Metadata-Version: 2.1
Name: neural-structured-learning
Version: 1.0.0
Summary: TensorFlow Neural Structured Learning
Home-page: https://github.com/tensorflow/neural-structured-learning
Author: Google LLC
License: UNKNOWN
Description: # Neural Structured Learning in TensorFlow
        
        ![](g3doc/images/nsl_overview.png)
        
        **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 [1,2,5] or implicit as
        induced by adversarial perturbation [3,4].
        
        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 [1] as well as to Adversarial
        Learning [3]. 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 pertubations (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. Please check out our tutorials for a practical introduction
        to NSL.
        
        ## Getting started
        
        Install the prebuilt pip package using
        
        ```bash
        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 repos using NSL on
            [tensorflow.org/neural_structured_learning](http://www.tensorflow.org/neural_structured_learning).
        
        Please be sure to review the
        [contribution guidelines](CONTRIBUTING.md#code-style-guidelines-and-best-practices)
        for guidelines on the coding style, best practices, etc.
        
        ## Issues and Questions
        
        For issues, please use [GitHub issues](https://github.com/tensorflow/neural-structured-learning/issues)
        for tracking requests and bugs. For questions, please direct them to [Stack Overflow](https://stackoverflow.com) with the
        ["nsl"](https://stackoverflow.com/questions/tagged/nsl)
        tag.
        
        ## References
        
        [[1] T. Bui, S. Ravi and V. Ramavajjala. "Neural Graph Learning: Training Neural Networks Using Graphs." WSDM 2018](https://ai.google/research/pubs/pub46568.pdf)
        
        
        [[2] T. Kipf and M. Welling. "Semi-supervised classification with graph convolutional networks." ICLR 2017](https://arxiv.org/pdf/1609.02907.pdf)
        
        
        [[3] I. Goodfellow, J. Shlens and C. Szegedy. "Explaining and harnessing adversarial examples." ICLR 2015](https://arxiv.org/pdf/1412.6572.pdf)
        
        
        [[4] T. Miyato, S. Maeda, M. Koyama and S. Ishii. "Virtual Adversarial Training: a Regularization Method for Supervised and Semi-supervised Learning." ICLR 2016](https://arxiv.org/pdf/1704.03976.pdf)
        
        [[5] D. Juan, C. Lu, Z. Li, F. Peng, A. Timofeev, Y. Chen, Y. Gao, T. Duerig, A. Tomkins and S. Ravi "Graph-RISE: Graph-Regularized Image Semantic Embedding." arXiv 2019](https://arxiv.org/abs/1902.10814)
        
        
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
