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_IsHRUzXPqhI | SHINE: SubHypergraph Inductive Neural nEtwork | Hypergraph neural networks can model multi-way connections among nodes of the graphs, which are common in real-world applications such as genetic medicine. In particular, genetic pathways or gene sets encode molecular functions driven by multiple genes, naturally represented as hyperedges. Thus, hypergraph-guided embed... | Accept | The paper proposed a GNN that explicitly treats hyperedges, and makes use of strongly dual attention, hypergraph regularization, and weighted subgraph attention. The proposed method shows better performance than existing baselines on two genetic medicine datasets. Explainability is also demonstrated.
Reviewers origi... | train | [
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" Dear Reviewer FtLi,\n\nThank you for your constructive feedbacks and suggestions again! We greatly appreciate the additional rigorous ablation studies and state-of-the-art baselines (e.g., AllSetTransformer and AllDeepSets) that you suggested, and our new results have further strengthened the paper. We also highl... | [
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nips_2022_0TDki1mlcwz | LASSIE: Learning Articulated Shapes from Sparse Image Ensemble via 3D Part Discovery | Creating high-quality articulated 3D models of animals is challenging either via manual creation or using 3D scanning tools. Therefore, techniques to reconstruct articulated 3D objects from 2D images are crucial and highly useful. In this work, we propose a practical problem setting to estimate 3D pose and shape of ani... | Accept | This paper had substantial discussion amongst reviewers, and concluded with mixed reviews (7, 7, 7, 4). The positive reviewers (mH51, rsf7, LjEQ) actively and strongly championed the paper. The remaining concern comes from boNQ, who (in reviewer-to-reviewer discussions) was primarily concerned with making sure that th... | train | [
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" I appreciate the authors' detailed response. I like the new experiments on novel image \"inference\". I am also satisfied with the response to the current limitations and have no further questions. I think this submission present important insight on reconstructing articulated objects from sparse images.",
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nips_2022_PO6cKxILdi | Bayesian Risk Markov Decision Processes | We consider finite-horizon Markov Decision Processes where parameters, such as transition probabilities, are unknown and estimated from data. The popular distributionally robust approach to addressing the parameter uncertainty can sometimes be overly conservative. In this paper, we propose a new formulation, Bayesian r... | Accept | Motivated by the often overly conservative characteristics of distributionally robust MDPs, this paper employs (nested) Bayesian posterior distributions to model the uncertainty over MDP parameters. The programming solution is similar to belief state approximation methods for POMDPs. The experiments (after revision) se... | test | [
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" Thank you for your reply and acknowledgement! We look forward to your final recommendation!",
" I want to thank the authors for their further clarifications over my follow-up comments. I believe I now have a better understanding of the merits of this submission, and I will discuss them with other reviewers befo... | [
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nips_2022_16nVkS8Twxo | Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization | Variance reduction techniques such as SPIDER/SARAH/STORM have been extensively studied to improve the convergence rates of stochastic non-convex optimization, which usually maintain and update a sequence of estimators for a single function across iterations. What if we need to track multiple functional mappings across... | Accept | The paper makes a nice contribution to the growing field of stochastic compositional optimization. In particular, it considers the case of coupled compositional problems and provides an algorithm that tracks all the inner-level objective information required in an efficient manner. Sample complexities (which are intuit... | train | [
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" Dear Authors, \n\nThanks for your detailed comments. It makes more sense now. ",
" Dear authors,\n\nThank you for the response! It clearly addressed all my concerns.",
" Thank you very much for your constructive comments and suggestions! We will revise accordingly.\n\n---\n\nQ1: It would be much more clear if... | [
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nips_2022_tuC6teLFZD | Synergy-of-Experts: Collaborate to Improve Adversarial Robustness | Learning adversarially robust models require invariant predictions to a small neighborhood of its natural inputs, often encountering insufficient model capacity. There is research showing that learning multiple sub-models in an ensemble could mitigate this insufficiency, further improving the generalization and the rob... | Accept | This paper proposes an ensemble-type solution for improving the adversarial robustness of a model. The proposed idea is simple, yet novel with theoretical supports. The authors did a good job clarifying reviewers' concerns and all reviewers finally recommend acceptance. AC also thinks that this is a good paper in vario... | val | [
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" Thanks for your reply! We would like to make every effort to clarify the unclear points.\n\n**Question 1:First, the generation of adversarial examples $\\tilde{x}''$ in Part 3 of your response is not discussed in t... | [
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nips_2022_Q8GnGqT-GTJ | Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning | Prompt learning approaches have made waves in natural language processing by inducing better few-shot performance while they still follow a parametric-based learning paradigm; the oblivion and rote memorization problems in learning may encounter unstable generalization issues. Specifically, vanilla prompt learning may ... | Accept | The paper proposes RetroPrompt, which builds a knowledge-store with training examples and improves few-shot and zero-shot performance.
All reviewers appreciate the improvements over competitive baselines and the quality of presentation. The main weaknesses are the lack of ablations to clarify where the gains come from... | test | [
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" Dear Reviewer,\n\nWe hope that you've had a chance to read our response. We would really appreciate a reply as to whether our response and clarifications have addressed the issues raised in your review, or whether there is anything else we can address.",
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nips_2022_Soadfc-JMeX | HSDF: Hybrid Sign and Distance Field for Modeling Surfaces with Arbitrary Topologies | Neural implicit function based on signed distance field (SDF) has achieved impressive progress in reconstructing 3D models with high fidelity. However, such approaches can only represent closed shapes.
Recent works based on unsigned distance function (UDF) are proposed to handle both watertight and open surfaces.
Non... | Accept | The reviewers agree that the paper's idea to include both sign and distance fields is a valuable contribution to 3D computer vision research.
Reviewers ask sensible clarifying questions (e.g. orienting the training data, sign network continuity) and the rebuttal's answers are illuminating and to the point.
