Graph neural networks (GNNs) have received remarkable success in link prediction (GNNLP) tasks. Existing efforts first predefine the subgraph for the whole dataset and then apply GNNs to encode edge representations by leveraging the neighborhood structure induced by the fixed subgraph. The prominence of GNNLP methods significantly relies on the adhoc subgraph. Since node connectivity in real-world graphs is complex, one shared subgraph is limited for all edges. Thus, the choices of subgraphs should be personalized to different edges. However, performing personalized subgraph selection is nontrivial since the potential selection space grows exponentially to the scale of edges. Besides, the inference edges are not available during training in link prediction scenarios, so the selection process needs to be inductive. To bridge the gap, we introduce a Personalized Subgraph Selector (PS2) as a plug-and-play framework to automatically, personally, and inductively identify optimal subgraphs for different edges when performing GNNLP. PS2 is instantiated as a bi-level optimization problem that can be efficiently solved differently. Coupling GNNLP models with PS2, we suggest a brand-new angle towards GNNLP training: by first identifying the optimal subgraphs for edges; and then focusing on training the inference model by using the sampled subgraphs. Comprehensive experiments endorse the effectiveness of our proposed method across various GNNLP backbones (GCN, GraphSage, NGCF, LightGCN, and SEAL) and diverse benchmarks (Planetoid, OGB, and Recommendation datasets). Our code is publicly available at \url{https://github.com/qiaoyu-tan/PS2}

图异常检测（GAD）是至关重要的任务，因为即使有一些异常也可能对良性用户构成巨大威胁。最近可以有效利用可用标签作为先验知识的半监督GAD方法比无监督的方法实现了卓越的性能。实际上，人们通常需要在新（子）图上识别异常以确保其业务，但他们可能缺乏培训有效检测模型的标签。一个自然的想法是将经过训练的GAD模型直接在新的（子）图中进行测试。但是，我们发现现有的半监督GAD方法遇到了不良的概括问题，即训练有素的模型无法在同一图的看不见的区域（即无法在培训中无法访问）上表现良好。这可能会造成极大的麻烦。在本文中，我们以这种现象为基础，并提出了广义图异常检测的一般研究问题，旨在有效地识别训练域图和看不见的测试图，以消除潜在的危险。然而，这是一项具有挑战性的任务，因为只有有限的标签可用，并且正常背景在培训和测试数据之间可能有所不同。因此，我们提出了一个名为\ textit {augan}（\ uline {augan}的数据增强方法，用于\ uline {a} nomaly和\ uline {n} ormal分布），以丰富培训数据并促进GAD模型的普遍性。实验验证了我们方法在改善模型推广性方面的有效性。

激活压缩训练〜（ACT）已被证明是减少训练深神经网络中记忆消耗的一种有希望的方法。但是，现有的ACT工作依赖于在深神经网络（DNN）训练期间寻找最佳的位宽度以减少量化噪声，从而使过程变得复杂且透明。为此，我们提出了一种简单有效的DNN培训方法。我们的方法是由观察结果激励的：\ emph {DNN向后传播主要取决于激活图的低频组分〜（LFC），而不是高频组件〜（HFC）}。它表明激活图的HFC在DNN训练过程中是高度冗余和可压缩的，这激发了我们提出的双重激活精度〜（分裂）。在培训期间，分裂估计激活图的LFC和HFC，并将HFC压缩到低精度副本中以消除冗余。这可以大大减少记忆消耗，而不会对DNN向后传播的精度产生负面影响。这样，部门可以实现可比的表现与正常培训。三个基准数据集的实验结果表明，在记忆消耗，模型准确性和跑步速度方面，分裂的表现优于最先进的基线方法。

关于公平建模的现有工作通常假设所有实例的敏感属性都已完全可用，由于获取敏感信息的高成本，在许多现实世界中，这可能并非如此。当未披露或可用的敏感属性时，需要手动注释培训数据的一小部分以减轻偏见。但是，跨不同敏感组的偏斜分布保留了带注释的子集中原始数据集的偏度，这导致了非最佳偏置缓解。为了应对这一挑战，我们提出了对歧视（APOD）的积极惩罚，这是一个交互式框架，以指导有限的注释以最大程度地消除算法偏见的影响。拟议的APOD将歧视惩罚与主动实例选择集成在一起，以有效利用有限的注释预算，从理论上讲，它可以限制算法偏见。根据五个基准数据集的评估，APOD在有限的注释预算下优于最先进的基线方法，并显示出与完全注释的偏见缓解相当的性能，这表明APOD可以使真实世界应用程序受益于敏感信息时的应用是有限的。

我们介绍了一种新颖的屏蔽图AutoEncoder（MGAE）框架，以在图形结构数据上执行有效的学习。从自我监督学习中欣识见，我们随机掩盖了大部分边缘，并在训练期间尝试重建这些缺失的边缘。 Mgae有两个核心设计。首先，我们发现掩蔽了输入图结构的高比率，例如70 \％$，产生一个非凡和有意义的自我监督任务，使下游应用程序受益。其次，我们使用图形神经网络（GNN）作为编码器，以在部分掩蔽的图表上执行消息传播。为了重建大量掩模边缘，提出了一种定制的互相关解码器。它可以捕获多粒度的锚边的头部和尾部节点之间的互相关。耦合这两种设计使MGAE能够有效且有效地培训。在多个开放数据集（Planetoid和OGB基准测试）上进行了广泛的实验，证明MGAE通常比链接预测和节点分类更好地表现优于最先进的无监督竞争对手。

可说明的机器学习吸引了越来越多的关注，因为它提高了模型的透明度，这有助于机器学习在真实应用中受到信任。然而，最近证明了解释方法易于操纵，在那里我们可以在保持其预测常数的同时轻松改变模型的解释。为了解决这个问题，已经支付了一些努力来使用更稳定的解释方法或更改模型配置。在这项工作中，我们从训练角度解决了问题，并提出了一种称为对抗的解释培训的新培训计划（ATEX），以改善模型的内部解释稳定性，无论应用的具体解释方法如何。而不是直接指定数据实例上的解释值，而是仅为模型预测提供了要求，该预测避免了涉及在优化中的二阶导数。作为进一步的讨论，我们还发现解释稳定性与模型的另一个性质密切相关，即暴露于对抗性攻击的风险。通过实验，除了表明ATEX改善了针对操纵靶向解释的模型鲁棒性，它还带来了额外的益处，包括平滑解释，并在应用于模型时提高对抗性训练的功效。

机器学习模型在高赌注应用中变得普遍存在。尽管在绩效方面有明显的效益，但该模型可以表现出对少数民族群体的偏见，并导致决策过程中的公平问题，导致对个人和社会的严重负面影响。近年来，已经开发了各种技术来减轻机器学习模型的偏差。其中，加工方法已经增加了社区的关注，在模型设计期间直接考虑公平，以诱导本质上公平的模型，从根本上减轻了产出和陈述中的公平问题。在本调查中，我们审查了加工偏置减缓技术的当前进展。基于在模型中实现公平的地方，我们将它们分类为明确和隐性的方法，前者直接在培训目标中纳入公平度量，后者重点介绍精炼潜在代表学习。最后，我们在讨论该社区中的研究挑战来讨论调查，以激励未来的探索。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

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.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.