Convolutional Neural Networks for Sentence Classification 简单来说就是作者将cnn应用到了文本分类任务,且取得了一个不错的效果。 在看该论文前,需要了解一下自然语言处理的背景知识,包括word2Vec等内容,可以参考该链接内容 深入浅出Word2Vec原理解析 - 知乎 (zhihu.com) cnn模型在此之前主要应用于计算机视觉领域,...
2. Neural networks for Classification problem We want h_\theta(x)\approx \begin{bmatrix} 1 \\ 0 \\ 0 \\ 0 \end{bmatrix} for Pedestrian. h_\theta(x)\approx \begin{bmatrix} 0 \\ 1 \\ 0 \\ 0 \end{bmatrix} for Cas.h_\theta(x)\approx \begin{bmatrix} 0 \\ 0 \\ 1 ...
Learning task-specific vectors through fine-tuning results in further improvements. 学习任务具体的vectors可以有改进。 We finally describe a simple modification to the architecture to allow for the use of both pre-trained and task-specific vectors by having multiple channels. 允许pre-trained和task-spec...
文章展示了少许超参数调整和静态词向量的简单CNN能在多个基准上达到完美的效果。学习任务特定的词向量通过微调获得了性能上的进一步提升。另外,提出了一个简单改进的架构,允许使用任务特定的词向量和静态的词向量。这里讨论的CNN模型在7项任务中有4项得到了state of art的结果,包括语义分析和问题分类。 模型 图一展示...
本文在事先进行单词向量的学习的基础上,利用卷积神经网络(CNN)进行句子分类,然后通过微调学习任务特定的向量,提高性能。 在从无监督神经语言模型中获得单词向量(Tomas Mikolov等人做过相关工作,即谷歌的word2vector完成,将原始的1/V模型变化为分布式低维表示)后利用一层卷积层的CNN进行学习。
论文:Multi-column Deep Neural Networks for Image Classification(Ciresan 2012) 可以参考部分中文译文《基于交通路标分类的多列深度神经网络(MCDNN)》 论文在intrudction部分比较详细的介绍了DNN的发展,从DNN的提出,到精华、简化,再到应用范围扩大到3D、人脸、手写字符,再到使用GPU训练。该论文也是基于GPU计算的,...
We adopt an architecture that mimics a visual geometry group (VGG) network that is used in image classification tasks32. In our research, the input three color channels of the RGB image size of the networks are\(32\times 32\)pixels. All the convolutional layers use small\(3\times 3\)rec...
Figure 1. Classification view, row percents style For categorical targets, this displays the cross-classification of observed versus predicted values in a heat map, plus the overall percent correct. Table styles. There are several different display styles, which are accessible from the Style drop...
For more information, see Neural Network Structure. example Mdl = fitcnet(Tbl,ResponseVarName) returns a neural network classification model Mdl trained using the predictors in the table Tbl and the class labels in the ResponseVarName table variable. Mdl = fitcnet(Tbl,formula) returns a neural...
从模型的角度,本文作者将RNN(Bi-LSTM)和max_pooling结合使用,提出RCNN模型,应用到了NLP的文本分类任务中,提高了分类精度。 ·参考文献: [1] Recurrent Convolutional Neural Networks for Text Classification 论文链接:http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/download/9745/9552...