neural-structured-learning

Neural Structured Learning (NSL) is 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 as induced by adversarial perturbation.

Structured signals are commonly used to represent relations or similarity among samples that may be labeled or unlabeled. Leveraging these signals during neural network training harnesses both labeled and unlabeled data, which can improve model accuracy, particularly when the amount of labeled data is relatively small. Additionally, models trained with samples that are generated by adversarial perturbation have been shown to be robust against malicious attacks, which are designed to mislead a model's prediction or classification.

NSL generalizes to Neural Graph Learning as well as to Adversarial Learning. The NSL framework in TensorFlow provides the following easy-to-use APIs and tools for developers to train models with structured signals:

• Keras APIs to enable training with graphs (explicit structure) and adversarial perturbations (implicit structure).

• TF ops and functions to enable training with structure when using lower-level TensorFlow APIs.

• Tools to build graphs and construct graph inputs for training.

The NSL framework is designed to be flexible and can be used to train any kind of neural network. For example, feed-forward, convolution, and recurrent neural networks can all be trained using the NSL framework. In addition to supervised and semi-supervised learning (a low amount of supervision), NSL can in theory be generalized to unsupervised learning. Incorporating structured signals is done only during training, so the performance of the serving/inference workflow remains unchanged.