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The top 100 papers

Article from Nature The discovery of high-temperature superconductors, the determination of DNA’s double-helix structure, the first observations that the expansion of the Universe is accelerating — all of these breakthroughs won Nobel prizes and i… → Read More: The top 100 papers

video text retouch

Several of us just returned from ACM UIST 2014 where we presented some new work as part of the cemint project.  One vision of the cemint project is to build applications for multimedia content manipulation and reuse that are as powerful as their analogues for text content.  We are working towards this goal by exploiting two key tools.  First, we […] → Read More: video text retouch

Machine-Learning Maestro Michael Jordan on the Delusions of Big Data and Other Huge Engineering Efforts

Big-data boondoggles and brain-inspired chips are just two of the things we’re really getting wrong The overeager adoption of big data is likely to result in catastrophes of analysis comparable to a national epidemic of collapsing bridges. Hardware d… → Read More: Machine-Learning Maestro Michael Jordan on the Delusions of Big Data and Other Huge Engineering Efforts

Why training and review (partly) break control sets

A technology-assisted review (TAR) process frequently begins with the creation of a control set—a set of documents randomly sampled from the collection, and coded by a human expert for relevance. The control set can then be used to estimate the richness (proportion relevant) of the collection, and also to gauge the effectiveness of a predictive […] → Read More: Why training and review (partly) break control sets

Why training and review (partly) break control sets

A technology-assisted review (TAR) process frequently begins with the creation of a control set—a set of documents randomly sampled from the collection, and coded by a human expert for relevance. The control set can then be used to estimate the richness (proportion relevant) of the collection, and also to gauge the effectiveness of a predictive […] → Read More: Why training and review (partly) break control sets

Why training and review (partly) break control sets

A technology-assisted review (TAR) process frequently begins with the creation of a control set—a set of documents randomly sampled from the collection, and coded by a human expert for relevance. The control set can then be used to estimate the richness (proportion relevant) of the collection, and also to gauge the effectiveness of a predictive […] → Read More: Why training and review (partly) break control sets

Why training and review (partly) break control sets

A technology-assisted review (TAR) process frequently begins with the creation of a control set—a set of documents randomly sampled from the collection, and coded by a human expert for relevance. The control set can then be used to estimate the richness (proportion relevant) of the collection, and also to gauge the effectiveness of a predictive […] → Read More: Why training and review (partly) break control sets

Why training and review (partly) break control sets

A technology-assisted review (TAR) process frequently begins with the creation of a control set—a set of documents randomly sampled from the collection, and coded by a human expert for relevance. The control set can then be used to estimate the richness (proportion relevant) of the collection, and also to gauge the effectiveness of a predictive […] → Read More: Why training and review (partly) break control sets

Why training and review (partly) break control sets

A technology-assisted review (TAR) process frequently begins with the creation of a control set—a set of documents randomly sampled from the collection, and coded by a human expert for relevance. The control set can then be used to estimate the richness (proportion relevant) of the collection, and also to gauge the effectiveness of a predictive […] → Read More: Why training and review (partly) break control sets

Why training and review (partly) break control sets

A technology-assisted review (TAR) process frequently begins with the creation of a control set—a set of documents randomly sampled from the collection, and coded by a human expert for relevance. The control set can then be used to estimate the richness (proportion relevant) of the collection, and also to gauge the effectiveness of a predictive […] → Read More: Why training and review (partly) break control sets

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