AI Open最新文献_第9页

Towards a universal continuous knowledge base 朝向一个通用的连续知识库

AI Open Pub Date : 2021-01-01 DOI: 10.1016/j.aiopen.2021.11.001

Gang Chen , Maosong Sun , Yang Liu

{"title":"Towards a universal continuous knowledge base","authors":"Gang Chen , Maosong Sun , Yang Liu","doi":"10.1016/j.aiopen.2021.11.001","DOIUrl":"10.1016/j.aiopen.2021.11.001","url":null,"abstract":"<div><p>In artificial intelligence (AI), knowledge is the information required by an intelligent system to accomplish tasks. While traditional knowledge bases use discrete, symbolic representations, detecting knowledge encoded in the continuous representations learned from data has received increasing attention recently. In this work, we propose a method for building a continuous knowledge base (CKB) that can store knowledge imported from multiple, diverse neural networks. The key idea of our approach is to define an interface for each neural network and cast knowledge transferring as a function simulation problem. Experiments on text classification show promising results: the CKB imports knowledge from a single model and then exports the knowledge to a new model, achieving comparable performance with the original model. More interesting, we import the knowledge from multiple models to the knowledge base, from which the fused knowledge is exported back to a single model, achieving a higher accuracy than the original model. With the CKB, it is also easy to achieve knowledge distillation and transfer learning. Our work opens the door to building a universal continuous knowledge base to collect, store, and organize all continuous knowledge encoded in various neural networks trained for different AI tasks.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"2 ","pages":"Pages 197-204"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666651021000280/pdfft?md5=6baa28b4172e47cb5e69435795e785e6&pid=1-s2.0-S2666651021000280-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85616245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

CokeBERT: Contextual knowledge selection and embedding towards enhanced pre-trained language models CokeBERT:面向增强预训练语言模型的上下文知识选择和嵌入

AI Open Pub Date : 2021-01-01 DOI: 10.1016/j.aiopen.2021.06.004

Yusheng Su , Xu Han , Zhengyan Zhang , Yankai Lin , Peng Li , Zhiyuan Liu , Jie Zhou , Maosong Sun

{"title":"CokeBERT: Contextual knowledge selection and embedding towards enhanced pre-trained language models","authors":"Yusheng Su , Xu Han , Zhengyan Zhang , Yankai Lin , Peng Li , Zhiyuan Liu , Jie Zhou , Maosong Sun","doi":"10.1016/j.aiopen.2021.06.004","DOIUrl":"10.1016/j.aiopen.2021.06.004","url":null,"abstract":"<div><p>Several recent efforts have been devoted to enhancing pre-trained language models (PLMs) by utilizing extra heterogeneous knowledge in knowledge graphs (KGs), and achieved consistent improvements on various knowledge-driven NLP tasks. However, most of these knowledge-enhanced PLMs embed static sub-graphs of KGs (“knowledge context”), regardless of that the knowledge required by PLMs may change dynamically according to specific text (“textual context”). In this paper, we propose a novel framework named Coke to dynamically select contextual knowledge and embed knowledge context according to textual context for PLMs, which can avoid the effect of redundant and ambiguous knowledge in KGs that cannot match the input text. Our experimental results show that Coke outperforms various baselines on typical knowledge-driven NLP tasks, indicating the effectiveness of utilizing dynamic knowledge context for language understanding. Besides the performance improvements, the dynamically selected knowledge in Coke can describe the semantics of text-related knowledge in a more interpretable form than the conventional PLMs. Our implementation and datasets are publicly available.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"2 ","pages":"Pages 127-134"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.aiopen.2021.06.004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78659489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

Lawformer: A pre-trained language model for Chinese legal long documents Lawformer:中文法律长文件的预训练语言模型

AI Open Pub Date : 2021-01-01 DOI: 10.1016/j.aiopen.2021.06.003

Chaojun Xiao , Xueyu Hu , Zhiyuan Liu , Cunchao Tu , Maosong Sun

{"title":"Lawformer: A pre-trained language model for Chinese legal long documents","authors":"Chaojun Xiao , Xueyu Hu , Zhiyuan Liu , Cunchao Tu , Maosong Sun","doi":"10.1016/j.aiopen.2021.06.003","DOIUrl":"10.1016/j.aiopen.2021.06.003","url":null,"abstract":"<div><p>Legal artificial intelligence (LegalAI) aims to benefit legal systems with the technology of artificial intelligence, especially natural language processing (NLP). Recently, inspired by the success of pre-trained language models (PLMs) in the generic domain, many LegalAI researchers devote their effort to applying PLMs to legal tasks. However, utilizing PLMs to address legal tasks is still challenging, as the legal documents usually consist of thousands of tokens, which is far longer than the length that mainstream PLMs can process. In this paper, we release the Longformer-based pre-trained language model, named as Lawformer, for Chinese legal long documents understanding. We evaluate Lawformer on a variety of LegalAI tasks, including judgment prediction, similar case retrieval, legal reading comprehension, and legal question answering. The experimental results demonstrate that our model can achieve promising improvement on tasks with long documents as inputs. The code and parameters are available at <span>https://github.com/thunlp/LegalPLMs</span><svg><path></path></svg>.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"2 ","pages":"Pages 79-84"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.aiopen.2021.06.003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73107746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 88

