几个名称的实体识别（NER）使我们能够使用很少的标记示例为新域构建一个NER系统。但是，该任务的现有原型网络具有大致估计的标签依赖性和紧密分布的原型，因此经常导致错误分类。为了解决上述问题，我们提出了EP-NET，这是一个实体级原型网络，通过分散分布的原型增强。EP-NET构建实体级原型，并认为文本跨度为候选实体，因此它不再需要标签依赖性。此外，EP-NET从头开始训练原型，以分散分配它们，并使用空间投影将跨度与嵌入空间中的原型对齐。两项评估任务和少量网络设置的实验结果表明，EP-NET在整体性能方面始终优于先前的强大模型。广泛的分析进一步验证了EP-NET的有效性。

随机梯度下降（SGD）是现代机器学习（ML）系统的基石。尽管具有其计算效率，但SGD仍需要随机数据访问，这些数据访问在依赖块可调地理的二级存储的系统中实现效率低下，例如HDD和SSD，例如TensorFlow/Pytorch和DB ML系统，而不是大文件。为了解决这种阻抗不匹配，已经提出了各种数据改组策略，以平衡SGD的收敛速率（有利于随机性）及其I/O性能（有利于顺序访问）。在本文中，我们首先对现有数据改组策略进行系统的实证研究，该研究表明，所有现有策略都有改进的空间 - 它们都在I/O性能或融合率方面受苦。考虑到这一点，我们提出了一种简单但新颖的分层数据改组策略Corgipile。与现有的策略相比，Corgipile避免了完整的数据洗牌，同时保持SGD的可比收敛速度，就好像执行了完整的混音一样。我们对Corgipile的融合行为提供了非平凡的理论分析。我们通过在新的CorgipileDataSet API中设计新的平行/分布式洗牌操作员来进一步将Corgipile整合到Pytorch中。我们还通过介绍具有优化的三个新的物理运营商，将Corgipile集成到PostgreSQL中。我们的实验结果表明，Corgipile可以与全面的SGD达到可比的收敛速率，以实现深度学习和广义线性模型。对于ImageNet数据集的深度学习模型，Corgipile比带有完整数据洗牌的Pytorch快1.5倍。对于具有线性模型的INDB ML，在HDD和SSD上，Corgipile的Corgipile比两个最先进的IN-DB ML系统（Apache Madlib和Bismarck）快1.6 x-12.8倍。

无监督域适应（UDA）的突破可以帮助将富含标签的源域的模型调整为未标记的目标域。尽管有这些进步，但缺乏对UDA算法的缺乏研究，特别是基于对抗性学习的算法，可以在分布式设置中工作。在现实世界应用中，目标域通常分布在数千个设备上，并且现有的对抗UDA算法 - 这些算法中集中在本质上 - 无法应用于这些设置。为了解决这一重要问题，我们介绍了弗鲁加：分布式对策UDA的端到端框架。通过对UDA文献进行仔细分析，我们确定了分布式UDA系统的设计目标，并提出了两种新算法，以提高分布式环境中对抗性UDA的适应准确性和培训效率。我们对具有五个图像和语音数据集的弗鲁加的评估表明，它可以将目标域精度升高至50％，并提高对抗越野UDA的培训效率至少11次。

近年来，目睹了分布式数据并行培训的越来越多的系统列表。现有系统很大程度上适合两个范例，即参数服务器和MPI样式的集体操作。在算法方面，研究人员提出了广泛的技术，以通过系统弛豫降低通信：量化，分散和通信延迟。然而，大多数情况下，如果不是全部，现有系统仅依赖于标准的同步和异步随机梯度（SG）的优化，因此不能利用机器学习社区最近发展的所有可能的优化。鉴于该系统和理论的当前景观之间的新出现差距，我们构建了一个MPI式通信库，提供了一种基元的集合，这既灵活又模块化，以支持分布式的最先进的系统松弛技术训练。 BAGUA提供了这种设计，拥有巨大的实现和扩展各种最先进的分布式学习算法的能力。在具有多达16台机器（128个GPU）的生产群集中，BAGUA可以在端到端培训时间内优于Pytorch-DDP，Horovod和ByTeps，在各种任务范围内的重大边缘（最多2次）。此外，我们进行严格的权衡探索，表明不同的算法和系统放松在不同的网络条件下实现了最佳性能。

This work focuses on unsupervised representation learning in person re-identification (ReID). Recent self-supervised contrastive learning methods learn invariance by maximizing the representation similarity between two augmented views of a same image. However, traditional data augmentation may bring to the fore undesirable distortions on identity features, which is not always favorable in id-sensitive ReID tasks. In this paper, we propose to replace traditional data augmentation with a generative adversarial network (GAN) that is targeted to generate augmented views for contrastive learning. A 3D mesh guided person image generator is proposed to disentangle a person image into id-related and id-unrelated features. Deviating from previous GAN-based ReID methods that only work in id-unrelated space (pose and camera style), we conduct GAN-based augmentation on both id-unrelated and id-related features. We further propose specific contrastive losses to help our network learn invariance from id-unrelated and id-related augmentations. By jointly training the generative and the contrastive modules, our method achieves new state-of-the-art unsupervised person ReID performance on mainstream large-scale benchmarks.

