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

内容簡介：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.

報告人：王建軍

報告人簡介：博士，西南大學教授（三級），博士生導師，重慶市學術帶頭人，重慶市創新創業領軍人才，巴渝學者特聘教授，重慶工業與應用數學學會、運籌學會副理事長，CSIAM全國大數據與人工智能專家委員會委員，美國數學評論評論員，曾獲重慶市自然科學獎勵。主要研究方向為：高維數據建模、機器學習（深度學習）、數據挖掘、壓縮感知、張量分析、函數逼近論等。在神經網絡(深度學習)逼近複雜性和高維數據稀疏建模等方面有一定的學術積累。主持國家自然科學基金5項，教育部科學技術重點項目1項，重慶市自然科學基金1項，主研8項國家自然、社會科學基金，參與國家重點基礎研究發展‘973’計劃一項；現主持國家重點研發課題1項，國家自然科學基金面上項目1項，多次出席國際、國内重要學術會議，并應邀做大會特邀報告30餘次。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，中國科學(A,F輯)(4),數學學報,計算機學報,電子學報(3)等知名專業期刊發表100餘篇學術論文 ,IEEE等系列刊物，NSR,SP，NN，PR,中國科學,計算機學報，電子學報，數學學報等知名期刊審稿人等。

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

内容簡介：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.

報告人：馬俊傑

報告人簡介：中國科學院數學與系統科學研究院優秀青年副研究員。2010年本科畢業于西安電子科技大學，2015年在香港城市大學取得博士學位。曾于香港城市大學、哥倫比亞大學和哈佛大學從事博士後研究。研究興趣包括信号處理、無線通信、信息論、機器學習等，近年來主要關注無線通信中的高維信号估計問題。曾入選中科院百人計劃，主持自然基金青年項目并參與中科院先導科技專項等科研項目，目前擔任中國運籌學會青年工作委員會副秘書長。

時  間：2023年11月26日（周日）上午10：00 開始

地  點：騰訊會議：994-379-410

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太阳集团1088vip/網絡空間安全學院

2023年11月23日