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AKBC 2014

Webpage for the AKBC 2014 Workshop at CIKM

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PAGE TITLE
AKBC 2014 | akbc.ws Reviews
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Webpage for the AKBC 2014 Workshop at CIKM
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1 speakers
2 schedule
3 papers
4 organization
5 camera ready instructions
6 previous workshops
7 tweet
8 supported by
9 topics of interest
10 invited speakers
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speakers,schedule,papers,organization,camera ready instructions,previous workshops,tweet,supported by,topics of interest,invited speakers,tom mitchell,andrew mccallum,chris re,stanford university,oren etzioni,ramanathan guha,google,william cohen,darpa
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AKBC 2014 | akbc.ws Reviews

https://akbc.ws

Webpage for the AKBC 2014 Workshop at CIKM

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AKBC 2013

http://www.akbc.ws/2013

Automated Knowledge Base Construction (AKBC) 2013: The 3rd Workshop on Knowledge Extraction at CIKM 2013. In San Francisco, October 27-28, 2013. Supported in part by. If you were an author or reviewer, please complete our OpenReview survey. The advances in information extraction, machine learning, and natural language processing have led to the creation of large knowledge bases. Microsoft Bing, and Google) nowadays experiment with semantic tools. The Semantic Web, too, benefits from the new approaches.

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Proposal for A New Publishing Model in Computer Science

http://yann.lecun.com/ex/pamphlets/publishing-models.html

Websites that I maintain. A New Publishing Model in Computer Science. Many computer Science researchers are complaining that our emphasis on highly selective conference publications, and our double-blind reviewing system stifles innovation and slow the rate of progress of Science and technology. This pamphlet proposes a new publishing model based on an open repository and open (but anonymous) reviews which creates a "market" between papers and reviewing entities. 2013-10-16: The ICLR 2014 call for paper.

rockt.github.io rockt.github.io

Tim Rocktäschel

https://rockt.github.io/index.html

Machine Learning, Deep Learning, Natural Language Processing, Artificial Intelligence. I am a final year PhD candidate in Sebastian Riedel. S Machine Reading group. At University College London. My studies are generously supported by a Microsoft Research PhD Scholarship. I worked on regularizing representations by first-order logic rules. And recently became interested in neural-symbolic approaches to theorem proving. And neural program induction. Proteins and diseases in biomedical publications. As well...

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Mathias Niepert

http://www.matlog.net/publications.html

Mathias Niepert. Discriminative Gaifman Models. In Proceedings of the 30th Annual Conference on Neural Information Processing Systems ( NIPS. Barcelona, Spain, 2016. Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov. Learning Convolutional Neural Networks for Graphs. In Proceedings of the 33rd International Conference on Machine Learning ( ICML. New York City, 2016. [ slides. Mathias Niepert and Pedro Domingos. Learning and Inference in Tractable Probabilistic Knowledge Bases. Austin, Texas, 2015.

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AKBC 2014

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