CPM-2: Large-scale cost-effective pre-trained language models CPM-2:大规模经济高效的预训练语言模型

AI Open Pub Date : 2021-01-01 DOI: 10.1016/j.aiopen.2021.12.003

Zhengyan Zhang , Yuxian Gu , Xu Han , Shengqi Chen , Chaojun Xiao , Zhenbo Sun, Yuan Yao, Fanchao Qi, Jian Guan, Pei Ke, Yanzheng Cai, Guoyang Zeng, Zhixing Tan, Zhiyuan Liu, Minlie Huang, Wentao Han, Yang Liu, Xiaoyan Zhu, Maosong Sun

{"title":"CPM-2: Large-scale cost-effective pre-trained language models","authors":"Zhengyan Zhang , Yuxian Gu , Xu Han , Shengqi Chen , Chaojun Xiao , Zhenbo Sun, Yuan Yao, Fanchao Qi, Jian Guan, Pei Ke, Yanzheng Cai, Guoyang Zeng, Zhixing Tan, Zhiyuan Liu, Minlie Huang, Wentao Han, Yang Liu, Xiaoyan Zhu, Maosong Sun","doi":"10.1016/j.aiopen.2021.12.003","DOIUrl":"10.1016/j.aiopen.2021.12.003","url":null,"abstract":"<div><p>In recent years, the size of pre-trained language models (PLMs) has grown by leaps and bounds. However, efficiency issues of these large-scale PLMs limit their utilization in real-world scenarios. We present a suite of cost-effective techniques for the use of PLMs to deal with the efficiency issues of pre-training, fine-tuning, and inference. (1) We introduce knowledge inheritance to accelerate the pre-training process by exploiting existing PLMs instead of training models from scratch. (2) We explore the best practice of prompt tuning with large-scale PLMs. Compared with conventional fine-tuning, prompt tuning significantly reduces the number of task-specific parameters. (3) We implement a new inference toolkit, namely <span>infmoe</span>, for using large-scale PLMs with limited computational resources. Based on our cost-effective pipeline, we pre-train two models: an encoder-decoder bilingual model with 11 billion parameters (CPM-2) and its corresponding MoE version with 198 billion parameters. In our experiments, we compare CPM-2 with mT5 on downstream tasks. Experimental results show that CPM-2 has excellent general language intelligence. Moreover, we validate the efficiency of <span>infmoe</span> when conducting inference of large-scale models having tens of billions of parameters on a single GPU. All source code and model parameters are available at <span>https://github.com/TsinghuaAI/CPM</span><svg><path></path></svg>.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"2 ","pages":"Pages 216-224"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666651021000310/pdfft?md5=46efc536c128aefd0ff69139f8627ddb&pid=1-s2.0-S2666651021000310-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90204116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 59

The information propagation model of Weibo network based on spiking neural P systems 基于脉冲神经P系统的微博网络信息传播模型

AI Open Pub Date : 2021-01-01 DOI: 10.1016/j.aiopen.2021.07.003

Tiancui Zhang , Xiaoliang Chen , Yajun Du , Xianyong Li

{"title":"The information propagation model of Weibo network based on spiking neural P systems","authors":"Tiancui Zhang , Xiaoliang Chen , Yajun Du , Xianyong Li","doi":"10.1016/j.aiopen.2021.07.003","DOIUrl":"10.1016/j.aiopen.2021.07.003","url":null,"abstract":"<div><p>Information propagation models in the Weibo network play a primary role in analyzing user behaviors, obtaining the propagation paths, determining the opinion leaders, and discovering the hot spots of public opinion. Existing research recognizes the critical role played by information propagation models from different aspects. However, few studies have investigated the specific details of information propagation in any systematic way. Spiking neural P (SNP, for short) systems are one of the most potential research carriers of information propagation by applying their concurrent structures and asynchronous firing rules. This paper proposes a simple and intuitive SNP variant, namely DWIP-SNP, for user behavior analysis in Weibo. The fundamental objects of information propagation in Weibo are represented by a similar SNP formalization. The forward, comment, delete, and other users’ behaviors in the Weibo network can be observed and proceeded more intuitively. Then, the DWIP-SNP systems are combined with time delays to indicate the dynamic information diffusion from the perspective of the Bio-computing systems. Finally, a real-world example of information propagation with Weibo data set is utilized to verify the effectiveness and feasibility of the model. The insights of the DWIP-SNP based propagation model gained from this study may be of assistance to user behavior understanding and information propagation in other complex networks.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"2 ","pages":"Pages 135-142"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.aiopen.2021.07.003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79850721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Robustness of deep learning models on graphs: A survey 图上深度学习模型的鲁棒性:综述

