我们在时间图上提出了一种新的邻居采样方法。在时间图中，预测不同节点的时变特性可能需要各种时间尺度的接收邻域。在这项工作中，我们提出了TNS（时间感知邻居采样）方法：TNS从时间信息学习，以便随时为每个节点提供自适应接收邻域。学习如何样本邻居是非琐碎的，因为邻居指数处于时间顺序是离散的且不可分辨。为了解决这一挑战，我们通过插入邻居的消息，我们将邻居指数从离散值转换为连续的索引。 TNS可以灵活地纳入流行的时间图网络，以提高其有效性，而不会增加时间复杂性。 TNS可以以端到端的方式训练。它不需要额外的监督，并自动和隐含地引导以对预测最有利的邻居进行样本。多个标准数据集的经验结果表明，TNS对边缘预测和节点分类产生了显着的增益。

表示标签分布作为一个热量矢量是培训节点分类模型中的常见做法。然而，单热表示可能无法充分反映不同类别中节点的语义特征，因为某些节点可以在其他类中的邻居语义上靠近其邻居。由于鼓励在对每个节点进行分类时，鼓励模型分配完全概率，因此会导致过度自信。虽然具有标签平滑的培训模型可以在某种程度上缓解此问题，但它仍然无法捕获图形结构隐含的节点的语义特征。在这项工作中，我们提出了一种新颖的SAL（\ Textit {Security-Aware标签平滑}）方法作为流行节点分类模型的增强组件。 SAL利用图形结构来捕获连接节点之间的语义相关性并生成结构感知标签分配以替换原始的单热标签向量，从而改善节点分类性能而不推广成本。七节点分类基准数据集的广泛实验揭示了我们对改进转膜和归纳节点分类的含量的有效性。经验结果表明，SALS优于标签平滑方法，增强节点分类模型以优于基线方法。

我们呈现多视图姿势变压器（MVP），用于从多视图图像估计多人3D姿势。而不是从昂贵的体积表示或从多个检测到的2D重建的每人3D姿势估计从昂贵的体积表示或从多个检测到的2D姿势进行估计3D联合位置，而是MVP以清洁和有效的方式直接回归多人3D姿势，而不依赖于中间任务。具体而言，MVP表示作为学习查询嵌入的骨架关节，并让它们从输入图像中逐渐参加和原因，以直接回归实际的3D联合位置。为了提高这种简单管道的准确性，MVP呈现了一个分层方案，简明地代表了多人骨架关节的查询嵌入，并引入了输入相关的查询适应方法。此外，MVP设计了一种新颖的几何引导注意力机制，称为投影注意力，更精确地熔化每个关节的跨视网膜信息。 MVP还介绍了RAYCONV操作，以将视图依赖的相机几何整合到特征表示中，以增加投射注意。我们通过实验展示我们的MVP模型在几个基准上占据了最先进的方法，同时更有效。值得注意的是，它在挑战的Panoptic DataSet上实现了92.3％的AP25，提高了先前的最佳方法[36]，提高了9.8％。 MVP是通用的，并且还可以扩展到恢复SMPL模型表示的人网格，因此可用于建模多人身体形状。代码和模型可在https://github.com/sail-sg/mvp上获得。

Recent investigations on rotation invariance for 3D point clouds have been devoted to devising rotation-invariant feature descriptors or learning canonical spaces where objects are semantically aligned. Examinations of learning frameworks for invariance have seldom been looked into. In this work, we review rotation invariance in terms of point cloud registration and propose an effective framework for rotation invariance learning via three sequential stages, namely rotation-invariant shape encoding, aligned feature integration, and deep feature registration. We first encode shape descriptors constructed with respect to reference frames defined over different scales, e.g., local patches and global topology, to generate rotation-invariant latent shape codes. Within the integration stage, we propose Aligned Integration Transformer to produce a discriminative feature representation by integrating point-wise self- and cross-relations established within the shape codes. Meanwhile, we adopt rigid transformations between reference frames to align the shape codes for feature consistency across different scales. Finally, the deep integrated feature is registered to both rotation-invariant shape codes to maximize feature similarities, such that rotation invariance of the integrated feature is preserved and shared semantic information is implicitly extracted from shape codes. Experimental results on 3D shape classification, part segmentation, and retrieval tasks prove the feasibility of our work. Our project page is released at: https://rotation3d.github.io/.

