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Several research progress in deep learning

吴庆标教授(浙江大学)
Friday, April 12th, 2019, 2:00 PM  闵行数学楼126室
 
报告人简介:
吴庆标,浙江大学数学科学学院教授、博士生导师。一直从事数值计算方法、数值代数与优化、大数据分析、图形图像处理等研究工作。现任浙江大学科学与工程计算研究所所长,浙江大学工业技术研究院院长助理,学术兼任浙江省应用数学会副理事长、中国优选筹法统筹法学会经济数学与管理数学分会副理事长、中国计算数学学会理事、中国优选法统筹法与经济数学会理事。
近十年来,主持和完成国家自然科学基金、军工重大项目、973子项目、浙江省重大科技研发项目、浙江自然科学基金重点项目和大数据分析、图形图像处理等重大产学研合作项目等四十多项,在《Journal of Computational Physics》、《Computer Methods in Applied Mechanics and Engineering》、《Journal of Computational and Applied Mathematics》和《Computer & Graphics》等学术杂志上发表SCI检索学术论文70多篇。拥有发明专利和软件著作权证书10多项。主持完成的研究成果曾获浙江省科学技术进步奖、浙江省高等学校科研成果奖和浙江省自然科学学术奖等。
报告内容简介:
In this report, we first introduce a class of probabilistic generative model based on undirected graph with special structure, namely Deep Boltzmann machine. Its principle is expounded, and a new shape completion algorithm is proposed according to its characteristics. By setting the appropriate mask and sampling from the Deep Boltzmann machine, the proposed method can deal with the task without the prior information of the missing region. Then we introduce a new kind of probabilistic generative model, namely the Neural Autoregressive Distribution Estimator, which is inspired by the Restricted Boltzmann Machine. Combining this model with the mean field method in the Deep Boltzmann machine training process, a better variational learning algorithm is proposed. Experiments show that the model trained with this algorithm has better performance than the original Deep Boltzmann machine.

主持人: 朱升峰 副教授
主办单位:数学科学学院 科技处
   
 
 
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