CFP | KDD 2018深度学习日征稿启事

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CFP | KDD 2018深度学习日征稿启事

2018 年 5 月 27 日 AI科技评论

学术青年有两大痛点：一个是寻找继续进修的offer机会；另一个是寻找学术会议workshop的投稿渠道。

而与此同时，企业研究院及高校研究机构也同样面临宣传自身招募/征稿信息的不便。

为了消除彼此的信息不对称，AI 科技评论从今年开始，开启招募每周一更的专栏，为学术青年提供两大领域的启事：

1.高校实验室博士生招生和博士后招募

2.会刊期刊相关或workshop的论文投稿

*投递简历或投稿记得在邮件主题加上【AI 科技评论】

*有相关需求的企业研究院或高校研究机构可以把详细的 JD发送至邮箱【zhongsimin@leiphone.com】

示例可参考：招募 | 2018年南京大学计算机科学与技术系暨人工智能学院本科生开放日报名通知

*如果有开发类应聘岗位需求可点击链接查看 AI 科技评论友军的推送「AI 职位内推时间！这些 AI 职位你不能错过」

KDD 2018深度学习日

征稿启事

AI科技评论按：德州农工大学胡侠教授、微软研究院Yuxiao Dong和斯坦福Jure Leskovec在KDD 2018推出深度学习日(Deep Learning Day)，给大家提供一个与业界和学界大咖深度交流的平台。

CFP详情如下：

KDD 2018 Deep Learning Day Call for Papers

KDD Deep Learning Day aims to provide an opportunity for participants from academia, industry, government and other related parties to present and discuss novel ideas on current and emerging topics relevant to deep learning. The KDD Deep Learning Day provides a single big plenary schedule with exciting invited speakers and leaders from both academia and industry, paper spotlight presentations, and a poster session. We wish to exchange ideas on recent approaches to the challenges related to deep structures, identify emerging fields of applications for such techniques, and provide opportunities for relevant interdisciplinary research or projects.

—- Submission Information—-

Submission Website: https://easychair.org/conferences/?conf=dlday18

The submitted manuscripts must be formatted according to the Standard ACM Conference Proceedings Template. The maximum length of papers is 10 pages in this format—shorter papers are also welcome. The paper submission should be in PDF. The accepted papers will be published on the workshop’s website, and will not be considered archival. This is intended to help preserve the authors’ ability to submit a revised version of their paper to a conference or journal. All submissions should clearly present the author information including the names of the authors, the affiliations and the emails.

Authors of all accepted papers must prepare a final version for publication and a poster for presentation. At least one author of each accepted paper is required to present their work in the poster session at the workshop.

—- Topics of Interest—-

Topic areas for the workshop include (but are not limited to) the following:

Unsupervised, semi-supervised, and supervised representation learning on various kinds of data (images, text, graphs, time series, etc.)
Interpretable deep learning
Hierarchical models
Reinforce learning
Optimization for deep learning
Multimodal deep learning
Theory of deep learning
Applications in vision, audio, speech, natural language processing, and human computer interaction
Applications in healthcare analytics and neuroscience
Applications in social computing, fraud detection, or any other field