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Learning personalized attribute preference via multi-task auc optimization

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arXiv 2019年

作者： Yang, Zhiyong Xu, Qianqian Cao, Xiaochun Huang, Qingming SKLOIS Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Lab of Intell. Info. Process. Inst. of Comput. Tech. CAS Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management CAS Beijing China

Traditionally, most of the existing attribute learning methods are trained based on the consensus of annotations aggregated from a limited number of annotators. However, the consensus might fail in settings, especially when a wide spectrum of annotators with different interests and comprehension about the attribute words are involved. In this paper, we develop a novel multi-task method to understand and predict personalized attribute annotations. Regarding the attribute preference learning for each annotator as a specific task, we first propose a multi-level task parameter decomposition to capture the evolution from a highly popular opinion of the mass to highly personalized choices that are special for each person. Meanwhile, for personalized learning methods, ranking prediction is much more important than accurate classification. This motivates us to employ an Area Under ROC Curve (AUC) based loss function to improve our model. On top of the AUC-based loss, we propose an efficient method to evaluate the loss and gradients. Theoretically, we propose a novel closed-form solution for one of our non-convex subproblem, which leads to provable convergence behaviors. Furthermore, we also provide a generalization bound to guarantee a reasonable performance. Finally, empirical analysis consistently speaks to the efficacy of our proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

iSplit LBI: Individualized partial ranking with ties via split LBI

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arXiv 2019年

作者： Xu, Qianqian Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Microsoft Research Asia State Key Laboratory of Information Security Institute of Information Engineering CAS School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management CAS Peng Cheng Laboratory Department of Mathematics Hong Kong University of Science and Technology Hong Kong

Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. However, in real-world scenarios, different individuals often hold distinct preferences. It might be misleading to merely look at a global partial ranking while ignoring personal diversity. In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective. Particularly, we formulate a unified framework which not only can be used for individualized partial ranking prediction, but also be helpful for abnormal user selection. This is realized by a variable splitting-based algorithm called iSplitLBI. Specifically, our algorithm generates a sequence of estimations with a regularization path, where both the hyperparameters and model parameters are updated. At each step of the path, the parameters can be decomposed into three orthogonal parts, namely, abnormal signals, personalized signals and random noise. The abnormal signals can serve the purpose of abnormal user selection, while the abnormal signals and personalized signals together are mainly responsible for individual partial ranking prediction. Extensive experiments on simulated and real-world datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives. The code is now availiable at https://***/qianqianxu010/ NeurIPS2019-iSplitLBI. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

QUANTUM LOVÁSZ LOCAL LEMMA: SHEARER’S BOUND IS TIGHT

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arXiv 2018年

作者： He, Kun Li, Qian Sun, Xiaoming Zhang, Jiapeng The Key Lab of Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing China Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China University of Southern California United States

The Lovász Local Lemma (LLL) is a very powerful tool in combinatorics and probability theory to show the possibility of avoiding all bad events under some weakly dependent conditions. In a seminal paper, Ambainis, Kempe, and Sattath (JACM 2012) introduced a quantum version LLL (QLLL) which shows the possibility of avoiding all "bad" Hamiltonians under some weakly dependent condition, and applied QLLL to the random k-QSAT problem. Sattath, Morampudi, Laumann, and Moessner (PNAS 2015) extended Ambainis, Kempe, and Sattath’s result and showed that Shearer’s bound is a sufficient condition for QLLL, and conjectured that Shearer’s bound is indeed the tight condition for QLLL. In this paper, we affirm this conjecture. Precisely, we prove that Shearer’s bound is tight for QLLL, i.e., the relative dimension of the smallest satisfying subspace is completely characterized by the independent set polynomial. Our result implies the tightness of Gilyén and Sattath’s algorithm (FOCS 2017), and also implies that the lattice gas partition function fully characterizes quantum satisfiability for almost all Hamiltonians with large enough qudits (Sattath, Morampudi, Laumann and Moessner, PNAS 2015). The commuting LLL (CLLL), which focuses on commuting local Hamiltonians, is also investigated here. We prove that the tight regions of CLLL and QLLL are different in general. This result indicates that it is possible to design an algorithm for CLLL which is still efficient beyond Shearer’s bound. Copyright © 2018, The Authors. All rights reserved.

关键词： Hamiltonians

Dual encoding for zero-example video retrieval

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arXiv 2018年

作者： Dong, Jianfeng Li, Xirong Xu, Chaoxi Ji, Shouling He, Yuan Yang, Gang Wang, Xun College of Computer and Information Engineering Zhejiang Gongshang University Key Lab of Data Engineering and Knowledge Engineering Renmin University of China AI & Media Computing Lab School of Information Renmin University of China College of Computer Science Zhejiang University Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies Alibaba Group

This paper attacks the challenging problem of zero-example video retrieval. In such a retrieval paradigm, an end user searches for unlabeled videos by ad-hoc queries described in natural language text with no visual example provided. Given videos as sequences of frames and queries as sequences of words, an effective sequence-to-sequence cross-modal matching is required. The majority of existing methods are concept based, extracting relevant concepts from queries and videos and accordingly establishing associations between the two modalities. In contrast, this paper takes a concept-free approach, proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own. Dual encoding is conceptually simple, practically effective and end-to-end. As experiments on three benchmarks, i.e. MSR-VTT, TRECVID 2016 and 2017 Ad-hoc Video Search show, the proposed solution establishes a new state-of-the-art for zero-example video retrieval. Copyright © 2018, The Authors. All rights reserved.

关键词： Signal encoding

Link prediction in author collaboration network based on BP neural network 3

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Link prediction in author collaboration network based on BP ...

