HicSuntLeones-CSDN博客

原创【算法基础】模式匹配：从 BF 到 KMP（图解 + 代码）

模式匹配 / 字符串匹配暴力BF + KMP，图解 + 代码

2022-11-25 06:53:25 203 1

原创【YOLO详解】You Only Look Once（一）：YOLO 原论文笔记

You Only Look Once:Unified, Real-Time Object Detection ; Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi; University of Washington, Allen Institute for AI, Facebook AI Research原文：https://arxiv.org/pdf/1506.02640.pdf我：和 YOLO 小打小闹半年了也是我：这是什.

2022-02-09 13:06:05 2373

原创【AAAI2020 论文笔记】HopRetriever：通过 Hop 检索回答开放领域的复杂多跳问题

HopRetriever:Retrieve Hops over Wikipedia to Answer Complex Questions ; Shaobo Li, Xiaoguang Li, Lifeng Shang, Xin Jiang,Qun Liu, Chengjie Sun, Zhenzhou Ji, Bingquan Liu; Harbin Institute of Technology; Huawei Noah’s Ark Lab原文：https://arxiv.org/pdf/201.

2021-09-22 03:27:25 312

原创【ICLR2020 论文笔记】可用于文本推理的模块神经网络（ Neural Module Networks + NLP + reasoning）

Neural Module Networks for Reasoning over Text ; Nitish Gupta, Kevin Lin, Dan Roth, Sameer Singh & Matt Gardner; University of Pennsylvania, Philadelphia, University of California, Berkeley, University of California, Irvine, Allen Institute for AI原文.

2021-07-06 04:08:50 755

原创【ACL16 论文笔记】Harnessing Deep Neural Networks with Logic Rules：结合逻辑规则的深层神经网络

Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, Eduard Hovy, Eric P. Xing, School of Computer Science, Carnegie Mellon University ; Harnessing Deep Neural Networks with Logic Rules原文：https://arxiv.org/pdf/1603.06318v6.pdf源码：（更贴切来说是一个官方应用案例）https://github.com/Zhi.

2021-04-06 01:18:09 524 1

原创【EMNLP20 论文笔记】HGN：基于分层图网络的多跳阅读理解模型

Yuwei Fang, Siqi Sun, Zhe Gan, Rohit Pillai, Shuohang Wang, Jingjing LiuMicrosoft Dynamics 365 AI Research; Hierarchical Graph Network for Multi-hop Question Answering原文：https://arxiv.org/pdf/1911.03631.pdf源码：https://github.com/yuwfan/HGN （official **.

2021-03-30 00:02:15 973

原创【ACL20 论文笔记】CorefQA：基于QA模式（提出问题 + 片段预测）的共指消解 / 指代消解模型

Wei Wu, Fei Wang, Arianna Yuan, Fei Wu and Jiwei Li, Department of Computer Science and Technology, Zhejiang University Computer Science Department, Stanford University, ShannonAI; CorefQA: Coreference Resolution as Query-based Span Prediction论文原文：https.

2021-03-19 01:21:55 458

原创【ACL19 论文笔记】EPAr：探索+提议+组装：多跳阅读理解的可解释模型

Yichen Jiang, Nitish Joshi, Yen-Chun Chen Mohit Bansal ; UNC Chapel Hill Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension论文原文：https://arxiv.org/pdf/1906.05210.pdf源码：https://github.com/jiangycTarheel/EPAr文章目录.

2021-03-18 02:06:57 597

原创【论文笔记】Retro-Reader：基于回溯式阅读器的机器阅读理解模型

Zhuosheng Zhang, Junjie Yang, Hai Zhao, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Retrospective Reader for Machine Reading Comprehension论文原文：https://arxiv.org/pdf/2001.09694v4.pdf源码：https://github.com/cooelf/Awesome.

2021-03-17 02:13:31 1015 2

原创【ACL19 论文笔记】KAR：实现机器阅读理解中对常识知识的显示应用

Chao Wang and Hui Jiang, Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Explicit Utilization of General Knowledge in Machine Reading Comprehension论文原文：https://arxiv.org/pdf/1809.03449v3.pdf.

2021-03-13 02:39:37 397

原创【ACL20 论文笔记】Self-Training MRC (STM)：基于软证据提取的机器阅读理解自训练方法

Yilin Niu, Fangkai Jiao, Mantong Zhou, Ting Yao, Jingfang Xu, Minlie Huang, A Self-Training Method for Machine Reading Comprehension with Soft Evidence Extraction论文原文：https://arxiv.org/pdf/2005.05189.pdf源代码：https://github.com/SparkJiao/Self-Training-MR.

