永利赌场娱乐城-永利赌场 天幕表演-永利高开户hbtyg

報(bào)告人：毛先領(lǐng)

簡(jiǎn)介：毛先領(lǐng)，北京理工大學(xué)副教授，博導(dǎo)。主要研究深度學(xué)習(xí)、機(jī)器學(xué)習(xí)與網(wǎng)絡(luò)數(shù)據(jù)挖掘，具體研究Information Extraction、 Question Answering and Dialogue和Learn to Hashing等方向。目前擔(dān)任計(jì)算機(jī)學(xué)會(huì)中文信息技術(shù)專(zhuān)委會(huì)委員，中文信息學(xué)會(huì)青工委委員以及語(yǔ)言與知識(shí)專(zhuān)委會(huì)委員；已在SIGIR、AAAI，IJCAI, TOIS, TKDE, CIKM, EMNLP, COLING等國(guó)際期刊會(huì)議上發(fā)表30余篇論文；分別獲NLPCC 2019和ICKG 2020最佳論文獎(jiǎng)；部分成果獲中國(guó)電子學(xué)會(huì)科技進(jìn)步一等獎(jiǎng)（2018）和浙江省科技進(jìn)步三等獎(jiǎng)（2018）；正在承擔(dān)或參與國(guó)家重點(diǎn)研發(fā)計(jì)劃子課題、國(guó)家自然科學(xué)基金重點(diǎn)項(xiàng)目和面上項(xiàng)目等多項(xiàng)。

題目:  Similarity-preserved Hashing: Diffusing from Images Retrieval to Other Scenarios

報(bào)告時(shí)間：2021年11月25日9:00—11:00

報(bào)告地點(diǎn)：信息大廈B1111

Abstract:In the past decade, we have witnessed an explosive growth of data on the Internet, and it brings both challenges and opportunities to traditional algorithms developed on small to median scale data sets. Particularly, nearest neighbor search (NN) has become a key ingredient in many large-scale machine learning and data management tasks. In fact, approximate nearest neighbors (ANN) are enough to achieve satisfactory performance in many applications, such as the image retrieval task in search engines. Due to the low storage cost and fast retrieval speed, similarity-preserved hashing is one of the popular solutions for ANN search. This talk will first review related methods for images, then introduce the ways how similarity-preserved hashing is enabling natural language processing. It will also highlight open problems that are being addressed by emerging research.