A short no... | train | [
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" Thanks to the reviewer nGbx for further explanation, we provide some point cloud data[A] (mentioned by reviewer oe6G) and the reconstruction script[B] provided by NDF's author in these links below. If you are interested, you can try it for the reconstruction of the point cloud.\n\n[A] https://ufile.io/kdu0hoja\n\... | [
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nips_2022_cRNl08YWRKq | Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks | Visual tasks vary a lot in their output formats and concerned contents, therefore it is hard to process them with an identical structure. One main obstacle lies in the high-dimensional outputs in object-level visual tasks. In this paper, we propose an object-centric vision framework, Obj2Seq. Obj2Seq takes objects as b... | Accept | The paper proposes an approach for formulating a few visual tasks as sequence prediction with class prompt. Reviewers are overall positive about the paper, especially the direction towards a unified vision model where the paper is exploring. However, it is also pointed out the paper should be more explicit about how th... | train | [
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nips_2022_1ryTomA0iKa | Riemannian Neural SDE: Learning Stochastic Representations on Manifolds | In recent years, the neural stochastic differential equation (NSDE) has gained attention for modeling stochastic representations with great success in various types of applications. However, it typically loses expressivity when the data representation is manifold-valued. To address this issue, we suggest a principled m... | Accept | There was a consensus towards weak acceptance among all the reviewers, and I agree with this consensus. This paper solves an important problem of applying SDEs to manifolds. It is clearly written, and all the reviewers agree that the claims are well-supported by strong experimental results. On the other hand, this clar... | train | [
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nips_2022_C9yUwd72yy | Learning Latent Seasonal-Trend Representations for Time Series Forecasting | Forecasting complex time series is ubiquitous and vital in a range of applications but challenging. Recent advances endeavor to achieve progress by incorporating various deep learning techniques (e.g., RNN and Transformer) into sequential models. However, clear patterns are still hard to extract since time series are o... | Accept | The paper presents a novel learning approach named LaST for time-series forecasting based on variational inference to disentangle the seasonal-trend representations in the latent space. Empirical results validate the effectiveness of the proposed method in comparison with several strong baselines. Reviewers generally a... | train | [
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nips_2022_tbId-oAOZo | QueryPose: Sparse Multi-Person Pose Regression via Spatial-Aware Part-Level Query | We propose a sparse end-to-end multi-person pose regression framework, termed QueryPose, which can directly predict multi-person keypoint sequences from the input image. The existing end-to-end methods rely on dense representations to preserve the spatial detail and structure for precise keypoint localization. However,... | Accept | The authors propose a novel framework for end-to-end multi-person pose estimation by employing a set of learnable part-level queries along with instance-level queries. Promising results are demonstrated on the challenging COCO and CrowdPose datasets. The provided author rebuttal successfully addressed all reviewer conc... | train | [
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" Thank you for the previous insightful comments. We also would like to receive your further response about our clarifications.",
" We are glad to hear that the concerns have been addressed. Thanks again for the time and effort in reviewing our paper. The constructive suggestions help us make our paper better.",
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nips_2022_OHkq7qNr72- | A Mixture Of Surprises for Unsupervised Reinforcement Learning | Unsupervised reinforcement learning aims at learning a generalist policy in a reward-free manner for fast adaptation to downstream tasks. Most of the existing methods propose to provide an intrinsic reward based on surprise. Maximizing or minimizing surprise drives the agent to either explore or gain control over its e... | Accept | This paper proposes a method for unsupervised skill discovery, which learns a mixture of policies that simultaneously maximizes and minimizes the surprise. All the reviewers agree that the paper tackles an important active research area. The paper is well written; the motivation is well explained; the proposed method i... | train | [
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" We appreciate the follow-up response from the reviewer. Although our objective switching mechanism appears to be ad-hoc, we consider its simplicity a strength for the following reasons: it does not increase the computational cost duri... | [
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nips_2022_pCrB8orUkSq | Monocular Dynamic View Synthesis: A Reality Check | We study the recent progress on dynamic view synthesis (DVS) from monocular video. Though existing approaches have demonstrated impressive results, we show a discrepancy between the practical capture process and the existing experimental protocols, which effectively leaks in multi-view signals during training. We defin... | Accept | Pre-rebuttal, this paper had mixed reviews. Post-rebuttal, the paper had two strong supporters, A6gt and vDbH, who argued that the paper provides valuable insights into an important field, as well as a supporter dLU6, who commented in the discussion below that they are in favor of the paper (although did not update the... | test | [
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nips_2022_jSorGn2Tjg | Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models for Protein Structures | Antibodies are immune system proteins that protect the host by binding to specific antigens such as viruses and bacteria. The binding between antibodies and antigens is mainly determined by the complementarity-determining regions (CDR) of the antibodies. In this work, we develop a deep generative model that jointly mod... | Accept | This is a very exciting and timely paper that eleganlty enables CDR sequence-structure co-design, sequence design given a certain backbone, and antibody optimization.
The reviewers and AC all appreciate the extensive feedback provided by the authors and the additional studies included in the supplements. We strongly... | train | [
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" Dear authors,\n\nThanks for addressing all of my comments! I increased my score to “weak accept”.\n\n[Q2] I agree with you that MSA based approaches such as AlphaFold2 or RosettaFold are less accurate for predicting CDR loops due to often insufficient homologs for building a reliable MSA. However, recent approach... | [
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nips_2022_QLGuUwDx4S | DropCov: A Simple yet Effective Method for Improving Deep Architectures | Previous works show global covariance pooling (GCP) has great potential to improve deep architectures especially on visual recognition tasks, where post-normalization of GCP plays a very important role in final performance. Although several post-normalization strategies have been studied, these methods pay more close a... | Accept | Both reviewer Fzo6, reviewer VWPn and reviewer 5eq4 have concerns and been questions regarding equation 5. Please clarify the clarifications on the paper and add intuition and more discussion of Eq. 5.
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nips_2022_XrECTbqRCfX | Approximate Secular Equations for the Cubic Regularization Subproblem | The cubic regularization method (CR) is a popular algorithm for unconstrained non-convex optimization. At each iteration, CR solves a cubically regularized quadratic problem, called the cubic regularization subproblem (CRS). One way to solve the CRS relies on solving the secular equation, whose computational bottleneck... | Accept | This paper proposes a new method for solving the cubic subproblem in the cubic regularized Newton method. The propose method is simple, but works very well in practice. The numerical experiments demonstrated that the ARC algorithm combined with the proposed new subproblem solver significantly outperforms ARC with diffe... | train | [
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" Dear reviewer MvBV,\n\nThanks again for your review and comments. Do you have further comments? We hope our response answers all your concerns well.\n\nWe would like to state something further.\n## Contribution\nin this paper, **our main contribution** is the proposed novel ASEM in solving cubic subproblems, theo... | [
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nips_2022_QqWqFLbllZh | Spatial Pruned Sparse Convolution for Efficient 3D Object Detection | 3D scenes are dominated by a large number of background points, which is redundant for the detection task that mainly needs to focus on foreground objects. In this paper, we analyze major components of existing sparse 3D CNNs and find that 3D CNNs ignores the redundancy of data and further amplifies it in the down-samp... | Accept | The paper shows that it is possible to obtain a good saving in both terms of FLOPS and latency using sparse convolutions for 3d object detection by leveraging the magnitude of features. After a strong rebuttal all 4 reviewers vote for acceptance of the paper with high-confidence.
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nips_2022_H5z5Q--YdYd | BMU-MoCo: Bidirectional Momentum Update for Continual Video-Language Modeling | Video-language models suffer from forgetting old/learned knowledge when trained with streaming data. In this work, we thus propose a continual video-language modeling (CVLM) setting, where models are supposed to be sequentially trained on five widely-used video-text datasets with different data distributions. Although ... | Accept | This work presents a study on continual video-language modeling. In addition to the modeling side of things (BMU-MoCO), the authors construct a new benchmark in which they compare a number of existing methods. While I think it's great that the authors came up with a new benchmark, it's always a somewhat difficult analy... | train | [
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" We sincerely appreciate all reviewers’ time and efforts in reviewing our paper. We are glad to find that reviewers generally recognized our contributions: \n\n* **Model.** Proposing a novel cross-modal MoCo-based model with bidirectional momentum update for continual learning [6RoA, ZHhB].\n* **Setting.** Introd... | [
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nips_2022_GAUwreODU5L | GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images | As several industries are moving towards modeling massive 3D virtual worlds, the need for content creation tools that can scale in terms of the quantity, quality, and diversity of 3D content is becoming evident. In our work, we aim to train performant 3D generative models that synthesize textured meshes which can be di... | Accept |
The paper proposes a generative model for synthesizing textured 3D meshes given only a collection of 2D images. The paper has received overwhelmingly positive reviews. Many reviewers find the idea interesting, the paper well-written, the results compelling, and the experiments comprehensive. The rebuttal further addre... | train | [
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" We thank the reviewer for the feedback! \n\nThat's really great point to interpolate the geometry code and texture code individually! We provide two such results in our revised main paper (see Sec 6.5, Fig 16 & 17). Please check it.\n\nFor each figure, at every row, we interpolate the geometry latent code while ... | [
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nips_2022_3vYkhJIty7E | Learning Optical Flow from Continuous Spike Streams | Spike camera is an emerging bio-inspired vision sensor with ultra-high temporal resolution. It records scenes by accumulating photons and outputting continuous binary spike streams. Optical flow is a key task for spike cameras and their applications. A previous attempt has been made for spike-based optical flow. Howeve... | Accept | This work is focused on the estimation of optical flow from a neuromorphic camera that produces Poisson spiking at each pixel with a rate governed by overall intensity. The authors use local space-time aggregation of spike-time differentials to identify features that are then corresponded via a convGRU decoder.
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nips_2022_7KBzV5IL7W | INRAS: Implicit Neural Representation for Audio Scenes | The spatial acoustic information of a scene, i.e., how sounds emitted from a particular location in the scene are perceived in another location, is key for immersive scene modeling. Robust representation of scene's acoustics can be formulated through a continuous field formulation along with impulse responses varied by... | Accept | This is a technically good paper, with some flaws. Parts of the paper are hard to read. Several questions remain, e.g. how to determine the optimal number and location of bouncepoints, and how they depend on room layout and content.
The motivation behind some of the comparisons, e.g. to AACs is unclear.