AI Open Pub Date : 2021-01-01 DOI: 10.1016/j.aiopen.2021.05.002

Jiarong Xu, Junru Chen, Siqi You, Zhiqing Xiao, Yang Yang, Jiangang Lu

{"title":"Robustness of deep learning models on graphs: A survey","authors":"Jiarong Xu, Junru Chen, Siqi You, Zhiqing Xiao, Yang Yang, Jiangang Lu","doi":"10.1016/j.aiopen.2021.05.002","DOIUrl":"10.1016/j.aiopen.2021.05.002","url":null,"abstract":"<div><p>Machine learning (ML) technologies have achieved significant success in various downstream tasks, e.g., node classification, link prediction, community detection, graph classification and graph clustering. However, many studies have shown that the models built upon ML technologies are vulnerable to noises and adversarial attacks. A number of works have studied the robust models against noise or adversarial examples in image domains and text processing domains, however, it is more challenging to learn robust models in graph domains. Adding noises or perturbations on graph data will make the robustness even harder to enhance – the noises and perturbations of edges or node attributes are easy to propagate to other neighbors via the relational information on a graph. In this paper, we investigate and summarize the existing works that study the robust deep learning models against adversarial attacks or noises on graphs, namely the robust learning (models) on graphs. Specifically, we first provide some robustness evaluation metrics of model robustness on graphs. Then, we comprehensively provide a taxonomy which groups robust models on graphs into five categories: anomaly detection, adversarial training, pre-processing, attention mechanism, and certifiable robustness. Besides, we emphasize some promising future directions in learning robust models on graphs. Hopefully, our works can offer insights for the relevant researchers, thus providing assistance for their studies.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"2 ","pages":"Pages 69-78"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.aiopen.2021.05.002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78272915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Incorporating bidirectional interactive information and regional features for relational facts extraction 结合双向交互信息和区域特征进行关系事实提取

AI Open Pub Date : 2021-01-01 DOI: 10.1016/j.aiopen.2021.09.002

Bingshan Zhu , Yang Yu , Mingying Zhang , Haopeng Ren , Canguang Li , Wenjian Hao , Lixi Wang , Yi Cai

{"title":"Incorporating bidirectional interactive information and regional features for relational facts extraction","authors":"Bingshan Zhu , Yang Yu , Mingying Zhang , Haopeng Ren , Canguang Li , Wenjian Hao , Lixi Wang , Yi Cai","doi":"10.1016/j.aiopen.2021.09.002","DOIUrl":"10.1016/j.aiopen.2021.09.002","url":null,"abstract":"<div><p>Extracting entity and relation jointly is often complicated since the relational triplets may be overlapped. In this paper, we propose a novel unified joint extraction model that considers the significant information which is useful for relation extraction between a pair of entities. We also consider bidirectional interaction between named entity recognition and relation extraction. To this end, we apply Bi-LSTM to capture sequential information and use Graph Convolutional Network to capture significant regional information in our encoding part. We use multi-layer structure in decoding part including first decode layer, interactive layer and final decode layer to fuse bidirectional interactive information between named entity recognition and relation extraction. In this way, our method can simultaneously extract all entities and their relations including overlapping relations. Experimental results show that our model performs better comparing with other baseline models in this task, and we achieve state-of-the-art performance on two public datasets.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"2 ","pages":"Pages 175-185"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666651021000255/pdfft?md5=97db58ca1e40caebd6ee57606b699005&pid=1-s2.0-S2666651021000255-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84724666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pre-trained models: Past, present and future 预训练模型:过去、现在和未来

AI Open Pub Date : 2021-01-01 DOI: 10.1016/j.aiopen.2021.08.002

Xu Han , Zhengyan Zhang , Ning Ding , Yuxian Gu , Xiao Liu , Yuqi Huo , Jiezhong Qiu , Yuan Yao , Ao Zhang , Liang Zhang , Wentao Han , Minlie Huang , Qin Jin , Yanyan Lan , Yang Liu , Zhiyuan Liu , Zhiwu Lu , Xipeng Qiu , Ruihua Song , Jie Tang , Jun Zhu

