips_2022_UDmPRm-P1nL | Distinguishing Learning Rules with Brain Machine Interfaces | Despite extensive theoretical work on biologically plausible learning rules, clear evidence about whether and how such rules are implemented in the brain has been difficult to obtain. We consider biologically plausible supervised- and reinforcement-learning rules and ask whether changes in network activity during learn... | Accept | This paper explores the question of experimentally distinguishing between different hypothesized classes of learning rules in the brain (specifically biased supervised learning and unbiased reinforcement learning). It derives a metric to distinguish between such learning rules based on changes in neural activity seen d... | train | [
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nips_2022_wYgRIJ-oK6M | BiT: Robustly Binarized Multi-distilled Transformer | Modern pre-trained transformers have rapidly advanced the state-of-the-art in machine learning, but have also grown in parameters and computational complexity, making them increasingly difficult to deploy in resource-constrained environments. Binarization of the weights and activations of the network can significantly ... | Accept | This paper proposes an innovative pipeline for quantizing transformers for extremely low precision (1-2) bits, while reducing the gap of previous methods to full precision by ~3X.
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nips_2022_1C36tFZn7sR | Learning Chaotic Dynamics in Dissipative Systems | Chaotic systems are notoriously challenging to predict because of their sensitivity to perturbations and errors due to time stepping. Despite this unpredictable behavior, for many dissipative systems the statistics of the long term trajectories are governed by an invariant measure supported on a set, known as the globa... | Accept | This paper proposes a neural network-based approach to estimate the Markov operator of dissipative chaotic systems. It introduces a novel combination of Sobolev and dissipativity losses. While the reviewers had initial concerns about clarity, assumption and application condition, and the choice of learning Markov opera... | train | [
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nips_2022_9YasTgzma8c | Trading off Image Quality for Robustness is not Necessary with Regularized Deterministic Autoencoders | The susceptibility of Variational Autoencoders (VAEs) to adversarial attacks indicates the necessity to evaluate the robustness of the learned representations along with the generation performance. The vulnerability of VAEs has been attributed to the limitations associated with their variational formulation. Determinis... | Accept | This paper received generally positive reviews that, after discussion, all backed acceptance. The paper was praised for its empirical evaluations, potential significance, clarity, and applicability. While some questions and lower-level issues were raised, I do not feel that the reviewers raised any significant issues... | train | [
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nips_2022_Upt5wsECVJe | Mean Estimation in High-Dimensional Binary Markov Gaussian Mixture Models | We consider a high-dimensional mean estimation problem over a binary hidden Markov model, which illuminates the interplay between memory in data, sample size, dimension, and signal strength in statistical inference. In this model, an estimator observes $n$ samples of a $d$-dimensional parameter vector $\theta_{*}\in\ma... | Accept | The paper addresses the problem of high-dimensional statistical inference from dependent samples. This is a recently emerging area, and the authors establish nearly tight minimax error rate bounds for a basic statistical model (gaussian hidden markov model).
