#2019年清华大学计算机系列讲座#
本次的报告会详情如下：
报告人：唐建https://t.cn/RdOdu9B
主题：Graph Representation Learning and Reasoning
时间：2019年6月28日11:00-12:30
地点：清华大学FIT楼1-315
报告摘要：Graphs,a general type of data structures for capturing interconnected objects，are ubiquitous in a variety of disciplines and domains.This talk is divided into two parts.In the first part,I will introduce our work on learning node representations(LINE,WWW`15),extremely low-dimensional node representation learning for graph and high-dimensional data visualization (LargeVis,WWW`16),knowledge graph embedding（RotatE,ICLR`19）,and a general and high-performance graph embedding system(GraphVite,WWW`19).In the second part,I will introduce our recent work on combining statistical relational learning and graph neural networks for predictions and reasoning on graphs(GMNN,ICML`19)
简介：Dr. Jian Tang is an assistant professor at Mila (Quebec AI institute) and HEC Montreal since December 2017. He is named to the first cohort of Canada CIFAR Artificial Intelligence Chairs (CIFAR AI Research Chair). His research interests focus on deep graph representation learning with a variety of applications such as knowledge graphs, drug discovery and recommender systems. He was a research fellow in University of Michigan and Carnegie Mellon University. He received his Ph.D degree from Peking University and was a visiting student in University of Michigan for two years. He was a researcher in Microsoft Research Asia for two years. His work on graph representation learning (e.g. LINE, LargeVis, and RotatE) are widely recognized. He received the best paper award of ICML' 14 and was nominated for the best paper of WWW' 16.
本次的报告会详情如下：
报告人：唐建https://t.cn/RdOdu9B
主题：Graph Representation Learning and Reasoning
时间：2019年6月28日11:00-12:30
地点：清华大学FIT楼1-315
报告摘要：Graphs,a general type of data structures for capturing interconnected objects，are ubiquitous in a variety of disciplines and domains.This talk is divided into two parts.In the first part,I will introduce our work on learning node representations(LINE,WWW`15),extremely low-dimensional node representation learning for graph and high-dimensional data visualization (LargeVis,WWW`16),knowledge graph embedding（RotatE,ICLR`19）,and a general and high-performance graph embedding system(GraphVite,WWW`19).In the second part,I will introduce our recent work on combining statistical relational learning and graph neural networks for predictions and reasoning on graphs(GMNN,ICML`19)
简介：Dr. Jian Tang is an assistant professor at Mila (Quebec AI institute) and HEC Montreal since December 2017. He is named to the first cohort of Canada CIFAR Artificial Intelligence Chairs (CIFAR AI Research Chair). His research interests focus on deep graph representation learning with a variety of applications such as knowledge graphs, drug discovery and recommender systems. He was a research fellow in University of Michigan and Carnegie Mellon University. He received his Ph.D degree from Peking University and was a visiting student in University of Michigan for two years. He was a researcher in Microsoft Research Asia for two years. His work on graph representation learning (e.g. LINE, LargeVis, and RotatE) are widely recognized. He received the best paper award of ICML' 14 and was nominated for the best paper of WWW' 16.
️昨夜睡前三个孩子叨家常
Oscar：招文锋，我爸爸买了新家，以后我们就要搬到新家啦[赞啊][赞啊]
carson：[酷][酷]哈哈哈我爸爸也买新家，以后我也要搬到新家
Oscar：哈哈哈，那你是搬到佛山的啊，不是我爸爸的新家
carson：哈哈，火什么山，我才不要搬到火山，烧死我了[嘻嘻][嘻嘻][嘻嘻]木头哥哥你是不是傻了
Mila:哈哈哈哈哈我们才不要搬到火山好热的
Oscar:啊啊，妹妹，才不是火山是佛山啊，佛山啊，你们两个是不是傻啊，搬去火山，搬去屁股山吧
：哈哈哈哈哈
:不说啦，赶紧睡吧
Oscar：招文锋，我爸爸买了新家，以后我们就要搬到新家啦[赞啊][赞啊]
carson：[酷][酷]哈哈哈我爸爸也买新家，以后我也要搬到新家
Oscar：哈哈哈，那你是搬到佛山的啊，不是我爸爸的新家
carson：哈哈，火什么山，我才不要搬到火山，烧死我了[嘻嘻][嘻嘻][嘻嘻]木头哥哥你是不是傻了
Mila:哈哈哈哈哈我们才不要搬到火山好热的
Oscar:啊啊，妹妹，才不是火山是佛山啊，佛山啊，你们两个是不是傻啊，搬去火山，搬去屁股山吧
：哈哈哈哈哈
:不说啦，赶紧睡吧
#萌煮[超话]#
小北鼻养成记
➅. ➁➃⃔ ❾点半
讨论课「米粉的选择和冲泡」
宝贝的第一口辅食你是不是充满了期待？
你也许会有这些疑问
米粉要选择哪种？如何冲泡？
第一次需要给多少？每天给多少顿？
今晚九点半，Mila妈妈来带你解开疑问
我们不见不散
快加入我们吧~
小北鼻养成记
➅. ➁➃⃔ ❾点半
讨论课「米粉的选择和冲泡」
宝贝的第一口辅食你是不是充满了期待？
你也许会有这些疑问
米粉要选择哪种？如何冲泡？
第一次需要给多少？每天给多少顿？
今晚九点半，Mila妈妈来带你解开疑问
我们不见不散
快加入我们吧~
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