Introducing Neural Structured Learning in TensorFlow

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. 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).