实体集扩展（ESE）是一项有价值的任务，旨在找到给定种子实体描述的目标语义类别的实体。由于其发现知识的能力，各种NLP和下游应用程序都受益于ESE。尽管现有的引导方法取得了巨大进展，但其中大多数仍然依赖手动预定义的上下文模式。预定义的上下文模式的不可忽略的缺点是，它们不能灵活地推广到各种语义类别，我们将这种现象称为“语义敏感性”。为了解决这个问题，我们设计了一个上下文模式生成模块，该模块利用自回归语言模型（例如GPT-2）自动为实体生成高质量的上下文模式。此外，我们提出了GAPA，这是一种新型ESE框架，利用上述生成的模式扩展目标实体。对三个广泛使用的数据集进行了广泛的实验和详细分析，证明了我们方法的有效性。我们实验的所有代码都将用于可重复性。

中文拼写检查（CSC）任务旨在检测和纠正中文拼写错误。近年来，相关研究的重点是引入“混乱设置”以增强CSC模型的角色相似性，忽略了包含更丰富信息的字符的上下文。为了更好地利用上下文相似性，我们为CSC任务提供了一个简单而有效的课程学习框架。借助我们设计的模型不足框架，现有的CSC型号将从人类学习汉字并取得进一步改进的培训。对广泛使用的Sighan数据集进行了广泛的实验和详细分析表明，我们的方法的表现优于先前的最新方法。

Bootstrap aggregating (Bagging) and boosting are two popular ensemble learning approaches, which combine multiple base learners to generate a composite model for more accurate and more reliable performance. They have been widely used in biology, engineering, healthcare, etc. This paper proposes BoostForest, which is an ensemble learning approach using BoostTree as base learners and can be used for both classification and regression. BoostTree constructs a tree model by gradient boosting. It increases the randomness (diversity) by drawing the cut-points randomly at node splitting. BoostForest further increases the randomness by bootstrapping the training data in constructing different BoostTrees. BoostForest generally outperformed four classical ensemble learning approaches (Random Forest, Extra-Trees, XGBoost and LightGBM) on 35 classification and regression datasets. Remarkably, BoostForest tunes its parameters by simply sampling them randomly from a parameter pool, which can be easily specified, and its ensemble learning framework can also be used to combine many other base learners.

设想制造部门受到基于人工智能的技术的严重影响，计算能力和数据量的大幅增加。制造业领域的一个核心挑战在于一般框架的要求，以确保满足不同制造应用中的诊断和监视性能。在这里，我们提出了一个通用数据驱动的端到端框架，用于监视制造系统。该框架是从深度学习技术中得出的，评估了融合的感觉测量值，以检测甚至预测故障和磨损条件。这项工作利用了深度学习的预测能力，从嘈杂的时间表数据中自动提取隐藏的降解功能。我们已经在从各种制造应用中绘制的十个代表性数据集上试验了拟议的框架。结果表明，该框架在检查的基准应用中表现良好，可以在不同的情况下应用，这表明其潜在用作智能制造中的关键角石。

Currently, the neural network architecture design is mostly guided by the indirect metric of computation complexity, i.e., FLOPs. However, the direct metric, e.g., speed, also depends on the other factors such as memory access cost and platform characterics. Thus, this work proposes to evaluate the direct metric on the target platform, beyond only considering FLOPs. Based on a series of controlled experiments, this work derives several practical guidelines for efficient network design. Accordingly, a new architecture is presented, called ShuffleNet V2.Comprehensive ablation experiments verify that our model is the stateof-the-art in terms of speed and accuracy tradeoff.

Recent research has reported a performance degradation in self-supervised contrastive learning for specially designed efficient networks, such as MobileNet and EfficientNet. A common practice to address this problem is to introduce a pretrained contrastive teacher model and train the lightweight networks with distillation signals generated by the teacher. However, it is time and resource consuming to pretrain a teacher model when it is not available. In this work, we aim to establish a stronger baseline for lightweight contrastive models without using a pretrained teacher model. Specifically, we show that the optimal recipe for efficient models is different from that of larger models, and using the same training settings as ResNet50, as previous research does, is inappropriate. Additionally, we observe a common issu e in contrastive learning where either the positive or negative views can be noisy, and propose a smoothed version of InfoNCE loss to alleviate this problem. As a result, we successfully improve the linear evaluation results from 36.3\% to 62.3\% for MobileNet-V3-Large and from 42.2\% to 65.8\% for EfficientNet-B0 on ImageNet, closing the accuracy gap to ResNet50 with $5\times$ fewer parameters. We hope our research will facilitate the usage of lightweight contrastive models.

