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Saturday, September 21, 2019

Structured Signals for Model Training

Technical but interesting point about how to add structured knowledge into otherwise non transparent networks.  Examining further.

Posted by Da-Cheng Juan (Senior Software Engineer) and Sujith Ravi (Senior Staff Research Scientist)

We are excited to introduce  Neural Structured Learning in TensorFlow, an easy-to-use framework that both novice and advanced developers can use for training neural networks with structured signals. Neural Structured Learning (NSL) can be applied to construct accurate and robust models for vision, language understanding, and prediction in general.

Neutral structured learning framework

Many machine learning tasks benefit from using structured data which contains rich relational information among the samples. For example, modeling citation networks, Knowledge Graph inference and reasoning on linguistic structure of sentences, and learning molecular fingerprints all require a model to learn from structured inputs, as opposed to just individual samples. These structures can be explicitly given (e.g., as a graph), or implicitly inferred (e.g., as an adversarial example). Leveraging structured signals during training allows developers to achieve higher model accuracy, particularly when the amount of labeled data is relatively small. Training with structured signals also leads to more robust models. These techniques have been widely used in Google for improving model performance, such as learning image semantic embedding.

Neural Structured Learning (NSL) is an open source framework for training deep neural networks with structured signals. It implements Neural Graph Learning, which enables developers to train neural networks using graphs. The graphs can come from multiple sources such as Knowledge graphs, medical records, genomic data or multimodal relations (e.g., image-text pairs). NSL also generalizes to Adversarial Learning where the structure between input examples is dynamically constructed using adversarial perturbation.  ... " 

See also:  https://www.datanami.com/2019/09/04/google-adds-structured-signals-to-model-training/

See also:  https://venturebeat.com/2019/09/03/google-launches-tensorflow-machine-learning-framework-for-graphical-data/ 

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