Sparse and Hierarchical Bipartite Clustering


Sparse and Hierarchical Bipartite Clustering – The proposed architecture is able to combine the features of previous approaches using the simple but effective concept of multidimensional multi-stage clustering. This approach is based on the idea that in multi-stage clustering a set of features are assigned to an input vector and a set of features are associated with each node in the input vector, leading to a hierarchical clustering. The hierarchical clustering is achieved by combining these features into an output in a unified form. This method is very similar to the clustering of linear multidimensional vectors by the Kripke-Meyer (K-M) clustering method, as shown in the example code.

We present an innovative approach for embedding Chinese and English texts in a unified framework, which is able to process and embed sentences in an end-to-end fashion.

We present a unified framework for automatic feature learning systems. In particular, it is proposed to utilize features from text-to-text to predict a human-level representation of a word in the English language. The proposed approach is based on a recurrent neural network (RNN) that is trained with a novel recurrent language model that represents word vectors over a sequence of text-structured data. Then, the RNN-RNN encodes the data using a convolutional neural network to perform word prediction. Our experiments on a dataset of English sentences show that the proposed method is effective and comparable to RNN-RNN’s performance for predicting speech words in Chinese text. We also describe a new approach for automatic feature learning in sentence embeddings.

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Sparse and Hierarchical Bipartite Clustering

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  • Learning to Predict With Pairwise Pairing

    Neural Embeddings for Sentiment ClassificationWe present an innovative approach for embedding Chinese and English texts in a unified framework, which is able to process and embed sentences in an end-to-end fashion.

    We present a unified framework for automatic feature learning systems. In particular, it is proposed to utilize features from text-to-text to predict a human-level representation of a word in the English language. The proposed approach is based on a recurrent neural network (RNN) that is trained with a novel recurrent language model that represents word vectors over a sequence of text-structured data. Then, the RNN-RNN encodes the data using a convolutional neural network to perform word prediction. Our experiments on a dataset of English sentences show that the proposed method is effective and comparable to RNN-RNN’s performance for predicting speech words in Chinese text. We also describe a new approach for automatic feature learning in sentence embeddings.


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