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arXiv 2022年

作者： Hou, Wenzheng Xu, Qianqian Yang, Zhiyong Bao, Shilong He, Yuan Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Alibaba Group Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen China

It is well-known that deep learning models are vulnerable to adversarial examples. Existing studies of adversarial training have made great progress against this challenge. As a typical trait, they often assume that the class distribution is overall balanced. However, long-tail datasets are ubiquitous in a wide spectrum of applications, where the amount of head class instances is larger than the tail classes. Under such a scenario, AUC is a much more reasonable metric than accuracy since it is insensitive toward class distribution. Motivated by this, we present an early trial to explore adversarial training methods to optimize AUC. The main challenge lies in that the positive and negative examples are tightly coupled in the objective function. As a direct result, one cannot generate adversarial examples without a full scan of the dataset. To address this issue, based on a concavity regularization scheme, we reformulate the AUC optimization problem as a saddle point problem, where the objective becomes an instance-wise function. This leads to an end-to-end training protocol. Furthermore, we provide a convergence guarantee of the proposed algorithm. Our analysis differs from the existing studies since the algorithm is asked to generate adversarial examples by calculating the gradient of a min-max problem. Finally, the extensive experimental results show the performance and robustness of our algorithm in three long-tail datasets. © 2022, CC BY.

关键词： Deep learning

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Semi-supervised classification on data streams with recurring concept drift and concept evolution

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knowledge-Based Systems 2021年 215卷

作者： Zheng, Xiulin Li, Peipei Hu, Xuegang Yu, Kui Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education China School of Computer Science and Information Engineering Hefei University of Technology Hefei 230601 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei 230601 Anhui China

Mining non-stationary stream is a challenging task due to its unique property of infinite length and dynamic characteristics let alone the issues of concept drift, concept evolution and limited labeled data. Although more attention has been attracted on the issues of concept drift and evolution in data streams, however, most of existing methods are supervised in nature, which probably result in a worse classification performance and lower efficiency in the case with scarcity of labeled data. Thus, in this paper, we proposed a semi-supervised framework with recurring concept drift and novel class detection called ESCR, which aims to detect recurring concept drift and concept evolution in data streams with partially labeled data. It is firstly built on an ensemble model consisted of several clustering-based classifiers. In terms of this framework, we adopt Jensen–Shannon divergence based change detection technique on classifier confidence score instead of classification error rate to detect recurring concept drifts. Meanwhile, we take concept evolution into consideration by monitoring the outliers with strong cohesion. Moreover, we further improve the execution efficiency of our framework by exploiting the recursive function and dynamic programming. Finally, extensive experiments conducted on both benchmark and synthetic data sets demonstrate the effectiveness and efficiency of our proposed semi-supervised framework in the handling of data streams with recurring concept drifts and concept evolution, as compared to several well-known semi-supervised data stream classification methods. © 2021 Elsevier B.V.

关键词： Concept evolution data stream classification Recurring concept drift

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Context-Dependent Propagating-Based Video Recommendation in Multimodal Heterogeneous Information Networks

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IEEE Transactions on Multimedia 2021年 23卷 2019-2032页

作者： Lei Sang Min Xu Shengsheng Qian Matt Martin Peter Li Xindong Wu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei China Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW Australia Institute of Automation Chinese Academy of Sciences Beijing China INTERACT Technology Sydney NSW Australia Mininglamp Academy of Sciences Mininglamp Technology Beijing China

With the emergence of online social networks (OSNs), video recommendation has come to play a crucial role in mitigating the semantic gap between users and videos. Conventional approaches to video recommendation primarily focus on exploiting content features or simple user-video interactions to model the users’ preferences. Although these methods have achieved promising results, they fail to model the complex video context interdependency, which is obscure/hidden in heterogeneous auxiliary data from OSNs. In this paper, we study the problem of video recommendation in Heterogeneous Information Networks (HINs) due to its excellence in characterizing heterogeneous and complex context information. We propose a Context-Dependent Propagating Recommendation network (CDPRec) to obtain accurate video embedding and capture global context cues among videos in HINs. The CDPRec can iteratively propagate the contexts of a video along links in a graph-structured HIN and explore multiple types of dependencies among the surrounding video nodes. Then, each video is represented as the composition of the multimodal content feature and global dependency structure information using an attention network. The learned video embedding with sequential based recommendation are jointly optimized for the final rating prediction. Experimental results on real-world YouTube video recommendation scenarios demonstrate the effectiveness of the proposed methods compared with strong baselines.

