To address them, we introduce the Recursive Neural Tensor Network. When trained on the new treebank, this model outperforms all previous methods on several metrics. It pushes the state of the art in single sentence positive/negative classification from 80% up to 85.4%. The accuracy of ...
Tensors, a high-level generalization of vector and matrix, can be used for image classification, reducing data storage costs while maintaining spatial correlation between pixels. Tensor networks, formed by the contraction of several tensors, can be decomposed into related lower-order core tensors to...
(“Deep Learning” AND “detection” OR “classification” OR “segmentation” OR “Localization”), (“Deep Learning” AND “CPU” OR “GPU” OR “FPGA”), (“Deep Learning” AND “Transfer Learning”), (“Deep Learning” AND “Imbalanced Data”), ...
In the past few decades, it is believed that only the protein-coding genes contain genetic information [1]. As the development continues to deepen, researchers found that the number of noncoding RNAs (ncRNAs) in the whole transcriptome is over 98% [2], which makes it confident to believe ...
This repository contains resources for Natural Language Processing (NLP) with a focus on the task of Text Classification. The content is mainly from paper 《A Survey on Text Classification: From Shallow to Deep Learning》(该repository主要总结自然语言处理(NLP)中文本分类任务的资料。内容主要来自文本分...
we introduce the Recursive Neural Tensor Network. When trained on the new treebank, this model outperforms all previous methods on several metrics. It pushes the state of the art in single sentence positive/negative classification from 80% up to 85.4%. The accuracy of predicting fine-grained se...