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_Il0ymeSnKyL | NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation | Optical computing has become emerging technology in next-generation efficient artificial intelligence (AI) due to its ultra-high speed and efficiency. Electromagnetic field simulation is critical to the design, optimization, and validation of photonic devices and circuits.
However, costly numerical simulation significa... | Accept | The authors propose a domain-specific extension of neural operators that is appropriate for photonics applications. This is an interesting application of neural operators which demonstrates the usefulness of building in physical priors. Some reviewers expressed concern about the topic being too far outside the usual fo... | train | [
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" I appreciate the authors’ thorough response to my questions. After reading the authors' response, now I think the paper’s contribution outweighs my initial concerns (especially regarding the significance). I still think the tackled problem of this paper is not very relevant to the general ML community, but seems ... | [
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nips_2022_Qq-ge2k8uml | Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields | Capitalizing on the recent advances in image generation models, existing controllable face image synthesis methods are able to generate high-fidelity images with some levels of controllability, e.g., controlling the shapes, expressions, textures, and poses of the generated face images. However, these methods focus on 2... | Accept | Paper attacks a hard problem and brings together state-of-the-art ideas to demonstrate substantial wins. Many good points were raised by the reviewers, and we ask the authors to carefully read through the feedback and address what they can for the final version. | train | [
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nips_2022_p62j5eqi_g2 | On the Robustness of Deep Clustering Models: Adversarial Attacks and Defenses | Clustering models constitute a class of unsupervised machine learning methods which are used in a number of application pipelines, and play a vital role in modern data science. With recent advancements in deep learning-- deep clustering models have emerged as the current state-of-the-art over traditional clustering app... | Accept | To investigate the adversarial attacks and robustness for deep clustering models, the authors propose a blackbox attack using Generative Adversarial Networks (GANs) where the adversary does not know which deep clustering model is being used, but can query it for outputs.
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" Thanks for the additional experiments and further improvements.\n\nYour clarifications in Items 2, 3, 4 has well-resolved our concerns. Regarding Items 5 and 6, we have got your explanations. Thanks.\n\nIn our ... | [
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nips_2022_lSfrwyww-FR | Blackbox Attacks via Surrogate Ensemble Search | Blackbox adversarial attacks can be categorized into transfer- and query-based attacks. Transfer methods do not require any feedback from the victim model, but provide lower success rates compared to query-based methods. Query attacks often require a large number of queries for success. To achieve the best of both app... | Accept | This paper proposes BASES, a query-efficient black-box adversarial attack by first generating adversarial perturbation with gradient-based attack using a weighted ensemble of surrogate models. The perturbed image is used to query the target model and its feedback is used to update the weights via zeroth-order optimizat... | train | [
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nips_2022_Qoow6uXwjnA | Quadproj: a Python package for projecting onto quadratic hypersurfaces | Quadratic hypersurfaces are a natural generalization of affine subspaces, and projections are elementary blocks of algorithms in optimization and machine learning. It is therefore intriguing that no proper studies and tools have been developed to tackle this nonconvex optimization problem. The quadproj package is a use... | Reject | The paper presents a software package to do projections on the non-cylindrical central quadratic hypersurfaces. While the problem is certainly interesting (all the reviewers agree), its motivation in the context of machine learning seems to be lacking in the paper. This is missing in the paper currently and is the main... | train | [
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" I do not think NeurIPS is markedly different from the SISC in expectations. The premise of the cited Call For Papers is \"We invite submissions presenting new and original research\". I already mentioned a concern on novelty.\n\nIf the paper would have been presented as suggested by Reviewer o4hv, or the library ... | [
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nips_2022_ymAsTHhrnGm | Inverse Game Theory for Stackelberg Games: the Blessing of Bounded Rationality | Optimizing strategic decisions (a.k.a. computing equilibrium) is key to the success of many non-cooperative multi-agent applications. However, in many real-world situations, we may face the exact opposite of this game-theoretic problem --- instead of prescribing equilibrium of a given game, we may directly observe the ... | Accept | High-level view: this paper presents some interesting observations around
learning against a Stackelberg follower that corresponds to a quantal response model.
The learning seemingly relies strongly on the follower being a quantal responder with a logit regularizer, but this is an interesting setting to study, and one ... | train | [
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" Sorry, I am little late in the discussion. First, I want to point out that NeurIPS allows updating the paper as a rebuttal revision to include new/modified things. I see that some other comments also asked for some discussion, etc. I do expect the authors to update the paper instead of saying that will add the di... | [
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nips_2022_NMTSIY6ykw7 | Semi-Discrete Normalizing Flows through Differentiable Tessellation | Mapping between discrete and continuous distributions is a difficult task and many have had to resort to heuristical approaches. We propose a tessellation-based approach that directly learns quantization boundaries in a continuous space, complete with exact likelihood evaluations. This is done through constructing norm... | Accept | The authors develop a tesselation based approach to map between discrete and continuous spaces. They use this approach to dequantize data to port likelihood based models on continuous spaces to discrete spaces and to scale mixture models where each mixture component has disjoint support. From the view of normalizing fl... | train | [
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" Thank you for the revision! The newly added figure 2 does explain the concept in much simpler terms. My apologies for not having found the supplementary material before, thank you for pointing it out. Including all these details in the appendix is indeed much appreciated!\n\n",
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nips_2022_z9poo2GhOh6 | Trajectory of Mini-Batch Momentum: Batch Size Saturation and Convergence in High Dimensions | We analyze the dynamics of large batch stochastic gradient descent with momentum (SGD+M) on the least squares problem when both the number of samples and dimensions are large. In this setting, we show that the dynamics of SGD+M converge to a deterministic discrete Volterra equation as dimension increases, which we anal... | Accept | The paper analyses an SGD with Momentum (SGD+M) in a setting where the dimension and number of samples are large. The authors provide a theoretical justification for a least square problem.
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nips_2022_pn5trhFskOt | A Closer Look at Weakly-Supervised Audio-Visual Source Localization | Audio-visual source localization is a challenging task that aims to predict the location of visual sound sources in a video. Since collecting ground-truth annotations of sounding objects can be costly, a plethora of weakly-supervised localization methods that can learn from datasets with no bounding-box annotations hav... | Accept | The authors seem to have addressed most if not all of the reviewers recommendations, leading to a much improved paper compared to the initial manuscript. The updated scores from the reviewers reflect the major improvements and therefore I recommend this paper be accepted in its updated form. | train | [
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nips_2022_CKbqDtZnSc | A Policy-Guided Imitation Approach for Offline Reinforcement Learning | Offline reinforcement learning (RL) methods can generally be categorized into two types: RL-based and Imitation-based. RL-based methods could in principle enjoy out-of-distribution generalization but suffer from erroneous off-policy evaluation. Imitation-based methods avoid off-policy evaluation but are too conservativ... | Accept | This paper proposes an interesting new idea that is well-motivated through illustrative examples and is thoroughly evaluated. There are some ways in which the paper could be improved, e.g. by including additional experiments (e.g. with high-dim observation spaces, transfer across action spaces, and discrete action spac... | train | [
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" Thank you for answering my questions. I decide to increase my score to a 6.",
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nips_2022_zAc2a6_0aHb | Posterior Collapse of a Linear Latent Variable Model | This work identifies the existence and cause of a type of posterior collapse that frequently occurs in the Bayesian deep learning practice. For a general linear latent variable model that includes linear variational autoencoders as a special case, we precisely identify the nature of posterior collapse to be the competi... | Accept | This paper analyzes the phenomenon of posterior collapse in linear variational autoencoders. While only the linear case is addressed, all reviewers found the work worthy of acceptance, citing its clear contributions to this line of literature that seeks to understand how deep architectures interact with the evidence l... | train | [
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nips_2022_pk1C2qQ3nEQ | Active Learning in Bayesian Neural Networks: Balanced Entropy Learning Principle | Acquiring labeled data is challenging in many machine learning applications with limited budgets. Active learning gives a procedure to select the most informative data points and improve data efficiency by reducing the cost of labeling. The info-max learning principle maximizing mutual information such as BALD has been... | Reject | The majority of reviewers found this paper to be confusing in its presentation, lacking novelty (e.g. Section 3), and not well motivated (e.g. BalEntAcq), with 3 out of 4 recommending rejection. I find that the paper particularly falters in its explanation of the point process entropy and derivation of the ultimate ac... | test | [
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nips_2022_gyZMZBiI9Cw | Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation | We address a practical yet challenging problem of training robot agents to navigate in an environment following a path described by some language instructions. The instructions often contain descriptions of objects in the environment. To achieve accurate and efficient navigation, it is critical to build a map that accu... | Accept | The paper received all positive reviews (3x accept ratings, 1x strong accept rating). The meta-reviewer agrees with the reviewers' assessment of the paper. | train | [
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nips_2022_iH4eyI5A7o | Learning Active Camera for Multi-Object Navigation | Getting robots to navigate to multiple objects autonomously is essential yet difficult in robot applications. One of the key challenges is how to explore environments efficiently with camera sensors only. Existing navigation methods mainly focus on fixed cameras and few attempts have been made to navigate with active c... | Accept | This paper proposes to decouple the camera policy from the navigation policy in goal-driven navigation agents trained using RL, and builds upon the local and global mapping and planning approach by adding an additional recurrent network that takes as inputs global reconstructed maps, heuristic directions, and navigatio... | train | [
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nips_2022_qqIrESv4f_L | Signal Processing for Implicit Neural Representations | Implicit Neural Representations (INRs) encoding continuous multi-media data via multi-layer perceptrons has shown undebatable promise in various computer vision tasks. Despite many successful applications, editing and processing an INR remains intractable as signals are represented by latent parameters of a neural netw... | Accept | The paper proposes a framework to perform signal processing tasks on a signal represented with an implicit neural representation directly in the representation space, without the need to instantiate the signal.
