As a powerful engine, vanilla convolution has promoted huge breakthroughs in various computer tasks. However, it often suffers from sample and content agnostic problems, which limits the representation capacities of the convolutional neural networks (CNNs). In this paper, we for the first time model the scene features as a combination of the local spatial-adaptive parts owned by the individual and the global shift-invariant parts shared to all individuals, and then propose a novel two-branch dual complementary dynamic convolution (DCDC) operator to flexibly deal with these two types of features. The DCDC operator overcomes the limitations of vanilla convolution and most existing dynamic convolutions who capture only spatial-adaptive features, and thus markedly boosts the representation capacities of CNNs. Experiments show that the DCDC operator based ResNets (DCDC-ResNets) significantly outperform vanilla ResNets and most state-of-the-art dynamic convolutional networks on image classification, as well as downstream tasks including object detection, instance and panoptic segmentation tasks, while with lower FLOPs and parameters.

知识图（KGS）代表作为三元组的事实已被广泛采用在许多应用中。 LIGHT预测和规则感应等推理任务对于KG的开发很重要。已经提出了知识图形嵌入式（KGES）将kg的实体和kg与持续向量空间的关系进行了建议，以获得这些推理任务，并被证明是有效和强大的。但在实际应用中申请和部署KGE的合理性和可行性尚未探索。在本文中，我们讨论并报告我们在真实域应用程序中部署KGE的经验：电子商务。我们首先为电子商务KG系统提供三个重要的探索者：1）注意推理，推理几个目标关系更为关注而不是全部; 2）解释，提供预测的解释，帮助用户和业务运营商理解为什么预测; 3）可转让规则，生成可重用的规则，以加速将千克部署到新系统。虽然非现有KGE可以满足所有这些DesiderATA，但我们提出了一种新颖的一种，可说明的知识图表注意网络，通过建模三元组之间的相关性而不是纯粹依赖于其头实体，关系和尾部实体嵌入来预测。它可以自动选择预测的注意力三倍，并同时记录它们的贡献，从该解释可以很容易地提供，可以有效地生产可转移规则。我们经验表明，我们的方法能够在我们的电子商务应用程序中满足所有三个DesiderATA，并从实际域应用程序中倾斜于数据集的典型基线。

Reasoning, as an essential ability for complex problem-solving, can provide back-end support for various real-world applications, such as medical diagnosis, negotiation, etc. This paper provides a comprehensive survey of cutting-edge research on reasoning with language model prompting. We introduce research works with comparisons and summaries and provide systematic resources to help beginners. We also discuss the potential reasons for emerging such reasoning abilities and highlight future research directions.

Robust Model-Agnostic Meta-Learning (MAML) is usually adopted to train a meta-model which may fast adapt to novel classes with only a few exemplars and meanwhile remain robust to adversarial attacks. The conventional solution for robust MAML is to introduce robustness-promoting regularization during meta-training stage. With such a regularization, previous robust MAML methods simply follow the typical MAML practice that the number of training shots should match with the number of test shots to achieve an optimal adaptation performance. However, although the robustness can be largely improved, previous methods sacrifice clean accuracy a lot. In this paper, we observe that introducing robustness-promoting regularization into MAML reduces the intrinsic dimension of clean sample features, which results in a lower capacity of clean representations. This may explain why the clean accuracy of previous robust MAML methods drops severely. Based on this observation, we propose a simple strategy, i.e., increasing the number of training shots, to mitigate the loss of intrinsic dimension caused by robustness-promoting regularization. Though simple, our method remarkably improves the clean accuracy of MAML without much loss of robustness, producing a robust yet accurate model. Extensive experiments demonstrate that our method outperforms prior arts in achieving a better trade-off between accuracy and robustness. Besides, we observe that our method is less sensitive to the number of fine-tuning steps during meta-training, which allows for a reduced number of fine-tuning steps to improve training efficiency.

Analogical reasoning is fundamental to human cognition and holds an important place in various fields. However, previous studies mainly focus on single-modal analogical reasoning and ignore taking advantage of structure knowledge. Notably, the research in cognitive psychology has demonstrated that information from multimodal sources always brings more powerful cognitive transfer than single modality sources. To this end, we introduce the new task of multimodal analogical reasoning over knowledge graphs, which requires multimodal reasoning ability with the help of background knowledge. Specifically, we construct a Multimodal Analogical Reasoning dataSet (MARS) and a multimodal knowledge graph MarKG. We evaluate with multimodal knowledge graph embedding and pre-trained Transformer baselines, illustrating the potential challenges of the proposed task. We further propose a novel model-agnostic Multimodal analogical reasoning framework with Transformer (MarT) motivated by the structure mapping theory, which can obtain better performance.