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nips_2022_d0stFTU2dTI | Exploration via Planning for Information about the Optimal Trajectory | Many potential applications of reinforcement learning (RL) are stymied by the large numbers of samples required to learn an effective policy. This is especially true when applying RL to real-world control tasks, e.g. in the sciences or robotics, where executing a policy in the environment is costly. In popular RL algor... | Accept | All reviewers acknowledged to have read the rebuttal. Reviewer iWun's reply isn't visible to the authors (posted too late), see end of metareview. The most important concerns of the reviewers have been addressed by extensive replies and additional experiments. Overall the method is sound and performs well. As acknowled... | train | [
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nips_2022_UZJHudsQ7d | Robust Calibration with Multi-domain Temperature Scaling | Uncertainty quantification is essential for the reliable deployment of machine learning models to high-stakes application domains. Uncertainty quantification is all the more challenging when training distribution and test distribution are different, even if the distribution shifts are mild. Despite the ubiquity of dist... | Accept | Reviewers find the paper original, useful, thorough in its numerics (in the revision), and clearly written. | test | [
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nips_2022_Q6DJ12oQjrp | Sparse Interaction Additive Networks via Feature Interaction Detection and Sparse Selection | There is currently a large gap in performance between the statistically rigorous methods like linear regression or additive splines and the powerful deep methods using neural networks. Previous works attempting to close this gap have failed to fully consider the exponentially growing number of feature combinations whi... | Accept | This paper proposes a scheme to augment a trained neural network (considering in particular the case of unstructured, tabular data) by extending generalized additive models to the multi-layer neural setting in an unusual manner by using higher-order derivatives from an initial deep neural network to select a sparse set... | train | [
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nips_2022_vfR3gtIFd8Y | Fast variable selection makes scalable Gaussian process BSS-ANOVA a speedy and accurate choice for tabular and time series regression | Many approaches for scalable GPs have focused on using a subset of data as inducing points. Another promising approach is the Karhunen-Loève (KL) decomposition, in which the GP kernel is represented by a set of basis functions which are the eigenfunctions of the kernel operator. Such kernels have the potential to be ve... | Reject | Reading the reviews, I think there are ultimately two challenges for the authors to address in this work. First I think ends up being a somewhat simple "background for the community" problem, as both several reviewers and the authors in their general comments point out: significantly more background on KL decomposed ke... | val | [
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nips_2022_Rqe-fJQtExY | Efficient and Effective Multi-task Grouping via Meta Learning on Task Combinations | As a longstanding learning paradigm, multi-task learning has been widely applied into a variety of machine learning applications. Nonetheless, identifying which tasks should be learned together is still a challenging fundamental problem because the possible task combinations grow exponentially with the number of tasks,... | Accept | The overall idea of using a meta-learning network with an active learner for grouped multi-task learning is interesting. The experimental results provided in the original submission and rebuttal are extensive to verify the effectiveness of the proposed method. A major limitation of the proposed method is the high compu... | train | [
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" We very appreciate this suggestion and will take your advice to discuss the limitations of this work in the revised paper. We also agree with you that when each task has its own dataset, the computational cost of N-task MTL will be significantly larger than that of two-task MTL.\n\nAs you have mentioned, this wor... | [
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nips_2022_nLKkHwYP4Au | CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds | We present a novel two-stage fully sparse convolutional 3D object detection framework, named CAGroup3D. Our proposed method first generates some high-quality 3D proposals by leveraging the class-aware local group strategy on the object surface voxels with the same semantic predictions, which considers semantic consiste... | Accept | 4 expert reviewers suggest acceptance, based mostly on a strong evaluation section that shows good improvements over previous methods. Novelty of the method is deemed sufficient and well ablated. Overall seems like a good quality paper, although a tiny bit on the incremental side, but enough for recommending acceptance... | train | [
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" We sincerely thank the reviewer for providing thoughtful review and positive feedback. Below are our responses to the questions and suggestions raised by the reviewer.\n\n**R4-Q1: Update the title/introduction.** \n**R4-A1:** Thanks. We agree that the title and introduction are somewhat misleading. Our model tak... | [
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nips_2022_Fm7Dt3lC_s2 | Adaptive Data Debiasing through Bounded Exploration | Biases in existing datasets used to train algorithmic decision rules can raise ethical and economic concerns due to the resulting disparate treatment of different groups. We propose an algorithm for sequentially debiasing such datasets through adaptive and bounded exploration in a classification problem with costly and... | Accept | This paper has seen a lot of discussion between reviewers and authors. Reviewers are fairly positive after the discussion/rebuttal phase and there have been significant score revisions upwards.
