We study a multi-factor block model for variable clustering and connect it to the regularized subspace clustering by formulating a distributionally robust version of the nodewise regression. To solve the latter problem, we derive a convex relaxation, provide guidance on selecting the size of the robust region, and hence the regularization weighting parameter, based on the data, and propose an ADMM algorithm for implementation. We validate our method in an extensive simulation study. Finally, we propose and apply a variant of our method to stock return data, obtain interpretable clusters that facilitate portfolio selection and compare its out-of-sample performance with other clustering methods in an empirical study.

Ensemble learning serves as a straightforward way to improve the performance of almost any machine learning algorithm. Existing deep ensemble methods usually naively train many different models and then aggregate their predictions. This is not optimal in our view from two aspects: i) Naively training multiple models adds much more computational burden, especially in the deep learning era; ii) Purely optimizing each base model without considering their interactions limits the diversity of ensemble and performance gains. We tackle these issues by proposing deep negative correlation classification (DNCC), in which the accuracy and diversity trade-off is systematically controlled by decomposing the loss function seamlessly into individual accuracy and the correlation between individual models and the ensemble. DNCC yields a deep classification ensemble where the individual estimator is both accurate and negatively correlated. Thanks to the optimized diversities, DNCC works well even when utilizing a shared network backbone, which significantly improves its efficiency when compared with most existing ensemble systems. Extensive experiments on multiple benchmark datasets and network structures demonstrate the superiority of the proposed method.

The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based image signal processing (ISP) pipeline replacing the standard mobile ISPs that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The runtime of the resulting models was evaluated on the Snapdragon's 8 Gen 1 GPU that provides excellent acceleration results for the majority of common deep learning ops. The proposed solutions are compatible with all recent mobile GPUs, being able to process Full HD photos in less than 20-50 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper.

住房质量是区域财富，安全和健康的重要代理。了解住房质量的分布对于揭示农村发展状况并提供政治建议至关重要。但是，目前的农村房屋质量数据在很大程度上取决于在国家或省级的自上而下，耗时的调查，但未能在村庄一级解开住房质量。为了填补准确描述农村住房质量条件和数据不足之间的空白，我们收集大量的农村图像，并邀请用户按大规模评估其住房质量。此外，提出了一个深度学习框架，以根据众包农村图像自动有效地预测住房质量。

尽管已经对音频驱动的说话的面部生成取得了重大进展，但现有方法要么忽略面部情绪，要么不能应用于任意主题。在本文中，我们提出了情感感知的运动模型（EAMM），以通过涉及情感源视频来产生一次性的情感谈话面孔。具体而言，我们首先提出了一个Audio2Facial-Dynamics模块，该模块从音频驱动的无监督零和一阶密钥点运动中进行说话。然后，通过探索运动模型的属性，我们进一步提出了一个隐性的情绪位移学习者，以表示与情绪相关的面部动力学作为对先前获得的运动表示形式的线性添加位移。全面的实验表明，通过纳入两个模块的结果，我们的方法可以在具有现实情感模式的任意主题上产生令人满意的说话面部结果。

数据增强是减少过度装备和提高学习性能的重要技术，但是现有的3D点云数据的数据增强的工作基于启发式。在这项工作中，我们建议使用Bilevel优化自动学习数据增强策略。增强器以类似的方式设计为条件发生器，并且在增强输入用于训练模型时最小化基础模型对验证集的损耗来进行优化。此配方提供了更为原则的方法来学习3D点云上的数据增强。我们评估了我们对标准点云分类任务的方法以及培训和验证/测试集之间的构成错位的更具挑战性。该拟议的战略在两个任务方面实现了竞争性能，我们提供了进一步了解增强者学习验证集分发的能力。