{"title":"Pre-trained models: Past, present and future","authors":"Xu Han , Zhengyan Zhang , Ning Ding , Yuxian Gu , Xiao Liu , Yuqi Huo , Jiezhong Qiu , Yuan Yao , Ao Zhang , Liang Zhang , Wentao Han , Minlie Huang , Qin Jin , Yanyan Lan , Yang Liu , Zhiyuan Liu , Zhiwu Lu , Xipeng Qiu , Ruihua Song , Jie Tang , Jun Zhu","doi":"10.1016/j.aiopen.2021.08.002","DOIUrl":"10.1016/j.aiopen.2021.08.002","url":null,"abstract":"<div><p>Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success and become a milestone in the field of artificial intelligence (AI). Owing to sophisticated pre-training objectives and huge model parameters, large-scale PTMs can effectively capture knowledge from massive labeled and unlabeled data. By storing knowledge into huge parameters and fine-tuning on specific tasks, the rich knowledge implicitly encoded in huge parameters can benefit a variety of downstream tasks, which has been extensively demonstrated via experimental verification and empirical analysis. It is now the consensus of the AI community to adopt PTMs as backbone for downstream tasks rather than learning models from scratch. In this paper, we take a deep look into the history of pre-training, especially its special relation with transfer learning and self-supervised learning, to reveal the crucial position of PTMs in the AI development spectrum. Further, we comprehensively review the latest breakthroughs of PTMs. These breakthroughs are driven by the surge of computational power and the increasing availability of data, towards four important directions: designing effective architectures, utilizing rich contexts, improving computational efficiency, and conducting interpretation and theoretical analysis. Finally, we discuss a series of open problems and research directions of PTMs, and hope our view can inspire and advance the future study of PTMs.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"2 ","pages":"Pages 225-250"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.aiopen.2021.08.002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76058793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 351

Neural, symbolic and neural-symbolic reasoning on knowledge graphs 知识图上的神经、符号和神经-符号推理

AI Open Pub Date : 2021-01-01 DOI: 10.1016/j.aiopen.2021.03.001

Jing Zhang, Bo Chen, Lingxi Zhang, Xirui Ke, Haipeng Ding

{"title":"Neural, symbolic and neural-symbolic reasoning on knowledge graphs","authors":"Jing Zhang, Bo Chen, Lingxi Zhang, Xirui Ke, Haipeng Ding","doi":"10.1016/j.aiopen.2021.03.001","DOIUrl":"10.1016/j.aiopen.2021.03.001","url":null,"abstract":"<div><p>Knowledge graph reasoning is the fundamental component to support machine learning applications such as information extraction, information retrieval, and recommendation. Since knowledge graphs can be viewed as the discrete symbolic representations of knowledge, reasoning on knowledge graphs can naturally leverage the symbolic techniques. However, symbolic reasoning is intolerant of the ambiguous and noisy data. On the contrary, the recent advances of deep learning have promoted neural reasoning on knowledge graphs, which is robust to the ambiguous and noisy data, but lacks interpretability compared to symbolic reasoning. Considering the advantages and disadvantages of both methodologies, recent efforts have been made on combining the two reasoning methods. In this survey, we take a thorough look at the development of the symbolic, neural and hybrid reasoning on knowledge graphs. We survey two specific reasoning tasks — knowledge graph completion and question answering on knowledge graphs, and explain them in a unified reasoning framework. We also briefly discuss the future directions for knowledge graph reasoning.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"2 ","pages":"Pages 14-35"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.aiopen.2021.03.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73071933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 55

Structure-enhanced meta-learning for few-shot graph classification 基于结构增强元学习的少镜头图分类

AI Open Pub Date : 2021-01-01 DOI: 10.1016/j.aiopen.2021.08.001

Shunyu Jiang , Fuli Feng , Weijian Chen , Xiang Li , Xiangnan He

{"title":"Structure-enhanced meta-learning for few-shot graph classification","authors":"Shunyu Jiang , Fuli Feng , Weijian Chen , Xiang Li , Xiangnan He","doi":"10.1016/j.aiopen.2021.08.001","DOIUrl":"10.1016/j.aiopen.2021.08.001","url":null,"abstract":"<div><p>Graph classification is a highly impactful task that plays a crucial role in a myriad of real-world applications such as molecular property prediction and protein function prediction. Aiming to handle the new classes with limited labeled graphs, few-shot graph classification has become a bridge of existing graph classification solutions and practical usage. This work explores the potential of metric-based meta-learning for solving few-shot graph classification. We highlight the importance of considering structural characteristics in the solution and propose a novel framework which explicitly considers <em>global structure</em> and <em>local structure</em> of the input graph. An implementation upon GIN, named SMF-GIN, is tested on two datasets, Chembl and TRIANGLES, where extensive experiments validate the effectiveness of the proposed method. The Chembl is constructed to fill in the gap of lacking large-scale benchmark for few-shot graph classification evaluation, which is released together with the implementation of SMF-GIN at: <span>https://github.com/jiangshunyu/SMF-GIN</span><svg><path></path></svg>.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"2 ","pages":"Pages 160-167"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.aiopen.2021.08.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87440687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8