Singular Value Decomposition (SVD) google
In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix, with many useful applications in signal processing and statistics. …

R Consortium google
The R Consortium, Inc. is a group of businesses organized under an open source governance and foundation model to provide support to the R community, the R Foundation and groups and individuals, using, maintaining and distributing R software.
The R language is an open source environment for statistical computing and graphics, and runs on a wide variety of computing platforms. The R language has enjoyed significant growth, and now supports over 2 million users. A broad range of industries have adopted the R language, including biotech, finance, research and high technology industries. The R language is often integrated into third party analysis, visualization and reporting applications.
The central mission of the R Consortium is to work with and provide support to the R Foundation and to the key organizations developing, maintaining, distributing and using R software through the identification, development and implementation of infrastructure projects.
From a governance perspective, the business of the consortium is managed by a Board of Directors. The technical aspects of the project, including the development and implementation of infrastructure projects, is overseen by an Infrastructure Steering Committee. While the initial members of the Infrastructure Steering Committee consist of representatives of the founding members of the R Consortium, Inc., project leads of key infrastructure projects will become voting members of the Infrastructure Steering Committee.
Potential infrastructure projects include:
• strengthening the R Forge infrastructure;
• assisting the Stanford University group running user!R 2016;
• developing documentation; and
• encouraging increased communication and collaboration among users and developers of the R language. …

Recurrent Ladder Network google
Ladder networks are a notable new concept in the field of semi-supervised learning by showing state-of-the-art results in image recognition tasks while being compatible with many existing neural architectures. We present the recurrent ladder network, a novel modification of the ladder network, for semi-supervised learning of recurrent neural networks which we evaluate with a phoneme recognition task on the TIMIT corpus. Our results show that the model is able to consistently outperform the baseline and achieve fully-supervised baseline performance with only 75% of all labels which demonstrates that the model is capable of using unsupervised data as an effective regulariser. …