This paper proposes a novel self-supervised based Cut-and-Paste GAN to perform foreground object segmentation and generate realistic composite images without manual annotations. We accomplish this goal by a simple yet effective self-supervised approach coupled with the U-Net based discriminator. The proposed method extends the ability of the standard discriminators to learn not only the global data representations via classification (real/fake) but also learn semantic and structural information through pseudo labels created using the self-supervised task. The proposed method empowers the generator to create meaningful masks by forcing it to learn informative per-pixel as well as global image feedback from the discriminator. Our experiments demonstrate that our proposed method significantly outperforms the state-of-the-art methods on the standard benchmark datasets.

With the rapid deployment of graph neural networks (GNNs) based techniques into a wide range of applications such as link prediction, node classification, and graph classification the explainability of GNNs has become an indispensable component for predictive and trustworthy decision-making. Thus, it is critical to explain why graph neural network (GNN) makes particular predictions for them to be believed in many applications. Some GNNs explainers have been proposed recently. However, they lack to generate accurate and real explanations. To mitigate these limitations, we propose GANExplainer, based on Generative Adversarial Network (GAN) architecture. GANExplainer is composed of a generator to create explanations and a discriminator to assist with the Generator development. We investigate the explanation accuracy of our models by comparing the performance of GANExplainer with other state-of-the-art methods. Our empirical results on synthetic datasets indicate that GANExplainer improves explanation accuracy by up to 35\% compared to its alternatives.

This work addresses an alternative approach for query expansion (QE) using a generative adversarial network (GAN) to enhance the effectiveness of information search in e-commerce. We propose a modified QE conditional GAN (mQE-CGAN) framework, which resolves keywords by expanding the query with a synthetically generated query that proposes semantic information from text input. We train a sequence-to-sequence transformer model as the generator to produce keywords and use a recurrent neural network model as the discriminator to classify an adversarial output with the generator. With the modified CGAN framework, various forms of semantic insights gathered from the query document corpus are introduced to the generation process. We leverage these insights as conditions for the generator model and discuss their effectiveness for the query expansion task. Our experiments demonstrate that the utilization of condition structures within the mQE-CGAN framework can increase the semantic similarity between generated sequences and reference documents up to nearly 10% compared to baseline models

High-utility sequential pattern mining (HUSPM) has emerged as an important topic due to its wide application and considerable popularity. However, due to the combinatorial explosion of the search space when the HUSPM problem encounters a low utility threshold or large-scale data, it may be time-consuming and memory-costly to address the HUSPM problem. Several algorithms have been proposed for addressing this problem, but they still cost a lot in terms of running time and memory usage. In this paper, to further solve this problem efficiently, we design a compact structure called sequence projection (seqPro) and propose an efficient algorithm, namely discovering high-utility sequential patterns with the seqPro structure (HUSP-SP). HUSP-SP utilizes the compact seq-array to store the necessary information in a sequence database. The seqPro structure is designed to efficiently calculate candidate patterns' utilities and upper bound values. Furthermore, a new upper bound on utility, namely tighter reduced sequence utility (TRSU) and two pruning strategies in search space, are utilized to improve the mining performance of HUSP-SP. Experimental results on both synthetic and real-life datasets show that HUSP-SP can significantly outperform the state-of-the-art algorithms in terms of running time, memory usage, search space pruning efficiency, and scalability.

Despite excellent performance in image generation, Generative Adversarial Networks (GANs) are notorious for its requirements of enormous storage and intensive computation. As an awesome ''performance maker'', knowledge distillation is demonstrated to be particularly efficacious in exploring low-priced GANs. In this paper, we investigate the irreplaceability of teacher discriminator and present an inventive discriminator-cooperated distillation, abbreviated as DCD, towards refining better feature maps from the generator. In contrast to conventional pixel-to-pixel match methods in feature map distillation, our DCD utilizes teacher discriminator as a transformation to drive intermediate results of the student generator to be perceptually close to corresponding outputs of the teacher generator. Furthermore, in order to mitigate mode collapse in GAN compression, we construct a collaborative adversarial training paradigm where the teacher discriminator is from scratch established to co-train with student generator in company with our DCD. Our DCD shows superior results compared with existing GAN compression methods. For instance, after reducing over 40x MACs and 80x parameters of CycleGAN, we well decrease FID metric from 61.53 to 48.24 while the current SoTA method merely has 51.92. This work's source code has been made accessible at https://github.com/poopit/DCD-official.