Few concerns that were highlighted during rebuttal/discussion phase are:
1) Multiple reviewers have pointed out that amongs... | train | [
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" Thank you for the response. I will update the numeric score to a 6, with possible further change after discussion with other reviewers.\nI am happy with the response given by the authors. I believe that the series of clarification given in the responses regarding Assumption 1 and its implications are definitely n... | [
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nips_2022_bfz-jhJ8wn | Bridging the Gap Between Vision Transformers and Convolutional Neural Networks on Small Datasets | There still remains an extreme performance gap between Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) when training from scratch on small datasets, which is concluded to the lack of inductive bias. In this paper, we further consider this problem and point out two weaknesses of ViTs in inductive bia... | Accept | Authors introduce 3 modifications to ViT architecture to introduce additional inductive biases to improve performance in low-data scenarios:
- SOPE: Sequential Overlapping Patch Embedding -- essentially convolutions before partitioning the image into patches.
- DAFF: Dynamic Aggregation Feed Forward -- a DWCONV operati... | train | [
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" Thanks again for your time, your detailed and insightful comments and kindess! It is a good trip of us these days and your suggestions greatly improve our work, making it more solid. Best wishes.",
" Thanks for your time and comments again. Your suggestions and insights help us rethink our work and make it more... | [
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nips_2022_Bq2-WN5csW | Loss Landscape Dependent Self-Adjusting Learning Rates in Decentralized Stochastic Gradient Descent | Distributed Deep Learning (DDL) is essential for large-scale Deep Learning (DL) training. Synchronous Stochastic Gradient Descent (SSGD) 1 is the de facto DDL optimization method. Using a sufficiently large batch size is critical to achieving DDL runtime speedup. In a large batch setting, the learning rate must be incr... | Reject | This paper compares all-reduce SGD (SSGD) with decentralized SGD (DPSGD) and argues that the latter can tolerate lager stepsize due to a smoothing effect induced by noise in DPSGD.
The reviewers found that the theoretical contribution is overclaimed. By the strong assumptions needed in the theory section (such as e.g... | train | [
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nips_2022_hcVlMF3Nvxg | MultiGuard: Provably Robust Multi-label Classification against Adversarial Examples | Multi-label classification, which predicts a set of labels for an input, has many applications. However, multiple recent studies showed that multi-label classification is vulnerable to adversarial examples. In particular, an attacker can manipulate the labels predicted by a multi-label classifier for an input via addi... | Accept | This paper studies adversarial examples for varieties of randomized smoothing, namely, ways to improve the robustness of a classifier by adding noise and averaging over inputs. The main contribution is MultiGuard, which is a provably robust defense for multi-label classification. Moreover, the method works for a variet... | train | [
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" Thanks the authors for providing the code. My concerns are addressed.",
" Many thanks for the comment! We really appreciate the constructive feedback, which significantly improves the paper. We will definitively integrate our clarifications into t... | [
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nips_2022_Yopob26XjmL | Natural gradient enables fast sampling in spiking neural networks | For animals to navigate an uncertain world, their brains need to estimate uncertainty at the timescales of sensations and actions. Sampling-based algorithms afford a theoretically-grounded framework for probabilistic inference in neural circuits, but it remains unknown how one can implement fast sampling algorithms in ... | Accept | Although some reviewers have reservations about strong modelling assumptions, the main contribution of the paper is clearly presented and technically sound. | train | [
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nips_2022_3AbigH4s-ml | CEBaB: Estimating the Causal Effects of Real-World Concepts on NLP Model Behavior | The increasing size and complexity of modern ML systems has improved their predictive capabilities but made their behavior harder to explain. Many techniques for model explanation have been developed in response, but we lack clear criteria for assessing these techniques. In this paper, we cast model explanation as the ... | Accept |
The paper presents a new benchmark dataset for assessing explanation methods in NLP, on the sentiment analysis domain. The dataset is unique in that it focuses on the casual effects of modifying specific aspects, providing minimal pairs where only one of the aspects is different. After constructing the benchmark, the ... | train | [
