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32nd International Conference on Machine Learning (ICML 2015)

32nd International Conference on Machine Learning (ICML 2015)
第32届国际机器学习会议(ICML 2015)
批准号:
1523346
负责人:
Kilian Weinberger
金额:
$3.5万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2015
资助国家:
美国
项目状态:
已结题
起止时间:
2015-06-01 至 2017-05-31

项目摘要

项目成果

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中文摘要
翻译
该奖项为来自美国机构的学生作者前往ICML 2015提供部分支持。该奖项将支持大约40名学生(他们已经撰写了被接受的投稿)前往会议并展示他们的研究成果。ICML是世界上最重要的机器学习会议之一,也是一个竞争激烈的场所。该奖项资助的学生奖学金为学生在顶级会议上向国际观众展示他们的作品创造了机会-这对研究生来说是一个重大的职业进步。因此,该奖项有助于在日益重要的机器学习领域对下一代研究人员和教育工作者进行教育和培训。特别注意扩大学生的参与,这些学生来自传统上在计算机科学中代表性较低的群体,特别是在机器学习中。选择将获得资助的学生是根据遴选委员会对他们的作者和经济需求进行审查的基础上进行的。学生奖学金对于鼓励学生参与和塑造该领域的未来非常重要。将特别注意扩大来自传统上在计算机科学特别是机器学习研究中代表性不足的群体的学生的参与,以及来自机器学习专门知识有限的机构的学生的参与,他们将受益于与来自世界各地的研究人员互动的机会。
英文摘要
This award provides partial support for student authors from US Institutions to travel to ICML 2015. The award will support roughly 40 students (who have authored accepted submissions) to travel to the conference and present their research. ICML is one of the premier Machine Learning conferences in the world, and a highly competitive venue. The student scholarships funded under this award creates opportunities for students to present their work to an international audience at a top conference --- a significant professional enhancement for graduate research students. The award thus contributes to the education and training of the next generation of researchers and educatiors in the increasingly important area of Machine Learning. Special attention is paid to broadening participation of students from groups traditionally underrepresented in Computer Science in general, and in Machine Learning in particular. The selection of students to be funded is based on a selection committee review of their authorship and financial needs. The student scholarships are very important for encouraging student participation and shaping the future of the field. Special attention will be paid to broadening the participation of students from groups that are traditionally underrepresented in Computer Science in general and in Machine Learning research in particular, and students from institutions with limited Machine Learning expertise who would benefit from the opportunity to interact with researchers from around the world.
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