His primary areas of investigation include Artificial intelligence, Information retrieval, Machine learning, Natural language processing and Pattern recognition. Yangqiu Song regularly links together related areas like Collaborative filtering in his Artificial intelligence studies. His work deals with themes such as Text mining and Interactive visualization, which intersect with Information retrieval.
His research in the fields of Transfer of learning and Decision boundary overlaps with other disciplines such as Contextual image classification. His Natural language processing study combines topics in areas such as Deep learning, Convolutional neural network, Leverage and Domain knowledge. His work investigates the relationship between Pattern recognition and topics such as Cluster analysis that intersect with problems in Data set and Parallel algorithm.
His primary scientific interests are in Artificial intelligence, Natural language processing, Machine learning, Information retrieval and Data mining. His Artificial intelligence study frequently draws parallels with other fields, such as Pattern recognition. His Natural language processing research incorporates themes from Context, Relation, Pronoun and Coreference.
His Machine learning research is multidisciplinary, relying on both Crowdsourcing and Usability. His Information retrieval study integrates concerns from other disciplines, such as Text mining, Social media and Knowledge base. Data mining is frequently linked to Cluster analysis in his study.
Yangqiu Song focuses on Artificial intelligence, Natural language processing, Commonsense knowledge, Theoretical computer science and Benchmark. His Artificial intelligence study incorporates themes from Crowdsourcing, Machine learning and Pattern recognition. His research in Natural language processing intersects with topics in Context, Relation and Resolution.
Yangqiu Song has included themes like Language model, Representation and Data science in his Commonsense knowledge study. His biological study spans a wide range of topics, including Logical consequence, Task analysis and Directed graph. His research on Benchmark also deals with topics like
His primary areas of study are Artificial intelligence, Natural language processing, Theoretical computer science, Commonsense knowledge and Data science. In most of his Artificial intelligence studies, his work intersects topics such as Pattern recognition. The study incorporates disciplines such as Discourse relation and Representation in addition to Natural language processing.
His work in the fields of Theoretical computer science, such as PageRank, overlaps with other areas such as Motif. He interconnects Categorization, Commonsense reasoning and Leverage in the investigation of issues within Commonsense knowledge. His study in Data science is interdisciplinary in nature, drawing from both Relation, Inference, Focus and Knowledge graph.
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Parallel Spectral Clustering in Distributed Systems
Wen-Yen Chen;Yangqiu Song;Hongjie Bai;Chih-Jen Lin.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2011)
TextFlow: Towards Better Understanding of Evolving Topics in Text
Weiwei Cui;Shixia Liu;Li Tan;Conglei Shi.
IEEE Transactions on Visualization and Computer Graphics (2011)
Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks
Huan Zhao;Quanming Yao;Jianda Li;Yangqiu Song.
knowledge discovery and data mining (2017)
TIARA: a visual exploratory text analytic system
Furu Wei;Shixia Liu;Yangqiu Song;Shimei Pan.
knowledge discovery and data mining (2010)
Large-Scale Hierarchical Text Classification with Recursively Regularized Deep Graph-CNN
Hao Peng;Jianxin Li;Yu He;Yaopeng Liu.
the web conference (2018)
Short text conceptualization using a probabilistic knowledgebase
Yangqiu Song;Haixun Wang;Zhongyuan Wang;Hongsong Li.
international joint conference on artificial intelligence (2011)
MetaGAN: an adversarial approach to few-shot learning
Ruixiang Zhang;Tong Che;Zoubin Ghahramani;Yoshua Bengio.
neural information processing systems (2018)
Semi-Supervised Multi-label Learning by Solving a Sylvester Equation
Gang Chen;Yangqiu Song;Fei Wang;Changshui Zhang.
siam international conference on data mining (2008)
A unified framework for semi-supervised dimensionality reduction
Yangqiu Song;Feiping Nie;Changshui Zhang;Shiming Xiang.
Pattern Recognition (2008)
Maximum-Likelihood Augmented Discrete Generative Adversarial Networks
Tong Che;Yanran Li;Ruixiang Zhang;R Devon Hjelm.
arXiv: Artificial Intelligence (2017)
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