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" Thank the authors for the response, which addresses many of my concerns. The new comparions are very helpful. It makes sense to use a compact neural representation to encode the acoustic fields, which can be computationally expensive to simulate. However, I am still not very convinced that the dataset used in the... | [
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nips_2022_Zzi8Od19DSU | Posterior and Computational Uncertainty in Gaussian Processes | Gaussian processes scale prohibitively with the size of the dataset. In response, many approximation methods have been developed, which inevitably introduce approximation error. This additional source of uncertainty, due to limited computation, is entirely ignored when using the approximate posterior. Therefore in prac... | Accept | Gaussian Processes are a very nice modelisation tool in Bayesian nonparametrics, with very nice uncertainty quantification. But they also lead to serious computational issues. So, in practice, it is difficult to know what part of the uncertainty is due to the data, and what is due to approximations in the computations.... | train | [
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" Thank you for the detailed explanations. I will keep my score as it is. ",
" Thank you for your response and feedback! We will make sure to include the details on the stopping criteria used and a clarification about the grey lines in Algorithm 1 in the final version.",
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nips_2022_9s3CbJh4vRP | Precise Regret Bounds for Log-loss via a Truncated Bayesian Algorithm | We study sequential general online regression, known also as sequential probability assignments, under logarithmic loss when compared against a broad class of experts. We obtain tight, often matching, lower and upper bounds for sequential minimax regret, which is defined as the excess loss incurred by the predictor ove... | Accept | This paper considers the problem of online learning with the logarithmic loss, and provides a new algorithm based on smoothing of the log loss which matches certain rates that were previously only achieved through non-constructive methods.
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nips_2022_UVF3yybAjF | Robust Testing in High-Dimensional Sparse Models | We consider the problem of robustly testing the norm of a high-dimensional sparse signal vector under two different observation models. In the first model, we are given $n$ i.i.d. samples from the distribution $\mathcal{N}\left(\theta,I_d\right)$ (with unknown $\theta$), of which a small fraction has been arbitrarily c... | Accept | The reviewers and I agree that this result is a solid, if not groundbreaking, result in the theory of robust statistics. It considers a very natural testing problem, and when the fraction of corrupted samples is constant, completely settles the statistical complexity of the problem. There are some concerns that its imm... | val | [
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nips_2022_VrJWseIN98 | VER: Scaling On-Policy RL Leads to the Emergence of Navigation in Embodied Rearrangement | We present Variable Experience Rollout (VER), a technique for efficiently scaling batched on-policy reinforcement learning in heterogenous environments (where different environments take vastly different times to generate rollouts) to many GPUs residing on, potentially, many machines. VER combines the strengths of and ... | Accept | The paper proposes a novel method that takes the best of both worlds: synchronous and asynchronous on-policy RL methods.
The rebuttal nicely addressed the concerns of most reviewers.
Why the method makes sense and has benefits is rather straightforward and intuitive (which is a good thing!). The paper is clearly an exp... | test | [
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" We hope that we have addressed your concerns. Are you satisfied with our response or do you have additional questions?",
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nips_2022_KFxIsdIvUj | Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning | While Reinforcement Learning (RL) aims to train an agent from a reward function in a given environment, Inverse Reinforcement Learning (IRL) seeks to recover the reward function from observing an expert's behavior. It is well known that, in general, various reward functions can lead to the same optimal policy, and henc... | Accept | The paper provides an investigation of conditions for recovering the reward function up to a constant from multiple experts. While the assumption that observations from multiple (entropy regularized experts) acting in different environments is quite strong, the authors did a good job in justifying and further explainin... | train | [
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" Dear reviewer,\n\nas the discussion period ends soon we were wondering if you have any other question on the differences between our work and (Ng et al. 1999). If that is the case, we are happy to have further discussion.\n\nBest,\nAuthors ",
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nips_2022_4kjQZTNz-NH | AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos | This paper studies the problem of real-world video super-resolution (VSR) for animation videos, and reveals three key improvements for practical animation VSR. First, recent real-world super-resolution approaches typically rely on degradation simulation using basic operators without any learning capability, such as blu... | Accept | The paper proposes a method for super-resolution of animation videos. The contribution is three-fold: a new approach to learned image degradations, a dataset of high-resolution animation videos, and a multiscale model architecture. The method demonstrated good empirical results while being substantially faster than pri... | train | [
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nips_2022_NgIf3FpcHie | Rethinking Alignment in Video Super-Resolution Transformers | The alignment of adjacent frames is considered an essential operation in video super-resolution (VSR). Advanced VSR models, including the latest VSR Transformers, are generally equipped with well-designed alignment modules. However, the progress of the self-attention mechanism may violate this common sense. In this pap... | Accept | This paper re-thinks the role of alignment in video super-resolution based on transformer models. The video alignment is costly which may need manual efforts. This paper proposed several inspiring and counter-intuitive remarks, such as that alignment is unnecessary and may be harmful to the transformer model. The autho... | train | [
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" Dear Reviewer CC5y:\n\nThanks again for your precious time and valuable comments.\n\nInitially, Reviewer F7UK (denoted as R3) and Reviewer TWT3 (denoted as R4) both thought positively about our work. After rebuttal, Reviewer j86q (denoted a... | [
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nips_2022_pGcTocvaZkJ | Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis | This paper considers doing quantile regression on censored data using neural networks (NNs). This adds to the survival analysis toolkit by allowing direct prediction of the target variable, along with a distribution-free characterisation of uncertainty, using a flexible function approximator. We begin by showing how an... | Accept | This paper studies the quantile regression of censored data. Neural network models are used as the statistical model. Numerical results show the proposed algorithm is computationally efficient and attains high prediction accuracy compared to existing methods. Since reviewers agree that this paper is well written and in... | train | [
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nips_2022_AsH-Tx2U0Ug | Effective Backdoor Defense by Exploiting Sensitivity of Poisoned Samples | Poisoning-based backdoor attacks are serious threat for training deep models on data from untrustworthy sources. Given a backdoored model, we observe that the feature representations of poisoned samples with trigger are more sensitive to transformations than those of clean samples. It inspires us to design a simple sen... | Accept | The authors propose a new method for defending against backdoor attacks which is based on the observation that poisoned samples are more sensitive to transformations than clean samples. They design a metric called \textit{feature consistency towards transformations (FCT)} to distinguish poisoned samples from clean samp... | train | [
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" Dear Reviewer,\n\nThanks for you constructive comments which are really beneficial to our work. And we will add the limitation on the feature dimensionality into the final manuscript.",
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nips_2022_er4GR0wHWQO | Asymptotically Unbiased Instance-wise Regularized Partial AUC Optimization: Theory and Algorithm | The Partial Area Under the ROC Curve (PAUC), typically including One-way Partial AUC (OPAUC) and Two-way Partial AUC (TPAUC), measures the average performance of a binary classifier within a specific false positive rate and/or true positive rate interval, which is a widely adopted measure when decision constraints ... | Accept | The paper presented a novel reformulation of maximzing PAUC in an asymptotically unbiased and instance-wise manner. Based on this formulation, the authors presented an efficient stochastic min-max algorithm for OPAUC and TPAUC maximization. Convergence and generalization analysis were conducted. The concerns and que... | train | [
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nips_2022_Lpla1jmJkW | Constants of motion network | The beauty of physics is that there is usually a conserved quantity in an always-changing system, known as the constant of motion. Finding the constant of motion is important in understanding the dynamics of the system, but typically requires mathematical proficiency and manual analytical work. In this paper, we presen... | Accept | 2 of the 3 reviewers highly appreciated the rebuttal and are now recommending the paper for acceptance without any reservations. The 3rd, most critical reviewer, FmUK did unfortunately not react. The new experiments "learning from pixels" nicely addresses the reviewer's concern about having to carefully choose the syst... | val | [
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" I thank the authors for their informative replies. Ideally I would have liked some more analysis of the QR part, but the paper is otherwise well written, the technical approach appears well founded, and it improves upon relevant baselines... | [
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nips_2022_gbXqMdxsZIP | OTKGE: Multi-modal Knowledge Graph Embeddings via Optimal Transport | Multi-modal knowledge graph embeddings (KGE) have caught more and more attention in learning representations of entities and relations for link prediction tasks. Different from previous uni-modal KGE approaches, multi-modal KGE can leverage expressive knowledge from a wealth of modalities (image, text, etc.), leading t... | Accept | This paper presents a method to learn multi-modal knowledge graph embeddings. To integrate the embeddings from different modalities, which is a difficult task because of the heterogeneity across the different modalities, the paper presents an optimal transport based method to learn multi-modal embeddings.
