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　　報(bào)告題目：Connectivity inference by asynchronously updated kinetic Ising model (利用異步更新的動(dòng)態(tài)伊辛模型重建網(wǎng)絡(luò)結(jié)構(gòu))

　　報(bào)告人：曾紅麗

　　時(shí)間：2014年12月24日14：30

　　地點(diǎn)：三牌樓校區(qū)科研樓712

　　主辦單位：電子科學(xué)與工程學(xué)院、科技處

　　報(bào)告人簡(jiǎn)介：

　　曾紅麗，于芬蘭阿爾托大學(xué)（Aalto University）獲物理學(xué)博士學(xué)位，現(xiàn)為瑞典烏普薩拉大學(xué)埃格斯特朗實(shí)驗(yàn)室（Angstrom Laboratory, Uppsala University）博士后研究員。曾入選芬蘭計(jì)算科學(xué)博士培養(yǎng)計(jì)劃FICS成員，多次受邀到波爾研究所（Niels Bohr Institute，Denmark）、北歐理論物理學(xué)會(huì)（Nordic Institute for Theoretical Physics (Nordita)，Sweden）、挪威科技大學(xué)（Norwegian University of Science and Technology (NTNU) ，Norway）開(kāi)展合作研究。在Phys. Rev. Lett.、 Phys. Rev. E等國(guó)際著名物理學(xué)、交叉科學(xué)刊物上發(fā)表學(xué)術(shù)論文。目前研究方向主要集中于inference problem（反推問(wèn)題）、stochastic process（隨機(jī)過(guò)程）、statistical machine learning （統(tǒng)計(jì)機(jī)器學(xué)習(xí)）、Patter analysis（圖像處理）等領(lǐng)域。

　　報(bào)告簡(jiǎn)介：

　　The talk focuses on the inference of network connections from statistical physics point of view. The reconstruction methods of the asynchronously updated kinetic Ising model with an asymmetric Sherrington-Kirkpatrick (SK) model are studied theoretically. Both approximate and exact learning rules for the couplings from the generated dynamical data are developed. All the learning rules are studied numerically. Good convergence is observed in accordance with the theoretical expectations. Several of the derived algorithms are applied to real experimental neural and financial dataset. Meaningful results are produced in both cases.