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nips_2022_0ucMtEKCihU | Stochastic Window Transformer for Image Restoration | Thanks to the powerful representation capabilities, transformers have made impressive progress in image restoration. However, existing transformers-based methods do not carefully consider the particularities of image restoration. In general, image restoration requires that an ideal approach should be translation-invari... | Accept | The paper proposes a new stochastic window strategy for image restoration. The stochastic window transformer layer is invariant to translations and is applied to the image degradation and mitigates loss of locality, hence making the approach potentially more robust.
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nips_2022_fJguu0okUY1 | An Empirical Study on Disentanglement of Negative-free Contrastive Learning | Negative-free contrastive learning methods have attracted a lot of attention with simplicity and impressive performances for large-scale pretraining. However, its disentanglement property remains unexplored. In this paper, we examine negative-free contrastive learning methods to study the disentanglement property empir... | Accept | There was a consensus among reviewers that this paper should be accepted. The key convincing arguments that this paper studies a novel setting: how to measure the disentanglement in high-dimensional spaces. For this, the authors perform extensive experiments and come up with a novel metric. The reviewers further felt t... | train | [
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nips_2022_59pMU2xFxG | What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods | A multitude of explainability methods has been described to try to help users better understand how modern AI systems make decisions. However, most performance metrics developed to evaluate these methods have remained largely theoretical -- without much consideration for the human end-user. In particular, it is not yet... | Accept | This paper introduces a human evaluation framework for benchmarking current explainers.
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nips_2022_K48UYo0glaJ | Theseus: A Library for Differentiable Nonlinear Optimization | We present Theseus, an efficient application-agnostic open source library for differentiable nonlinear least squares (DNLS) optimization built on PyTorch, providing a common framework for end-to-end structured learning in robotics and vision. Existing DNLS implementations are application specific and do not always inco... | Accept | This paper presents Theseus, a software library which provides a new layer in the form of a differentiable nonlinear least squares (DNLS) solver. Forward pass solves the problem and the backward pass provides derivates for the optimum with respect to parameters. The reviewers uniformly appreciated the presentation of t... | train | [
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nips_2022_IiCsx9KNVa0 | Unsupervised Representation Learning from Pre-trained Diffusion Probabilistic Models | Diffusion Probabilistic Models (DPMs) have shown a powerful capacity of generating high-quality image samples. Recently, diffusion autoencoders (Diff-AE) are proposed to explore DPMs for representation learning via autoencoding and succeed in various downstream tasks. Their key idea is to jointly train an encoder for d... | Accept | This paper presents a new unsupervised learning method by making full use of pre-trained diffusion probabilistic models. Extensive experiments show that the proposed method can obtain an improvement in performance and learning time. Four reviewers voted for accepting the paper after the rebuttal and the discussion. All... | train | [
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nips_2022_JVoKzM_-lhz | SPoVT: Semantic-Prototype Variational Transformer for Dense Point Cloud Semantic Completion | Point cloud completion is an active research topic for 3D vision and has been widely
studied in recent years. Instead of directly predicting missing point cloud from
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nips_2022_GoOuIrDHG_Y | End-to-end Symbolic Regression with Transformers | Symbolic regression, the task of predicting the mathematical expression of a function from the observation of its values, is a difficult task which usually involves a two-step procedure: predicting the "skeleton" of the expression up to the choice of numerical constants, then fitting the constants by optimizing a non-c... | Accept | The paper proposes a transformer-based approach to perform end-to-end symbolic regression. All three reviewers seem to agree on the usefulness of the proposed approach to reduce inference time. As pointed out by Reviewer Hxn5, although the performance is not superior, the advantage of using pre-training over GP-based ... | train | [
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nips_2022_BRIL0EFvTgc | Pay attention to your loss : understanding misconceptions about Lipschitz neural networks | Lipschitz constrained networks have gathered considerable attention in the deep learning community, with usages ranging from Wasserstein distance estimation to the training of certifiably robust classifiers. However they remain commonly considered as less accurate, and their properties in learning are still not fully u... | Accept | The submission proposes a series of novel results for Lipschitz models on robustness, generalization, and empirical performances opening a new venue for working on Lipschitz neural networks for example. While these results are important and interesting, the authors have struggled to provide a clear takeaway from this s... | train | [
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nips_2022_hH9ohGbhyv | Panchromatic and Multispectral Image Fusion via Alternating Reverse Filtering Network | Panchromatic (PAN) and multi-spectral (MS) image fusion, named Pan-sharpening, refers to super-resolve the low-resolution (LR) multi-spectral (MS) images in the spatial domain to generate the expected high-resolution (HR) MS images, conditioning on the corresponding high-resolution PAN images. In this paper, we present... | Accept | The paper presents a pan sharpening image fusion approach using deep learning. The overall review sentiment leaned towards accepting the paper. The reviewers appreciated the reformulation of the problem as an iterative reverse filtering process and thought the technique was generalizable, broadening its potential impac... | train | [
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nips_2022_ZChgD8OoGds | Joint Entropy Search for Multi-Objective Bayesian Optimization | Many real-world problems can be phrased as a multi-objective optimization problem, where the goal is to identify the best set of compromises between the competing objectives. Multi-objective Bayesian optimization (BO) is a sample efficient strategy that can be deployed to solve these vector-valued optimization problems... | Accept | The authors propose an entropy search method for multi-objective Bayesian optimization that considers the mutual information gain of the location and value of the optimizer simultaneously while selecting query points. Most reviewers found the approach to be interesting. The work is commendable in its attempt to rigor... | val | [
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nips_2022_b9APFSTylGT | Prompt Learning with Optimal Transport for Vision-Language Models | With the increasing attention to large vision-language models such as CLIP, there has been a significant amount of effort dedicated to building efficient prompts. Unlike conventional methods of only learning one single prompt, we propose to learn multiple comprehensive prompts to describe diverse characteristics of cat... | Reject | This paper presents a novel perspective of prompt tuning for few-shot visual recognition: a dynamic matching algorithm between the prompt candidate and the visual features. Compared to the existing CoOp and CoCoOp algorithm, the proposed "Optimal Transportation" idea definitely sounds better and indeed achieves better ... | train | [
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nips_2022_G1vrYk9uX-_ | Mining Unseen Classes via Regional Objectness: A Simple Baseline for Incremental Segmentation | Incremental or continual learning has been extensively studied for image classification tasks to alleviate catastrophic forgetting, a phenomenon in which earlier learned knowledge is forgotten when learning new concepts. For class incremental semantic segmentation, such a phenomenon often becomes much worse due to the ... | Accept | Most of the reviewers pointed out that the motivation of the method is clear, and the method is novel and interesting. The proposed method is also effective on multiple benchmarks. One of the reviewer has concerns about the choice of a parameter (K), and another reviewer has concerns about details of the method. AC adm... | train | [
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nips_2022_zfo2LqFEVY | Multi-modal Grouping Network for Weakly-Supervised Audio-Visual Video Parsing | The audio-visual video parsing task aims to parse a video into modality- and category-aware temporal segments. Previous work mainly focuses on weakly-supervised approaches, which learn from video-level event labels. During training, they do not know which modality perceives and meanwhile which temporal segment contains... | Accept | The authors propose an approach for weakly supervised audio-visual parsing of videos. They propose using learnable categorical embedding to do class-aware unimodal grouping, combined with cross-modal grouping to time-stamp audio, visual and audio-visual events using only video level labels.