3rd International Conference on Mechanical, Electronic and Information Technology engineering, ICMITE 2017

作者： Chen, Chaoqun Deng, Sanhong Lu, Jing School of Information Management Nanjing University Nanjing210023 China Jiangsu Key Lab of Data Engineering and Knowledge Service Nanjing210023 China Jiangsu Institute of Geological Survey Nanjing210018 China

Recently, more and more authors have been encouraged for collaboration because it often produces good results. However, the author collaboration network contains experts in various research directions within various fields, and it is difficult for individual authors to decide which authors are best suited to their expertise. This paper uses the relationships among authors to predict new relationships that may arise, recommending each author with the collaborators they may be interested in. The data source comes from 4-year data in DBLP from 2001 to 2004. After data cleaning, the training set and test set are constructed and then used BP neural network to build model. At the same time, this article compares the performance with Logistic Regression, SVM and Random Forest. The experiment shows that the BP neural network can get better result, and it is feasible to predict links in the author collaboration network. © The Authors, published by EDP Sciences, 2017.

关键词： Forecasting

Automatic Chinese Multiple-Choice Question Generation for Human Resource Performance Appraisal

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Procedia Computer Science 2018年 139卷 165-172页

作者： Pei Quan Yong Shi Lingfeng Niu Ying Liu Tianlin Zhang School of Computer and Control Engineering University of Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha NE 68182 USA

Performance appraisal has always been an important research topic in human resource management. A reasonable performance appraisal plan lays a solid foundation for the development of an enterprise. Especially as globalization and technology advance, in order to meet the fast-changing strategic goals and increasing cross-functional tasks, enterprises face new challenges in performance appraisal. How to improve employees’ ability to accept new knowledge efficiently and constantly has been an urgent problem for enterprises. In this paper, we propose an automatic method which generation multiple-choice questions by utilizing the relations between different terminology. Graphical model is used to extract core concept from different corpus while word embedding technology is used to indicate the relevant relations. Experimental results demonstrate that the proposed question generation method outperforms the traditional manual method in both efficiency and confusion.

关键词： Human resource management multiple-choice question generation natural language processing word embedding

NODE IMPORTANCE MEASURE FOR SCIENTIFIC RESEARCH COLlabORATION FROM HYPERNETWORK PERSPECTIVE

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2016年第2期23卷 397-404页

作者： Xiao, Quan Jiangxi Univ Finance & Econ Sch Informat Technol Yuping West St 556 Nanchang 330032 Jiangxi Peoples R China Jiangxi Key Lab Data & Knowledge Engn Yuping West St 556 Nanchang 330032 Jiangxi Peoples R China

Collaboration has become main stream and trend in interdisciplinary fields. In research collaboration organizations, to evaluate the contributions of researchers to the organization and then to identify core researchers is an important issue to carry out performance appraisal and crisis management of brain drain. Scientific research collaboration network is a basic model to investigate this question, but under the context of increasingly complex collaborative behaviour, it shows its limitations for semantic representations. In this paper, by introducing hypernetwork, a more powerful modelling tool than traditional network, and taking scientific paper co-authorship as object to construct scientific research collaboration hypernetwork (SRCH), we measure the importance of researchers in two aspects, as collaborative relationship structure and collaborative achievement value from a hypernetwork perspective. An additive weighting method with adjustable parameters is utilized to integrate the evaluation indicators of the two aspects, and then the synthetical importance evaluation of researchers is obtained. Analysis of data instance verifies that our node importance measure for scientific research collaboration from hypernetwork perspective is reasonable and effective.

关键词： adjustable parameter hyper network node importance scientific research collaboration network

Author Correction:data augmentation in microscopic images for material data mining

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npj Computational Materials 2020年第1期6卷 476-476页

作者： Boyuan Ma Xiaoyan Wei Chuni Liu Xiaojuan Ban Haiyou Huang Hao Wang Weihua Xue Stephen Wu Mingfei Gao Qing Shen Michele Mukeshimana Adnan Omer Abuassba Haokai Shen Yanjing Su Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China Institute for Advanced Materials and Technology University of Science and Technology Beijing Beijing China School of Materials Science and Engineering University of Science and Technology Beijing Beijing China School of Materials Science and Technology Liaoning Technical University Liaoning China The Institute of Statistical Mathematics Research Organization of Information and Systems Tachikawa Tokyo Japan National Intellectual Property Administration Beijing China Faculty of Engineering Sciences University of Burundi Bujumbura Burundi Faculty of Engineering and Technology Palestine Technical University – Kadoorie Tulkarem Palestine College of Information Science and Engineering China University of Petroleum Beijing China Key Lab of Petroleum Data Mining China University of Petroleum Beijing China

The original version of this article omitted the following from the Acknowledgements:“This work was supported by Beijing Top Discipline for Artificial Intelligent Science and engineering,University of Science and Tec... 详细信息

关键词： HTML knowledge mining

Hybrid Learning Network: A Novel Architecture for Fast Learning

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Procedia Computer Science 2017年 122卷 622-628页

作者： Ying Liu Chao Xiang School of Computer and Engineering University of Chinese Academy of Sciences Beijing 100190 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China

There’re many effective architectures of the artificial neural network(ANN). For which the training is a hard work. The cost for training an ANN increases exponentially when the ANN gets deeper or wider. We therefore propose a novel architecture, the Hybrid Learning Network(HLN), to achieve a fast learning with good stablity. The HLN can learn from both labeled data and unlabeled data at the same time in a hybrid learning manner. It uses a Self Organizing Map unified by the specially designed nonlinear function as the sparsity mask for a hidden layer to improve the training speed. We experiment our architecture on a synthetic dataset to test its regression capability against the traditional architecture, the result is promising.

关键词： hybrid learning neural network sparsity mask self organizing map fast learning