"A general framework for unsupervised processing of structured data".
![cacheman network share recursive events cacheman network share recursive events](https://www.tutorialspoint.com/unix/images/unix_architecture.jpg)
Typically, stochastic gradient descent (SGD) is used to train the network. Recursive neural tensor networks use one, tensor-based composition function for all nodes in the tree. Unsupervised RNN Ī framework for unsupervised RNN has been introduced in 2004. RecCC is a constructive neural network approach to deal with tree domains with pioneering applications to chemistry and extension to directed acyclic graphs. This architecture, with a few improvements, has been used for successfully parsing natural scenes, syntactic parsing of natural language sentences, and recursive autoencoding and generative modeling of 3D shape structures in the form of cuboid abstractions. If c 1 and c 2 are n-dimensional vector representation of nodes, their parent will also be an n-dimensional vector, calculated as
![cacheman network share recursive events cacheman network share recursive events](https://www.capsl.udel.edu/codelets_parallella/img/cachemanFlowDiagram.png)
![cacheman network share recursive events cacheman network share recursive events](https://s3.manualzz.com/store/data/029338713_1-b57ea18671e58a9f0acaf803b3c94e8f.png)
In the most simple architecture, nodes are combined into parents using a weight matrix that is shared across the whole network, and a non-linearity such as tanh. A simple recursive neural network architecture