tensorflow/neural-structured-learning
A TensorFlow library for training neural networks with structured signals including graph-based and adversarial learning techniques.
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Neural Structured Learning (NSL) is a TensorFlow framework that improves neural network training by leveraging structured signals such as graphs and adversarial perturbations. It provides Keras APIs and low-level TF ops for incorporating explicit (graph) and implicit (adversarial) structure during training, along with tools for graph construction. The framework supports supervised, semi-supervised, and unsupervised learning scenarios and is designed to work with any neural network architecture including feed-forward, CNNs, and RNNs.
Frequently asked
- What is tensorflow/neural-structured-learning?
- A TensorFlow library for training neural networks with structured signals including graph-based and adversarial learning techniques.
- Is neural-structured-learning open source?
- Yes — tensorflow/neural-structured-learning is open source, released under the Apache-2.0 license.
- What language is neural-structured-learning written in?
- tensorflow/neural-structured-learning is primarily written in Python.
- How popular is neural-structured-learning?
- tensorflow/neural-structured-learning has 1k stars on GitHub.
- Where can I find neural-structured-learning?
- tensorflow/neural-structured-learning is on GitHub at https://github.com/tensorflow/neural-structured-learning.