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检索条件"机构=Intelligent Computing & Machine Learning Lab"
76 条 记 录,以下是31-40 订阅
排序:
Emotion classification with data augmentation using generative adversarial networks  22nd
Emotion classification with data augmentation using generati...
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22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018
作者: Zhu, Xinyue Liu, Yifan Li, Jiahong Wan, Tao Qin, Zengchang Intelligent Computing and Machine Learning Lab School of ASEE Beihang University Beijing China School of Electronic Engineering Bejing University of Posts and Telecommunications Beijing China Beijing San Kuai Yun Technology Co. Ltd. Beijing China School of Biological Science and Medical Engineering Beijing Advanced Innovation Centre for Biomedical Engineering Beihang University Beijing China
It is a difficult task to classify images with multiple class labels using only a small number of labeled examples, especially when the label (class) distribution is imbalanced. Emotion classification is such an examp... 详细信息
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A sequential guiding network with attention for image captioning
arXiv
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arXiv 2018年
作者: Sow, Daouda Qin, Zengchang Niasse, Mouhamed Wan, Tao Intelligent Computing and Machine Learning Lab School of ASEE Beihang University China Keep Labs Keep Inc. Beijing China School of EEE North China Electric Power University China
The recent advances of deep learning in both computer vision (CV) and natural language processing (NLP) provide us a new way of understanding semantics, by which we can deal with more challenging tasks such as automat... 详细信息
来源: 评论
Modeling Text with Graph Convolutional Network for Cross-Modal Information Retrieval
arXiv
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arXiv 2018年
作者: Yu, Jing Lu, Yuhang Qin, Zengchang Liu, Yanbing Tan, Jianlong Guo, Li Zhang, Weifeng Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China Intelligent Computing and Machine Learning Lab School of Asee Beihang University China Hangzhou Dianzi University China
Cross-modal information retrieval aims to find heterogeneous data of various modalities from a given query of one modality. The main challenge is to map different modalities into a common semantic space, in which dist... 详细信息
来源: 评论
Generative Cooperative Net for Image Generation and Data Augmentation 2018
arXiv
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arXiv 2017年
作者: Xu, Qiangeng Qin, Zengchang Wan, Tao Intelligent Computing and Machine Learning Lab Beihang University Beijing China
How to build a good model for image generation given an abstract concept is a fundamental problem in computer vision. In this paper, we explore a generative model for the task of generating unseen images with desired ... 详细信息
来源: 评论
Stock volatility prediction using recurrent neural networks with sentiment analysis  30th
Stock volatility prediction using recurrent neural networks ...
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30th International Conference on Industrial, Engineering, and Other Applications of Applied intelligent Systems, IEA/AIE 2017
作者: Liu, Yifan Qin, Zengchang Li, Pengyu Wan, Tao Intelligent Computing and Machine Learning Lab School of ASEE Beihang University Beijing100191 China School of Mechanical Engineering and Automation Beihang University Beijing100191 China School of Biological Science and Medical Engineering Beihang University Beijing100191 China
In this paper, we propose a model to analyze sentiment of online stock forum and use the information to predict the stock volatility in the Chinese market. We have labeled the sentiment of the online financial posts a... 详细信息
来源: 评论
A Bayesian model of game decomposition  30th
A Bayesian model of game decomposition
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30th International Conference on Industrial, Engineering, and Other Applications of Applied intelligent Systems, IEA/AIE 2017
作者: Zhao, Hanqing Qin, Zengchang Liu, Weijia Wan, Tao Intelligent Computing and Machine Learning Lab School of ASEE Beihang University Beijing100191 China École Centrale de Pékin Beihang University Beijing100191 China School of Biological Science and Medical Engineering Beihang University Beijing100191 China
In this paper, we propose a Bayesian probabilistic model to describe collective behavior generated by a finite number of agents competing for limited resources. In this model, the strategy for each agent is a binary c... 详细信息
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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence
arXiv
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arXiv 2025年
作者: Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U
Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI technique... 详细信息
来源: 评论
Why is the Winner the Best?
Why is the Winner the Best?
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Conference on Computer Vision and Pattern Recognition (CVPR)
作者: M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from t...
来源: 评论
Text generation based on generative adversarial nets with latent variable
arXiv
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arXiv 2017年
作者: Wang, Heng Qin, Zengchang Wan, Tao Intelligent Computing and Machine Learning Lab School of ASEE Beihang University Beijing100191 China School of Biological Science and Medical Engineering Beihang University Beijing100191 China
In this paper, we propose a model using generative adver- sarial net (GAN) to generate realistic text. Instead of using standard GAN, we combine variational autoencoder (VAE) with generative ad- versarial net. The use... 详细信息
来源: 评论
Logical parsing from natural language based on a neural translation model
arXiv
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arXiv 2017年
作者: Li, Liang Li, Pengyu Liu, Yifan Wan, Tao Qin, Zengchang Intelligent Computing and Machine Learning Lab School of ASEE Beihang University Beijing100191 China Biomedical Imaging and Informatics Lab School of Biological Science and Medical Engineering Beihang University Beijing100191 China
Semantic parsing has emerged as a significant and powerful paradigm for natural language interface and question answering systems. Traditional methods of building a semantic parser rely on high-quality lexicons, hand-... 详细信息
来源: 评论