Sentence examples for recursive neural networks from inspiring English sources

Propensity and other local tactical information are fed into recursive neural networks, derived from a probabilistic Bayesian network architecture.

The sentiment prediction methods using recursive neural networks and deep convolution neural networks are bit complex in capturing compositionality of words.

The recursive neural networks in turn integrate local information across the board in all four cardinal directions and produce local outputs that represent local territory ownership probabilities.

Machine learning methods that can handle variable-size structured data such as sequences and graphs include Bayesian networks (BNs) and Recursive Neural Networks (RNNs).

In the adopted approach [18], recursive neural networks models are used to build representations that capture the structure of the sentences, obtaining in this way their sentiment based on the meaning of each word.

DNNs have also been applied with promising results to the prediction of Drug-Induced Liver Injury [35], although a different descriptor was used than the conventional fingerprints, i.e. directed acyclic graph recursive neural networks [36].

Their proposed model is a matrix-vector recursive neural network model.

In this paper, we introduce a new recursive neural network model able to process directed acyclic graphs with labelled edges.

Luong et al. [32] use a recursive neural network (RNN) to model the morphological structures of words and learn morphologically-aware embeddings.

We demonstrate that a recursive neural network approach can identify genetic regulation circuits from expression data for ribosomal and genome stability genes.

A recursive neural network QSPR model that can take directly molecular structures as input was applied to the prediction of the glass transition temperature of 277 poly meth)acrylates.