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迎校庆“百大名家进校园”系列讲座之亚博最新娱乐网址学术报告-第【2020033】号
2020-11-07 10:54   审核人:

应我校亚博最新娱乐网址邀请,东南大学的闫亮教授于2020117日为我院教师和研究生讲学。欢迎亚博最新娱乐网址及全校相关教师、博士生、硕士生参加!

报告题目: Stein variational gradient descent with local approximations

人: 闫亮 教授 Prof. Liang Yan

报告人单位:东南大学

间:2020年11月7日(周六)下午16:10-16:40

腾讯会议:466 431 061,密码:1107

 

闫亮教授简介:闫亮,东南大学教授、博士生导师。主要从事不确定性量化、贝叶斯反问题理论与算法的研究。2018年入选东南大学“至善青年学者”(A层次)支持计划,2017年入选江苏省高校“青蓝工程”优秀青年骨干教师培养对象。目前主持国家自然科学基金面上项目一项,主持完成国家自然科学基金青年项目和江苏省自然科学基金青年项目各一项。已经在《SIAM J. Sci. Comput.》、《Inverse Problems》、《J. Comput. Phys.》、《Int. J. Numer. Meth. Eng.》等国内外刊物上发表20多篇学术论文.

报告摘要Bayesian computation plays an important role in modern machine learning and statistics to reason about uncertainty. A key computational challenge in Bayesian inference is to develop efficient techniques to approximate, or draw samples from posterior distributions. Stein variational gradient decent (SVGD) has been shown to be a powerful approximate inference algorithm for this issue. However, the vanilla SVGD requires calculating the gradient of the target density and cannot be applied when the gradient is unavailable or too expensive to evaluate. In this talk we explore one way to address this challenge by the construction of a local surrogate for the target distribution which the gradient can be obtained in a much more computationally feasible manner. The key idea is to approximate the forward model using a deep neural network (DNN) which is trained on a carefully chosen training set, which also determines the quality of the surrogate. To this end we propose a general adaptation procedure to refine the local approximation online without destroying the convergence of the resulting SVGD. This significantly reduces the computational cost of SVGD and leads to a suite of algorithms that are straightforward to implement. The new algorithm is illustrated on a set of challenging Bayesian inverse problems, and numerical experiments demonstrate a clear improvement in performance and applicability of standard SVGD.

欢迎各位老师同学届时参加!

 

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