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arXiv 2023年

作者： Du, Pan Zhao, Suyun Sheng, Zisen Li, Cuiping Chen, Hong Key Lab of Data Engineering and Knowledge Engineering of MOE Renmin University of China China Renmin University of China Beijing China

Semi-Supervised Learning (SSL) under class distribution mismatch aims to tackle a challenging problem wherein unlabeled data contain lots of unknown categories unseen in the labeled ones. In such mismatch scenarios, traditional SSL suffers severe performance damage due to the harmful invasion of the instances with unknown categories into the target classifier. In this study, by strict mathematical reasoning, we reveal that the SSL error under class distribution mismatch is composed of pseudo-labeling error and invasion error, both of which jointly bound the SSL population risk. To alleviate the SSL error, we propose a robust SSL framework called Weight-Aware Distillation (WAD) that, by weights, selectively transfers knowledge beneficial to the target task from unsupervised contrastive representation to the target classifier. Specifically, WAD captures adaptive weights and high-quality pseudo-labels to target instances by exploring point mutual information (PMI) in representation space to maximize the role of unlabeled data and filter unknown categories. Theoretically, we prove that WAD has a tight upper bound of population risk under class distribution mismatch. Experimentally, extensive results demonstrate that WAD outperforms five state-of-the-art SSL approaches and one standard baseline on two benchmark datasets, CIFAR10 and CIFAR100, and an artificial cross-dataset. The code is available at https://***/RUC-DWBI-ML/research/tree/main/WAD-master. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Few-Shot Charge Prediction with Multi-grained Features and Mutual Information 20

Few-Shot Charge Prediction with Multi-grained Features and M...

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20th China National Conference on Computational Linguistics, CCL 2021

作者： Zhang, Han Dou, Zhicheng Zhu, Yutao Wen, Jirong School of Information Renmin University of China Beijing China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Université de Montréal Montreal Canada Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China

ISBN: (纸本)9783030841850

Charge prediction aims to predict the final charge for a case according to its fact description and plays an important role in legal assistance systems. With deep learning based methods, prediction on high-frequency charges has achieved promising results but that on few-shot charges is still challenging. In this work, we propose a framework with multi-grained features and mutual information for few-shot charge prediction. Specifically, we extract coarse- and fine-grained features to enhance the model’s capability on representation, based on which the few-shot charges can be better distinguished. Furthermore, we propose a loss function based on mutual information. This loss function leverages the prior distribution of the charges to tune their weights, so the few-shot charges can contribute more on model optimization. Experimental results on several datasets demonstrate the effectiveness and robustness of our method. Besides, our method can work well on tiny datasets and has better efficiency in the training, which provides better applicability in real scenarios. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

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Effect factors on secondary structure of protein sequence pattern

Effect factors on secondary structure of protein sequence pa...

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International Workshop on Intelligent Systems and Applications

作者： Liu, Tao Li, Minghui Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing 100872 China School of Information Renmin University of China Beijing 100872 China School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China

ISBN: (纸本)9781424498574

Discovering the relationship between protein sequence pattern and protein secondary structure is important for accurately predicting secondary structure of protein sequence. A protein secondary structure pattern dictionary is constructed using protein sequence pattern and its corresponding secondary structure in this paper. Based on the constructed dictionary, we propose four effect factors on secondary structure of protein sequence pattern, including 1) the core pattern itself;2) patterns or amino acid residues that neighbor with the core pattern;3) patterns or amino acid residues that are far away from the core pattern;and 4) amino acid sequence segment that match the core pattern. Statistical measures are adopted to analyze these factors. The experimental result shows the reliability of these factors. The recognition of these effect factors presents new directions to predict protein secondary structure based on protein pattern dictionary. © 2011 IEEE.