Based on the feedback provi... | val | [
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" **Q6**\n*In Equation (10), it is not clear why learned weights are needed to transform both the class tokens and the modality specific features. Is it not equivalent to just transform the features? That is $Ax\\cdot By = x^TA^TBy = B^TAx\\cdot y = Wx\\cdot y$, where $W=B^TA$.*\n\nYes, they are theoretically equiv... | [
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nips_2022_MG3YN3z1J4M | Unveiling The Mask of Position-Information Pattern Through the Mist of Image Features | Recent studies show that paddings in convolutional neural networks encode absolute position information which can negatively affect the model performance for certain tasks. However, existing metrics for quantifying the strength of positional information remain unreliable and frequently lead to erroneous results. To add... | Reject | The three reviewers all leaned towards rejection for this paper. One reviewer was concerned with the relatively small number of images used in the experiment and how valid the conclusions can be from that for PPP as a better metric. Another confusion was over how optimality in padding can be defined. This was important... | val | [
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nips_2022_oW4Zz0zlbFF | Understanding Benign Overfitting in Gradient-Based Meta Learning | Meta learning has demonstrated tremendous success in few-shot learning with limited supervised data. In those settings, the meta model is usually overparameterized. While the conventional statistical learning theory suggests that overparameterized models tend to overfit, empirical evidence reveals that overparameteriz... | Accept | This paper explores the generalization of minimum norm optima for various meta-learning objectives, including basic ERM, model-agnostic meta-learning (MAML) and implicit MAML (iMAML). The generative model considered is "mixed linear regression", in which each of tasks follows a linear + Gaussian noise data model (a dif... | val | [
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nips_2022_nJWcpq2fco3 | Representing Spatial Trajectories as Distributions | We introduce a representation learning framework for spatial trajectories. We represent partial observations of trajectories as probability distributions in a learned latent space, which characterize the uncertainty about unobserved parts of the trajectory. Our framework allows us to obtain samples from a trajectory fo... | Accept | This paper presents a new method for learning spatial partial trajectories. The trajectories are embedded as probability distributions in a learned latent space. The proposed framework is shown to interpolate and extrapolate partially observed trajectories. Experiments on three real datasets show that the proposed meth... | train | [
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nips_2022_Sj2z__i1wX- | Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and its Application to Optimization | The stochastic gradient Langevin Dynamics is one of the most fundamental algorithms to solve sampling problems and non-convex optimization appearing in several machine learning applications. Especially, its variance reduced versions have nowadays gained particular attention. In this paper, we study two variants of this... | Accept | This paper proposes an improved convergence rate for stochastic gradient Langevin dynamics with variance reduction under smoothness and Log-Sobolev inequality assumptions, which improves a long line of prior works. After author response and reviewer discussion, the paper receives unanimous support from the reviewers. T... | train | [
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" The paper deserves to be published. I raise my score to 7.\n\n\nminor remark: The hyperlink (Section 4) in the discussion section does not work.\n",
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nips_2022_DgM7-7eMkq0 | Decoupling Features in Hierarchical Propagation for Video Object Segmentation | This paper focuses on developing a more effective method of hierarchical propagation for semi-supervised Video Object Segmentation (VOS). Based on vision transformers, the recently-developed Associating Objects with Transformers (AOT) approach introduces hierarchical propagation into VOS and has shown promising results... | Accept | The paper obtains three accept and one borderline reject recommendations. Yet all reviewers pointed out that the paper has novelty and originality in the domain of video object segmentation, and also the method works quite well on the tested datasets. The reviewer recommending rejection does not comment at the post-reb... | train | [
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nips_2022_ZYKWi6Ylfg | Harmonizing the object recognition strategies of deep neural networks with humans | The many successes of deep neural networks (DNNs) over the past decade have largely been driven by computational scale rather than insights from biological intelligence. Here, we explore if these trends have also carried concomitant improvements in explaining the visual strategies humans rely on for object recognition.... | Accept | The reviewers have brought up important concerns around the framing of the paper contributions, the presentation and application of the neural harmonizer method, and the use of saliency maps. The authors have addressed some of these concerns in their rebuttal.
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nips_2022_JoZyVgp1hm | Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification | Computer-aided pathology diagnosis based on the classification of Whole Slide Image (WSI) plays an important role in clinical practice, and it is often formulated as a weakly-supervised Multiple Instance Learning (MIL) problem. Existing methods solve this problem from either a bag classification or an instance classifi... | Accept | This submission was reviewed by three reviewers. All three reviewers provided detailed comments during the review period. The authors provided detailed responses to the initial set of reviews. The rebuttals lead to improved scores of some reviewers while other reviewers confirmed that their concerns have been addressed... | test | [
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nips_2022_-deKNiSOXLG | RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection | The task of out-of-distribution (OOD) detection is crucial for deploying machine learning models in real-world settings. In this paper, we observe that the singular value distributions of the in-distribution (ID) and OOD features are quite different: the OOD feature matrix tends to have a larger dominant singular value... | Accept | Thanks for your submission to NeurIPS.