关键词： Semantics Collaboration YouTube Australia Visualization Context modeling

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Near-optimal representation learning for linear bandits and linear RL

arXiv

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arXiv 2021年

作者： Hu, Jiachen Chen, Xiaoyu Jin, Chi Li, Lihong Wang, Liwei Key Laboratory of Machine Perception MOE School of EECS Peking University China Department of Electrical and Computer Engineering Princeton University United States Amazon United States Center for Data Science Peking University Beijing Institute of Big Data Research China

This paper studies representation learning for multi-task linear bandits and multi-task episodic RL with linear value function approximation. We first consider the setting where we play M linear bandits with dimension d concurrently, and these bandits share a common k-dimensional linear representation so that k d and k M. We propose a sample-efficient algorithm, MTLR-OFUL, which leverages the shared representation to achieve Õ(M√dkT + d√kMT) regret, with T being the number of total steps. Our regret significantly improves upon the baseline Õ(Md√T) achieved by solving each task independently. We further develop a lower bound that shows our regret is near-optimal when d > M. Furthermore, we extend the algorithm and analysis to multi-task episodic RL with linear value function approximation under low inherent Bellman error (Zanette et al., 2020a). To the best of our knowledge, this is the first theoretical result that characterizes the benefits of multi-task representation learning for exploration in RL with function approximation. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Research progress of CDT 8

Research progress of CDT

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8th International Conference on Bioinformatics and Biomedical Science, ICBBS 2019

作者： Yuting, Gao Shutong, Shen Sanhong, Deng School of Information Management Nanjing University China Key Laboratory of Data Engineering and Knowledge Services in Jiangsu China

ISBN: (纸本)9781450372510

With the high mortality rate for the cases of malignant tumors, the discovery and early treatment of cancer is critical to improving the 5-year survival rate of cancer. The biggest challenge in control and prevention is how to detect tumors as early as possible, so that the most effective means can be used for treatment. With the help of the WOS database, researchers found that liquid biopsy, genetic material/protein expression and computer imaging play an important role in cancer detection. Through analysis of the history and current status of cancer detection technology development, this paper also aims to provide guidance for the clinical practice of current cancer screening and provide inspiration for future technological development. © 2019 Association for Computing Machinery.

关键词： Gene expression

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Robust and Fast Measure of Information via Low-rank Representation

arXiv

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arXiv 2022年

作者： Dong, Yuxin Gong, Tieliang Yu, Shujian Chen, Hong Li, Chen School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Ministry of Education Xi’an710049 China Machine Learning Group UiT - The Arctic University of Norway Norway College of Science Huazhong Agriculture University Wuhan430070 China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan430070 China

The matrix-based Rényi’s entropy allows us to directly quantify information measures from given data, without explicit estimation of the underlying probability distribution. This intriguing property makes it widely applied in statistical inference and machine learning tasks. However, this information theoretical quantity is not robust against noise in the data, and is computationally prohibitive in large-scale applications. To address these issues, we propose a novel measure of information, termed low-rank matrix-based Rényi’s entropy, based on low-rank representations of infinitely divisible kernel matrices. The proposed entropy functional inherits the specialty of of the original definition to directly quantify information from data, but enjoys additional advantages including robustness and effective calculation. Specifically, our low-rank variant is more sensitive to informative perturbations induced by changes in underlying distributions, while being insensitive to uninformative ones caused by noises. Moreover, low-rank Rényi’s entropy can be efficiently approximated by random projection and Lanczos iteration techniques, reducing the overall complexity from O(n3) to O(n2s) or even O(ns2), where n is the number of data samples and s n. We conduct large-scale experiments to evaluate the effectiveness of this new information measure, demonstrating superior results compared to matrix-based Rényi’s entropy in terms of both performance and computational efficiency. Copyright © 2022, The Authors. All rights reserved.

关键词： Entropy

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Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview

arXiv

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arXiv 2021年

作者： Fan, Zhaoxin Zhu, Yazhi He, Yulin Sun, Qi Liu, Hongyan He, Jun Key Laboratory of Data Engineering and Knowledge Engineering of MOE School of Information Renmin University of China Beijing China No. 59 Zhongguancun Street Haidian Dist Beijing100872 China Institute of Information Science Beijing Jiaotong University No.3 Shangyuancun Haidian Dist. Beijing China School of Economics and Management Tsinghua University Haidian Dist Beijing100084 China