关键词： Amino acids

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Context-aware recommendation using GPU based parallel tensor decomposition

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8933卷 213-226页

作者： Zou, Benyou Lan, Mengwei Li, Cuiping Tan, Liwen Chen, Hong Key Lab of Data Engineering and Knowledge Engineering of MOE Renmin University of China Beijing100872 China School of Information Renmin University of China Beijing100872 China

Recommender system plays an important role in many practical applications that help users to deal with information overload and provide personalized recommendations to them. The context in which a choice is made has been recognized as an important factor for recommendation systems. Recently, researchers extend the classical matrix factorization and allows for a generic integration of contextual information by modeling the data as a tensor. However, current tensor factorization methods suffer from the limitation that the computing cost can be very high in practice. In this paper, we propose GALS, a GPU based parallel tensor factorization algorithm, to accelerate the tensor factorization on large data sets to support the efficient context-aware recommendation. Experiments show that the proposed method can achieve 10 times faster than the current tensor factorization methods. © Springer International Publishing Switzerland 2014.

关键词： Collaborative filtering

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Location-based recommendation using incremental tensor factorization model

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8933卷 227-238页

作者： Zou, Benyou Zou, Benyou Li, Cuiping Tan, Liwen Tan, Liwen Chen, Hong Key Lab of Data Engineering and Knowledge Engineering of MOE Renmin University of China Beijing100872 China School of Information Renmin University of China Beijing100872 China

Newly emerging location-based online social services, such as Meetup and Douban, have experienced increased popularity and rapid growth. The classical Matrix Factorization methods usually only consider the user-item matrix. Recently, Researchers have extended the matrix adding location context as a tensor and used the Tensor Factorization methods for this scenario. However, in real scenario, the users and events are changing over time, the classical Tensor Factorization methods suffers the limitation that it can only be applied for static settings. In this paper, we propose a general Incremental Tensor Factorization model, which models the appearance changes of a tensor by adaptively updating its previous factorized components rather than recomputing them on the whole data every time the data changed. Experiments show that the proposed methods can offer more effective recommendations than baselines, and significantly improve the efficiency of providing location recommendations. © Springer International Publishing Switzerland 2014.

关键词： Location based services

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Contrastive Coding for Active Learning under Class Distribution Mismatch

Contrastive Coding for Active Learning under Class Distribut...

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International Conference on Computer Vision (ICCV)

作者： Pan Du Suyun Zhao Hui Chen Shuwen Chai Hong Chen Cuiping Li Key Lab of Data Engineering and Knowledge Engineering of MOE Renmin University of China Beijing China Renmin University of China Beijing China

ISBN: (纸本)9781665428132

Active learning (AL) is successful based on the assumption that labeled and unlabeled data are obtained from the same class distribution. However, its performance deteriorates under class distribution mismatch, wherein the un-labeled data contain many samples out of the class distribution of labeled data. To effectively handle the problems under class distribution mismatch, we propose a contrastive coding based AL framework named CCAL. Unlike the existing AL methods that focus on selecting the most informative samples for annotating, CCAL extracts both semantic and distinctive features by contrastive learning and combines them in a query strategy to choose the most informative un-labeled samples with matched categories. Theoretically, we prove that the AL error of CCAL has a tight upper bound. Experimentally, we evaluate its performance on CIFAR10, CIFAR100, and an artificial cross-dataset that consists of five datasets; consequently, CCAL achieves state-of-the-art performance by a large margin with remarkably lower annotation cost. To the best of our knowledge, CCAL is the first work related to AL for class distribution mismatch.

关键词： Computer vision Costs Upper bound Annotations Semantics Text categorization Computer architecture

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Superclass Learning with Representation Enhancement

Superclass Learning with Representation Enhancement

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zeyu Gan Suyun Zhao Jinlong Kang Liyuan Shang Hong Chen Cuiping Li Key Lab of Data Engineering and Knowledge Engineering of MOE Renmin University of China Beijing China Renmin University of China Beijing China

In many real scenarios, data are often divided into a handful of artificial super categories in terms of expert knowledge rather than the representations of images. Concretely, a superclass may contain massive and various raw categories, such as refuse sorting. Due to the lack of common semantic features, the existing classification techniques are intractable to recognize superclass without raw class labels, thus they suffer severe performance damage or require huge annotation costs. To narrow this gap, this paper proposes a superclass learning framework, called SuperClass Learning with Representation Enhancement(SCLRE), to recognize super categories by leveraging enhanced representation. Specifically, by exploiting the self-attention technique across the batch, SCLRE collapses the boundaries of those raw categories and enhances the representation of each superclass. On the enhanced representation space, a superclassaware decision boundary is then reconstructed. Theoretically, we prove that by leveraging attention techniques the generalization error of SCLRE can be bounded under superclass scenarios. Experimentally, extensive results demonstrate that SCLRE outperforms the baseline and other contrastive-based methods on CIFAR-100 datasets and four high-resolution datasets.