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nips_2022_ikXoMuy_H4 | In the Eye of the Beholder: Robust Prediction with Causal User Modeling | Accurately predicting the relevance of items to users is crucial to the success of many social platforms. Conventional approaches train models on logged historical data; but recommendation systems, media services, and online marketplaces all exhibit a constant influx of new content---making relevancy a moving target, t... | Accept | This paper studies user-item relevance prediction and proposes a novel learning framework that is robust to distributional shifts in observed user-item attributes. All the reviewers appreciated the significance of the problem, the novelty of the solution, and the thorough empirical evaluation. The reviewers were confus... | train | [
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nips_2022_2OdAggzzF3z | ResT V2: Simpler, Faster and Stronger | This paper proposes ResTv2, a simpler, faster, and stronger multi-scale vision Transformer for visual recognition. ResTv2 simplifies the EMSA structure in ResTv1 (i.e., eliminating the multi-head interaction part) and employs an upsample operation to reconstruct the lost medium- and high-frequency information caused by... | Accept | This paper introduced an improvement over ResT by addressing the issues introduced by downsampling operations in MSA. All reviewers have recognized the contribution of this paper and the impressive performance achieved by the proposed algorithm. In the rebuttal, the authors have well-fixed reviewers' major concerns an... | train | [
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nips_2022_CCBJf9xJo2X | Dataset Inference for Self-Supervised Models | Self-supervised models are increasingly prevalent in machine learning (ML) since they reduce the need for expensively labeled data. Because of their versatility in downstream applications, they are increasingly used as a service exposed via public APIs. At the same time, these encoder models are particularly vulnerable... | Accept | This paper joins an interesting area that tackles the use of models in the real world that are accessible publicly via APIs. In these cases, there may be adversaries that attempt to steal the model. This can be done by accessing information about the model from particular queries. One of the approaches used to tackle ... | val | [
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nips_2022_RYZyj_wwgfa | Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks | We propose an algorithm that compresses the critical information of a large dataset into compact addressable memories. These memories can then be recalled to quickly re-train a neural network and recover the performance (instead of storing and re-training on the full original dataset). Building upon the dataset distill... | Accept | This paper proposes a new dataset distillation method that achieves SotA results on several benchmarks. Authors were very responsive to answer reviewers' questions, and made significant improvements to the manuscript, also adding additional results confirming the benefits of their approach. At the end of the discussion... | train | [
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nips_2022_QK38rpF8RWL | GenSDF: Two-Stage Learning of Generalizable Signed Distance Functions | We investigate the generalization capabilities of neural signed distance functions (SDFs) for learning 3D object representations for unseen and unlabeled point clouds. Existing methods can fit SDFs to a handful of object classes and boast fine detail or fast inference speeds, but do not generalize well to unseen shapes... | Accept | This paper studies the generalization ability of neural signed distance functions by proposing a two-stage semi-supervised meta-learning framework. The method has been tested on both synthetic data and real point clouds. The paper received a total of 4 reviews. After the rebuttal, Reviewers 84Fy (accept), 8obm (week a... | train | [
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nips_2022_wxWTyJtiJZ | Product Ranking for Revenue Maximization with Multiple Purchases | Product ranking is the core problem for revenue-maximizing online retailers. To design proper product ranking algorithms, various consumer choice models are proposed to characterize the consumers' behaviors when they are provided with a list of products. However, existing works assume that each consumer purchases at mo... | Accept | The paper studies the problem of choosing a ranked list of products to show to consumers in a regret minimization model. Consumers are assumed to follow a certain search rule to purchase a subset of presented products, and the goal is to maximize the revenue of the product listing under this search model. The model mak... | train | [
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nips_2022_32Ryt4pAHeD | Explainable Reinforcement Learning via Model Transforms | Understanding emerging behaviors of reinforcement learning (RL) agents may be difficult since such agents are often trained in complex environments using highly complex decision making procedures. This has given rise to a variety of approaches to explainability in RL that aim to reconcile discrepancies that may arise b... | Accept | This paper is about explainable AI: explaining a black-box agent's learned behavior via how it aligns with an observers anticipated behaviour
This paper was a bit polarizing with the reviewers. First let's summarize on the agreements between the reviewers. They all agreed:
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" Dear reviewers, \nAs the author-reviewer discussion period is about to end, we would like to know if you have any additional concerns or questions in light of our responses? If so, we will be happy to address them.",
" The reviewer made a valid point in this statement \"Conversely, if the actor's policy is alre... | [
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nips_2022_kb33f8J83c | One Model to Edit Them All: Free-Form Text-Driven Image Manipulation with Semantic Modulations | Free-form text prompts allow users to describe their intentions during image manipulation conveniently. Based on the visual latent space of StyleGAN[21] and text embedding space of CLIP[34], studies focus on how to map these two latent spaces for text-driven attribute manipulations. Currently, the latent mapping betwee... | Accept | The paper develops an image manipulation method FF-CLIP (Freeform CLIP) to edit image semantics based on the text prompt guidance. A cross-attention module is developed to align the visual representations and text semantic embeddings. The results show the effectiveness of the approach. Reviewers had concerns on the nov... | train | [
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nips_2022_6tRhLrki6b8 | Privacy-Preserving Logistic Regression Training with A Faster Gradient Variant | Logistic regression training over encrypted data has been an attractive idea to security concerns for years. In this paper, we propose a faster gradient variant called quadratic gradient to implement logistic regression training in a homomorphic encryption domain, the core of which can be seen as an extension of the s... | Reject | Reviewers remained concerned about the novelty of the contribution, about the extent/limitations of experiments/comparisons to other methods, as well as about the fact that the method does not seem to outperform competitors in certain cases. | train | [
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nips_2022_T5TtjbhlAZH | Towards Practical Control of Singular Values of Convolutional Layers | In general, convolutional neural networks (CNNs) are easy to train, but their essential properties, such as generalization error and adversarial robustness, are hard to control. Recent research demonstrated that singular values of convolutional layers significantly affect such elusive properties and offered several met... | Accept | This paper introduced a tensor decomposition, and associated theory, which allows for the control of singular values in convolutional layers.
Based upon the reviews, rebuttal, and reviewer discussion, I recommend paper acceptance. All reviewers recommend acceptance. The rebuttal was effective, with one reviewer who in... | train | [
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nips_2022_QUyasQGv1Nl | Hyperbolic Contrastive Learning for Visual Representations beyond Objects | Despite the rapid progress in visual representation learning driven by self-/un-supervised methods, both objects and scenes have been primarily treated using the same lens. In this paper, we focus on learning representations for objects and scenes explicitly in the same space. Motivated by the observation that visually... | Reject | Overall, reviewers found that the method is sound but the results are marginal.
There are numerous frameworks for self-supervised learning today. The one introduced here underperforms compared to others, like ORL and Dense-CL, as pointed out by the reviewers. The authors in their response then combined their method wi... | val | [
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nips_2022_ZMrZ5SC2G3_ | Towards Versatile Embodied Navigation | With the emergence of varied visual navigation tasks (e.g., image-/object-/audio-goal and vision-language navigation) that specify the target in different ways, the community has made appealing advances in training specialized agents capable of handling individual navigation tasks well. Given plenty of embodied navigat... | Accept | This paper introduces a novel indoor navigation dataset that is both continuous and audio+visual. Within this setting, they include popular tasks and their audio-generalizations (e.g. image-goal nav --> audio-goal nav). Particularly of note is the leveraging of unification of these tasks during training for a better ... | train | [
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" Thanks for your feedback. The range of training noise is: *vision-language nav.* ($0.19$ SPL), *image-goal nav.* ($0.50$ SPL), *audio-goal nav.* ($0.22$ SPL), *object-goal nav.* ($0.42$ SPL). Most of the numbers in Table 3 are beyond the range of noi... | [
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nips_2022_wfel7CjOYk | Resource-Adaptive Federated Learning with All-In-One Neural Composition | Conventional Federated Learning (FL) systems inherently assume a uniform processing capacity among clients for deployed models. However, diverse client hardware often leads to varying computation resources in practice. Such system heterogeneity results in an inevitable trade-off between model complexity and data acces... | Accept | This paper proposes a method to cope with heterogeneous computation capabilities of clients in federated learning. The initial reviews were positive, but some the high-score reviewers indicated low confidence. The following concerns were raised.