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nips_2022_aLNWp0pn1Ij | GAR: Generalized Autoregression for Multi-Fidelity Fusion | In many scientific research and engineering applications, where repeated simulations of complex systems are conducted, a surrogate is commonly adopted to quickly estimate the whole system. To reduce the expensive cost of generating training examples, it has become a promising approach to combine the results of low-fideli... | Accept | This paper considers the problem of multi-fidelity fusion using generalized autoregression. The authors especially take on problems such as high-dimensionality and non-subsetness with this approach. The reviewers agree that the paper is well written and makes a significant contribution to MF-fusion. I recommend accepta... | train | [
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nips_2022_nrksGSRT7kX | RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning | Offline reinforcement learning (RL) aims to find performant policies from logged data without further environment interaction. Model-based algorithms, which learn a model of the environment from the dataset and perform conservative policy optimisation within that model, have emerged as a promising approach to this prob... | Accept | This paper introduces the idea of Robust Adversarial RL for offline model-based RL, which could have a high impact. It is well organized and the writing is very comprehensive; the authors manage to convey their idea in concise but informative language. The proposed RAMBO approach performs reasonably well in the present... | train | [
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nips_2022_k713e8vXzwR | Large-Scale Differentiable Causal Discovery of Factor Graphs | A common theme in causal inference is learning causal relationships between observed variables, also known as causal discovery. This is usually a daunting task, given the large number of candidate causal graphs and the combinatorial nature of the search space. Perhaps for this reason, most research has so far focused o... | Accept | In this paper, the authors propose a new DAG constraint for low-rank adjacency matrices., which can scale to larger graphs. All the reviewers consider this paper is sound and the experiments are well designed. However, one question about the case of different graph spaces from other reviewer should be addressed in the ... | train | [
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nips_2022_VnAwNNJiwDb | Generating Long Videos of Dynamic Scenes | We present a video generation model that accurately reproduces object motion, changes in camera viewpoint, and new content that arises over time. Existing video generation methods often fail to produce new content as a function of time while maintaining consistencies expected in real environments, such as plausible dyn... | Accept | All four reviewers enjoyed this paper and were particularly impressed by the videos provided in the supplementary material. The results are very impressive indeed. The reviewers also agreed that using a multi stage approach was interesting and effective. The two new datasets were deemed useful to the generation communi... | train | [
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nips_2022_-e2SBzFDE8x | Adaptively Exploiting d-Separators with Causal Bandits | Multi-armed bandit problems provide a framework to identify the optimal intervention over a sequence of repeated experiments. Without additional assumptions, minimax optimal performance (measured by cumulative regret) is well-understood. With access to additional observed variables that d-separate the intervention from... | Accept | This paper exploits the causal structure in the multi-armed bandits setting and gives a set of novel and strong results, including (1) the conditional benign property -- a nice and simple generalization of prior assumptions; (2) an impossibility result for the previous algorithm C-UCB; and (3) a new algorithm gives sub... | train | [
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nips_2022_dMK7EwoTYp | MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction | In recent years, neural implicit surface reconstruction methods have become popular for multi-view 3D reconstruction. In contrast to traditional multi-view stereo methods, these approaches tend to produce smoother and more complete reconstructions due to the inductive smoothness bias of neural networks. State-of-the-ar... | Accept | There was a range of reactions to this paper from borderline reject to strong accept. Although several of the reviewers highlighted that the contribution could be viewed as incremental, it is clearly described, and robust across different types of scenes, and I concur with the three reviewers that give positive rating... | test | [
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nips_2022_Euv1nXN98P3 | TarGF: Learning Target Gradient Field for Object Rearrangement | Object Rearrangement is to move objects from an initial state to a goal state. Here, we focus on a more practical setting in object rearrangement, i.e., rearranging objects from shuffled layouts to a normative target distribution without explicit goal specification. However, it remains challenging for AI agents, as it ... | Accept | After a strong rebuttal from the authors and an extensive discussion among the reviewers, I believe this work will be a valuable contribution to NeurIPS. I recommend it for acceptance and encourage the authors to address the reviewers comments for the camera-ready version of the paper, especially the point about the si... | train | [