With the attention mechanism, transformers achieve significant empirical successes. Despite the intuitive understanding that transformers perform relational inference over long sequences to produce desirable representations, we lack a rigorous theory on how the attention mechanism achieves it. In particular, several intriguing questions remain open: (a) What makes a desirable representation? (b) How does the attention mechanism infer the desirable representation within the forward pass? (c) How does a pretraining procedure learn to infer the desirable representation through the backward pass? We observe that, as is the case in BERT and ViT, input tokens are often exchangeable since they already include positional encodings. The notion of exchangeability induces a latent variable model that is invariant to input sizes, which enables our theoretical analysis. - To answer (a) on representation, we establish the existence of a sufficient and minimal representation of input tokens. In particular, such a representation instantiates the posterior distribution of the latent variable given input tokens, which plays a central role in predicting output labels and solving downstream tasks. - To answer (b) on inference, we prove that attention with the desired parameter infers the latent posterior up to an approximation error, which is decreasing in input sizes. In detail, we quantify how attention approximates the conditional mean of the value given the key, which characterizes how it performs relational inference over long sequences. - To answer (c) on learning, we prove that both supervised and self-supervised objectives allow empirical risk minimization to learn the desired parameter up to a generalization error, which is independent of input sizes. Particularly, in the self-supervised setting, we identify a condition number that is pivotal to solving downstream tasks.

In the new era of personalization, learning the heterogeneous treatment effect (HTE) becomes an inevitable trend with numerous applications. Yet, most existing HTE estimation methods focus on independently and identically distributed observations and cannot handle the non-stationarity and temporal dependency in the common panel data setting. The treatment evaluators developed for panel data, on the other hand, typically ignore the individualized information. To fill the gap, in this paper, we initialize the study of HTE estimation in panel data. Under different assumptions for HTE identifiability, we propose the corresponding heterogeneous one-side and two-side synthetic learner, namely H1SL and H2SL, by leveraging the state-of-the-art HTE estimator for non-panel data and generalizing the synthetic control method that allows flexible data generating process. We establish the convergence rates of the proposed estimators. The superior performance of the proposed methods over existing ones is demonstrated by extensive numerical studies.

Label noise is ubiquitous in various machine learning scenarios such as self-labeling with model predictions and erroneous data annotation. Many existing approaches are based on heuristics such as sample losses, which might not be flexible enough to achieve optimal solutions. Meta learning based methods address this issue by learning a data selection function, but can be hard to optimize. In light of these pros and cons, we propose Selection-Enhanced Noisy label Training (SENT) that does not rely on meta learning while having the flexibility of being data-driven. SENT transfers the noise distribution to a clean set and trains a model to distinguish noisy labels from clean ones using model-based features. Empirically, on a wide range of tasks including text classification and speech recognition, SENT improves performance over strong baselines under the settings of self-training and label corruption.

The high feature dimensionality is a challenge in music emotion recognition. There is no common consensus on a relation between audio features and emotion. The MER system uses all available features to recognize emotion; however, this is not an optimal solution since it contains irrelevant data acting as noise. In this paper, we introduce a feature selection approach to eliminate redundant features for MER. We created a Selected Feature Set (SFS) based on the feature selection algorithm (FSA) and benchmarked it by training with two models, Support Vector Regression (SVR) and Random Forest (RF) and comparing them against with using the Complete Feature Set (CFS). The result indicates that the performance of MER has improved for both Random Forest (RF) and Support Vector Regression (SVR) models by using SFS. We found using FSA can improve performance in all scenarios, and it has potential benefits for model efficiency and stability for MER task.

A general, {\em rectangular} kernel matrix may be defined as $K_{ij} = \kappa(x_i,y_j)$ where $\kappa(x,y)$ is a kernel function and where $X=\{x_i\}_{i=1}^m$ and $Y=\{y_i\}_{i=1}^n$ are two sets of points. In this paper, we seek a low-rank approximation to a kernel matrix where the sets of points $X$ and $Y$ are large and are not well-separated (e.g., the points in $X$ and $Y$ may be ``intermingled''). Such rectangular kernel matrices may arise, for example, in Gaussian process regression where $X$ corresponds to the training data and $Y$ corresponds to the test data. In this case, the points are often high-dimensional. Since the point sets are large, we must exploit the fact that the matrix arises from a kernel function, and avoid forming the matrix, and thus ruling out most algebraic techniques. In particular, we seek methods that can scale linearly, i.e., with computational complexity $O(m)$ or $O(n)$ for a fixed accuracy or rank. The main idea in this paper is to {\em geometrically} select appropriate subsets of points to construct a low rank approximation. An analysis in this paper guides how this selection should be performed.

Existing 3D-aware image synthesis approaches mainly focus on generating a single canonical object and show limited capacity in composing a complex scene containing a variety of objects. This work presents DisCoScene: a 3Daware generative model for high-quality and controllable scene synthesis. The key ingredient of our method is a very abstract object-level representation (i.e., 3D bounding boxes without semantic annotation) as the scene layout prior, which is simple to obtain, general to describe various scene contents, and yet informative to disentangle objects and background. Moreover, it serves as an intuitive user control for scene editing. Based on such a prior, the proposed model spatially disentangles the whole scene into object-centric generative radiance fields by learning on only 2D images with the global-local discrimination. Our model obtains the generation fidelity and editing flexibility of individual objects while being able to efficiently compose objects and the background into a complete scene. We demonstrate state-of-the-art performance on many scene datasets, including the challenging Waymo outdoor dataset. Project page: https://snap-research.github.io/discoscene/