Le Song mostly deals with Artificial intelligence, Machine learning, Pattern recognition, Data mining and Theoretical computer science. All of his Artificial intelligence and Deep learning, Convolutional neural network, Embedding, Graphical model and Kernel method investigations are sub-components of the entire Artificial intelligence study. Le Song interconnects Event, Parametric statistics and Social network in the investigation of issues within Machine learning.
His Pattern recognition research integrates issues from CURE data clustering algorithm, Fuzzy clustering and Key. His biological study spans a wide range of topics, including Stochastic process, Transmission, Structure and Cluster analysis. The Theoretical computer science study combines topics in areas such as Artificial neural network, Decoupling and Graph.
His primary areas of investigation include Artificial intelligence, Algorithm, Machine learning, Mathematical optimization and Theoretical computer science. Artificial intelligence and Pattern recognition are frequently intertwined in his study. The various areas that Le Song examines in his Algorithm study include Kernel method, Reproducing kernel Hilbert space, Kernel, Artificial neural network and Embedding.
The study incorporates disciplines such as Nonparametric statistics and Kernel in addition to Kernel. His work on Recurrent neural network as part of his general Machine learning study is frequently connected to Process, thereby bridging the divide between different branches of science. His Theoretical computer science study integrates concerns from other disciplines, such as Graph and Graph.
Le Song mainly investigates Artificial intelligence, Graph, Theoretical computer science, Artificial neural network and Algorithm. His Artificial intelligence study frequently involves adjacent topics like Machine learning. His work carried out in the field of Graph brings together such families of science as Node, Graphical model, Computation and Mathematical optimization.
His Theoretical computer science study combines topics in areas such as Point process, Property, Feature learning and Graph algorithms, Graph. His Artificial neural network research is multidisciplinary, incorporating elements of Perspective and Bayes' theorem. Le Song works mostly in the field of Algorithm, limiting it down to topics relating to Feature and, in certain cases, Enhanced Data Rates for GSM Evolution, Embedding, Bridging and Representation.
Le Song spends much of his time researching Artificial intelligence, Graph, Deep learning, Inference and Algorithm. His multidisciplinary approach integrates Artificial intelligence and Meta learning in his work. His study explores the link between Graph and topics such as Graph that cross with problems in Attention network, Directed graph, Comprehension, Theoretical computer science and Natural language understanding.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
SphereFace: Deep Hypersphere Embedding for Face Recognition
Weiyang Liu;Yandong Wen;Zhiding Yu;Ming Li.
computer vision and pattern recognition (2017)
A Hilbert space embedding for distributions
Alex Smola;Arthur Gretton;Le Song;Bernhard Schölkopf.
algorithmic learning theory (2007)
A Kernel Statistical Test of Independence
Arthur Gretton;Kenji Fukumizu;Choon H. Teo;Le Song.
neural information processing systems (2007)
Recurrent Marked Temporal Point Processes: Embedding Event History to Vector
Nan Du;Hanjun Dai;Rakshit Trivedi;Utkarsh Upadhyay.
knowledge discovery and data mining (2016)
Feature selection via dependence maximization
Le Song;Alex Smola;Arthur Gretton;Justin Bedo.
Journal of Machine Learning Research (2012)
Learning combinatorial optimization algorithms over graphs
Hanjun Dai;Elias B. Khalil;Yuyu Zhang;Bistra Dilkina.
neural information processing systems (2017)
Learning Social Infectivity in Sparse Low-rank Networks Using Multi-dimensional Hawkes Processes
Ke Zhou;Hongyuan Zha;Le Song.
international conference on artificial intelligence and statistics (2013)
Scalable Influence Estimation in Continuous-Time Diffusion Networks
Nan Du;Le Song;Manuel Gomez-Rodriguez;Hongyuan Zha.
neural information processing systems (2013)
The 'when' and 'where' of perceiving signals of threat versus non-threat.
Leanne M. Williams;Donna M. Palmer;Belinda J. Liddell;Le Song.
Estimating time-varying networks
Mladen Kolar;Le Song;Amr Ahmed;Eric P. Xing.
The Annals of Applied Statistics (2010)
Profile was last updated on December 6th, 2021.
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