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Few-example video event retrieval using tag propagation 14

Few-example video event retrieval using tag propagation

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2014 4th ACM International Conference on Multimedia Retrieval, ICMR 2014

作者： Mazloom, Masoud Li, Xirong Snoek, Cees G. M. ISLA Faculty of Science University of Amsterdam Science Park 904 1098 XH Amsterdam Netherlands Key Lab of Data Engineering and Knowledge Engineering Renmin University of China Beijing 100872 China

ISBN: (纸本)1595930361

An emerging topic in multimedia retrieval is to detect a complex event in video using only a handful of video examples. Different from existing work which learns a ranker from positive video examples and hundreds of negative examples, we aim to query web video for events using zero or only a few visual examples. To that end, we propose in this paper a tag-based video retrieval system which propagates tags from a tagged video source to an unlabeled video collection without the need of any training examples. Our algorithm is based on weighted frequency neighbor voting using concept vector similarity. Once tags are propagated to unlabeled video we can rely on off-the-shelf language models to rank these videos by the tag similarity. We study the behavior of our tag-based video event retrieval system by performing three experiments on web videos from the TRECVID multimedia event detection corpus, with zero, one and multiple query examples that beats a recent alternative. Copyright 2014 ACM.

关键词： Image retrieval

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Managing a Large Shared Bank of Unstructured data by Using Free-Table

Managing a Large Shared Bank of Unstructured Data by Using F...

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International Asia-Pacific Web Conference (APWEB)

作者： Xiao Zhang Xiaoyong Du Jinchuan Chen Shan Wang Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing China

This paper presents a reference framework, called BUD, to manage a large shared bank of unstructured data. This paper lists several important issues on managing or maintaining the unstructured data in BUD. BUD stores and manages the ever-growing unstructured data by introducing a novel technique called free-table, which is a conceptual view for end-users and a physical entity maintained by transactional storage manager of BUD. Free-table is cell-oriented but not column-oriented as relational table. It can store various types of unstructured data in cell with different versions. Additionally, we study two cases, VMP and PXRDB, to show that our proposal is feasible and tractable.

关键词： data models Conference management knowledge management engineering management data engineering Proposals Maintenance engineering knowledge engineering data mining Relational databases

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MapReduce-Based SimRank Computation and Its Application in Social Recommender System

MapReduce-Based SimRank Computation and Its Application in S...

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IEEE International Conference on Services Economics (SE)

作者： Lina Li Cuiping Li Hong Chen Xiaoyong Du Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing China

Recently there has been a lot of interest in graph-based analysis, with examples including social network analysis, recommendation systems, document classification and clustering, and so on. A graph is an abstraction that naturally captures data objects as well as relationships among those objects. Objects are represented as nodes and relationships are represented as edges in the graph. There are many cases in which similarities among nodes are required to compute. SimRank is one of the simple and intuitive algorithms for this purpose. It is rigidly based on the random walk theorem. Existing methods on SimRank computation suffer from one limitation: the computing cost can be very high in practice. In order to optimize the computation of SimRank, a few techniques have been proposed. However, the performance of these methods are still limited by the processing ability of the single computer. Ideally, we would like to develop new parallel solutions that can offer improved processing power to compute SimRank on large data set. In this paper, we propose parallel algorithms for SimRank computation on Map-Reduce framework, and more specifically its open source implementation, Hadoop. Two different parallel methods are proposed and their performances are evaluated and compared. Furthermore, we employ the proposed methods to do the similarity computation in order to recommend appropriate products to users in social recommender systems.

关键词： Computers Iterative methods Recommender systems Computational modeling Parallel processing Equations Sorting

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