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nips_2022_rnJzy8JnaX | Rethinking Resolution in the Context of Efficient Video Recognition | In this paper, we empirically study how to make the most of low-resolution frames for efficient video recognition. Existing methods mainly focus on developing compact networks or alleviating temporal redundancy of video inputs to increase efficiency, whereas compressing frame resolution has rarely been considered a pro... | Accept | After the rebuttal and discussion, two reviewers recommend acceptance, one borderline rejection. Most concerns of the raised in the borderline review were addressed at a sufficient detail in the rebuttal. The AC sees no reason the reject this paper. | val | [
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" Dear Reviewer THu4,\n\nThank you for your feedback. As we are approaching the end of the discussion period, we would like to ask whether there are any remaining concerns regarding our paper or our response? We are happy to answer any further questions.\n\nWe sincerely thank you for your efforts in reviewing our p... | [
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nips_2022_hBaI5MY0CBz | Feature-Proxy Transformer for Few-Shot Segmentation | Few-shot segmentation~(FSS) aims at performing semantic segmentation on novel classes given a few annotated support samples. With a rethink of recent advances, we find that the current FSS framework has deviated far from the supervised segmentation framework: Given the deep features, FSS methods typically use an intric... | Accept | This paper studies the plain segmentation framework (feature extractor + linear classification) for few-shot segmentation. It introduces a prompt based query and support interaction method to enable this framework to work well. All the reviewers recognize the proposed method is novel and the performance is good. Though... | train | [
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" Thanks for the further comments. \n\n---\n**Q4:** The CNN backbones are typically fixed to alleviate overfitting. In contrast, the ViT backbone is much larger and yet shows resistance against overfitting. Even the baseline with a plain vision ... | [
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nips_2022_5JdyRvTrK0q | Private Synthetic Data for Multitask Learning and Marginal Queries | We provide a differentially private algorithm for producing synthetic data simultaneously useful for multiple tasks: marginal queries and multitask machine learning (ML). A key innovation in our algorithm is the ability to directly handle numerical features, in contrast to a number of related prior approaches which re... | Accept | This paper provides a method for generating synthetic differentially-private datasets for use in answering statistical queries, including Mixed Marginal Queries, Class Conditional Linear Threshold Queries, and "Querying the Error." The is an improvement over previous work. A solid paper that all reviewers are positive... | train | [
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" Terrific --- and thank you for the time you spent reviewing our paper! Your feedback has been valuable.",
" Thanks you for the response!\nI am happy with the responses given. I think that the clarific... | [
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nips_2022_fXq93VpCIy | Sauron U-Net: Simple automated redundancy elimination in medical image segmentation via filter pruning | We present Sauron, a filter pruning method that eliminates redundant feature maps by discarding the corresponding filters with automatically-adjusted layer-specific thresholds. Furthermore, Sauron minimizes a regularization term that, as we show with various metrics, promotes the formation of feature maps clusters. In ... | Reject | The paper proposed a method for pruning filters in image segmentation networks by removing filters during training that are closely clustered. Unlike prior works, the approach is described as single-phase, meaning it prunes during normal training. To obtain smaller networks, a term which promotes feature map clustering... | train | [
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nips_2022_TrsAkAbC96 | Implicit Warping for Animation with Image Sets | We present a new implicit warping framework for image animation using sets of source images through the transfer of motion of a driving video. A single cross-modal attention layer is used to find correspondences between the source images and the driving image, choose the most appropriate features from different source ... | Accept | Consistent reviews, both in content and in score.
The cross-identity motion transfer is a good test of the paper's capability -- it would improve the paper to provide more such examples, which are clearly more challenging than the same-identity case.