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nips_2022_m6DJxSuKuqF | Keypoint-Guided Optimal Transport with Applications in Heterogeneous Domain Adaptation | Existing Optimal Transport (OT) methods mainly derive the optimal transport plan/matching under the criterion of transport cost/distance minimization, which may cause incorrect matching in some cases. In many applications, annotating a few matched keypoints across domains is reasonable or even effortless in annotation ... | Accept | In this paper the authors propose a novel Optimal Transport problem that uses a small number of annotated keypoints in both source and target domain to encode additional information and guide the OT plan in the problem. The authors propose a variant of the sinkhorn algorithm to solve the problem and show that it can b... | train | [
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nips_2022_qSYVigfakqS | Weak-shot Semantic Segmentation via Dual Similarity Transfer | Semantic segmentation is a practical and active task, but severely suffers from the expensive cost of pixel-level labels when extending to more classes in wider applications. To this end, we focus on the problem named weak-shot semantic segmentation, where the novel classes are learnt from cheaper image-level labels wi... | Accept | Two reviewers give a weak accept rating while the other one gives a borderline reject rating. Considering the low confidence of the negative comment and the contrary comments in paper writing (confident "easy to follow" vs. unconfident "hard to understand"), the AC would lean to accept this paper. | train | [
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nips_2022_V03mpOjCwtg | Learning Generalizable Part-based Feature Representation for 3D Point Clouds | Deep networks on 3D point clouds have achieved remarkable success in 3D classification, while they are vulnerable to geometry variations caused by inconsistent data acquisition procedures. This results in a challenging 3D domain generalization (3DDG) problem, that is to generalize a model trained on source domain to an... | Accept | The paper works on domain generalization of 3D point cloud classification, and proposes a part-based domain generalization network for the purpose, whose key idea is to build a common feature space of part template and align the part-level features wherein. Three reviewers appreciate the contributions, including the cl... | train | [
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nips_2022_Bv8GV6d76Sy | Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks | Monte Carlo (MC) integration is the _de facto_ method for approximating the predictive distribution of Bayesian neural networks (BNNs). But, even with many MC samples, Gaussian-based BNNs could still yield bad predictive performance due to the posterior approximation's error. Meanwhile, alternatives to MC integration a... | Accept | The paper proposes a method to refine Gaussian approximations of the posterior in Bayesian computations by using the normalizing flow. Such Gaussian approximations are usually cheap to obtain, via Laplace approximation or variational Bayes. The method proposed by the authors outperform standard MC approaches and is com... | train | [
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nips_2022_VMU-hMsonit | Training and Inference on Any-Order Autoregressive Models the Right Way | Conditional inference on arbitrary subsets of variables is a core problem in probabilistic inference with important applications such as masked language modeling and image inpainting. In recent years, the family of Any-Order Autoregressive Models (AO-ARMs) -- closely related to popular models such as BERT and XLNet -- ... | Accept | This paper introduces an improved training method for auto-regressive generative models. Specifically, the paper identifies a redundancy problem in common auto-regressive models and proposes a way to fix this. The reviewers found the contribution significant and important, and it is likely that the paper will have subs... | train | [
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nips_2022_nax3ATLrovW | Versatile Multi-stage Graph Neural Network for Circuit Representation | Due to the rapid growth in the scale of circuits and the desire for knowledge transfer from old designs to new ones, deep learning technologies have been widely exploited in Electronic Design Automation (EDA) to assist circuit design. In chip design cycles, we might encounter heterogeneous and diverse information sourc... | Accept | This paper proposes a GNN approach to EDA using the construction of a circuit graph that combines geometric and topological information, as well as features generated from physical properties of circuit components. While reviewers have raised certain concerns (some addressed already in rebuttal), they all settled (post... | train | [
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nips_2022_tvwkeAIcRP8 | S$^3$-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint | In this paper, we address the "dual problem" of multi-view scene reconstruction in which we utilize single-view images captured under different point lights to learn a neural scene representation. Different from existing single-view methods which can only recover a 2.5D scene representation (i.e., a normal / depth map ... | Accept | This paper had reviews ranging from borderline reject to strong accept. The most negative reviewer had concerns about the assumptions in the framework (point light sources), and the loss of accuracy as the number of light sources decreases, but the remaining reviewers were compelled by the ability to hand scenes with ... | test | [
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nips_2022_YCPmfirAcc | High-dimensional Additive Gaussian Processes under Monotonicity Constraints | We introduce an additive Gaussian process (GP) framework accounting for monotonicity constraints and scalable to high dimensions. Our contributions are threefold. First, we show that our framework enables to satisfy the constraints everywhere in the input space. We also show that more general componentwise linear inequ... | Accept | This paper deals with the problem of regression with an additive Gaussian process prior and a linear inequality constraint. A finite-dimensional approximation is proposed to the Gaussian process in terms of a linear combination of triangular basis functions with Gaussian weights. The weights are then estimated by solvi... | train | [
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nips_2022_Qt4rKNYzcO | Enhanced Latent Space Blind Model for Real Image Denoising via Alternative Optimization | Motivated by the achievements in model-based methods and the advances in deep networks, we propose a novel enhanced latent space blind model based deep unfolding network, namely ScaoedNet, for complex real image denoising. It is derived by introducing latent space, noise information, and guidance constraint into the de... | Accept | The paper under review introduces a deep unrolling network driven by a latent space blind model for image denoising. Although the network combines known components, it has novel elements and good algorithms, the experimental results are robust, the implementation details are rich, and the ablation research is extensive... | train | [
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nips_2022_2nYz4WZAne4 | Generative Evolutionary Strategy For Black-Box Optimizations | Many scientific and technological problems are related to optimization. Among them, black-box optimization in high-dimensional space is particularly challenging. Recent neural network-based black-box optimization studies have shown noteworthy achievements. However, their capability in high-dimensional search space is s... | Reject | While the topic of the paper and the reported experimental results appeared to be of interest of the reviewing team, a number of limitations were put to the fore by the reviewers, who graded the paper with scores between 2 and 5, and often emphasized various issues such as a lacunary literature review (See in particula... | val | [
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nips_2022_q0XxMcbaZH9 | Learning Equivariant Segmentation with Instance-Unique Querying | Prevalent state-of-the-art instance segmentation methods fall into a query-based scheme, in which instance masks are derived by querying the image feature using a set of instance-aware embeddings. In this work, we devise a new training framework that boosts query-based models through discriminative query embedding lear... | Accept | This paper leverages dataset-level uniqueness and transformation equivariance to improve state-of-the-art instance segmentation methods.
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nips_2022_kADW_LsENM | Video-based Human-Object Interaction Detection from Tubelet Tokens | We present a novel vision Transformer, named TUTOR, which is able to learn tubelet tokens, served as highly-abstracted spatial-temporal representations, for video-based human-object interaction (V-HOI) detection. The tubelet tokens structurize videos by agglomerating and linking semantically-related patch tokens along ... | Accept | *Summary*
This paper presents a novel vision Transformer TUTOR for human-object interaction detection in videos. TUTOR structurizes a video into a few tubelet tokens by agglomerating and linking semantically-related patch tokens along spatial and temporal domains. Experiments are conducted on VidHOI and CAD-120, showi... | train | [
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nips_2022_r70ZpWKiCW | Semi-Supervised Semantic Segmentation via Gentle Teaching Assistant | Semi-Supervised Semantic Segmentation aims at training the segmentation model with limited labeled data and a large amount of unlabeled data. To effectively leverage the unlabeled data, pseudo labeling, along with the teacher-student framework, is widely adopted in semi-supervised semantic segmentation. Though proved t... | Accept | The paper was reviewed by four expert reviewers in the field. The initial ratings were three weak accept and one weak reject.
