学术报告与学术会议

江颖教授、李斯教授和林荣荣研究员受邀进行讲座

发布时间: 2018-10-15 23:34:14

Multi-task Learning in Vector-valued Reproducing Kernel Banach Spaces with the L1 Norm.

报告人：林荣荣研究员中山大学

时间：2018-10-12 09:00--10:00

地点：数学科学学院401

报告人简介:

林荣荣博士于2017年6月在中山大学数学学院取得博士学位。博士期间作为科研助理曾访问加拿大阿尔伯特大学一年。2017年7月聘为中山大学数据科学与计算机学院副研究员。已在机器学习核函数方法和时频分析研究领域发表多篇论文。

摘要：

Motivated by sparse multi-task learning, we constructed a class of vector-valued reproducing kernel Banach spaces with the l1 norm based on multi-task admissible kernels. The linear representer theorem holds for regularization networks in the obtained spaces if and only if the Lebesgue constant of multi-task admissible kernels is bounded by 1. Examples including the Brownian bridge kernel, the exponential kernel and the covariance of Brownian motion admissible for constructions are given. In order to accommodate more kernels, we consider relaxed representer theorems that need a weaker condition on the Lebesgue constant. Finally, we present numerical experiments for both synthetic data and real-world benchmark data to demonstrate the advantages of the proposed construction and regularization models. This is a joint work with Prof.Guohui Song (Clarkson U) and Haizhang Zhang (SYSU).

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林荣荣研究员在作报告

A Higher-Order Polynomial Method for SPECT Reconstruction

报告人：李斯中山大学

时间：2018-10-12 10:00--11:00

地点：数学科学学院401

李斯教授个人简介:

2008年和2013年分别获得中山大学信息与计算科学学士学位和计算数学博士学位，期间自2010年11月至2012年3月访问美国纽约州立大学上州医科大学（SUNY Upstate）放射系。尔后至2015年10月于中山大学数学与计算科学学院从事博士后研究工作，2015年11月至2018年6月于中山大学数据科学与计算机学院任特聘研究员。主要研究方向为医学影像重建、最优化理论与算法、反问题与积分方程。获美国专利授权1件，主持国家自然科学基金青年基金1项，参与国家重点研发计划高性能计算重点专项子课题1项，发表学术论文十来篇，曾获广东省计算数学优秀青年论文奖一等奖。

摘要：

Existing single-photon emission computed tomography (SPECT) reconstruction methods are most based on discrete models that may be viewed as piecewise constant approximations of certain continuous data acquisition process. Due to low accuracy order of piecewise constant approximations, traditional discrete models introduce irreducible model errors which are a bottleneck of the quality improvement of reconstructed images in clinical applications. To overcome this drawback, we develop a higher-order polynomial method for SPECT reconstruction. Specifically, we represent the data acquisition of SPECT imaging by using an integral equation model, approximate the solution of the underlying integral equation by higher-order piecewise polynomials leading to a new discrete system and introduce two novel regularizers for the system, by exploring the a priori knowledge of the radiotracer distribution, suitable for the approximation.

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李斯教授在作报告

高维稀疏网格逼近及其在积分方程、随机微分方程中的应用

报告人：江颖教授中山大学

时间：2018-10-12 11:00--12:00

地点：数学科学学院401

江颖教授简介:

中山大学数据科学与计算机学院副教授、博士生导师，数据科学系副系主任，广东省计算科学重点实验室主任助理。主要从事高维数据稀疏表示、积分方程快速算法等方面的研究，在Siam. Numer. Anal.、Math. Comp.、J. Sci. Comput.、JCAM等计算数学重要期刊上发表SCI论文10余篇。主持国家自然科学基金2项、广州市重点项目1项。

摘要：

This talk is about a kind of high-dimension approximation techniques, called sparse grids,which are widely used in solving partial differential equations, integral equations,designing high-dimension quadrature formula, data mining, etc. The approximation schemes on sparse grids achieve quasi-linear computational cost when the schemes on full grids suffer from the ``curse of dimensionality'', since the computational complexity increases exponentially as the dimension grows. At same time, the approximation schemes on sparse grids enjoy the optimal approximation order as the schemes on full grids do.

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江颖教授在作报告

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叶颀教授与江颖教授、李斯教授和林荣荣研究员合影