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本文(从经典股书提炼精华 07 从《股票作手回忆录》谈读书的方法_PDF.pdf)为本站会员(静寂夜)主动上传,文库网仅提供信息存储空间,仅对用户上传内容的表现方式做保护处理,对上载内容本身不做任何修改或编辑。 若此文所含内容侵犯了您的版权或隐私,请立即通知文库网(发送邮件至13560552955@163.com或直接QQ联系客服),我们立即给予删除!

从经典股书提炼精华 07 从《股票作手回忆录》谈读书的方法_PDF.pdf

1、n making w/ all the information System 1 Intuitive System 2 Analytic 39 CogQA: Cognitive Graph for QA An iterative framework corresponding to dual process theory System 1 extract entities to build the cognitive graph generate semantic vectors for each node System 2 Do reasoning based on semantic vec

2、tors and graph Feed clues to System 1 to extract next-hop entities Question Quality caf Todd Phillips Gone in 60 seconds Old school Los Angeles Dominic Sena System 1 System 2 Cognitive Graph location featured in a number of Hollywood films, including Old School, Gone in 60 Seconds input clues predic

3、t 40 Cognitive Graph: DL + Dual Process Theory 1.M. Ding, C. Zhou, Q. Chen, H. Yang, and J. Tang. Cognitive Graph for Multi-Hop Reading Comprehension at Scale. ACL19. ? System 1: implicit knowledge expansion System 2: explicit decision 41 Cognitive Graph: DL + Dual Process Theory 1.M. Ding, C. Zhou,

4、 Q. Chen, H. Yang, and J. Tang. Cognitive Graph for Multi-Hop Reading Comprehension at Scale. ACL19. System 1: implicit knowledge expansion System 2: explicit decision 42 Cognitive Graph: Representation, Reasoning, and Decision ? 43 认知与推理 Trillion-scale common-sense knowledge graph Tim Berners Lee T

5、uring Award Winner Edward Feigenbaum Turing Award Winner * AI = Knowledge + Intelligence 1. J. Tang, J. Zhang, L. Yao, J. Li, L. Zhang, and Z. Su. ArnetMiner: Extraction and Mining of Academic Social Networks. KDD08. pp.990-998. Big DataKnowledgeIntelligence 44 Related Publications Ming Ding, Chang

6、Zhou, Qibin Chen, Hongxia Yang, and Jie Tang. Cognitive Graph for Multi-Hop Reading Comprehension at Scale. ACL19. Jie Zhang, Yuxiao Dong, Yan Wang, Jie Tang, and Ming Ding. ProNE: Fast and Scalable Network Representation Learning. IJCAI19. Yukuo Cen, Xu Zou, Jianwei Zhang, Hongxia Yang, Jingren Zho

7、u and Jie Tang. Representation Learning for Attributed Multiplex Heterogeneous Network. KDD19. Fanjin Zhang, Xiao Liu, Jie Tang, Yuxiao Dong, Peiran Yao, Jie Zhang, Xiaotao Gu, Yan Wang, Bin Shao, Rui Li, and Kuansan Wang. OAG: Toward Linking Large-scale Heterogeneous Entity Graphs. KDD19. Qibin Che

8、n, Junyang Lin, Yichang Zhang, Hongxia Yang, Jingren Zhou and Jie Tang. Towards Knowledge-Based Personalized Product Description Generation in E-commerce. KDD19. Yifeng Zhao, Xiangwei Wang, Hongxia Yang, Le Song, and Jie Tang. Large Scale Evolving Graphs with Burst Detection. IJCAI19. Yu Han, Jie Ta

9、ng, and Qian Chen. Network Embedding under Partial Monitoring for Evolving Networks. IJCAI19. Yifeng Zhao, Xiangwei Wang, Hongxia Yang, Le Song, and Jie Tang. Large Scale Evolving Graphs with Burst Detection. IJCAI19. Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, and Jie Tang.

10、NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization. WWW19. Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, and Jie Tang. DeepInf: Modeling Influence Locality in Large Social Networks. KDD18. Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang. Network

11、 Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec. WSDM18. Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zhang, and Zhong Su. ArnetMiner: Extraction and Mining of Academic Social Networks. KDD08. For more, please check here 45 Jie Tang, KEG, Tsinghua U Download all data

12、 & Codes Thank you! 蔀桬襺搀漀挀昀昀挀昀戀挀挀攀昀攀攀昀攀攀愀愀1qAC6682 .doc0989d9c623070e72e4d8e404a76baab1628c7e847bd21261a5a55dbf0fb0eadf0甀搀漀挀挀搀搀挀挀攀愀挀搀戀搀愀昀戀搀昀愀攀愀攀搀戀搀戀攀攀攀攀I山东省发展和改革委员会 山东省能源局 .pdfa6b19e165ade7d4f0fbb9fddd5aa1bab0de6dd33b1fd40c153eded3b5c1ad1030洀瀀搀昀搀愀挀昀愀挀搀愀攀挀搀挀搀昀愀昀挀挀戀攀攀攀攀愀戀搀搀挀3s山杏带来美与富 .pdfad748b41889a9b7b4f6f5d55a2370560714b9a040018b21cb6e8b77e908421f70

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