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nips_2022_JSha3zfdmSo | Faster Stochastic Algorithms for Minimax Optimization under Polyak-{\L}ojasiewicz Condition | This paper considers stochastic first-order algorithms for minimax optimization under Polyak-{\L}ojasiewicz (PL) conditions.
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nips_2022_mTXQIpXPDbh | Back Razor: Memory-Efficient Transfer Learning by Self-Sparsified Backpropogation | Transfer learning from the model trained on large datasets to customized downstream tasks has been widely used as the pre-trained model can greatly boost the generalizability. However, the increasing sizes of pre-trained models also lead to a prohibitively large memory footprints for downstream transferring, making the... | Accept | This paper focuses on pruning the backpropogation activation to reduce the memory footprint in transfer learning. The paper is well structured and the method is simple to understand. All the reviewers acknowledge that the experimental results are convincing. Overall, the meta-reviewer recommends acceptance of the paper... | train | [
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nips_2022__atSgd9Np52 | DreamShard: Generalizable Embedding Table Placement for Recommender Systems | We study embedding table placement for distributed recommender systems, which aims to partition and place the tables on multiple hardware devices (e.g., GPUs) to balance the computation and communication costs. Although prior work has explored learning-based approaches for the device placement of computational graphs, ... | Accept | The paper proposes DreamShard, a RL-based framework for placing embedding tables across multiple devices in distributed recommender systems. DreamShard jointly trains a cost model (to predict the cost of communication and operator fusion for new configurations) and a policy network to make placement decisions based on ... | train | [
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nips_2022_YBsLfudKlBu | Learning Viewpoint-Agnostic Visual Representations by Recovering Tokens in 3D Space | Humans are remarkably flexible in understanding viewpoint changes due to visual cortex supporting the perception of 3D structure. In contrast, most of the computer vision models that learn visual representation from a pool of 2D images often fail to generalize over novel camera viewpoints. Recently, the vision architec... | Accept | This paper presents a method for transformers to upgrade the 2D image input to pseudo-3D. It proposes a neural layer that estimates per-token depth and also a camera pose (pitch yaw roll), then unproject token coordinates to 3D, encodes these coordinates into embeddings, then adds these with the existing embeddings, an... | train | [
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nips_2022_fVslVNBfjd8 | Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels? | We investigate whether self-supervised learning (SSL) can improve online reinforcement learning (RL) from pixels. We extend the contrastive reinforcement learning framework (e.g., CURL) that jointly optimizes SSL and RL losses and conduct an extensive amount of experiments with various self-supervised losses. Our obser... | Accept |
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nips_2022_8FuITQn6rG3 | CRAFT: explaining using Concepts from Recursive Activation FacTorization | Despite their considerable potential, concept-based explainability methods have received relatively little attention, and explaining what’s driving models’ decisions and where it’s located in the input is still an open problem. To tackle this, we revisit unsupervised concept extraction techniques for explaining the dec... | Reject | Reviewers generally agreed that this paper is innovative (the decomposition of high-level concepts into sub-concepts in particular sets this paper apart from existing concept-based methods), and appreciated its potential practical utility for the explainability community (for example by providing localization in input ... | test | [
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nips_2022_MAMOi89bOL | Masked Autoencoders that Listen | This paper studies a simple extension of image-based Masked Autoencoders (MAE) to self-supervised representation learning from audio spectrograms. Following the Transformer encoder-decoder design in MAE, our Audio-MAE first encodes audio spectrogram patches with a high masking ratio, feeding only the non-masked tokens ... | Accept | The paper has two strong accepts and two borderline reject reviews. However, as one of the reviewers did not engage with the authors post-rebuttal, I had to interpret the authors' response to the reviewer's concerns, and they seem to properly address them (even including a new experiment into the paper). The work seems... | train | [
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nips_2022_Ho_zIH4LA90 | MinVIS: A Minimal Video Instance Segmentation Framework without Video-based Training | We propose MinVIS, a minimal video instance segmentation (VIS) framework that achieves state-of-the-art VIS performance with neither video-based architectures nor training procedures. By only training a query-based image instance segmentation model, MinVIS outperforms the previous best result on the challenging Occlude... | Accept | All four reviewers are positive about this work. Reviewers appreciate the clear writing, simple yet highly effective idea, and strong experimental validation on three Video Instance Segmentation datasets. The authors responses further clarified and sufficiently addressed the concerns from the reviewers. The AC reads th... | train | [
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nips_2022_7b7iGkuVqlZ | Unsupervised Learning of Equivariant Structure from Sequences | In this study, we present \textit{meta-sequential prediction} (MSP), an unsupervised framework to learn the symmetry from the time sequence of length at least three.
Our method leverages the stationary property~(e.g. constant velocity, constant acceleration) of the time sequence to learn the underlying equivariant str... | Accept |
While there was a certain lack of enthusiasm in the scores of the reviewers, the author's answers cleared the concerns of the reviewers participating in the discussion and overall the recommendation leans towards acceptance. This paper is, in the reviewers' opinions, sound and adds to the literature on unsupervised le... | train | [
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nips_2022_HxZpawUrv9Q | A Conditional Randomization Test for Sparse Logistic Regression in High-Dimension | Identifying the relevant variables for a classification model with correct confidence levels is a central but difficult task in high-dimension. Despite the core role of sparse logistic regression in statistics and machine learning, it still lacks a good solution for accurate inference in the regime where the number of ... | Accept | The decision is to accept this paper.
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nips_2022_A6EmxI3_Xc | Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network? | Modern deep neural networks for classification usually jointly learn a backbone for representation and a linear classifier to output the logit of each class. A recent study has shown a phenomenon called neural collapse that the within-class means of features and the classifier vectors converge to the vertices of a simp... | Accept | This paper examines the use of a random equiangular tight frame (ETF) as a replacement mechanism for the final classification layer in a deep neural network, and demonstrates experimental advantages in class-imbalanced training scenarios.
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nips_2022_4R7YrAGhnve | SegViT: Semantic Segmentation with Plain Vision Transformers | We explore the capability of plain Vision Transformers (ViTs) for semantic segmentation and propose the SegViT. Previous ViT-based segmentation networks usually learn a pixel-level representation from the output of the ViT. Differently, we make use of the fundamental component—attention mechanism, to generate masks for... | Accept | This submission has received comments from 4 official reviewers. The authors have made very detailed replies to the reviewer's comments. The authors and reviewers had quite rich discussions. After these discussions, 3 reviewers recommended weak acceptance, and 1 recommended rejection.
