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KDD-DeepLearningDay 2018 : ACM SIGKDD 2018 Deep Learning Day Call for Papers


When Aug 19, 2018 - Aug 23, 2018
Where London, UK
Submission Deadline Jun 15, 2018
Notification Due Jun 25, 2018
Final Version Due Jul 10, 2018
Categories    deep learning   data mining   neural networks   artificial intelligence

Call For Papers

KDD 2018 Deep Learning Day

--- Overview ---
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:

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

--- Important Dates ---
Workshop paper submissions: July 1st, 2018
Paper acceptance notifications: July 15th, 2018
Camera-ready submission: August 1st, 2018

--- General Chairs ---
Anima Anandkumar, Caltech/Amazon
Jure Leskovec, Stanford/Pinterest
Joan Bruna, NYU

--- Organizing Committee ---
Pierre Richemond, Imperial College London
Douglas Mcilwraith, Imperial College London
Kevin Webster, Imperial College London

--- Program Chairs ---
Xia “Ben” Hu, Texas A&M University
Yuxiao Dong, Microsoft Research

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