The concerns about the limited diversity of example subjects mentio... | test | [
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" I thank the authors for their detailed response and my main concerns are addressed. I would encourage the authors to make the updates they describe and am happy to upgrade my review to Accept. ",
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nips_2022_cA8Zor8wFr5 | AttCAT: Explaining Transformers via Attentive Class Activation Tokens | Transformers have improved the state-of-the-art in various natural language processing and computer vision tasks. However, the success of the Transformer model has not yet been duly explained. Current explanation techniques, which dissect either the self-attention mechanism or gradient-based attribution, do not necessa... | Accept | This is an interesting paper with good contribution to the field. Most reviews are positive. | val | [
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nips_2022_O5arhQvBdH | Trading off Utility, Informativeness, and Complexity in Emergent Communication | Emergent communication (EC) research often focuses on optimizing task-specific utility as a driver for communication. However, there is increasing evidence that human languages are shaped by task-general communicative constraints and evolve under pressure to optimize the Information Bottleneck (IB) tradeoff between the... | Accept | From the ratings alone this paper appears borderline leaning towards acceptance, however, I want to highlight to the authors that in discussion with reviewers and my own reading of the paper there are aspects that shifted this even closer to the decision boundary. In the end, my own conflicted views of the work and the... | test | [
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nips_2022_4pwCvvel8or | Online PAC-Bayes Learning | Most PAC-Bayesian bounds hold in the batch learning setting where data is collected at once, prior to inference or prediction. This somewhat departs from many contemporary learning problems where data streams are collected and the algorithms must dynamically adjust. We prove new PAC-Bayesian bounds in this online learn... | Accept | PAC-Bayes theory provides upper-bounds on the risk of aggregation of predictors in the batch setting. Many PAC-Bayes bounds are actually minimized by EWA (Exponentially Weighted Aggregation), but these bounds can also be applied on (slightly) sub-optimal aggregation procedure, and allow to control their level of sub-op... | train | [
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nips_2022_siG_S8mUWxf | Learning Physical Dynamics with Subequivariant Graph Neural Networks | Graph Neural Networks (GNNs) have become a prevailing tool for learning physical dynamics. However, they still encounter several challenges: 1) Physical laws abide by symmetry, which is a vital inductive bias accounting for model generalization and should be incorporated into the model design. Existing simulators eith... | Accept | Overall this is an interesting paper. It proposed a new formulation of the equivariant graph neural network, subequivariant GNN. Reviewers agree that the proposed idea could be useful to the community, albeit with perhaps small application scope. So on the novelty side, this paper is okay. The biggest concern among the... | val | [
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" Dear Reviewer bh6f,\n\nThank you very much! We really enjoy the discussion with you, during which your insightful comments have helped greatly improve the paper. Thanks again!\n\nBest, \\\nAuthors",
" Thank you for the discussion. I will revise my score.",
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nips_2022_XtyeppctGgc | Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning | Existing fine-tuning methods either tune all parameters of the pre-trained model (full fine-tuning), which is not efficient, or only tune the last linear layer (linear probing), which suffers a significant accuracy drop compared to the full fine-tuning. In this paper, we propose a new parameter-efficient fine-tuning me... | Accept |
This paper provides a simple method to avoid full fine-tuning of vision transformers, namely very simple linear adapters that can be trained and then subsumed into the existing linear layers during inference, which is an interesting characteristic as it prevents added computation during inference (unlike the use of ... | train | [
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" Thank you authors for the detailed responses to my questions. I have reviewed them and they seem to answers my concerns. I have updated my rating accordingly.",
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nips_2022_qm5LpHyyOUO | MCMAE: Masked Convolution Meets Masked Autoencoders | Vision Transformers (ViT) become widely-adopted architectures for various vision tasks. Masked auto-encoding for feature pretraining and multi-scale hybrid convolution-transformer architectures can further unleash the potentials of ViT, leading to state-of-the-art performances on image classification, detection and sem... | Accept | The reviewers are positive about this submission initially. After the authors' rebuttal, one reviewer pointed out that the name `ConvMAE' is not proper to describe the current work. The authors respond by claiming using an alternative name, which is acknowledged by the reviewer. Overall, all the reviewers stand positiv... | train | [