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nips_2022_Vt3_mJNrjt | Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure | This paper presents a new efficient black-box attribution method built on Hilbert-Schmidt Independence Criterion (HSIC). Based on Reproducing Kernel Hilbert Spaces (RKHS), HSIC measures the dependence between regions of an input image and the output of a model using the kernel embedding of their distributions. It thus ... | Accept | The paper proposes a novel black-box explanation method. The proposed method uses HSIC to measure the dependence between randomly-masked inputs and the corresponding outputs, and identifies relevance patches. Based on the decomposition property, the proposed method can also find interactions between patches. Experim... | train | [
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nips_2022_-ZQOx6yaVa- | Causally motivated multi-shortcut identification and removal | For predictive models to provide reliable guidance in decision making processes, they are often required to be accurate and robust to distribution shifts. Shortcut learning--where a model relies on spurious correlations or shortcuts to predict the target label--undermines the robustness property, leading to models with... | Accept | The reviewers agreed the paper is a worthwhile contribution in a growing area of identifying and removing shortcuts for robustness to distributional shift. Please take the reviewers feedback into consideration for the camera-ready. | train | [
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nips_2022_jtq4KwZ9_n9 | Geometry-aware Two-scale PIFu Representation for Human Reconstruction | Although PIFu-based 3D human reconstruction methods are popular, the quality of recovered details is still unsatisfactory. In a sparse (e.g., 3 RGBD sensors) capture setting, the depth noise is typically amplified in the PIFu representation, resulting in flat facial surfaces and geometry-fallible bodies. In this paper,... | Accept | All reviewers were in favor of acceptance. The AC examined the paper, reviews, and author response, and is inclined to agree. The AC would encourage the authors to incorporate their responses to the reviewers into the final version of the paper. | test | [
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nips_2022_SLdfxFdIFeN | A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective | We propose the first unified theoretical analysis of mixed sample data augmentation (MSDA), such as Mixup and CutMix. Our theoretical results show that regardless of the choice of the mixing strategy, MSDA behaves as a pixel-level regularization of the underlying training loss and a regularization of the first layer pa... | Accept | This work proposes a theoretical analysis and unified specification for mixed sample data augmentation methods. The reviewers praise the extensive theoretical analysis as well as the strong empirical results in the paper. The authors and reviewers engaged in substantial discussion, which led multiple reviewers to revis... | train | [
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" We are happy to hear that our revision version of the paper clarifies our contribution. Thanks for the recommendation of the state-of-the-art MSDA papers. We note that we already mentioned [1,2, 4-8] in the original paper. We added the dis... | [
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nips_2022_qf12cWVSksq | Inception Transformer | Recent studies show that transformer has strong capability of building long-range dependencies, yet is incompetent in capturing high frequencies that predominantly convey local information. To tackle this issue, we present a novel and general-purpose $\textit{Inception Transformer}$, or $\textit{iFormer}$ for short, th... | Accept | This paper proposes a novel multi-branch style architecture for vision tasks, motivated by a frequency perspective of deep network behaviors. All reviewers are very positive about the motivation, presentation and experimental results. The AC believes this should be a good contribution to the neural architecture design ... | val | [
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nips_2022_luGXvawYWJ | Dataset Distillation via Factorization | In this paper, we study dataset distillation (DD), from a novel perspective and introduce a \emph{dataset factorization} approach, termed \emph{HaBa}, which is a plug-and-play strategy portable to any existing DD baseline. Unlike conventional DD approaches that aim to produce distilled and representative samples, \emph... | Accept | The reviewers originally had concerns but these have been well addressed by the authors in a thorough rebuttal and there is a consensus for acceptance. We encourage the authors to incorporate all the comments from the reviewers in the final version. | test | [
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nips_2022_q6bZruC3dWJ | Teach Less, Learn More: On the Undistillable Classes in Knowledge Distillation | Knowledge distillation (KD) can effectively compress neural networks by training a smaller network (student) to simulate the behavior of a larger one (teacher). A counter-intuitive observation is that a more expansive teacher does not make a better student, but the reasons for this phenomenon remain unclear. In this pa... | Accept | This paper makes an interesting observation on knowledge distillation such that excluding certain undistillable classes improves performance. This observation is quite interesting and potentially impactful for a better understanding of knowledge distillation. The authors use this observation to consistently improve the... | test | [
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nips_2022_xnuN2vGmZA0 | VITA: Video Instance Segmentation via Object Token Association | We introduce a novel paradigm for offline Video Instance Segmentation (VIS), based on the hypothesis that explicit object-oriented information can be a strong clue for understanding the context of the entire sequence. To this end, we propose VITA, a simple structure built on top of an off-the-shelf Transformer-based im... | Accept | All four reviewers are positive about this work (with three Accept and one Weak Accept). All reviewers appreciate the clear writing, solid results, and the idea of using local attentions in transformers to associate object token extracted at each frame. During the discussion phase, the authors further clarified some of... | train | [
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nips_2022_kZnGYt-3f_X | Hilbert Distillation for Cross-Dimensionality Networks | 3D convolutional neural networks have revealed superior performance in processing volumetric data such as video and medical imaging. However, the competitive performance by leveraging 3D networks results in huge computational costs, which are far beyond that of 2D networks. In this paper, we propose a novel Hilbert cur... | Accept | This submission was reviewed by four reviewers. All reviewers provided detailed and informative reviews. During rebuttal, the authors actively submitted detailed rebuttals, which lead to improved evaluations by the reviewers with improved scores. Overall, this is an interesting paper and an accept is recommended. | test | [
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nips_2022_ucNDIDRNjjv | Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting | Transformers have shown great power in time series forecasting due to their global-range modeling ability. However, their performance can degenerate terribly on non-stationary real-world data in which the joint distribution changes over time. Previous studies primarily adopt stationarization to attenuate the non-statio... | Accept | The paper introduces a transformer-based method for non-stationary time series forecasting.
This research addresses a clear need, as acknowledged by the reviewers. Also, most reviewers found the method clearly described and the experiments compelling, demonstrating an improvement of the state of the art.
The reviewer... | train | [
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nips_2022_lme1MKnSMb | VCT: A Video Compression Transformer | We show how transformers can be used to vastly simplify neural video compression. Previous methods have been relying on an increasing number of architectural biases and priors, including motion prediction and warping operations, resulting in complex models. Instead, we independently map input frames to representations ... | Accept | This paper uses transformers for video compression, using less components compared to competing methods. Video compression is an important application in machine learning, and the use of transformers is well-timed w.r.t. generally strong interest in the architecture. There were some concerns over clarity of presentatio... | train | [
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nips_2022_sGugMYr3Hdy | Pragmatically Learning from Pedagogical Demonstrations in Multi-Goal Environments | Learning from demonstration methods usually leverage close to optimal demonstrations to accelerate training. By contrast, when demonstrating a task, human teachers deviate from optimal demonstrations and pedagogically modify their behavior by giving demonstrations that best disambiguate the goal they want to demonstrat... | 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_bntkx18xEb4 | HUMANISE: Language-conditioned Human Motion Generation in 3D Scenes | Learning to generate diverse scene-aware and goal-oriented human motions in 3D scenes remains challenging due to the mediocre characters of the existing datasets on Human-Scene Interaction (HSI); they only have limited scale/quality and lack semantics. To fill in the gap, we propose a large-scale and semantic-rich synt... | Accept | Paper was reviewed by four reviewers and received: 1 x Borderline Accept, 1 x Borderline Reject, 1 x Weak Accept and 1 x Accept. The general sentiment of reviewers was positive. Main identified concerns were with lack of diversity in the dataset and potential realism issues arising from construction of the dataset (pla... | train | [
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nips_2022_2-REuflJDT | Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images | We present a simple yet effective fully convolutional one-stage 3D object detector for LiDAR point clouds of autonomous driving scenes, termed FCOS-LiDAR. Unlike the dominant methods that use the bird-eye view (BEV), our proposed detector detects objects from the range view (RV, a.k.a. range image) of the LiDAR points.... | Accept | After the rebuttal and discussion two reviewers recommend acceptance, one rejection. In their rebuttal, the authors were able to convincingly resolve all issues raised. Thus the AC sees no reason to reject this paper. | train | [
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nips_2022_mMuVRbsvPyw | GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models | Prevalent semantic segmentation solutions are, in essence, a dense discriminative classifier of p(class|pixel feature). Though straightforward, this de facto paradigm neglects the underlying data distribution p(pixel feature|class), and struggles to identify out-of-distribution data. Going beyond this, we propose GMMSe... | Accept | This paper proposes to learn generative model (mixture of Gaussian) on the discriminative features. The proposed method achieves strong performance on semantic segmentation and it is capable of anomaly detection.
The paper was reviewed by 4 reviewers.
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nips_2022_9aLbntHz1Uq | Counterfactual Fairness with Partially Known Causal Graph | Fair machine learning aims to avoid treating individuals or sub-populations unfavourably based on \textit{sensitive attributes}, such as gender and race. Those methods in fair machine learning that are built on causal inference ascertain discrimination and bias through causal effects. Though causality-based fair learni... | Accept | This paper has divergent views in the sense two reviewers have given positive assessments (6 and 7) while the other reviewer has given a negative assessment (score of 3). This paper also had very 'heavy' discussions between the reviewer with negative opinion and the authors.
First of all I would like to thank the revi... | test | [
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nips_2022_LMuh9bS4tqF | Learning Distinct and Representative Modes for Image Captioning | Over the years, state-of-the-art (SoTA) image captioning methods have achieved promising results on some evaluation metrics (e.g., CIDEr). However, recent findings show that the captions generated by these methods tend to be biased toward the "average" caption that only captures the most general mode (a.k.a, language p... | Accept | The paper tackles the problem of mode collapse in image captioning and provide a method for generating diverse captions. The proposed approach uses a VAE to learn various modes, each of which can produce a different caption, along with various technical innovations to train the model. Experiments with two models on MS ... | train | [
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nips_2022_7rcuQ_V2GFg | Parameter-Efficient Masking Networks | A deeper network structure generally handles more complicated non-linearity and performs more competitively. Nowadays, advanced network designs often contain a large number of repetitive structures (e.g., Transformer). They empower the network capacity to a new level but also increase the model size inevitably, which i... | Accept | The paper studies the use of random weights together with learnable masks. Authors demonstrate that such training approach for neural network can reduce the model storage requirements and has applications to network compression.
