Journal
WSDM '21: PROCEEDINGS OF THE 14TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING
Volume -, Issue -, Pages 1150-1153Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3437963.3441666
Keywords
Neural networks; Structured signals; Graph learning; Adversarial learning; Regularization; TensorFlow
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Neural Structured Learning (NSL) is a new learning paradigm in TensorFlow that trains neural networks by leveraging structured signals. NSL can represent structure explicitly or implicitly, and is widely used in various products and services at Google.
We present Neural Structured Learning (NSL) in TensorFlow [1], a new learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs. Structure can be explicit as represented by a graph, or implicit, either induced by adversarial perturbation or inferred using techniques like embedding learning. NSL is open-sourced as part of the TensorFlow [2] ecosystem and is widely used in Google across many products and services. In this tutorial, we provide an overview of the NSL framework including various libraries, tools, and APIs as well as demonstrate the practical use of NSL in different applications. The NSL website is hosted at www.tensorflow.org/neural_structured_learning, which includes details about the theoretical foundations of the technology, extensive API documentation, and hands-on tutorials.
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