Object pose detection and tracking has recently attracted increasing attention due to its wide applications in many areas, such as autonomous driving, robotics, and augmented reality. Among methods for object pose detection and tracking, deep learning is the most promising one that has shown better performance than others. However, survey study about the latest development of deep learning-based methods is lacking. Therefore, this study presents a comprehensive review of recent progress in object pose detection and tracking that belongs to the deep learning technical route. To achieve a more thorough introduction, the scope of this study is limited to methods taking monocular RGB/RGBD data as input and covering three kinds of major tasks: instance-level monocular object pose detection, category-level monocular object pose detection, and monocular object pose tracking. In our work, metrics, datasets, and methods of both detection and tracking are presented in detail. Comparative results of current state-of-the-art methods on several publicly available datasets are also presented, together with insightful observations and inspiring future research directions. Copyright © 2021, The Authors. All rights reserved.

关键词： Augmented reality

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Robust Low-rank Deep Feature Recovery in CNNs: Toward Low Information Loss and Fast Convergence

Robust Low-rank Deep Feature Recovery in CNNs: Toward Low In...

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IEEE International Conference on data Mining (ICDM)

作者： Jiahuan Ren Zhao Zhang Jicong Fan Haijun Zhang Mingliang Xu Meng Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China School of Data Science The Chinese University of Hong Kong (Shenzhen) & Shenzhen Research Institute of Big Data Shenzhen China Harbin Institute of Technology (Shenzhen) Shenzhen China School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9781665423991

Convolutional Neural Networks (CNNs)-guided deep models have obtained impressive performance for image representation, however the representation ability may still be restricted and usually needs more epochs to make the model converge in training, due to the useful information loss during the convolution and pooling operations. We therefore propose a general feature recovery layer, termed Low-rank Deep Feature Recovery (LDFR), to enhance the representation ability of the convolutional features by seamlessly integrating low-rank recovery into CNNs, which can be easily extended to all existing CNNs-based models. To be specific, to recover the lost information during the convolution operation, LDFR aims at learning the low-rank projections to embed the feature maps onto a low-rank subspace based on some selected informative convolutional feature maps. Such low-rank recovery operation can ensure all convolutional feature maps to be reconstructed easily to recover the underlying subspace with more useful and detailed information discovered, e.g., the strokes of characters or the texture information of clothes can be enhanced after LDFR. In addition, to make the learnt low-rank subspaces more powerful for feature recovery, we design a fusion strategy to obtain a generalized subspace, which averages over all learnt sub-spaces in each LDFR layer, so that the convolutional feature maps in test phase can be recovered effectively via low-rank embedding. Extensive results on several image datasets show that existing CNNs-based models equipped with our LDFR layer can obtain better performance.

关键词： Training Image recognition Convolution Conferences Image representation data mining Convolutional neural networks

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Rankings never really quantify contributions: A quantitative and qualitative study on universities and their libraries

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Journal of Library Science in China 2020年 117-126页

作者： Shelia X.WEI Ronda J.ZHANG Howell Y.WANG CAO Cong Fred Y.YE International Joint Informatics Laboratory & Jiangsu Key Laboratory of Data Engineering and Knowledge Services School of Information Management Nanjing University Nottingham University Business School University of Nottingham

By comparing quantitative ranking with qualitative contributions, we reveal that academic assessment has to put real contributions ahead of quantitative indicators and that rankings have nothing to do with universities’ and their libraries’ true values. The greatness of a university lies in its impacts on the progress for human knowledge and the promotion for social development. Although ranking of universities by way of quantitative indicators can reflect some information, we should pay more attention to qualitative contributions.

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Xdn: towards efficient inference of residual neural networks on cambricon chips 2nd

Xdn: towards efficient inference of residual neural networks...

引用

2nd International Symposium on Benchmarking, Measuring, and Optimization, Bench 2019

作者： Li, Guangli Wang, Xueying Ma, Xiu Liu, Lei Feng, Xiaobing State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China College of Computer Science and Technology Jilin University Changchun China MOE Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University Changchun China

ISBN: (纸本)9783030495558

In this paper, we present XDN, an optimization and inference engine for accelerating residual neural networks on Cambricon chips. We leverage a channel pruning method to compress the weights of ResNet-50. By exploring the optimization opportunities in computational graphs, we propose a layer fusion strategy, which dramatically decreases the number of scalar computation layers, such as Batch Normalization, Scale. Furthermore, we design an efficient implementation of XDN, including data preprocessing, hyper-parameter auto-tuning, etc. The experimental results show that the ResNet-50 model can achieve significant speedup without accuracy loss by using our XDN engine. © Springer Nature Switzerland AG 2020.

关键词： Engines

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