題目一：Non-uniform random sampling and reconstruction in sparse multivate trigonometric polynomial spaces
内容簡介：In this talk, we will intruduce the problem of random sampling and reconstruction in trigonometric polynomials signal sapces. Also, we take sparse condition into account. It turns out that with overwhelming probability, randomly samples drawn from non-uniform distribution on a bounded interval form a stable sampling set. We also consider the reconstruction of sparse signals by RIP condition.
題目二：Relevant sampling in finitely generated shift-invariant spaces (I)
内容簡介：We consider random sampling in finitely generated shift-invariant spaces $V(/Phi) /subset {/rm L}^2(/mathbb{R}^n)$ generated by a vector $/Phi = (/varphi_1,/ldots,/varphi_r) /in ( {/rm L}^2(/mathbb{R}^n))^r$. Following the approach introduced by Bass and Gr/"ochenig, we consider certain relatively compact subsets $V_{R,/delta}(/Phi)$ of such a space, defined in terms of a concentration inequality with respect to a cube with side lengths $R$. Under very mild assumptions on the generators, we show that for $R$ sufficiently large, taking $O(R^n log(R))$ many random samples (taken independently uniformly distributed within $C_R$) yields a sampling set for $V_{R,/delta}(/Phi)$ with high probability. We give explicit estimates of all involved constants in terms of the generators $/varphi_1, /ldots, /varphi_r$.
報告人：中山大學 冼軍 教授
報告人簡介：博士生導師、廣東省千百十人才工程入選者、國家優秀青年基金獲得者。2004年畢業于中山大學數學系獲理學博士學位，同年進入浙江大學博士後流動站，2006年返回中山大學數學學院任副教授、教授、碩士研究生導師、博士研究生導師。主要研究方向為應用調和分析、采樣理論及其在信号處理中的應用。2004年至今訪問過美國耶魯大學、美國中佛羅裡達大學、加拿大Alberta大學，德國亞琛工業大學、法國馬賽大學、新加坡國立大學、香港城市大學等高校，相關論文發表在APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS，JOURNAL OF FOURIER ANALYSIS AND APPLICATIONS，BMC BIOINFORMATICS，SIGNAL PROCESSING，PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY，JOURNAL OF APPROXIMATION THEORY等國内外核心期刊。
題目三：Recovery algorithms design for generalized linear models via approximate standard Bayesian inference algorithms
内容簡介：In this talk, designing recovery algorithms for generalized linear models (GLMs) using approximate standard Bayesian inference algorithms (approximate message passing (AMP), vector approximate message passing (VAMP), sparse Bayesian learning (SBL), variational Bayesian inference (VBI)) will be presented. Substantial examples such as image classification, parameter estimation from quantized data and phase retrieval can be formulated as a GLM problem. Compared to the standard linear models (SLMs), solving the GLMs is more challenging because of the coupling of the linear and nonlinear transforms. Although the generalized approximate message passing (GAMP) algorithm has been proposed to solve the GLMs, it does not provide any insight into the relationship between the SLMs and GLMs. According to expectation propagation (EP), the GLM can be iteratively approximated as a sequence of SLM subproblems, and thus the standard Bayesian algorithm can be easily extended to solve the GLMs.This talk is based on joint work with Xiangming Meng and Sheng Wu.
報告人：浙江大學 朱江 講師
報告人簡介：朱江博士分别于2011年和2016年獲得哈爾濱工程大學電子科學與技術學士學位和信息與通信工程博士學位。博士期間以訪問學生身份在美國裡海大學交流半年。從2016年6月開始擔任浙江大學海洋學院講師。IEEE和中國電子學會會員。目前感興趣的方向主要包括：廣義線性模型下的貝葉斯算法設計、線譜估計問題、檢測估計、無标簽感知等信号處理問題。
時 間：2018年11月16日（周五）上午10：00始
地 點：南海樓330室
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2018年11月13日