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nips_2022_Lp-QFq2QRXA | Decision Trees with Short Explainable Rules | Decision trees are widely used in many settings where interpretable models are preferred or required. As confirmed by recent empirical studies, the interpretability/explanability of a decision tree critically depends on some of its structural parameters, like size and the average/maximum depth of its leaves. There is... | Accept | The paper presents an interesting approach for using decision trees in order to provide explainable classifiers | train | [
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" Thank you for your responses, especially the clarification regarding pruning algorithms. I appreciate the addition of post-pruning results in the final version, as well as the EC2 results added in the supplement. Given that my main concern was thes... | [
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nips_2022_hOVEBHpHrMu | MsSVT: Mixed-scale Sparse Voxel Transformer for 3D Object Detection on Point Clouds | 3D object detection from the LiDAR point cloud is fundamental to autonomous driving. Large-scale outdoor scenes usually feature significant variance in instance scales, thus requiring features rich in long-range and fine-grained information to support accurate detection. Recent detectors leverage the power of window-ba... | Accept | After the rebuttal and discussion two reviewers are positive, one remains negative. The reviewers liked the overall approach, the writing, and the core experimental results. Some reviewers asked for additional broader experiments and comparisons, which the authors were able to provide. The main concern of reviewer QRYf... | train | [
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nips_2022_IvJj3CvjqHC | Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems | Predicting conversion rate (e.g., the probability that a user will purchase an item) is a fundamental problem in machine learning based recommender systems. However, accurate conversion labels are revealed after a long delay, which harms the timeliness of recommender systems. Previous literature concentrates on utilizi... | Accept | The paper presents an approach for dealing with delayed feedback in online learning settings such as large scale recommender systems, where the delays may be significant as in the case of predicting conversion rate for online shopping where a user may spend days or weeks deciding to finally click "purchase" after first... | train | [
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nips_2022_r9b6T088_75 | Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging | In coded aperture snapshot spectral compressive imaging (CASSI) systems, hyperspectral image (HSI) reconstruction methods are employed to recover the spatial-spectral signal from a compressed measurement. Among these algorithms, deep unfolding methods demonstrate promising performance but suffer from two issues. Firstl... | Accept | This paper integrates a Half-shuffle Transformer (HST) into the deep unfolding framework, establishing an effective method for hyperspectral image (HSI) reconstruction. The reviewers generally agree that the paper is well-written and technically-solid. The majority of the reviews assert that the technical novelty is no... | train | [
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nips_2022_Blbzv2ZjT7 | PerfectDou: Dominating DouDizhu with Perfect Information Distillation | As a challenging multi-player card game, DouDizhu has recently drawn much attention for analyzing competition and collaboration in imperfect-information games. In this paper, we propose PerfectDou, a state-of-the-art Doudizhu AI system that summits the game, in an actor-critic framework with a proposed technique named ... | Accept | The reviewers appreciate both main contributions, namely the PTIE concept and the feature and reward engineering. While there are concerns that neither may generalize beyond the specific game of DouDizhu, and that PTIE may be somewhat incremental given CTDE (or even not novel at all; several CTDE works use the entire s... | test | [
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nips_2022_ouXTjiP0ffV | NCP: Neural Correspondence Prior for Effective Unsupervised Shape Matching | We present Neural Correspondence Prior (NCP), a new paradigm for computing correspondences between 3D shapes. Our approach is fully unsupervised and can lead to high quality correspondences even in challenging cases such as sparse point clouds or non-isometric meshes, where current methods fail. Our first key observati... | Accept | This paper received mixed scores, with three reviewer recommending acceptance and one rejection. The reviewers appreciated the simplicity and effectiveness of the method, but nonetheless raised many questions about the method, requesting the authors to clarify several points. The authors' feedback addressed most of the... | train | [
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nips_2022_SsA-0BZa7B_ | A2: Efficient Automated Attacker for Boosting Adversarial Training | Based on the significant improvement of model robustness by AT (Adversarial Training), various variants have been proposed to further boost the performance. Well-recognized methods have focused on different components of AT (e.g., designing loss functions and leveraging additional unlabeled data). It is generally accep... | Accept | Based on the idea of AutoML, this paper proposes an attack method that efficiently generates strong adversarial perturbations. The main idea is to use an attention mechanism to score possible attacks in the attacker space, then sample the attack to perform based on the assigned scores. The experimental results show tha... | train | [
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nips_2022_V9ngeCMsZK3 | Efficient learning of nonlinear prediction models with time-series privileged information | In domains where sample sizes are limited, efficient learning algorithms are critical. Learning using privileged information (LuPI) offers increased sample efficiency by allowing prediction models access to auxiliary information at training time which is unavailable when the models are used. In recent work, it was show... | Accept | This paper considers a particular setting of time series prediction with privileged information. A special case can be described as predicting x(t+k) from x(t). At training time one is also given x(t+1), x(t+2), ..., x(t+k-1) and a latent dynamics is assumed. The paper presents a learning algorithm that leverages privi... | train | [
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nips_2022_lAN7mytwrIy | ElasticMVS: Learning elastic part representation for self-supervised multi-view stereopsis | Self-supervised multi-view stereopsis (MVS) attracts increasing attention for learning dense surface predictions from only a set of images without onerous ground-truth 3D training data for supervision. However, existing methods highly rely on the local photometric consistency, which fails to identify accurately dense c... | Accept | All the reviewers acknowledged the strength of the paper: self-supervised learning for MVS using contrastive learning to help correspondence based on learned features, SOTA results are obtained on DTU and T&T benchmarks, and the evaluations/ablation studies are well presented. The reviewers also shared weaknesses: the ... | train | [
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nips_2022_SeHslYhFx5- | Interaction Modeling with Multiplex Attention | Modeling multi-agent systems requires understanding how agents interact. Such systems are often difficult to model because they can involve a variety of types of interactions that layer together to drive rich social behavioral dynamics. Here we introduce a method for accurately modeling multi-agent systems. We present ... | Accept | The reviewers agreed this paper was presented well and a valuable contribution. We urge the authors to take the reviewers' comments into account in the final version.
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" Thank you for the rebuttal. \nI understood your responses other than the third one. \nFor visualizing layers of relations, I saw Figures 10 and 11. I understood Figure 10, but I cannot interpret the second layer in Figure 11 (right bottom). \nTotally, my unclear points are clarified, but probably due to the combi... | [
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nips_2022_uP9RiC4uVcR | When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment | AI systems are becoming increasingly intertwined with human life. In order to effectively collaborate with humans and ensure safety, AI systems need to be able to understand, interpret and predict human moral judgments and decisions. Human moral judgments are often guided by rules, but not always. A central challenge f... | Accept | This paper addresses an important question of whether LLMs understand human flexible moral judgments. This is a crucial task in AI safety, as it deals with the capability of understanding ethics in relation to heterogeneous contexts. The proposed approach consists in a prompting strategy that generates a sequence of qu... | train | [
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nips_2022_9njZa1fm35 | Matryoshka Representation Learning | Learned representations are a central component in modern ML systems, serving a multitude of downstream tasks. When training such representations, it is often the case that computational and statistical constraints for each downstream task are unknown. In this context rigid, fixed capacity representations can be either... | Accept | This paper proposes a Matryoshka Representation Learning paradigm to learn representations at multiple granularities, which can adapt to downstream tasks with different computational budgets. All the reviewers find the idea simple and interesting, and acknowledge that the experiments are thorough and impressive. The au... | train | [
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nips_2022_NIrbtCdxfBl | Deep Fourier Up-Sampling | Existing convolutional neural networks widely adopt spatial down-/up-sampling for multi-scale modeling. However, spatial up-sampling operators (e.g., interpolation, transposed convolution, and un-pooling) heavily depend on local pixel attention, incapably exploring the global dependency. In contrast, the Fourier domai... | Accept | This paper proposes using Fourier up-sampling for multi-scale modeling. The paper received initial scores of 8 8 5 3. After the rebuttal and in-depth discussions, most reviewers are satisfied with the authors' replies. Reviewer-tPpX who gives negative scores still has concerns about the novelty and presentation of this... | train | [
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nips_2022_Mftcm8i4sL | Trajectory Inference via Mean-field Langevin in Path Space | Trajectory inference aims at recovering the dynamics of a population from snapshots of its temporal marginals. To solve this task, a min-entropy estimator relative to the Wiener measure in path space was introduced in [Lavenant et al., 2021], and shown to consistently recover the dynamics of a large class of drift-diff... | Accept | This paper studies the challenging problem of inferring the trajectory of a stochastic process from sample observations of its marginals. Earlier work of Lavenant et al. introduced a consistent estimator based on an optimization problem over continuous time. The main contributions of this paper are in (1) introducing a... | train | [
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nips_2022_NhrbIME2Ljl | Divert More Attention to Vision-Language Tracking | Relying on Transformer for complex visual feature learning, object tracking has witnessed the new standard for state-of-the-arts (SOTAs). However, this advancement accompanies by larger training data and longer training period, making tracking increasingly expensive. In this paper, we demonstrate that the Transformer-r... | Accept | All three reviewers lean towards the acceptance of the paper. Reviewer YvUr was not 100% excited about the paper, pointing out the simplicity of the approach and lacking ablations. We encourage the authors to include the new materials they prepared for the rebuttal in the final version of the paper. | train | [
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nips_2022_znNmsN_O7Sh | Object Scene Representation Transformer | A compositional understanding of the world in terms of objects and their geometry in 3D space is considered a cornerstone of human cognition. Facilitating the learning of such a representation in neural networks holds promise for substantially improving labeled data efficiency. As a key step in this direction, we make ... | Accept | The paper received positive leaning reviews (2x borderline accept, 1x weak accept, 1x accept). The meta-reviewer agrees with the reviewers' assessment of the paper. | train | [
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nips_2022_xs9Sia9J_O | Rethinking Individual Global Max in Cooperative Multi-Agent Reinforcement Learning | In cooperative multi-agent reinforcement learning, centralized training and decentralized execution (CTDE) has achieved remarkable success. Individual Global Max (IGM) decomposition, which is an important element of CTDE, measures the consistency between local and joint policies. The majority of IGM-based research focu... | Accept | This paper revisits the notion of Individual Global Max in multi-agent reinforcement learning, in particular considering how to address the fact that individual greedy actions may not be globally optimal in cooperative settings.