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nips_2022_d4JmP1T45WE | Training Spiking Neural Networks with Event-driven Backpropagation | Spiking Neural networks (SNNs) represent and transmit information by spatiotemporal spike patterns, which bring two major advantages: biological plausibility and suitability for ultralow-power neuromorphic implementation. Despite this, the binary firing characteristic makes training SNNs more challenging. To learn ... | Accept | The authors propose a novel training algorithm to train spiking neural networks (SNNs) in an event-driven manner with backpropagation. They perform experiments on standard benchmarks such as CIFAR-10 and CIFAR-100 to verify the effectiveness of the method. The algorithm achieves SOTA performance on these data sets. Eve... | test | [
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" Dear Reviewer 1S4Q,\n\nAs you suggested, we have checked and added recommended publications in the new version of our paper. We organize the other concerns as follows:\n1) Aiming at your question on the contribution of our paper (which is also asked by Reviewer tMmb), we have clarified the contribution of our pap... | [
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nips_2022_mjUrg0uKpQ | I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification | Despite the tremendous progress in zero-shot learning (ZSL), the majority of existing methods still rely on human-annotated attributes, which are difficult to annotate and scale. An unsupervised alternative is to represent each class using the word embedding associated with its semantic class name. However, word embedd... | Accept | The authors propose a method to learn a joint representation of an image with a document of the object present in the image. Experiments show that the proposed model outperforms state-of-the-art models. Although the final reviews between reviewers are not aligned, I think authors solved most of their proposed questions... | train | [
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nips_2022_fLIgyyQiJqz | Temporal Effective Batch Normalization in Spiking Neural Networks | Spiking Neural Networks (SNNs) are promising in neuromorphic hardware owing to utilizing spatio-temporal information and sparse event-driven signal processing. However, it is challenging to train SNNs due to the non-differentiable nature of the binary firing function. The surrogate gradients alleviate the training prob... | Accept | The paper proposes a method of batch normalization that takes into account the temporal dimension (TEBN) and empirically shows that TEBN can significantly improve the accuracy of spiking neural networks (SNNs). Theoretical analysis also provides new insights into how SNNs should be trained to improve accuracy (particu... | train | [
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nips_2022_js2ssA77fX | Masked Generative Adversarial Networks are Data-Efficient Generation Learners | This paper shows that masked generative adversarial network (MaskedGAN) is robust image generation learners with limited training data. The idea of MaskedGAN is simple: it randomly masks out certain image information for effective GAN training with limited data. We develop two masking strategies that work along orthogo... | Accept | This paper proposes two masking strategies to improve GANs with limited data. The idea is novel and these two strategies can nicely complement each other. The experiment results are promising. The reviewers unanimously raised questions on missing comparison, which seem to be well addressed after author-reviewer discuss... | train | [
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nips_2022_w5DacXWzQ-Q | SAViT: Structure-Aware Vision Transformer Pruning via Collaborative Optimization | Vision Transformers (ViTs) yield impressive performance across various vision tasks. However, heavy computation and memory footprint make them inaccessible for edge devices. Previous works apply importance criteria determined independently by each individual component to prune ViTs. Considering that heterogeneous compo... | Accept | The paper received three positive reviews and one negative review. The raised issues contain technical correctness, ImageNet-22K pertaining, insufficient experiments and speedup on GPUs, computational cost, clarity on ablation studies. During the rebuttal and discussion phases, most of the issues are addressed and revi... | train | [
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nips_2022_ZG5Bi1N4V0U | SeqPATE: Differentially Private Text Generation via Knowledge Distillation | Protecting the privacy of user data is crucial for text generation models, which can leak sensitive information during generation. Differentially private (DP) learning methods provide guarantees against identifying the existence of a training sample from model outputs. PATE is a recent DP learning algorithm that achiev... | Accept | The paper studies PATE framework for text generation models and proposes algorithm based on KD to handle large output space. Reviewers think that proposed methods should generate interest among the NeurIPS audience. We encourage the authors to incorporate comments of the reviewers to improve the paper. | train | [
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nips_2022_wlEOsQ917F | A framework for bilevel optimization that enables stochastic and global variance reduction algorithms | Bilevel optimization, the problem of minimizing a value function which involves the arg-minimum of another function, appears in many areas of machine learning. In a large scale empirical risk minimization setting where the number of samples is huge, it is crucial to develop stochastic methods, which only use a few samp... | Accept | The main topic of this work is stochastic bilevel optimization. It provides an efficient algorithm for this task, and provides theoretical results in this setting.