Metric-based meta-learning is one of the de facto standards in few-shot learning. It composes of representation learning and metrics calculation designs. Previous works construct class representations in different ways, varying from mean output embedding to covariance and distributions. However, using embeddings in space lacks expressivity and cannot capture class information robustly, while statistical complex modeling poses difficulty to metric designs. In this work, we use tensor fields (``areas'') to model classes from the geometrical perspective for few-shot learning. We present a simple and effective method, dubbed hypersphere prototypes (HyperProto), where class information is represented by hyperspheres with dynamic sizes with two sets of learnable parameters: the hypersphere's center and the radius. Extending from points to areas, hyperspheres are much more expressive than embeddings. Moreover, it is more convenient to perform metric-based classification with hypersphere prototypes than statistical modeling, as we only need to calculate the distance from a data point to the surface of the hypersphere. Following this idea, we also develop two variants of prototypes under other measurements. Extensive experiments and analysis on few-shot learning tasks across NLP and CV and comparison with 20+ competitive baselines demonstrate the effectiveness of our approach.

Open Relation Extraction (OpenRE) aims to discover novel relations from open domains. Previous OpenRE methods mainly suffer from two problems: (1) Insufficient capacity to discriminate between known and novel relations. When extending conventional test settings to a more general setting where test data might also come from seen classes, existing approaches have a significant performance decline. (2) Secondary labeling must be performed before practical application. Existing methods cannot label human-readable and meaningful types for novel relations, which is urgently required by the downstream tasks. To address these issues, we propose the Active Relation Discovery (ARD) framework, which utilizes relational outlier detection for discriminating known and novel relations and involves active learning for labeling novel relations. Extensive experiments on three real-world datasets show that ARD significantly outperforms previous state-of-the-art methods on both conventional and our proposed general OpenRE settings. The source code and datasets will be available for reproducibility.

预训练的语言模型（PLM）在自然语言理解中的许多下游任务中取得了显着的性能增长。已提出了各种中文PLM，以学习更好的中文表示。但是，大多数当前模型都使用中文字符作为输入，并且无法编码中文单词中包含的语义信息。虽然最近的预训练模型同时融合了单词和字符，但它们通常会遭受不足的语义互动，并且无法捕获单词和字符之间的语义关系。为了解决上述问题，我们提出了一个简单而有效的PLM小扣手，该小扣子采用了对单词和性格表示的对比度学习。特别是，Clower通过对多透明信息的对比学习将粗粒的信息（即单词）隐式编码为细粒度表示（即字符）。在现实的情况下，小电动器具有很大的价值，因为它可以轻松地将其纳入任何现有的基于细粒的PLM中而无需修改生产管道。在一系列下游任务上进行的扩展实验表明，小动物的卓越性能超过了几个最先进的实验 - 艺术基线。

多层erceptron（MLP），作为出现的第一个神经网络结构，是一个大的击中。但是由硬件计算能力和数据集的大小限制，它一旦沉没了数十年。在此期间，我们目睹了从手动特征提取到带有局部接收领域的CNN的范式转变，以及基于自我关注机制的全球接收领域的变换。今年（2021年），随着MLP混合器的推出，MLP已重新进入敏捷，并吸引了计算机视觉界的广泛研究。与传统的MLP进行比较，它变得更深，但改变了完全扁平化以补丁平整的输入。鉴于其高性能和较少的需求对视觉特定的感应偏见，但社区无法帮助奇迹，将MLP，最简单的结构与全球接受领域，但没有关注，成为一个新的电脑视觉范式吗？为了回答这个问题，本调查旨在全面概述视觉深层MLP模型的最新发展。具体而言，我们从微妙的子模块设计到全局网络结构，我们审查了这些视觉深度MLP。我们比较了不同网络设计的接收领域，计算复杂性和其他特性，以便清楚地了解MLP的开发路径。调查表明，MLPS的分辨率灵敏度和计算密度仍未得到解决，纯MLP逐渐发展朝向CNN样。我们建议，目前的数据量和计算能力尚未准备好接受纯的MLP，并且人工视觉指导仍然很重要。最后，我们提供了开放的研究方向和可能的未来作品的分析。我们希望这项努力能够点燃社区的进一步兴趣，并鼓励目前为神经网络进行更好的视觉量身定制设计。