知识蒸馏是一种有前途的学习范式，用于提高资源有效的图形神经网络（GNNS）的性能和可靠性使用更多富有表现力而繁琐的教师模型。过去的GNNS蒸馏工作提出了局部结构保存损失（LSP），它与学生和教师节点嵌入空间的局部结构关系匹配。在本文中，我们提出了两个关键贡献：从方法的角度来看，我们研究了是否保留了教师嵌入图数据的全球拓扑结构对于GNN的更有效的蒸馏物目标，因为真实世界的图表通常包含潜在的相互作用和嘈杂边缘。通过预定义边缘的纯粹本地LSP目标无法实现这一目标，因为它忽略了断开的节点之间的关系。我们提出了两种新方法，更好地保留了全球拓扑结构：（1）全局结构保存损失（GSP），其扩展了LSP掺入所有成对相互作用; （2）曲线图对比度表示蒸馏（G-CRD），它使用对比学学习将学生节点嵌入的学生节点嵌入到参与表示空间中的教师。从实验的角度来看，我们在大型现实世界数据集中介绍了一组扩展的基准，教师和学生GNN之间的性能差距是不可忽略的。我们认为这对于测试知识蒸馏的功效和稳健性至关重要，但是从LSP研究中缺少，使用具有琐碎性能间隙的合成数据集。 4个数据集和14个异构GNN架构的实验表明，G-CRD始终如一地提高了轻量级GNN型号的性能和稳健性，优于维护方法，LSP和GSP的结构，以及由2D计算机视觉调整的基线。

Sensory and emotional experiences such as pain and empathy are essential for mental and physical health. Cognitive neuroscience has been working on revealing mechanisms underlying pain and empathy. Furthermore, as trending research areas, computational pain recognition and empathic artificial intelligence (AI) show progress and promise for healthcare or human-computer interaction. Although AI research has recently made it increasingly possible to create artificial systems with affective processing, most cognitive neuroscience and AI research do not jointly address the issues of empathy in AI and cognitive neuroscience. The main aim of this paper is to introduce key advances, cognitive challenges and technical barriers in computational pain recognition and the implementation of artificial empathy. Our discussion covers the following topics: How can AI recognize pain from unimodal and multimodal information? Is it crucial for AI to be empathic? What are the benefits and challenges of empathic AI? Despite some consensus on the importance of AI, including empathic recognition and responses, we also highlight future challenges for artificial empathy and possible paths from interdisciplinary perspectives. Furthermore, we discuss challenges for responsible evaluation of cognitive methods and computational techniques and show approaches to future work to contribute to affective assistants capable of empathy.

基于学习的3D形状分割通常被配制为语义标记问题，假设训练形状的所有部分都用给定的一组标签注释。然而，这种假设对于学习细粒度的细分来说是不切实际的。虽然大多数现成的CAD模型是由施工组成的细粒度，但它们通常会错过语义标签并标记那些细粒度的部分非常乏味。我们接近深群体的问题，其中关键的想法是从带有细粒度分割的形状数据集中学习部分前导者，但没有部分标签。给定点采样3D形状，我们通过相似矩阵模拟点的聚类前沿，通过最小化新的低级损失来实现部分分割。为了处理高度密集的采样点集，我们采用了分裂和征服策略。我们将大点分区设置为多个块。每个块使用以类别 - 不可知方式培训的基于深度基于集群的基于网络的部分进行分段。然后，我们会培训图形卷积网络以合并所有块的段以形成最终的分段结果。我们的方法是用细粒细分的具有挑战性的基准进行评估，显示出最先进的性能。

在非凸优化的背景下，研究Langevin扩散的温度控制问题。这种问题的经典最优控制是Bang-Bang类型，这对错误过于敏感。补救措施是允许扩散探索其他温度值，从而平滑爆炸控制。我们通过一种随机轻松的控制配方来实现这一点，该配方包括温度控制的随机性并规范其熵。我们得出了一个国家相关的截断的指数分布，其可用于在HJB偏微分方程的解决方案方面采样LangeVin算法中的温度。我们对一维基线示例进行数值实验，其中HJB方程可以很容易地解决，以比较算法与三个其他可用算法的性能，以搜索全局最优。