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nips_2022_bMYU8_qD8PW | A Unified Model for Multi-class Anomaly Detection | Despite the rapid advance of unsupervised anomaly detection, existing methods require to train separate models for different objects. In this work, we present UniAD that accomplishes anomaly detection for multiple classes with a unified framework. Under such a challenging setting, popular reconstruction networks may fa... | Accept | This paper is on a highly-important topic, and makes solid contributions. Anomaly detection for multi-class datasets without class information is an underexplored area. Reviewers have appreciated the strong experimental results (especially on the important MVtech benchmark), high quality paper writing, and explainabili... | train | [
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nips_2022_0tG59j2efs | Learning from Future: A Novel Self-Training Framework for Semantic Segmentation | Self-training has shown great potential in semi-supervised learning. Its core idea is to use the model learned on labeled data to generate pseudo-labels for unlabeled samples, and in turn teach itself. To obtain valid supervision, active attempts typically employ a momentum teacher for pseudo-label prediction yet obser... | Accept | This paper introduces an approach for reducing confirmation bias during self-training for semantic segmentation, by “learning from the future”, i.e. updating the teacher at a given timestep in self-training with a virtually updated version of the student, without actually using the gradients to update the student yet. ... | train | [
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nips_2022_gRK9SLQHTDV | Don't Roll the Dice, Ask Twice: The Two-Query Distortion of Matching Problems and Beyond | In most social choice settings, the participating agents express their preferences over the different alternatives in the form of linear orderings. While this clearly simplifies preference elicitation, it inevitably leads to poor performance with respect to optimizing a cardinal objective, such as the social welfare, s... | Accept | This work studies a narrow, but important problem of how much cardinal information is needed to achieve near optimal matchings. The authors show that with just two queries (one is required for any non-trivial results) they can achieve non-trivial results in a very general setting. Moreover, they show that their results... | test | [
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nips_2022_1bE24ZURBqm | Biologically Inspired Dynamic Thresholds for Spiking Neural Networks | The dynamic membrane potential threshold, as one of the essential properties of a biological neuron, is a spontaneous regulation mechanism that maintains neuronal homeostasis, i.e., the constant overall spiking firing rate of a neuron. As such, the neuron firing rate is regulated by a dynamic spiking threshold, which h... | Accept | The paper proposes a biologically plausible dynamic thresholding mechanism. Spiking neural nets with dynamic thresholding appears to be novel. The paper does a good job of motivating the choice of the model and illustrating its benefits across a series of control tasks. All reviewers support the acceptance of the paper... | train | [
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nips_2022_-bLLVk-WRPy | Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport | Despite recent advances in automated machine learning, model selection is still a complex and computationally intensive process. For Gaussian processes (GPs), selecting the kernel is a crucial task, often done manually by the expert. Additionally, evaluating the model selection criteria for Gaussian processes typically... | Accept | This is a strong submission that benefitted greatly from productive and clarifying discussion between the authors and reviewers, after which the reviewers reached a unanimous stance in favor of acceptance. I recommend the authors to revise the manuscript accordingly in light of these discussions. | train | [
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nips_2022_19MmorTQhho | One Inlier is First: Towards Efficient Position Encoding for Point Cloud Registration | Transformer architecture has shown great potential for many visual tasks, including point cloud registration. As an order-aware module, position encoding plays an important role in Transformer architecture applied to point cloud registration task. In this paper, we propose a one-inlier based position encoding method fo... | Accept | Thanks in large part to the rebuttal conversation, the reviewers converged to accept this paper. The reviewers recognize the interest and value of the approach and careful empirical results, bolstered by additional results introduced during the discussion.
In preparing the camera-ready, the authors of this paper are ... | train | [
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