Overall, the general sentiment is that this is interesting work with a useful contribution... | train | [
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nips_2022_-GgDBzwZ-e7 | Discrete-Convex-Analysis-Based Framework for Warm-Starting Algorithms with Predictions | Augmenting algorithms with learned predictions is a promising approach for going beyond worst-case bounds. Dinitz, Im, Lavastida, Moseley, and Vassilvitskii~(2021) have demonstrated that warm-starts with learned dual solutions can improve the time complexity of the Hungarian method for weighted perfect bipartite matchi... | Accept | In this paper, the authors provide new theoretical guarantees for augmenting algorithms with learned predictions. Based on discrete convex analysis (DCA), they generalize previous results of Dinitz et al, and obtain better time complexity bounds for a number of online problems. The application of DCA to online algorith... | test | [
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nips_2022_vExdPu73R2z | R^2-VOS: Robust Referring Video Object Segmentation via Relational Cycle Consistency | Referring video object segmentation (R-VOS) aims to segment the object masks in a video given a referring linguistic expression to the object. It is a recently introduced task attracting growing research attention. However, all existing works make a strong assumption: The object depicted by the expression must exist in... | Reject | This paper presents an approach for video object segmentation. The paper considers the possibility that an (object) expression may not correspond to any object in the given video. The approach is based on relational cycle consistency, which the reviewers find technically sound. The paper also has a dataset contribution... | train | [
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nips_2022_u4dXcUEsN7B | Exploring Example Influence in Continual Learning | Continual Learning (CL) sequentially learns new tasks like human beings, with the goal to achieve better Stability (S, remembering past tasks) and Plasticity (P, adapting to new tasks). Due to the fact that past training data is not available, it is valuable to explore the influence difference on S and P among training... | Accept | There was a consensus among reviewers that this paper should be accepted. The paper investigates an interesting direction of combining research on Example Influence with Continual Learning. The methods they introduce was considered to be novel and well-motivated by the reviewers and the experiments show good improvemen... | train | [
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nips_2022_9t-j3xDm7_Q | Motion Transformer with Global Intention Localization and Local Movement Refinement | Predicting multimodal future behavior of traffic participants is essential for robotic vehicles to make safe decisions. Existing works explore to directly predict future trajectories based on latent features or utilize dense goal candidates to identify agent's destinations, where the former strategy converges slowly si... | Accept | This paper proposes to model traffic vehicles using a transformer-based architecture for iteratively refining multimodal trajectory predictions. While the method is related to and builds upon several similar works in the area, it does also introduce some interesting new components such as the iterative refinement and t... | train | [
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" Thank you for uploading the revised paper. It's a great work and I increased my score to 7.",
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nips_2022_aKXBrj0DHm | Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection | Existing open-vocabulary object detectors typically enlarge their vocabulary sizes by leveraging different forms of weak supervision. This helps generalize to novel objects at inference. Two popular forms of weak-supervision used in open-vocabulary detection (OVD) include pretrained CLIP model and image-level supervisi... | Accept | The paper receives overall positive ratings after rebuttal. The major concern before rebuttal is that the benefits and limitations from using MViT are unclear. The rebuttal has addressed most concerns from reviewers. AC encourages authors to make the final revision with review comments. | train | [
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nips_2022_6avZnPpk7m9 | What Makes a "Good" Data Augmentation in Knowledge Distillation - A Statistical Perspective | Knowledge distillation (KD) is a general neural network training approach that uses a teacher to guide a student. Existing works mainly study KD from the network output side (e.g., trying to design a better KD loss function), while few have attempted to understand it from the input side. Especially, its interplay with ... | Accept | After a lively and interactive author discussion period all reviewers ended up recommending to accept this paper.
The work examines the ways in which different data augmentation schemes can increase knowledge distillation performance, providing some theoretical analysis with actionable insights and experiments to back ... | test | [
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" Thank you *so much* for generously raising the score! Your suggestions are well-taken. We *promise* to materialize the 3 conditional changes in our revised version. Thanks again!",
" **Edited review**\n\nIn light of the authors _active discussion and contributions_ during this phase, as well as their addressal ... | [
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nips_2022_agTr-vRQsa | Behavior Transformers: Cloning $k$ modes with one stone | While behavior learning has made impressive progress in recent times, it lags behind computer vision and natural language processing due to its inability to leverage large, human-generated datasets. Human behavior has a wide variance, multiple modes, and human demonstrations naturally do not come with reward labels. Th... | Accept | *Summary*
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nips_2022_ievxJqXwPCm | Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation | In unsupervised domain adaptation (UDA), directly adapting from the source to the target domain usually suffers significant discrepancies and leads to insufficient alignment. Thus, many UDA works attempt to vanish the domain gap gradually and softly via various intermediate spaces, dubbed domain bridging (DB). However,... | Accept | **Summary**: This paper proposes an effective Deliberated Domain Bridging (DDB) approach for domain adaptive semantic segmentation (DASS). It leverages two data mixing techniques: region-level mix and class-level mix, to train two corresponding teacher models, which then guide one student model on the target domain. It... | train | [
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" Thanks very much for your appreciation and recognition，but may I respectively ask you to rise your rating for our paper to promise an acceptance, thanks.",
" Dear authors, your response has cleared my concerns.\nThanks,\ni4C... | [
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nips_2022_b90lKL1IqcF | VoxGRAF: Fast 3D-Aware Image Synthesis with Sparse Voxel Grids | State-of-the-art 3D-aware generative models rely on coordinate-based MLPs to parameterize 3D radiance fields. While demonstrating impressive results, querying an MLP for every sample along each ray leads to slow rendering.
Therefore, existing approaches often render low-resolution feature maps and process them with an ... | Accept | It is valuable now to introduce this technical idea, even if the results do not quite match existing methods. Future work building on this idea may well do so, and it would impede the progress of the subfield to demand both the new idea and SOTA results.
