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A new learning paradigm to train neural networks by leveraging structured signals

A new learning paradigm to train neural networks by leveraging structured signals

Neural Structured Learning in TensorFlow

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:

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. Please check out our tutorials for a practical introduction
to NSL.

Getting started

Install the prebuilt pip package using

pip install neural-structured-learning

Contributing to NSL

Contributions are welcome and highly appreciated - there are several ways to
contribute to TF Neural Structured Learning:

  • Case studies. If you are interested in applying NSL, consider wrapping up
    your usage as a tutorial, a new dataset, or an example model that others
    could use for experiments and/or development.

  • Product excellence. If you are interested in improving NSL's product
    excellence and developer experience, the best way is to clone this repo,
    make changes directly on the implementation in your local repo, and then
    send us pull request to integrate your changes.

  • New algorithms. If you are interested in developing new algorithms for NSL,
    the best way is to study the implementations of NSL libraries, and to think
    of extensions to the existing implementation (or alternative approaches). If
    you have a proposal for a new algorithm, we recommend starting by staging
    your project in the research directory and including a colab notebook to
    showcase the new features.

    If you develop new algorithms in your own repository, we are happy to
    feature pointers to academic publications and/or repositories that use NSL,
    on tensorflow.org/neural_structured_learning.