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Bio
My contributions to Machine Learning have largely focussed on improving algorithm performance without increasing computational load. This work is fundamental in nature, and has involved the development and analysis of a variety of approaches to feature selection, feature sharing, and data-dependent hashing. My research in this area was initially motivated by the desire to improve the performance of real-time tracking in video surveillance.
My work in the area has developed new efficient methods for carrying out standard tasks in Machine Learning. The scale at which Machine Learning algorithms operate is one of their key limitations. It limits the ability to produce practical outcomes, and to deliver the broader potential benefits that might come from operating at the same scale as human beings.
My work in the area has developed new efficient methods for carrying out standard tasks in Machine Learning. The scale at which Machine Learning algorithms operate is one of their key limitations. It limits the ability to produce practical outcomes, and to deliver the broader potential benefits that might come from operating at the same scale as human beings.
Research Interests
Papers共 406 篇Author StatisticsCo-AuthorSimilar Experts
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Gaoxiang Cong,Yuankai Qi,Liang Li, Amin Beheshti, Zhedong Zhang,Anton van den Hengel,Ming-Hsuan Yang,Chenggang Yan, Qingming Huang
CoRR (2024)
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CoRR (2024)
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CVPR 2024 (2024)
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ICLR 2024 (2024)
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Intelligence-Based Cardiology and Cardiac Surgerypp.157-162, (2024)
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