题目一：Low-Rank High-Order Tensor Completion with Applications in Visual Data

内容简介：Recently, tensor Singular Value Decomposition (t-SVD)-based low-rank tensor completion has achieved unprecedented success in addressing various pattern analysis issues. However, existing studies mostly focus on third-order tensors while order-d (d ≥ 4) tensors are commonly encountered in real-world applications, like fourth-order color videos, fifth-order light-field images, and sixth-order bidirectional texture functions. Aiming at addressing this critical issue, this talk reported an order-d tensor recovery framework including the model, algorithm and theories by innovatively developing a novel algebraic foundation for order-d t-SVD, thereby achieving exact completion for any order-d low t-SVD rank tensors with missing values with an overwhelming probability. Empirical studies on synthetic data and real-world visual data illustrate that compared with other state-of-the-art recovery methods, the proposed one achieves highly competitive performance in terms of both qualitative and quantitative metrics.

报告人：王建军

报告人简介：博士，西南大学教授（三级），博士生导师，重庆市学术带头人，重庆市创新创业领军人才，巴渝学者特聘教授，重庆工业与应用数学学会、运筹学会副理事长，CSIAM全国大数据与人工智能专家委员会委员，美国数学评论评论员，曾获重庆市自然科学奖励。主要研究方向为：高维数据建模、机器学习（深度学习）、数据挖掘、压缩感知、张量分析、函数逼近论等。在神经网络(深度学习)逼近复杂性和高维数据稀疏建模等方面有一定的学术积累。主持国家自然科学基金5项，教育部科学技术重点项目1项，重庆市自然科学基金1项，主研8项国家自然、社会科学基金，参与国家重点基础研究发展‘973’计划一项；现主持国家重点研发课题1项，国家自然科学基金面上项目1项，多次出席国际、国内重要学术会议，并应邀做大会特邀报告30余次。已在IEEE TPAMI（5），IEEE TIT，IEEE TIP, IEEE TNNLS（3），ACHA（2）,PR，INF SCI, Inverse Problems, AAAI，ACM MM，Neural Networks, Signal Processing(2), IEEESPL(3), JCAM, ICASSP，中国科学(A,F辑)(4),数学学报,计算机学报,电子学报(3)等知名专业期刊发表100余篇学术论文 ,IEEE等系列刊物，NSR,SP，NN，PR,中国科学,计算机学报，电子学报，数学学报等知名期刊审稿人等。

题目二：Orthogonal approximate message passing for signal estimation with rotationally-invariant models

内容简介：Approximate message passing (AMP) algorithms are low-cost iterative algorithms for solving high-dimensional linear regression problems. With independent Gaussian measurements, the performance of AMP can be described by a state evolution recursion in the proportional asymptotic regime. Moreover, for various high-dimensional signal estimation problems, AMP achieves the statistically optimal performance among a wide class of algorithms. In this talk, we will discuss a variant of AMP based on divergence-free nonlinearities. This algorithm, which we call orthogonal AMP, admits simple state evolution characterization for general rotationally-invariant models, without the need of complicated Onsager correction terms tailored to the matrix spectrum. The simple state evolution structure makes it an appealing template for designing efficient and analyzable algorithms for various signal estimation problems, as we will briefly mention in this talk.

报告人：马俊杰

报告人简介：中国科学院数学与系统科学研究院优秀青年副研究员。2010年本科毕业于西安电子科技大学，2015年在香港城市大学取得博士学位。曾于香港城市大学、哥伦比亚大学和哈佛大学从事博士后研究。研究兴趣包括信号处理、无线通信、信息论、机器学习等，近年来主要关注无线通信中的高维信号估计问题。曾入选中科院百人计划，主持自然基金青年项目并参与中科院先导科技专项等科研项目，目前担任中国运筹学会青年工作委员会副秘书长。

时  间：2023年11月26日（周日）上午10：00 开始

地  点：腾讯会议：994-379-410

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2023年11月23日