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nips_2022_owZdBnUiw2 | Look More but Care Less in Video Recognition | Existing action recognition methods typically sample a few frames to represent each video to avoid the enormous computation, which often limits the recognition performance. To tackle this problem, we propose Ample and Focal Network (AFNet), which is composed of two branches to utilize more frames but with less computat... | Accept | The proposed architecture, AFNet, is simple yet effective for end-to-end efficient video action recognition.
It has good idea, well written paper, and relative solid experimental results to support the claim.
The emergency reviewer gives the highest score (6), while the other reviewers do have some concerns with this ... | val | [
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nips_2022_evRyKOjOx20 | Optimistic Mirror Descent Either Converges to Nash or to Strong Coarse Correlated Equilibria in Bimatrix Games | We show that, for any sufficiently small fixed $\epsilon > 0$, when both players in a general-sum two-player (bimatrix) game employ optimistic mirror descent (OMD) with smooth regularization, learning rate $\eta = O(\epsilon^2)$ and $T = \Omega(poly(1/\epsilon))$ repetitions, either the dynamics reach an $\epsilon$-app... | Accept | This paper proves a new phenomenon about the Optimistic Mirror Descent (OMD) algorithm in two-player general-sum matrix games (bimatrix games): The iterates either converge to an approximate Nash Equilibrium (NE), or converge to a Strong Coarse Correlated Equilibrium (CCE). This result links and improves over two exist... | train | [
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nips_2022_kEPAmGivMD | Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference | We propose a general purpose Bayesian inference algorithm for expensive likelihoods, replacing the stochastic term in the Langevin equation with a deterministic density gradient term. The particle density is evaluated from the current particle positions using a Normalizing Flow (NF), which is differentiable and has goo... | Accept | This paper proses an inference method that combines gradient ascent and normalizing flows. The idea is that one could, in principle, simulate the deterministic Fokker-Planck equation, but this would require access to the density of the evolving approximating density, which is intractable. Thus, the paper proposes to ma... | test | [
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nips_2022_CZwh1XdAhNv | Uncoupled Learning Dynamics with $O(\log T)$ Swap Regret in Multiplayer Games | In this paper we establish efficient and \emph{uncoupled} learning dynamics so that, when employed by all players in a general-sum multiplayer game, the \emph{swap regret} of each player after $T$ repetitions of the game is bounded by $O(\log T)$, improving over the prior best bounds of $O(\log^4 (T))$. At the same tim... | Accept | Given the unanimous support from the reviewers, to which I genuinely agree, the paper is recommended for acceptance. I encourage the authors to pay close attention to the reviewers comments and suggestions (and in particular to the comments of Reviewer Wcat) when working on their final version. | train | [
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nips_2022_fT9W53lLxNS | SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos | The visual world can be parsimoniously characterized in terms of distinct entities with sparse interactions. Discovering this compositional structure in dynamic visual scenes has proven challenging for end-to-end computer vision approaches unless explicit instance-level supervision is provided. Slot-based models levera... | Accept | Three out of four reviewers provided positive reviews and scores for this submission. They agreed that SAVI++ makes meaningful improvements over a previously proposed SAVI model. Importantly, while most past approaches evaluate on synthetic data, this submission evaluates the proposed model on a real world dataset. The... | test | [
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nips_2022_mXP-qQcYCBN | AutoLink: Self-supervised Learning of Human Skeletons and Object Outlines by Linking Keypoints | Structured representations such as keypoints are widely used in pose transfer, conditional image generation, animation, and 3D reconstruction. However, their supervised learning requires expensive annotation for each target domain. We propose a self-supervised method that learns to disentangle object structure from the... | Accept | Building from works on unsupervised keypoint discovery for a domain of 2D images, this work proposes to jointly learn a skeletal structure that links discovered keypoints, and further proposes a novel image masking strategy for extracting limited background information, to force the keypoints to capture maximum informa... | train | [
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nips_2022_f3zNgKga_ep | Video Diffusion Models | Generating temporally coherent high fidelity video is an important milestone in generative modeling research. We make progress towards this milestone by proposing a diffusion model for video generation that shows very promising initial results. Our model is a natural extension of the standard image diffusion architectu... | Accept | This paper proposes a diffusion model for video capable of generating long and high-resolution videos. Diffusion models have generated some more excitement around generative models as well, so the paper is well-timed. The reviewers had a few concerns regarding additional experiments and clarifications, and it appears t... | train | [
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nips_2022_ZJe-XahpyBf | UDC: Unified DNAS for Compressible TinyML Models for Neural Processing Units | Deploying TinyML models on low-cost IoT hardware is very challenging, due to limited device memory capacity. Neural processing unit (NPU) hardware address the memory challenge by using model compression to exploit weight quantization and sparsity to fit more parameters in the same footprint. However, designing compress... | Accept | In this paper, the authors present a new way to obtain compressible neural networks to fit on resource-constrained NPU-based hardware.
Initial reviews were mixed, but the authors successfully managed to respond to reviewers' concerns during the rebuttal period. Several reviewers pointed out clarity issues, but (1) som... | train | [
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nips_2022_vkGk2HI8oOP | Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debias | It has become cognitive inertia to employ cross-entropy loss function in classification related tasks. In the untargeted attacks on graph structure, the gradients derived from the attack objective are the attacker's basis for evaluating a perturbation scheme. Previous methods use negative cross-entropy loss as the atta... | Accept | The authors study graph modification attack (through editing the edges) in the setting of untargeted poisoning and show that negative cross entropy is not a good candidate for the attack loss. Instead they propose a novel attack objective to study the problem.
The reviewers found the topic timely and of interest to t... | train | [
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nips_2022_319xcX5qIcO | Signal Recovery with Non-Expansive Generative Network Priors | We study compressive sensing with a deep generative network prior. Initial theoretical guarantees for efficient recovery from compressed linear measurements have been developed for signals in the range of a ReLU network with Gaussian weights and logarithmic expansivity: that is when each layer is larger than the previo... | Accept | This paper focuses on theoretically studying signal reconstruction with non-expansive generative networks. In short the authors show that with a random Gaussian generator, any signal in its range can be reconstructed from Gaussian measurements. This holds as long as the number of measurements and the width of all layer... | train | [
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nips_2022_1ItkxrZP0rg | A Spectral Approach to Item Response Theory | The Rasch model is one of the most fundamental models in item response theory and has wide-ranging applications from education testing to recommendation systems. In a universe with $n$ users and $m$ items, the Rasch model assumes that the binary response $X_{li} \in \{0,1\}$ of a user $l$ with parameter $\theta^*_l$ to... | Accept | This is a strong paper with interesting theoretical results and important practical contributions (much faster parameter learning than prior methods with little performance drop-off). All reviewers agreed it was above the bar for acceptance to NeurIPS. | train | [
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nips_2022_1pHC-yZfaTK | Regret Bounds for Information-Directed Reinforcement Learning | Information-directed sampling (IDS) has revealed its potential as a data-efficient algorithm for reinforcement learning (RL). However, theoretical understanding of IDS for Markov Decision Processes (MDPs) is still limited. We develop novel information-theoretic tools to bound the information ratio and cumulative inform... | Accept | This paper has been well-received by the reviewers already in the initial round, and the reviewers were all happy with the authors' responses. The updates already made to the manuscript clearly showed the commitment of the authors to take all the reviewers' comments into account for the final version. After some discus... | train | [
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nips_2022_VOPiHQUevh5 | TUSK: Task-Agnostic Unsupervised Keypoints | Existing unsupervised methods for keypoint learning rely heavily on the assumption that a specific keypoint type (e.g. elbow, digit, abstract geometric shape) appears only once in an image. This greatly limits their applicability, as each instance must be isolated before applying the method—an issue that is never discu... | Accept | The meta reviewer has carefully read the paper, reviews, rebuttals, and discussions. The authors did a good job in rebuttal. The additional results and clarifications addressed the reviewers' concerns. The manuscript crosses the acceptance bar. The authors are still suggested to revise the paper considering the review... | train | [
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nips_2022_mmzkqUKNVm | Semantic Diffusion Network for Semantic Segmentation | Precise and accurate predictions over boundary areas are essential for semantic segmentation. However, the commonly used convolutional operators tend to smooth and blur local detail cues, making it difficult for deep models to generate accurate boundary predictions. In this paper, we introduce an operator-level approac... | Accept | This submission got a mixed rating: 1 borderline reject, 2 week accept and 1 accept.
