CIKM 2014 Accepted Papers http://cikm2014.fudan.edu.cn/index.php/Index/info/id/11 Deep Learning KDD 2014 Tutorial http://www.cs.toronto.edu/~rsalakhu/kdd.html ��Russ Salakhutdinov��KDD 2014�и����Ĺ���Deep Learning ��tutorial������RBMs, DBMs, DBNs, ��multimodal learning��������Ӻʹ������http://deeplearning.cs.toronto.edu/�� Tutorial: Statistical Methods for Mining Big Text Data http://www.itee.uq.edu.au/dke/filething/get/855/text-mining-ChengXiangZhai.pdf �Գ�����ʦ(UIUC)�����ڰĴ��������ݿⲩʿ��ѵ��Ľ̳�:"Statistical Methods for Mining Big Text Data" �������ֻ���ͳ������ģ��(Statistics Language Model)�Ļ�������ģ��(Topic Model): LDA��PLSA��ԭ��Ӧ�á�����г�����δ���о����� ���о�Ժ���ڷ������ѧϰ���ۻ������Դ http://research.microsoft.com/en-us/events/fs2013/agenda_collapsed.aspx ����Li Deng, John Platt ������Yoshua Bengio������������ѧ����Honglak Lee����Ъ����, Andrew Ng ��˹̹����, Ruslan Salakhutdinov�����ࣩ���˵ı���PPT����Ƶ�� ˹̹�����ģ�������ݼ���ȫ https://snap.stanford.edu/data/ ��˹̹������Jure Leskovec������������ء�������ʮ���ֲ�ͬ���͵��������ݼ����罻;��������;�����ʼ�;����;Web�ȵȣ�������Friendster���ݼ���6ǧ5����ڵ㣬18�����ߡ� ��ŦԼʱ������ע���ݼ� https://code.google.com/p/nyt-salience/ ѵ��������100,834�ļ���19,261,118��עʵ�塣���Լ��ϰ���9,706�ļ���187,080��עʵ�塣 ����ר����Graph-Based Semi-Supervised Learning http://www.morganclaypool.com/doi/abs/10.2200/S00590ED1V01Y201408AIM029 While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer vision, natural language processing, and other areas of Artificial Intelligence. Recognizing this promising and emerging area of research, this synthesis lecture focuses on graph-based SSL algorithms (e.g., label propagation methods). Our hope is that after reading this book, the reader will walk away with the following: (1) an in-depth knowledge of the current state-of-the-art in graph-based SSL algorithms, and the ability to implement them; (2) the ability to decide on the suitability of graph-based SSL methods for a problem; and (3) familiarity with different applications where graph-based SSL methods have been successfully applied.
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