2021-03-12 02:47:14 234

原创【ACL19 论文笔记】AGGCN：基于注意力导向图卷积神经网络的关系提取模型

guaZhijiang Guo, Yan Zhang and Wei Lu, StatNLP Research Group, Singapore University of Technology and Design; Attention Guided Graph Convolutional Networks for Relation Extraction论文原文：https://arxiv.org/pdf/1906.07510v8.pdf源码：https://github.com/Cartus/

2021-03-11 01:57:31 1055

原创【基础整理】attention：浅谈注意力机制与自注意力模型（附键值对注意力 + 多头注意力）

Vaswani, Ashish, et al. Attention is all you need. Advances in Neural Information Processing Systems. 2017.论文原文：https://arxiv.org/pdf/1706.03762v5.pdf源码：https://github.com/tensorflow/tensor2tensor （tensorflow / official）** https://github.com/facebookre.

2021-03-09 22:41:30 4751 2

原创【ACL19 论文笔记】KT-NET：结合丰富知识增强预训练语言表达以辅助机器阅读理解

An Yang, Quan Wang, Jing Liu, Kai Liu, Yajuan Lyu, Hua Wu, Qiaoqiao She and Sujian Li; Key Laboratory of Computational Linguistics, Peking University, MOE, China, Baidu Inc., Beijing, China; Enhancing Pre-Trained Language Representations with Rich Knowle.

2021-03-06 02:44:22 589

原创【ACL19 论文笔记】HDEGraph：基于异构图推理实现跨文档的多跳阅读理解

Ming Tu, Guangtao Wang, Jing Huang, Yun Tang, Xiaodong He, Bowen ZhouJD AI Research; Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs论文原文：https://arxiv.org/pdf/1905.07374v2.pdf源码：https://github.com/JD-A.

2021-03-05 01:54:31 489 7

原创【ACL19 论文笔记】CogQA：基于认知图谱的多跳阅读理解

Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, Jie Tang, Department of Computer Science and Technology, Tsinghua University, DAMO Academy, Alibaba GroupCognitive Graph for Multi-Hop Reading Comprehension at Scale论文原文：https://arxiv.org/pdf/1905.05460v.

2021-03-04 01:00:57 1009 2

原创【ACL19 论文笔记】RE3QA：检索+阅读+重新排序：端到端的多文档阅读理解

Minghao Hu, Yuxing Peng, Zhen Huang, Dongsheng Li, National University of Defense Technology, Changsha, China (ACL19) Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension原论文：https://arxiv.org/pdf/1906.04618.pdf源码：https://gith.

2021-03-03 01:37:17 548

原创【论文笔记】AKGE：基于注意力知识图谱嵌入的个性化推荐系统

Sha, X. , Sun, Z. , & Zhang, J. . (2019). Attentive knowledge graph embedding for personalized recommendation.原文：https://arxiv.org/pdf/1910.08288.pdf1 introduction知识图谱（KG）已经被广泛用于推荐系统中，但是主要思路仍集中于以下两个方向：基于路径提取的思路（path-based methods）：有的是要求人手动设计.

2021-02-10 00:31:17 1769 2

原创【论文笔记】KGCN：知识图谱 + 图卷积神经网络的推荐系统

Knowledge Graph Convolutional Networks for Recommender SystemsHongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, Minyi Guo.In Proceedings of The 2019 Web Conference (WWW 2019)论文原文（arXiv）：https://arxiv.org/pdf/1904.12575v1.pdf源码：https://github.com/hwwang55.

2021-02-09 18:04:41 4099 8

原创【论文笔记】KGAT：融合知识图谱的 CKG 表示 + 图注意力机制的推荐系统

Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu and Tat-Seng Chua (2019). KGAT: Knowledge Graph Attention Network for Recommendation. Paper in ACM DL or Paper in arXiv. In KDD’19, Anchorage, Alaska, USA, August 4-8, 2019.原文：https://arxiv.org/pdf/1905.07854.

2021-02-09 03:10:00 2797 4

原创【论文笔记】RippleNet : 知识图谱+用户偏好传播的推荐系统

RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender SystemsHongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, Minyi GuoThe 27th ACM International Conference on Information and Knowledge Management (CIKM .

2021-02-08 19:45:05 3503 1

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