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Whats new on arXiv

转载自:https://analytixon.com/2018/12/12/whats-new-on-arxiv-842/

Michael Laux


发表于 2018-12-11

Improved Knowledge Graph Embedding using Background Taxonomic Information

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Prior distributions for covariance matrices

转载自:https://andrewgelman.com/2018/12/10/prior-distributions-covariance-matrices/

Andrew


发表于 2018-12-10

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Whats new on arXiv

转载自:https://analytixon.com/2018/12/11/whats-new-on-arxiv-841/

Michael Laux


发表于 2018-12-10

A new system-wide diversity measure for recommendations with efficient algorithms

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Enter the

转载自:http://feedproxy.google.com/~r/RBloggers/~3/fgshfeplNnM/

Hugo Bowne-Anderson


发表于 2018-12-10

We’ll be back with Season 2 early in 2019 and to keep you thinking, curious and data focused in between seasons, we’re having a DataFramed challenge. The winner will get to join me on a segment here on DataFramed: the challenge is to listen to as many episodes as you can & to tweet excerpts that you find illuminating to @DataCamp, @hugobowne & the relevant guest using the hashtag #DataFramedChallenge.

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Great post Yash!

转载自:http://feedproxy.google.com/~r/RBloggers/~3/uw3K_K6eiok/

Matt.0


发表于 2018-12-10

Great post Yash! For those readers interested in getting data from the fitbit API using R I’ve documented the process here: https://towardsdatascience.com/the-gamification-of-fitbit-how-an-api-provided-the-next-level-of-training-eaf7b267af00

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Whats new on arXiv

转载自:https://analytixon.com/2018/12/10/whats-new-on-arxiv-838/

Michael Laux


发表于 2018-12-10

Making BREAD: Biomimetic strategies for Artificial Intelligence Now and in the Future

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Whats new on arXiv

转载自:https://analytixon.com/2018/12/10/whats-new-on-arxiv-840/

Michael Laux


发表于 2018-12-10

Efficient and Robust Machine Learning for Real-World Systems

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Document worth reading: “Can Machines Design An Artificial General Intelligence Approach”

转载自:https://analytixon.com/2018/12/10/document-worth-reading-can-machines-design-an-artificial-general-intelligence-approach/

Michael Laux


发表于 2018-12-10

Can machines design Can they come up with creative solutions to problems and build tools and artifacts across a wide range of domains Recent advances in the field of computational creativity and formal Artificial General Intelligence (AGI) provide frameworks for machines with the general ability to design. In this paper we propose to integrate a formal computational creativity framework into the G\’odel machine framework. We call this machine a design G\’odel machine. Such a machine could solve a variety of design problems by generating novel concepts. In addition, it could change the way these concepts are generated by modifying itself. The design G\’odel machine is able to improve its initial design program, once it has proven that a modification would increase its return on the utility function. Finally, we sketch out a specific version of the design G\’odel machine which specifically aims at the design of complex software and hardware systems. Future work could be the development of a more formal version of the Design G\’odel machine and a potential implementation. Can Machines Design An Artificial General Intelligence Approach

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未命名



发表于 2019-01-13
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Personal Data Analytics

转载自:https://rare-technologies.com/pii_analytics/

Radim Řehůřek


发表于 2018-12-10

The latest 2.0 release of PII Tools brings a brand new SAR dashboard, allowing targeted personal data search, filtering and analytics.

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