美獅貴賓官方網(wǎng)站學(xué)術(shù)報告預(yù)告
報告一
時間：2019年7月2日 星期二 9:00-9:40
地點(diǎn)：第一教學(xué)樓1211室
題目：On the Restricted Boltzmann Machines for Deep Learning
報告人簡介： 楊力華，中山大學(xué)數(shù)學(xué)學(xué)院教授、博士生導(dǎo)師。先后在湖南師范大學(xué), 北京師范大學(xué)和中山大學(xué)獲得學(xué)士, 碩士和博士學(xué)位。1996年至1998年在中科院數(shù)學(xué)研究所從事博士后工作。歷任全國計(jì)算數(shù)學(xué)學(xué)會理事、廣東省計(jì)算數(shù)學(xué)學(xué)會理事長、IEEE高級會員。研究領(lǐng)域?yàn)?/span>: 函數(shù)逼近與小波分析, 信號處理與機(jī)器學(xué)習(xí)， 迄今為止發(fā)表論文100余篇，合作出版專著一本，譯著三本，教材一本。
內(nèi)容提要：
Based on the structure of Deep Belief Networks, we discuss the mathematical problems in deep learning, including the expression power and learning algorithm of Boltzmann Machines。
報告二
時間：2019年7月2日 星期二 9:45-10:25
地點(diǎn)：第一教學(xué)樓1211室
題目：Tensor Decomposition for Multilayer Networks Clustering
報告人簡介：陳川，現(xiàn)任中山大學(xué)數(shù)據(jù)科學(xué)與計(jì)算機(jī)學(xué)院副研究員。2016年于香港浸會大學(xué)數(shù)學(xué)系獲得博士學(xué)位，2016-2017 年于比利時魯汶大學(xué)電子工程系任博士后研究員。主要研究方向?yàn)椋簲?shù)值優(yōu)化，機(jī)器學(xué)習(xí)及大數(shù)據(jù)分析。近年來發(fā)表SCI索引國際期刊論文及AAAI, ICML, IJCAI等國際會議論文近30篇。擔(dān)任 IEEE TIP/TSP等多份國際期刊審稿人，擔(dān)任IJCAI ECAI CBPM等多個國際學(xué)術(shù)會議的程序委員會成員及論壇主義，廣東省計(jì)算機(jī)協(xié)會區(qū)塊鏈專委會委員?，F(xiàn)主持國家青年科學(xué)基金，廣東省創(chuàng)新研究基金，CCF協(xié)會項(xiàng)目基金, 與包括美圖，微信，華為，中國電信，平安保險在內(nèi)多家企業(yè)開展橫向項(xiàng)目研究。
內(nèi)容提要：
Clustering on multilayer networks has been shown to be a promising approach to enhance accuracy. Various multilayer networks clustering algorithms assume all networks derive from a latent clustering structure and jointly learn the compatible and complementary information from different networks to excavate one shared underlying structure. However, such assumptions are in conflict with many emerging real-life applications due to the existence of noisy/irrelevant
networks. A key challenge here is to integrate different data representations automatically to achieve better predictive performance. To address this issue, we propose Centroid-based Multilayer Network Clustering (CMNC), a novel approach which can divide irrelevant relationships into different network groups and uncover the cluster structure in each group simultaneously. The multilayer networks are represented within a unified tensor framework for simultaneously capturing multiple types of relationships between a set of entities. By imposing the rank-(Lr; Lr; 1) block term decomposition with nonnegativity constraints, we are able to have well interpretations on the multiple clustering results based on graph cut theory. Numerically, we transform this tensor decomposition problem to an unconstrained optimization, thus can solve it efficiently under the nonlinear least squares (NLS) framework. Extensive experimental results on synthetic and real-world datasets show the effectiveness and robustness of our method against noise and irrelevant data.
報告三
時間：2019年7月2日 星期二 10:30-11:10
地點(diǎn)：第一教學(xué)樓1211室
題目：Gersgorin-type E-eigenvalue localization sets and the positive definiteness of even order tensors
報告人簡介：趙建興（Jianxing Zhao），男，1981年1月生，貴州民族大學(xué)數(shù)據(jù)科學(xué)與信息工程學(xué)院,教授，應(yīng)用數(shù)學(xué)博士，主要從事數(shù)值代數(shù)、特殊矩陣（張量）特征值估計(jì)等方向的研究，主持國家自然科學(xué)基金青年基金項(xiàng)目、貴州省科技廳科學(xué)技術(shù)基金、貴州省教育廳科技拔尖人才支持項(xiàng)目各一項(xiàng)，入選貴州省普通高等學(xué)?？萍及渭馊瞬牛?/span>2016年），中組部“西部之光”訪問學(xué)者（2018年），以第一作者或通訊作者發(fā)表SCI期刊論文20余篇。
內(nèi)容提要：
In this talk, some existing Z-eigenvalues of tensors are first recalled. Secondly, a Gersgorin-type E-eigenvalue localization set with applications to judge the positive (semi-)defniteness of tensors is introduced. As an application, an upper bound for the Z-spectral radius of weakly symmetric nonnegative tensors is obtained. Finally, numerical examples are given to verify the theoretical results.
歡迎老師、同學(xué)們參加！
美獅貴賓官方網(wǎng)站
2019年6月26日