Most of the concerns lie in the explanations on the details and experimental comparison with certain baselines/variants. The authors addressed them well by providing additional experiment results in their response.
The remained con... | test | [
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nips_2022_q__FmUtPZd9 | Social-Inverse: Inverse Decision-making of Social Contagion Management with Task Migrations | Considering two decision-making tasks $A$ and $B$, each of which wishes to compute an effective decision $Y$ for a given query $X$, can we solve task $B$ by using query-decision pairs $(X, Y)$ of $A$ without knowing the latent decision-making model? Such problems, called inverse decision-making with task migrations, ar... | Accept | Strengths:
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Weaknesses:
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nips_2022_LCIZmSw1DuE | Fair and Optimal Decision Trees: A Dynamic Programming Approach | Interpretable and fair machine learning models are required for many applications, such as credit assessment and in criminal justice. Decision trees offer this interpretability, especially when they are small. Optimal decision trees are of particular interest because they offer the best performance possible for a given... | Accept | I recommend acceptance due to the strengths identified by the positive reviews, despite some doubts expressed by more negative reviews. This paper modifies existing dynamic programming approaches for learning decision trees to accommodate non-monotonic constraints, motivated in particular by group fairness. Experiment ... | train | [
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nips_2022_v6CqBssIwYw | Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees | Gradient-boosted regression trees (GBRTs) are hugely popular for solving tabular regression problems, but provide no estimate of uncertainty. We propose Instance-Based Uncertainty estimation for Gradient-boosted regression trees (IBUG), a simple method for extending any GBRT point predictor to produce probabilistic pre... | Accept | This paper presents a method for extending any GBRT point predictor to produce probabilistic predictions such that the aleatoric uncertainty can be quantified. It computes a nonparametric distribution around a prediction using the k NNs where the distance is measured by a kernel that is similar to the random forest ke... | train | [
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nips_2022_zkQho-Jxky9 | Counterfactual harm | To act safely and ethically in the real world, agents must be able to reason about harm and avoid harmful actions. However, to date there is no statistical method for measuring harm and factoring it into algorithmic decisions. In this paper we propose the first formal definition of harm and benefit using causal models.... | Accept | All reviewers agreed that this paper should be accepted because of the strong author response during the rebuttal phase. Specifically the reviewers appreciated the motivation of the paper, its clarity, and the author clarification of the method, its assumptions, and scope during the rebuttal. Authors: please carefully ... | train | [
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nips_2022_DDEwoD608_l | Hand-Object Interaction Image Generation | In this work, we are dedicated to a new task, i.e., hand-object interaction image generation, which aims to conditionally generate the hand-object image under the given hand, object and their interaction status. This task is challenging and research-worthy in many potential application scenarios, such as AR/VR games an... | Accept | On the surface, this paper seems to be split between three borderline rejects (4) and one strong champion of the paper (10). However, this is not the full story, since two of the reject-inclined reviewers, Bc9p and zW9y did not participate post-rebuttal, despite multiple prods from the AC. The AC examined the stated we... | train | [
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" Dear reviewer p4XG,\n\nWe would like to thank again for your time and valuable comments.\nHopefully, our rebuttal and new submitted revision could properly address your concerns.\nWe look forward to your feedback and will appreciate it if you could upgrade your score.\n\nWish you a nice day.\n\nBest,\n\nAuthors o... | [
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nips_2022_8wtaJ9dE9Y2 | Predicting Label Distribution from Multi-label Ranking | Label distribution can provide richer information about label polysemy than logical labels in multi-label learning. There are currently two strategies including LDL (label distribution learning) and LE (label enhancement) to predict label distributions. LDL requires experts to annotate instances with label distribution... | Accept | This paper studies the problem of predicting label distribution from multi-label ranking. First, the authors give a theoretical analysis to prove the superiority of the multi-label ranking over the logical labels. Then an end-to-end framework called DRAM is proposed for recovering and learning label distributions. The ... | train | [
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" We appreciate your suggestions. Below we give the point-by-point responses to your questions.\n\n**Q1: It seems that the theoretical analysis has little relatedness with the proposed framework and there are not experiments to support the theoretical results. It is something that looks beautiful.**\n\nA: The theor... | [
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nips_2022_tbdk6XLYmZj | Learning Best Combination for Efficient N:M Sparsity | By forcing N out of M consecutive weights to be non-zero, the recent N:M fine-grained network sparsity has received increasing attention with its two attractive advantages over traditional irregular network sparsity methods: 1) Promising performance at a high sparsity. 2) Significant speedups when performed on NVIDIA A... | Accept | The paper presents a novel method on training N:M sparse-weight neural networks, which can be significantly accelerated by NVIDIA A100 GPUs. The optimal N:M pattern can be found via jointly solving a series of combinatorial problems with finite collections of candidates. Majority of the reviewers found the paper
The ... | train | [
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" We sincerely thank you for your timely and constructive feedback. Also, we appreciate your effort in reviewing this paper. We believe we introduce a sound approach and have made a strong contribution to N:M sparsity, which are already appraised by the other three reviewers. Our further responses are provided belo... | [
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nips_2022_6H2pBoPtm0s | ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation | Although no specific domain knowledge is considered in the design, plain vision transformers have shown excellent performance in visual recognition tasks. However, little effort has been made to reveal the potential of such simple structures for pose estimation tasks. In this paper, we show the surprisingly good capabi... | Accept | This submission received positive reviews. After rebuttal and discussions, all the reviewers feel positive about this submission with raised concerns addressed. After checking all the reviews and rebuttal, the AC stands on the reviewers' side and believe the current work is suitable for publication in this venue. The a... | train | [
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" Thanks for your valuable comments and suggestions! We are encouraged by the resolution of your major concerns and appreciate your constructive comments to improve our work. We promise that we will incorporate all feedback in the revised version and carefully amend the paper.",
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nips_2022_QNBzcgY0f4e | Easy incremental learning methods to consider for commercial fine-tuning applications | Fine-tuning deep learning models for commercial use cases is growing exponentially as more and more companies are adopting AI to enhance their core products and services, as well as automate their diurnal processes and activities. However, not many countries like the U.S. and those in Europe follow quality data collect... | Reject | This paper motivates problems related to fine tuning of pre-trained deep learning models for commercial applications and proposes three solutions for incremental learning: Fisher Shut-off, Fractional Data Retention and Border Control). The reviewers thought the work was well-motivated and they were in agreement that t... | train | [
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" FDR may not be novel, but Border Control or anything similar has not been proposed yet.\n\nIdea of the paper was to introduce easy incremental learning methods for commercial applications so that an infrastructure could be built out of it. And so visuals on the performance of these methods was key for the paper t... | [
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nips_2022_Ho6oWAslz5L | Saliency-Aware Neural Architecture Search | Recently a wide variety of NAS methods have been proposed and achieved considerable success in automatically identifying highly-performing architectures of neural networks for the sake of reducing the reliance on human experts. Existing NAS methods ignore the fact that different input data elements (e.g., image pixels)... | Accept | This paper proposed a novel method that reweights data using saliency maps and searches architecture using saliency-reweighted data.
There are four official reviewers for this submission. The reviewers consistently agree with the novelty, presentation, and experimental validation of this submission. The ratings are: ... | train | [
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" We thank the reviewer for reading our response and raising the score. We highly appreciate the reviewer's constructive suggestions.",
" Thank you for your feedback. The paper seems a good one to be further discussed officially in the community. I am raising the score.",
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