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" Thank you, that answers my questions.",
" Thank you for the quick reply to our rebuttal.\n\n1. Yes, exactly. We ran all methods using ou... | [
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nips_2022_wQ2QNNP8GtM | Cross Aggregation Transformer for Image Restoration | Recently, Transformer architecture has been introduced into image restoration to replace convolution neural network (CNN) with surprising results. Considering the high computational complexity of Transformer with global attention, some methods use the local square window to limit the scope of self-attention. However, t... | Accept | This paper proposes a cross aggregation transformer for image restoration. The Rwin-SA with axial-shift is introduced to aggregates the features cross different windows and the locality complementary module (LCM) is introduced to capture both local and global information. Massive experiments on different datasets and t... | train | [
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nips_2022_iCxRsZcVVAH | Optimistic Curiosity Exploration and Conservative Exploitation with Linear Reward Shaping | In this work, we study the simple yet universally applicable case of reward shaping in value-based Deep Reinforcement Learning (DRL). We show that reward shifting in the form of a linear transformation is equivalent to changing the initialization of the $Q$-function in function approximation. Based on such an equivalen... | Accept | This paper proposes a simple but general way to improve exploration in RL based on the equivalence between reward shifting and the initialization of value function. The paper shows that it is straightforward to implement conservative exploitation and curiosity-driven exploration based on this idea. The results on a var... | val | [
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" We would like to thank all reviewers for their time, generous comments, and suggestions for improving the paper. \n\n... | [
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nips_2022_lgj33-O1Ely | TotalSelfScan: Learning Full-body Avatars from Self-Portrait Videos of Faces, Hands, and Bodies | Recent advances in implicit neural representations make it possible to reconstruct a human-body model from a monocular self-rotation video. While previous works present impressive results of human body reconstruction, the quality of reconstructed face and hands are relatively low. The main reason is that the image reg... | Accept | This paper was reviewed by three experts in the field. Based on the reviewers' feedback, the decision is to recommend the paper for acceptance to NeurIPS 2022.
The reviewers did raise some valuable concerns that should be addressed in the final camera-ready version of the paper. For example, more discussion can be ad... | test | [
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nips_2022_EAcWgk7JM58 | PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies | PointNet++ is one of the most influential neural architectures for point cloud understanding. Although the accuracy of PointNet++ has been largely surpassed by recent networks such as PointMLP and Point Transformer, we find that a large portion of the performance gain is due to improved training strategies, i.e. data a... | Accept | This paper presents a series of training strategies and settings that can improve PointNet++ to match the performance of state-of-the-art architectures. The AC agrees with reviewer jBW5 that the novelty of the paper is limited and some phenomena were observed before. However, the detailed training strategies might have... | train | [
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nips_2022_wtuYr8_KhyM | Stochastic Adaptive Activation Function | The simulation of human neurons and neurotransmission mechanisms has been realized in deep neural networks based on the theoretical implementations of activation functions. However, recent studies have reported that the threshold potential of neurons exhibits different values according to the locations and types of ind... | Accept | Reviewers appreciated the novelty of the proposed activation function, the theoretical motivation and its connection to the SwisH activation.
In terms of presentation and soundness of the results, Reviewers pointed out some weaknesses in the initial reviews for this paper. In particular, the reviews voiced some concern... | train | [
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nips_2022_YTXIIc7cAQ | Improved Fine-Tuning by Better Leveraging Pre-Training Data | As a dominant paradigm, fine-tuning a pre-trained model on the target data is widely used in many deep learning applications, especially for small data sets. However, recent studies have empirically shown that training from scratch has the final performance that is no worse than this pre-training strategy once the numb... | Accept | The paper studies reuse of source data (originally used for pre-training) in the fine-tuning phase. Due to the difference between source and target data, use of the entire source data for fine-tuning can degrade generalization for the target task. However, the paper shows that by carefully choosing a subset of the sour... | train | [
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nips_2022_0cn6LSqwjUv | RainNet: A Large-Scale Imagery Dataset and Benchmark for Spatial Precipitation Downscaling | AI-for-science approaches have been applied to solve scientific problems (e.g., nuclear fusion, ecology, genomics, meteorology) and have achieved highly promising results. Spatial precipitation downscaling is one of the most important meteorological problem and urgently requires the participation of AI. However, the la... | Accept | This paper describes SPDNet, a dataset for spatial precipitation downscaling.
Experiments are provided using a fairly wide set of alternative methods - 14 models (including Kriging which is a widely used standard method in the meteorological community) - as well as a novel architecture proposed by the authors. The au... | test | [
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nips_2022_XdDl3bFUNn5 | Towards Robust Blind Face Restoration with Codebook Lookup Transformer | Blind face restoration is a highly ill-posed problem that often requires auxiliary guidance to 1) improve the mapping from degraded inputs to desired outputs, or 2) complement high-quality details lost in the inputs. In this paper, we demonstrate that a learned discrete codebook prior in a small proxy space largely red... | Accept | This work establishes a face restoration algorithm via integrating and optimizing several existing techniques, including VQ-GAN, Codebook prediction and Transformer. The key innovation comes from a Transformer-based prediction network, named CodeFormer, which may somehow exploit the global contexts helpful for codebook... | train | [
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nips_2022_fiBnhdazkyx | A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning | Federated learning is a learning paradigm to enable collaborative learning across different parties without revealing raw data. Notably, vertical federated learning (VFL), where parties share the same set of samples but only hold partial features, has a wide range of real-world applications. However, most existing stud... | Accept | This paper proposes a VFL technique that is effective in practice (for some datasets) but intuitively may not be general enough for a significant portion of common settings, such as when the identifiers are names. While we recommend to accept this work, we hope the authors can seriously revise this paper in the final v... | train | [
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nips_2022_x8DNliTBSYY | Memorization and Optimization in Deep Neural Networks with Minimum Over-parameterization | The Neural Tangent Kernel (NTK) has emerged as a powerful tool to provide memorization, optimization and generalization guarantees in deep neural networks. A line of work has studied the NTK spectrum for two-layer and deep networks with at least a layer with $\Omega(N)$ neurons, $N$ being the number of training samples... | Accept | solid contribution to ntk theory | train | [
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nips_2022_pMumil2EJh | Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks | Modeling multivariate time series (MTS) is critical in modern intelligent systems. The accurate forecast of MTS data is still challenging due to the complicated latent variable correlation. Recent works apply the Graph Neural Networks (GNNs) to the task, with the basic idea of representing the correlation as a static g... | Accept | This well-written paper has been carefully evaluated by four competent reviewers. Three of them rated the work as marginally acceptable, one gave it full accept score. In despite of a few identified deficiencies, including limited cohort of comparison models, overstated claims about performance of the proposed model at... | train | [
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nips_2022_Zk1SbbdZwS | Model-Based Imitation Learning for Urban Driving | An accurate model of the environment and the dynamic agents acting in it offers great potential for improving motion planning. We present MILE: a Model-based Imitation LEarning approach to jointly learn a model of the world and a policy for autonomous driving. Our method leverages 3D geometry as an inductive bias and l... | Accept | This work introduced a model-based framework for offline imitation learning of autonomous driving policies in simulated urban environments. The proposed model MILE jointly learns a world model and predicts expert actions using a variational generative model. This paper was reviewed by three expert reviewers. At the ini... | train | [
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nips_2022_2EwEWrNADpT | Learning Multi-resolution Functional Maps with Spectral Attention for Robust Shape Matching | In this work, we present a novel non-rigid shape matching framework based on multi-resolution functional maps with spectral attention. Existing functional map learning methods all rely on the critical choice of the spectral resolution hyperparameter, which can severely affect the overall accuracy or lead to overfitting... | Accept | All reviewers voted for acceptance of the paper. Reviewers acknowledge that the paper addresses an important problem: choosing the size of the truncated Eigenbasis for matching using functional maps. Also strong empirical performance on a number of datasets was noted. The rebuttal also addressed many points raised by r... | train | [
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" I would like thank the authors for their hard work to address my comments (including performing additional experiments). After reading authors' response as well as ... | [
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nips_2022_evWx_rWWJuG | Fully Sparse 3D Object Detection | As the perception range of LiDAR increases, LiDAR-based 3D object detection becomes a dominant task in the long-range perception task of autonomous driving. The mainstream 3D object detectors usually build dense feature maps in the network backbone and prediction head. However, the computational and spatial costs on th... | Accept | After the rebuttal and discussion all reviewers are positive, and recommend acceptance. The AC agrees with this recommendation. | test | [
