由于其效率，一声神经架构搜索（NAS）已被广泛用于发现架构。但是，先前的研究表明，由于架构之间的操作参数过度共享（即大共享范围），架构的一声绩效估计可能与他们在独立培训中的表现没有很好的相关性。因此，最近的方法构建了更高参数化的超级链，以降低共享程度。但是这些改进的方法引入了大量额外的参数，因此在培训成本和排名质量之间导致不良的权衡。为了减轻上述问题，我们建议将课程学习应用于共享范围（接近），以有效地训练超级网。具体而言，我们在一开始就以很大的共享范围（简单的课程）训练超网，并逐渐降低了超级网的共享程度（更难的课程）。为了支持这种培训策略，我们设计了一个新颖的超级网（闭合性），该超级网（CLESENET）将参数从操作中解耦，以实现灵活的共享方案和可调节的共享范围。广泛的实验表明，与其他一击的超级网络相比，Close可以在不同的计算预算限制中获得更好的排名质量，并且在与各种搜索策略结合使用时能够发现出色的体系结构。代码可从https://github.com/walkerning/aw_nas获得。

有效的骨干网络对于基于深度学习的可变形医学图像注册（DMIR）很重要，因为它可以提取和匹配两个图像之间的特征，以发现互联网的相互对应。但是，现有的深网关注单图像，并且在配对图像上执行的注册任务有限。因此，我们推进了一个新型的骨干网络Xmorpher，用于DMIR中有效的相应特征表示。 1）它提出了一种新颖的完整变压器体系结构，包括双重平行特征提取网络，通过交叉注意交换信息，从而在逐渐提取相应的特征以逐渐提取最终有效注册时发现了多层次的语义对应。 2）它推进了交叉注意变压器（CAT）块，以建立图像之间的注意机制，该图像能够自动找到对应关系并提示特征在网络中有效融合。 3）它限制了基本窗口和搜索不同尺寸的窗口之间的注意力计算，因此着重于可变形注册的局部转换，并同时提高了计算效率。我们的Xmorpher没有任何铃铛和哨子，可在DSC上提高2.8％的素孔，以证明其对DMIR中配对图像的特征的有效表示。我们认为，我们的Xmorpher在更多配对的医学图像中具有巨大的应用潜力。我们的Xmorpher在https://github.com/solemoon/xmorpher上开放

发现危险场景在测试中至关重要，进一步改善驾驶政策。然而，进行有效的驾驶政策测试面临两个关键挑战。一方面，在测试训练有素的自主驾驶策略时，自然遇到危险情景的可能性很低。因此，通过纯粹的现实世界的道路测试发现这些情景非常昂贵。另一方面，这项任务需要正确确定事故责任。收集错误归属责任的情景将导致过度保守的自治驾驶策略。更具体地说，我们的目标是发现是自主车辆负责（AV-Orderible），即测试驾驶政策的脆弱性的危险场景。为此，这项工作通过基于多智能体增强学习来查找AV负责的方案（星）提出了安全测试框架。星星指导其他交通参与者生产AV-Consocalize情景，并通过引入危险仲裁奖励（Har）来制作不受检测的驾驶政策行为不端行为。哈尔使我们的框架能够发现多样化，复杂和AV负责任的危险场景。针对三种环境中四种不同驾驶政策的实验结果表明星星可以有效地发现AV负责任的危险情景。这些方案确实对应于测试驾驶策略的漏洞，因此对其进一步的改进是有意义的。

我们介绍了课程学习算法，变分自动课程学习（VIVL），用于解决具有挑战性的目标条件的合作多功能增强学习问题。我们通过变分的角度激励我们的范式，其中学习目标可以分解为两种术语：任务学习当前任务分发以及新任务分发的课程更新。第二任期内的本地优化表明，课程应该逐步扩展培训任务，易于努力。我们的Vivl算法用两个实际组件，任务扩展和实体进展实现了这种变分的范例，它在任务配置以及任务中的实体数量产生培训课程。实验结果表明，Vacl解决了大量代理商的稀疏奖励问题的集合。特别是，使用单个桌面机器，VACL在简单扩展的基准测试中实现了100个代理的98％覆盖率，并再现最初在Openai隐藏项目中显示的斜坡使用行为。我们的项目网站位于https://sites.google.com/view/vacl-neurips-2021。

管状结构跟踪是计算机视觉和医学图像分析领域的关键任务。基于最小的路径的方法在跟踪管状结构中表现出具有强的能力，通过该方法可以自然地建模，作为用合适的测地度量计算的最小测地路径。然而，现有的基于路径的基于路径的跟踪方法仍然遭受诸如快捷方式和短分支组合问题的困难，特别是在处理涉及复杂的管状树结构或背景的图像时。在本文中，我们介绍了一种新的最小路径基于基于路径的基于型号，用于尽可能多的交互管结构中心线提取与感知分组方案。基本上，我们考虑了规定的管状轨迹和曲率惩罚的测地路，以寻求合适的最短路径。所提出的方法可以从管状结构上的局部平​​滑度和基于使用的图形的路径搜索方案的全球最优性中受益。合成和实图像的实验结果证明，该模型确实获得了与最新的基于路径的管状结构跟踪算法比较的优惠。

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

Blind image quality assessment (BIQA) remains challenging due to the diversity of distortion and image content variation, which complicate the distortion patterns crossing different scales and aggravate the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and little research has been done on learning strategies to make the regression model produce better performance. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model's performance.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

Given the increasingly intricate forms of partial differential equations (PDEs) in physics and related fields, computationally solving PDEs without analytic solutions inevitably suffers from the trade-off between accuracy and efficiency. Recent advances in neural operators, a kind of mesh-independent neural-network-based PDE solvers, have suggested the dawn of overcoming this challenge. In this emerging direction, Koopman neural operator (KNO) is a representative demonstration and outperforms other state-of-the-art alternatives in terms of accuracy and efficiency. Here we present KoopmanLab, a self-contained and user-friendly PyTorch module of the Koopman neural operator family for solving partial differential equations. Beyond the original version of KNO, we develop multiple new variants of KNO based on different neural network architectures to improve the general applicability of our module. These variants are validated by mesh-independent and long-term prediction experiments implemented on representative PDEs (e.g., the Navier-Stokes equation and the Bateman-Burgers equation) and ERA5 (i.e., one of the largest high-resolution data sets of global-scale climate fields). These demonstrations suggest the potential of KoopmanLab to be considered in diverse applications of partial differential equations.

A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equiangular tight frame at the terminal phase of training for classification. In this paper, we explore the corresponding structures of the last-layer feature centers and classifiers in semantic segmentation. Based on our empirical and theoretical analysis, we point out that semantic segmentation naturally brings contextual correlation and imbalanced distribution among classes, which breaks the equiangular and maximally separated structure of neural collapse for both feature centers and classifiers. However, such a symmetric structure is beneficial to discrimination for the minor classes. To preserve these advantages, we introduce a regularizer on feature centers to encourage the network to learn features closer to the appealing structure in imbalanced semantic segmentation. Experimental results show that our method can bring significant improvements on both 2D and 3D semantic segmentation benchmarks. Moreover, our method ranks 1st and sets a new record (+6.8% mIoU) on the ScanNet200 test leaderboard. Code will be available at https://github.com/dvlab-research/Imbalanced-Learning.