TEA Functions (TEA) google
• Transformations are functions that take existing input data and apply a function to it such that it changes form. A simple example could be combining first name, middle name, and last name fields in source data and creating a full name field that is the combination of the three sub fields.
• Enrichments are functions that take existing input data, combined with additional data sources, and create new information that could not be gleaned from either source independently. For example, one could take two different lists of individuals and use pattern matching to create relationships that are not apparent from either list itself.
• Augmentations are functions that add data of use in combination with the input data. The result is a more complete set of information that combines data from multiple sources. For example, a set of business entities gleaned from a conference attendee list, combined with Dun and Bradstreet profiles for those entities, creates a more complete set of information for each business entity. …


SEARNN google
We propose SEARNN, a novel training algorithm for recurrent neural networks (RNNs) inspired by the ‘learning to search’ (L2S) approach to structured prediction. RNNs have been widely successful in structured prediction applications such as machine translation or parsing, and are commonly trained using maximum likelihood estimation (MLE). Unfortunately, this training loss is not always an appropriate surrogate for the test error: by only maximizing the ground truth probability, it fails to exploit the wealth of information offered by structured losses. Further, it introduces discrepancies between training and predicting (such as exposure bias) that may hurt test performance. Instead, SEARNN leverages test-alike search space exploration to introduce global-local losses that are closer to the test error. We demonstrate improved performance over MLE on three different tasks: OCR, spelling correction and text chunking. Finally, we propose a subsampling strategy to enable SEARNN to scale to large vocabulary sizes. …

Shark google
SHARK is a fast, modular, feature-rich open-source C++ machine learning library. It provides methods for linear and nonlinear optimization, kernel-based learning algorithms, neural networks, and various other machine learning techniques (see the feature list below). It serves as a powerful toolbox for real world applications as well as research. Shark depends on Boost and CMake. It is compatible with Windows, Solaris, MacOS X, and Linux. Shark is licensed under the permissive GNU Lesser General Public License. …

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