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" Thank you for your efforts on the additional experiments and detailed response. They have resolved most of my concerns. Therefore, I will increase my rating to 6. Great work :)",
" We really appreciate your positive comments, which means a lot to us!\\\nWe will definitely follow all reviewers' comments to impro... | [
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nips_2022_fU-m9kQe0ke | Q-ViT: Accurate and Fully Quantized Low-bit Vision Transformer | The large pre-trained vision transformers (ViTs) have demonstrated remarkable performance on various visual tasks, but suffer from expensive computational and memory cost problems when deployed on resource-constrained devices. Among the powerful compression approaches, quantization extremely reduces the computation and... | Accept | This paper proposes a novel method for Vision Transformers quantization. The IRM and DGD scheme is developed to solve the bottleneck of low-bit quantized Vision Transformers. All the reviewers agree that the proposed method is novel and effective. The concerns and questions are well addressed during the rebuttal period... | test | [
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nips_2022_QYD9bDWR3R_ | Stability and Generalization of Kernel Clustering: from Single Kernel to Multiple Kernel | Multiple kernel clustering (MKC) is an important research topic that has been widely studied for decades. However, current methods still face two problems: inefficient when handling out-of-sample data points and lack of theoretical study of the stability and generalization of clustering. In this paper, we propose a nov... | Accept | The paper introduces a methodology for clustering out-of-sample data in the multiple kernel clustering (MKC) problem by leveraging the relationship between the empirical kernel matrix and the integral operator of the kernel function. Clustering risk bounds for the proposed method are provided that compare favorably wit... | train | [
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" The author-rebuttal phase closes today. Please acknowledge the author rebuttal and state if your position has changed. Thanks!",
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nips_2022_IzpgGB5pC_s | UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup | Subpopulation shift widely exists in many real-world machine learning applications, referring to the training and test distributions containing the same subpopulation groups but varying in subpopulation frequencies. Importance reweighting is a normal way to handle the subpopulation shift issue by imposing constant or a... | Accept | The reviewers unanimously agreed here that incorporating uncertainty scores as importance weights for mixup, and empirically the authors' method seems to lead to substantial quantitative performance improvements. I think the heuristic use of the model's parameter history to estimate uncertainty is reasonable. However, ... | train | [
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nips_2022_QRKmc0dRP75 | On the Strong Correlation Between Model Invariance and Generalization | Generalization and invariance are two essential properties of machine learning models. Generalization captures a model's ability to classify unseen data while invariance measures consistency of model predictions on transformations of the data. Existing research suggests a positive relationship: a model generalizing we... | Accept | This work proposes a very simple to implement, yet effective, metric (effective invariance or EI) to assess the invariance of a model with respect to some input transformation. The main novelty of the proposed method is that it does not rely on the true label, but rather on the agreement between the predictions given a... | train | [
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nips_2022__w2-1nXNjvv | Unsupervised Multi-Object Segmentation by Predicting Probable Motion Patterns | We propose a new approach to learn to segment multiple image objects without manual supervision. The method can extract objects form still images, but uses videos for supervision. While prior works have considered motion for segmentation, a key insight is that, while motion can be used to identify objects, not all obje... | Accept | This paper presents an approach for unsupervised multi-object segmentation. The majority of the reviewers believe the paper contains interesting technical materials that warrants its acceptance. The (only) remaining concern is from Reviewer bRe4, pointing out that the paper uses a more advanced backbone than the baseli... | train | [
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nips_2022_QjurhjyTAb | Roadblocks for Temporarily Disabling Shortcuts and Learning New Knowledge | Deep learning models have been found with a tendency of relying on shortcuts, i.e., decision rules that perform well on standard benchmarks but fail when transferred to more challenging testing conditions. Such reliance may hinder deep learning models from learning other task-related features and seriously affect their... | Accept | The submission describes a new method to avoid the shortcut learning behaviour in DNNs. After the rebuttal and discussion, most of the reviewers are positive about this submission since the proposed method does not require prior knowledge about the dataset and the strong empirical results for the debasing task. On the ... | train | [
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nips_2022_RgWjps_LdkJ | Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning | Consider making a prediction over new test data without any opportunity to learn from a training set of labelled data - instead given access to a set of expert models and their predictions alongside some limited information about the dataset used to train them. In scenarios from finance to the medical sciences, and eve... | Accept | This work suggests that in cases where data is sensitive it might be easier to gain access to pre-trained models instead of to the data used for training them. However, since these models were trained on different distributions, their prediction may be better/worse depending on whether the point of interest in in the s... | train | [
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nips_2022_eQfuHqEsUj | 4D Unsupervised Object Discovery | Object discovery is a core task in computer vision. While fast progresses have been made in supervised object detection, its unsupervised counterpart remains largely unexplored. With the growth of data volume, the expensive cost of annotations is the major limitation hindering further study. Therefore, discovering obj... | Accept | This paper focuses on expanding the problem of unsupervised object discovery (detection) to a new setup, where a 3D point cloud is available as well as an RGB sequence. The paper received three detailed reviews from expert reviewers, all of which had their major concerns about the paper resolved through the author rebu... | test | [
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" Hi,\n\nThe provided response answer my questions. Thanks!",
" Thanks for your valuable comments. Appreciation for the approval and constructive suggestions.\n\nQ1: Thanks for this. We agree that it is important for object discovery to reduce human effort by automatically generating object labels. We attempt to ... | [
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nips_2022_BNqRpzwyOFU | Hierarchical Normalization for Robust Monocular Depth Estimation | In this paper, we address monocular depth estimation with deep neural networks. To enable training of deep monocular estimation models with various sources of datasets, state-of-the-art methods adopt image-level normalization strategies to generate affine-invariant depth representations. However, learning with the imag... | Accept | This paper addresses the problem of training a monocular depth estimation network from variable sources of data. As opposed to only using a single scaling factor as in existing work, the authors propose local schemes for normalising. While the proposed approaches are conceptually simple, they result in a non-trivial bo... | train | [
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" Thanks for your suggestions. We will add this part to our manuscript and elaborate more. Yes, noise is an important reason for using median, and the mean vary per change in the shift. For example, inaccurate predictions in distant areas may constitute noise in the mean representations. When the depth values of al... | [
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nips_2022_TN4UpY_Qzo | Whitening Convergence Rate of Coupling-based Normalizing Flows | Coupling-based normalizing flows (e.g. RealNVP) are a popular family of normalizing flow architectures that work surprisingly well in practice. This calls for theoretical understanding. Existing work shows that such flows weakly converge to arbitrary data distributions. However, they make no statement about the stricte... | Accept | In this work, the authors analyze the convergence of affine coupling flows by providing a theoretical analysis of the whitening convergence rate. While previous analyses were derived for the optimal transport, the reviewers have appreciated the point of view provided by viewing the affine coupling layers as whitening t... | train | [
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" Thank you very much for your answers and clarifications. I updated my review and raised the score.\n\n",
" I thank the authors for addressing my concerns. I upgraded my score to 7.",
" We cordially thank you for your helpful feedback and hope to address the limitations you mentioned in the following:\n\n> Whe... | [
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nips_2022_GwXrGy_vc8m | Estimating Noise Transition Matrix with Label Correlations for Noisy Multi-Label Learning | In label-noise learning, the noise transition matrix, bridging the class posterior for noisy and clean data, has been widely exploited to learn statistically consistent classifiers. The effectiveness of these algorithms relies heavily on estimating the transition matrix. Recently, the problem of label-noise learning in... | Accept | Estimating the noisy transition matrix for handling noisy labels with multi-labels. Good experimental work illustrating estimating transition matrices. reviewers liked theory and the writeup. Paper has had improved citations and writing.
There was some discussion about the assumptions. Nuances of this should be a... | train | [
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" Thanks again for your kind comments. We will carefully consider your suggestions to further revise our paper.",
" Thanks a lot for your kind reminder. We will further carefully consider Eq.(1) and add more explanations in the revised version.",
" Thanks very much for your careful and insightful review! We al... | [
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