由于选择偏差，观察数据估算平均治疗效果（ATE）是有挑战性的。现有作品主要以两种方式应对这一挑战。一些研究人员建议构建满足正交条件的分数函数，该函数确保已建立的估计量“正交”更加健壮。其他人探索表示模型，以实现治疗组和受控群体之间的平衡表示。但是，现有研究未能进行1）在表示空间中歧视受控单元以避免过度平衡的问题； 2）充分利用“正交信息”。在本文中，我们提出了一个基于最新协变量平衡表示方法和正交机器学习理论的中等平衡的表示学习（MBRL）框架。该框架可保护表示形式免于通过多任务学习过度平衡。同时，MBRL将噪声正交性信息纳入培训和验证阶段，以实现更好的ATE估计。与现有的最新方法相比，基准和模拟数据集的全面实验表明，我们方法对治疗效应估计的优越性和鲁棒性。

经济学和医疗保健方面的许多实际决策问题寻求从观察数据中估算平均治疗效果（ATE）。双重/辩护的机器学习（DML）是观察性研究中估计吃量的普遍方法之一。但是，DML估计器可能会遇到错误的问题，甚至在倾向分数被弄错或非常接近0或1时进行极端估计。现有文献从理论的角度解决了这个问题。在本文中，我们提出了一种健壮的因果学习（RCL）方法，以抵消DML估计量的缺陷。从理论上讲，RCL估计量i）与DML估计器一样一致且双重稳健，ii）可以摆脱错误混合问题。从经验上讲，全面的实验表明，i）RCL估计器比DML估计器给出了因果参数的稳定估计，ii）RCL估计器在模拟和基准标准数据集上应用不同的机器学习模型时，RCL估计器优于传统估计器及其变体。 。

迅速的学习方法通​​过诱导更好的几次表现，在他们仍然遵循基于参数的学习范式的同时，引起了自然语言处理的波动。学习中的遗忘和死记硬背的记忆问题可能会遇到不稳定的概括问题。具体而言，香草及时的学习可能难以利用死记硬背的非典型实例，在完全监督的培训或过度贴身模式的情况下使用低射击数据。为了减轻此类局限性，我们以将知识从记忆中解耦的动机发展为有助于模型在概括和记忆之间取得平衡。与香草及时学习相反，重新启动构造了培训实例中的开放式知识店，并在输入，培训和推理过程中实现检索机制，从而使该模型能够从培训语料库中检索相关环境作为能力为提示增强。广泛的实验表明，Retroppt可以在几次射击和零拍设置中获得更好的性能。此外，我们进一步说明，我们提出的撤退可以通过新数据集获得更好的概括能力。对记忆的详细分析确实显示逆转可以减少语言模型对记忆的依赖；因此，改善下游任务的概括。

旨在从非结构化文本中提取结构信息的知识提取（KE）通常会遭受数据稀缺性和新出现的看不见类型，即低资源场景。许多低资源KE的神经方法已广泛研究并取得了令人印象深刻的表现。在本文中，我们在低资源场景中介绍了对KE的文献综述，并将现有作品分为三个范式：（1）利用更高的资源数据，（2）利用更强的模型，（3）利用数据和模型一起。此外，我们描述了有前途的应用，并概述了未来研究的一些潜在方向。我们希望我们的调查能够帮助学术和工业界更好地理解这一领域，激发更多的想法并提高更广泛的应用。

我们展示了一个新的开源和可扩展知识提取工具包，称为Deepke（基于深度学习的知识提取），支持标准完全监督，低资源少拍摄和文档级方案。 Deepke实现了各种信息提取任务，包括命名实体识别，关系提取和属性提取。使用统一的框架，DeePke允许开发人员和研究人员根据其要求，自定义数据集和模型以从非结构化文本中提取信息。具体而言，DeePke不仅为不同的任务和场景提供了各种功能模块和模型实现，而且还通过一致的框架组织所有组件以维持足够的模块化和可扩展性。此外，我们在\ URL {http://deepke.zjukg.cn/}中介绍一个在线平台，用于实时提取各种任务。 Deepke已经配备了Google Colab教程和初学者的综合文件。我们用演示视频发布\ url {https://github.com/zjunlp/deepke}源代码。